Supply Chain Management ST R AT E G Y , P L A N N I N G , A N D OPE R AT I ON

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S i x t h E d i t i o n

Sunil Chopra Kellogg School of Management

Peter Meindl Kepos Capital

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Library of Congress Cataloging-in-Publication Data Chopra, Sunil Supply chain management : strategy, planning, and operation / Sunil Chopra, Kellogg School of Management, Peter Meindl, Kepos Capital.—Sixth Edition. pages cm ISBN 978-0-13-380020-3—ISBN 0-13-380020-2 1. Marketing channels—Management. 2. Delivery of goods—Management. 3. Physical distribution of goods— Management. 4. Customer services—Management. 5. Industrial procurement. 6. Materials management. I. Meindl, Peter, 1970– II. Title. HF5415.13.C533 2015 658.7—dc23

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Dedication

I would like to thank my colleagues at Kellogg for all I have learned from them about logistics and supply chain management. I am grateful for the love and encouragement that my parents, Krishan and Pushpa, and sisters, Sudha and Swati, have always provided during every endeavor in my life. I thank my

children, Ravi and Rajiv, for the joy they have brought me. Finally, none of this would have been possible without the constant love, caring, and support

of my wife, Maria Cristina.

—Sunil Chopra

I would like to thank three mentors—Sunil Chopra, Hau Lee, and Gerry Lieberman—who have taught me a great deal. Thank you also to my parents and sister for their love, and to my sons, Jamie and Eric, for making me smile and teaching me what life is truly all about. Most important, I thank my wife,

Sarah, who makes life wonderful and whom I love with all my heart.

—Peter Meindl

ABOUT THE AUTHORS

SUNIL CHOPRA

Sunil Chopra is the IBM Distinguished Professor of Operations Management and Information Systems at the Kellogg School of Management. He has served as the interim dean and senior associate dean for curriculum and teaching, and the codirector of the MMM program, a joint dual-degree program between the Kellogg School of Manage- ment and the McCormick School of Engineering at Northwestern University. He has a PhD in operations research from SUNY at Stony Brook. Prior to joining Kellogg, he taught at New York University and spent a year at IBM Research.

Professor Chopra’s research and teaching interests are in supply chain and logistics management, operations management, and the design of telecommunication networks. He has won several teaching awards at the MBA and Executive programs of Kellogg. He has authored more than 40 papers and two books.

He has been a department editor for Management Science and an associate editor for Man- ufacturing & Service Operations Management, Operations Research, and Decision Sciences Journal. His recent research has focused on understanding supply chain risk and devising effective risk mitigation strategies. He has also consulted for several firms in the area of supply chain and operations management.

PETER MEINDL

Peter Meindl is a portfolio manager with Kepos Capital in New York. Previously, he was a research officer with Barclays Global Investors, a consultant with the Boston Consult- ing Group and Mercer Management Consulting, and the director of strategy with i2 Technologies. He holds PhD, MS, BS, and BA degrees from Stanford, and an MBA from the Kellogg School of Management at Northwestern.

The first edition of this book won the prestigious Book of the Year award in 2002 from the Institute of Industrial Engineers.

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CONTENTS

Preface x

Part I Building a Strategic Framework to Analyze Supply Chains

Chapter 1 UNDERSTANDING THE SUPPLY CHAIN 1 1.1 What Is a Supply Chain? 1 1.2 The Objective of a Supply Chain 3 1.3 The Importance of Supply Chain Decisions 5 1.4 Decision Phases in a Supply Chain 6 1.5 Process Views of a Supply Chain 8 1.6 Examples of Supply Chains 13 1.7 Summary of Learning Objectives 17

Discussion Questions 17 18

Chapter 2 SUPPLY CHAIN PERFORMANCE: ACHIEVING STRATEGIC FIT AND SCOPE 19 2.1 Competitive and Supply Chain Strategies 19 2.2 Achieving Strategic Fit 21 2.3 Expanding Strategic Scope 31 2.4 Challenges to Achieving and Maintaining

Strategic Fit 34 2.5 Summary of Learning Objectives 35

Discussion Questions 36 36 ▶ CASE STUDY: The Demise of Blockbuster 37

Chapter 3 SUPPLY CHAIN DRIVERS AND METRICS 40 3.1 Financial Measures of Performance 40 3.2 Drivers of Supply Chain Performance 44 3.3 Framework for Structuring Drivers 46 3.4 Facilities 47 3.5 Inventory 49 3.6 Transportation 52 3.7 Information 53 3.8 Sourcing 56 3.9 Pricing 57 3.10 Summary of Learning Objectives 59

Discussion Questions 60 61 ▶ CASE STUDY: Seven-Eleven Japan Co. 61 ▶ CASE STUDY: Financial Statements for Walmart Stores Inc. and

Macy’s Inc. 67

iv

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Part II Designing the Supply Chain Network

Chapter 4 DESIGNING DISTRIBUTION NETWORKS AND APPLICATIONS TO ONLINE SALES 69 4.1 The Role of Distribution in the Supply Chain 69 4.2 Factors Influencing Distribution Network Design 71 4.3 Design Options for a Distribution Network 74 4.4 Online Sales and the Distribution Network 87 4.5 Distribution Networks in Practice 100 4.6 Summary of Learning Objectives 101

Discussion Questions 102 102 ▶ CASE STUDY: Blue Nile and Diamond Retailing 103

Chapter 5 NETWORK DESIGN IN THE SUPPLY CHAIN 108 5.1 The Role of Network Design in the Supply Chain 108 5.2 Factors Influencing Network Design Decisions 109 5.3 Framework for Network Design Decisions 114 5.4 Models for Facility Location and Capacity Allocation 116 5.5 Making Network Design Decisions in Practice 132 5.6 Summary of Learning Objectives 133

Discussion Questions 133 134 138 ▶ CASE STUDY: Managing Growth at SportStuff.com 139 ▶ CASE STUDY: Designing the Production Network at CoolWipes 140

Chapter 6 DESIGNING GLOBAL SUPPLY CHAIN NETWORKS 142 6.1 The Impact of Globalization on Supply Chain Networks 142 6.2 The Offshoring Decision: Total Cost 144 6.3 Risk Management in Global Supply Chains 147 6.4 Discounted Cash Flows 151 6.5 Evaluating Network Design Decisions Using Decision Trees 153 6.6 To Onshore or Offshore: Evaluation of Global Supply Chain

Design Decisions Under Uncertainty 160 6.7 Making Global Supply Chain Design Decisions Under

Uncertainty in Practice 168 6.8 Summary of Learning Objectives 169

Discussion Questions 169 170 171 ▶ CASE STUDY: BioPharma, Inc. 172 ▶ CASE STUDY: The Sourcing Decision at Forever Young 175

Part III Planning and Coordinating Demand and Supply in a Supply Chain

Chapter 7 DEMAND FORECASTING IN A SUPPLY CHAIN 177 7.1 The Role of Forecasting in a Supply Chain 177 7.2 Characteristics of Forecasts 178

Contents v

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vi Contents

7.3 Components of a Forecast and Forecasting Methods 179 180

7.5 Time-Series Forecasting Methods 182 7.6 Measures of Forecast Error 192

195 7.8 Forecasting Demand at Tahoe Salt 197 7.9 The Role of IT in Forecasting 202 7.10 Forecasting in Practice 203 7.11 Summary of Learning Objectives 204

Discussion Questions 204 205 206 ▶ CASE STUDY: Specialty Packaging Corporation 207

Chapter 8 AGGREGATE PLANNING IN A SUPPLY CHAIN 209 8.1 The Role of Aggregate Planning in a Supply Chain 209 8.2 The Aggregate Planning Problem 211 8.3 Aggregate Planning Strategies 213 8.4 Aggregate Planning at Red Tomato Tools 214 8.5 Aggregate Planning Using Linear Programming 215 8.6 Aggregate Planning in Excel 220

224 8.8 The Role of IT in Aggregate Planning 225 8.9 Implementing Aggregate Planning in Practice 225 8.10 Summary of Learning Objectives 226

Discussion Questions 227 227 229 ▶ CASE STUDY: Kloss Planters and Harvesters 229

Chapter 9 SALES AND OPERATIONS PLANNING: PLANNING SUPPLY AND DEMAND IN A SUPPLY CHAIN 231 9.1 Responding to Predictable Variability in the Supply Chain 231 9.2 Managing Supply 232 9.3 Managing Demand 234 9.4 Sales and Operations Planning at Red Tomato 235 9.5 Implementing Sales and Operations Planning in Practice 241 9.6 Summary of Learning Objectives 242

Discussion Questions 242 242 244 ▶ CASE STUDY: Mintendo Game Girl 245 ▶ CASE STUDY: Promotion Challenges at Gulmarg Skis 246

Chapter 10 COORDINATION IN A SUPPLY CHAIN 248 248

10.2 The Effect on Performance of Lack of Coordination 250 10.3 Obstacles to Coordination in a Supply Chain 252 10.4 Managerial Levers to Achieve Coordination 256 10.5 Continuous Replenishment and Vendor-Managed

Inventories 261

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Contents vii

10.6 Collaborative Planning, Forecasting, and Replenishment 261 10.7 Achieving Coordination in Practice 265 10.8 Summary of Learning Objectives 266

Discussion Questions 267 267

Part IV Planning and Managing Inventories in a Supply Chain

Chapter 11 MANAGING ECONOMIES OF SCALE IN A SUPPLY CHAIN: CYCLE INVENTORY 268 11.1 The Role of Cycle Inventory in a Supply Chain 268 11.2 Estimating Cycle Inventory-Related Costs in Practice 271 11.3 Economies of Scale to Exploit Fixed Costs 273 11.4 Aggregating Multiple Products in a Single Order 278 11.5 Economies of Scale to Exploit Quantity Discounts 286 11.6 Short-Term Discounting: Trade Promotions 297 11.7 Managing Multiechelon Cycle Inventory 302 11.8 Summary of Learning Objectives 305

Discussion Questions 306 306 309 ▶ CASE STUDY: Delivery Strategy at MoonChem 310 ▶ CASE STUDY: Pricing and Delivery at KAR Foods 312

Appendix 11A: Economic Order Quantity 313

Chapter 12 MANAGING UNCERTAINTY IN A SUPPLY CHAIN: SAFETY INVENTORY 314 12.1 The Role of Safety Inventory in a Supply Chain 314 12.2 Factors Affecting the Level of Safety Inventory 316 12.3 Determining the Appropriate Level of Safety Inventory 318 12.4 Impact of Supply Uncertainty on Safety Inventory 327 12.5 Impact of Aggregation on Safety Inventory 330 12.6 Impact of Replenishment Policies on Safety Inventory 342 12.7 Managing Safety Inventory in a Multiechelon Supply Chain 346 12.8 The Role of IT in Inventory Management 346 12.9 Estimating and Managing Safety Inventory in Practice 347 12.10 Summary of Learning Objectives 348

Discussion Questions 349 349 353 ▶ CASE STUDY: Managing Inventories at ALKO Inc. 353 ▶ CASE STUDY: Should Packing Be Postponed to the DC? 356

Appendix 12A: The Normal Distribution 357 358

Appendix 12C: Expected Shortage per Replenishment Cycle 358 Appendix 12D: Evaluating Safety Inventory for Slow-Moving

Items 359

Chapter 13 DETERMINING THE OPTIMAL LEVEL OF PRODUCT AVAILABILITY 361 13.1 The Importance of the Level of Product Availability 361 13.2 Factors Affecting Optimal Level of Product Availability 362

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viii Contents

13.3 Managerial Levers to Improve Supply Chain Profitability 372 13.4 Setting Product Availability for Multiple Products Under

Capacity Constraints 386 13.5 Setting Optimal Levels of Product Availability in Practice 389 13.6 Summary of Learning Objectives 389

Discussion Questions 390 390 392 ▶ CASE STUDY: The Need for Speed at Winner Apparel 393

Appendix 13A: Optimal Level of Product Availability 394 395

Appendix 13C: Expected Profit from an Order 396 Appendix 13D: Expected Overstock from an Order 396 Appendix 13E: Expected Understock from an Order 397 Appendix 13F: Simulation Using Spreadsheets 397

Part V Designing and Planning Transportation Networks

Chapter 14 TRANSPORTATION IN A SUPPLY CHAIN 400 14.1 The Role of Transportation in a Supply Chain 400 14.2 Modes of Transportation and Their Performance

Characteristics 402 14.3 Transportation Infrastructure and Policies 406 14.4 Design Options for a Transportation Network 409 14.5 Mumbai Dabbawalas: A Highly Responsive Distribution

Network 415 14.6 Trade-Offs in Transportation Design 416 14.7 Tailored Transportation 425 14.8 The Role of IT in Transportation 427 14.9 Making Transportation Decisions in Practice 427 14.10 Summary of Learning Objectives 428

Discussion Questions 429 429 ▶ CASE STUDY: Designing the Distribution Network for Michael’s

Hardware 430 ▶ CASE STUDY: The Future of Same-Day Delivery: Same as the Past? 431 ▶ CASE STUDY: Selecting Transportation Modes for China Imports 432

Part VI Managing Cross-Functional Drivers in a Supply Chain

Chapter 15 SOURCING DECISIONS IN A SUPPLY CHAIN 433 15.1 The Role of Sourcing in a Supply Chain 433 15.2 In-House or Outsource? 435 15.3 Examples of Successful Third-Party Suppliers 441 15.4 Total Cost of Ownership 443 15.5 Supplier Selection—Auctions and Negotiations 446 15.6 Sharing Risk and Reward in the Supply Chain 448 15.7 The Impact of Incentives When Outsourcing 459

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Contents ix

15.8 Designing a Sourcing Portfolio: Tailored Sourcing 461 15.9 Making Sourcing Decisions in Practice 463 15.10 Summary of Learning Objectives 464

Discussion Questions 465 465 466

Chapter 16 PRICING AND REVENUE MANAGEMENT IN A SUPPLY CHAIN 468

468

Segments 470 477 484

Contracts 484 486

16.7 Summary of Learning Objectives 487 Discussion Questions 488 488 489

▶ CASE STUDY: To Savor or to Groupon? 490

Chapter 17 SUSTAINABILITY AND THE SUPPLY CHAIN 492 492

494 17.3 Key Pillars of Sustainability 497

500 504 505

17.7 Summary of Learning Objectives 507 Discussion Questions 508 508

Part VII Online Chapter

Chapter A INFORMATION TECHNOLOGY IN A SUPPLY CHAIN

Summary of Learning Objectives

509

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PREFACE

This book is targeted toward an academic as well as a practitioner audience. On the academic side, it should be appropriate for MBA students, engineering master’s students, and senior under- graduate students interested in supply chain management and logistics. It should also serve as a suitable reference for both concepts as well as providing a methodology for practitioners in con- sulting and industry.

NEW TO THIS EDITION The sixth edition has focused on allowing students to learn more as they study with the book. We have tightened the link between examples in the book and associated spreadsheets and have added exercises and cases in several chapters. We have also added changes based on spe- cific reviewer feedback that we believe significantly improve the book and its use by faculty and students.

– ters 2, 8, 9, 11, 13, 14, and 16. Information in other cases has been updated to be current.

students can use to understand the concept. These spreadsheets are referred to in the book and allow the student to try different “what-if” analyses. These spreadsheets are available at www.pearsonhighered.com/chopra along with basic guidance on how they may be used.

– out using Solver if they so desire. For faculty that wants to continue using Solver, all mate- rial in the chapters has been even more tightly linked to the associated spreadsheets. We have also added a couple of new mini-cases to give students a chance to apply the concepts in the chapters.

advanced concepts, we have tightened the linkage to the associated spreadsheets. We have also added a mini-case.

dabbawalas, a responsive distri- bution network. We have tightened the linkage of examples to associated spreadsheets and added a couple of mini-cases.

third parties as well as the impact of incentives and the sharing of risk and reward in the supply chain.

updated and placed online at www.pearsonhighered.com/chopra.

pricing of sustainability.

bringing in more global examples.

The book has grown from a course on supply chain management taught to second-year MBA students at the Kellogg School of Management at Northwestern University. The goal of this class was not only to cover high-level supply chain strategy and concepts, but also to give

x

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Preface xi

students a solid understanding of the analytical tools necessary to solve supply chain problems. With this class goal in mind, our objective was to create a book that would develop an under- standing of the following key areas and their interrelationships:

Our first objective in this book is for the reader to learn the strategic importance of good supply

good supply chain management can be a competitive advantage, whereas weaknesses in the supply chain can hurt the performance of a firm. We use many examples to illustrate this idea and develop a framework for supply chain strategy.

Within the strategic framework, we identify facilities, inventory, transportation, informa- tion, sourcing, and pricing as the key drivers of supply chain performance. Our second goal in the book is to convey how these drivers may be used on conceptual and practical levels during supply chain design, planning, and operation to improve performance. We have presented a variety of cases that can be used to illustrate how a company uses various drivers to improve supply chain performance. For each driver of supply chain performance, our goal is to provide readers with practical managerial levers and concepts that may be used to improve supply chain performance.

Using these managerial levers requires knowledge of analytic methodologies for supply chain analysis. Our third goal is to give the reader an understanding of these methodologies. Every methodological discussion is illustrated with its application in Excel. In this discussion, we also stress the managerial context in which the methodology is used and the managerial levers for improvement that it supports.

variety of examples that show how a combination of concepts is needed to achieve significant increases in performance.

FOR INSTRUCTORS

register to gain access to a variety of instructor resources available with this text in downloadable format. If assistance is needed, our dedicated technical support team is ready to help with the

– quently asked questions and toll-free user support phone numbers.

®

For Students

of the example discussed, but are live and allow the student to try different what-if analyses.

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xii Preface

ACKNOWLEDGMENTS We would like to thank the many people who helped us throughout this process. We thank the reviewers whose suggestions significantly improved the book, including: Steven Brown, Arizona State University; Ming Chen, California State University, Long Beach; Sameer Kumar, Univer- sity of Saint Thomas; Frank Montabon, Iowa State University; Brian Sauser, University of North Texas; and Paul Venderspek, Colorado State University.

We are grateful to the students at the Kellogg School of Management who suffered through typo-ridden drafts of earlier versions of the book. We would also like to thank our editor, Dan Tylman, and the staff at Pearson, including Liz Napolitano, senior production project manager; Anne Fahlgren, executive product marketing manager; Claudia Fernandes, program manager; and Linda Albelli, editorial assistant, for their efforts with the book. Finally, we would like to thank you, our readers, for reading and using this book. We hope it contributes to all your efforts to improve the performance of companies and supply chains throughout the world. We would be pleased to hear your comments and suggestions for future editions of this text.

Sunil Chopra Kellogg School of Management, Northwestern University

Peter Meindl Kepos Capital

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In this chapter, we provide a conceptual understanding of what a supply chain is and the various issues that must be considered when designing, planning, or operating a supply chain. We discuss the significance of supply chain decisions and supply chain performance for the success of a firm. We also provide several examples from different industries to empha- size the variety of supply chain issues that companies need to consider at the strategic, planning, and operational levels.

1.1 WHAT IS A SUPPLY CHAIN?

A supply chain consists of all parties involved, directly or indirectly, in fulfilling a customer request. The supply chain includes not only the manufacturer and suppliers, but also transporters, warehouses, retailers, and even customers themselves. Within each organization, such as a manu- facturer, the supply chain includes all functions involved in receiving and filling a customer request. These functions include, but are not limited to, new product development, marketing, operations, distribution, finance, and customer service.

Consider a customer walking into a Walmart store to purchase detergent. The supply chain begins with the customer and his or her need for detergent. The next stage of this supply chain is the Walmart retail store that the customer visits. Walmart stocks its shelves using inventory that may have been supplied from a finished-goods warehouse or a distributor using trucks supplied

Understanding the Supply Chain

C H A P T E R

1

LEARNING OBJECTIVES After reading this chapter, you will be able to

1

1. Discuss the goal of a supply chain and explain the impact of supply chain decisions on the success of a firm.

2. Identify the three key supply chain decision phases and explain the significance of each one.

3. Describe the cycle and push/pull views of a supply chain.

4. Classify the supply chain macro processes in a firm.

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2

by a third party. The distributor, in turn, is stocked by the manufacturer (say, Procter & Gamble [P&G] in this case). The P&G manufacturing plant receives raw material from a variety of sup- pliers, which may themselves have been supplied by lower-tier suppliers. For example, packag- ing material may come from Pactiv Corporation, whereas Pactiv receives raw materials to manufacture the packaging from other suppliers. This supply chain is illustrated in Figure 1-1, with the arrows corresponding to the direction of physical product flow.

A supply chain is dynamic and involves the constant flow of information, product, and funds among different stages. In our example, Walmart provides the product, as well as pric- ing and availability information, to the customer. The customer transfers funds to Walmart. Walmart conveys point-of-sales data and replenishment orders to the warehouse or distribu- tor, which transfers the replenishment order via trucks back to the store. Walmart transfers funds to the distributor after the replenishment. The distributor also provides pricing infor- mation and sends delivery schedules to Walmart. Walmart may send back packaging mate-

supply chain. In another example, when a customer makes a purchase online from Amazon, the supply

chain includes, among others, the customer, Amazon’s website, the Amazon warehouse, and all of Amazon’s suppliers and their suppliers. The website provides the customer with information regarding pricing, product variety, and product availability. After making a product choice, the customer enters the order information and pays for the product. The customer may later return

order information to fill the request. That process involves an additional flow of information, product, and funds among various stages of the supply chain.

These examples illustrate that the customer is an integral part of the supply chain. In fact, the primary purpose of any supply chain is to satisfy customer needs and, in the process, generate profit for itself. The term supply chain conjures up images of product or supply moving from suppliers to manufacturers to distributors to retailers to customers along a chain. This is certainly part of the supply chain, but it is also important to visualize information, funds, and product flows along both directions of this chain. The term supply chain may also imply that only one player is involved at each stage. In reality, a manufacturer may receive material from several sup- pliers and then supply several distributors. Thus, most supply chains are actually networks. It may be more accurate to use the term supply network or supply web to describe the structure of most supply chains, as shown in Figure 1-2.

CustomerWalmartStore

Walmart or Third Party DC

P&G or Other Manufacturer

Timber Company

Paper Manufacturer

Pactiv Corporation

Chemical Manufacturer

Plastic Producer

FIGURE 1-1 Stages of a Detergent Supply Chain

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3

A typical supply chain may involve a variety of stages, including the following:

Each stage in a supply chain is connected through the flow of products, information, and funds. These flows often occur in both directions and may be managed by one of the stages or an intermediary. Each stage in Figure 1-2 need not be present in a supply chain. As discussed in Chapter 4, the appropriate design of the supply chain depends on both the customer’s needs and the roles played by the stages involved. For example, Dell has two supply chain structures that it uses to serve its customers. For its server business, Dell builds to order; that is, a customer order initiates manufacturing at Dell. For the sale of servers, Dell does not have a separate retailer, distributor, or wholesaler in the supply chain. Dell also sells consumer products such as PCs and tablets through retailers such as Walmart, which carry Dell products in inventory. This supply chain thus contains an extra stage (the retailer), compared with the direct sales model used by Dell for servers. In the case of other retail stores, the supply chain may also contain a wholesaler or distributor between the store and the manufacturer.

1.2 THE OBJECTIVE OF A SUPPLY CHAIN

The objective of every supply chain should be to maximize the overall value generated. The value (also known as supply chain surplus) a supply chain generates is the difference between what the value of the final product is to the customer and the costs the entire supply chain incurs in filling the customer’s request.

Supply Chain Surplus = Customer Value – Supply Chain Cost

The value of the final product may vary for each customer and can be estimated by the maximum amount the customer is willing to pay for it. The difference between the value of the product and its price remains with the customer as consumer surplus. The rest of the supply chain surplus becomes supply chain profitability, the difference between the revenue generated from

Supplier Distributor Retailer CustomerManufacturer

Supplier Distributor Retailer CustomerManufacturer

Supplier Distributor Retailer CustomerManufacturer

FIGURE 1-2 Supply Chain Stages

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the customer and the overall cost across the supply chain. For example, a customer purchasing a wireless router from Best Buy pays $60, which represents the revenue the supply chain receives. Customers who purchase the router clearly value it at or above $60. Thus, part of the supply chain surplus is left with the customer as consumer surplus. The rest stays with the supply chain as profit. Best Buy and other stages of the supply chain incur costs to convey information, produce components, store them, transport them, transfer funds, and so on. The difference between the $60 that the customer paid and the sum of costs incurred across all stages by the supply chain to produce and distribute the router represents the supply chain profitability: the total profit to be shared across all supply chain stages and intermediaries. The higher the supply chain profitability, the more successful the supply chain. For most profit-making supply chains, the supply chain

terms of supply chain surplus and not in terms of the profits at an individual stage. (In subsequent chapters, we see that a focus on profitability at individual stages may lead to a reduction in overall supply chain surplus.) A focus on growing the supply chain surplus pushes all members of the supply chain toward growing the size of the overall pie.

Having defined the success of a supply chain in terms of supply chain surplus, the next logical step is to look for sources of value, revenue, and cost. For any supply chain, there is only one source of revenue: the customer. The value obtained by a customer purchasing detergent at Walmart depends on several factors, including the functionality of the detergent, how far the customer must travel to Walmart, and the likelihood of finding the detergent in stock. The cus- tomer is the only one providing positive cash flow for the Walmart supply chain. All other cash flows are simply fund exchanges that occur within the supply chain, given that different stages have different owners. When Walmart pays its supplier, it is taking a portion of the funds the customer provides and passing that money on to the supplier. All flows of information, product, or funds generate costs within the supply chain. Thus, the appropriate management of these flows is a key to supply chain success. Effective supply chain management involves the manage- ment of supply chain assets and product, information, and fund flows to grow the total supply chain surplus. A growth in supply chain surplus increases the size of the total pie, allowing con- tributing members of the supply chain to benefit.

In this book, we have a strong focus on analyzing all supply chain decisions in terms of their impact on the supply chain surplus. These decisions and their impact can vary for a wide variety of reasons. For instance, consider the difference in the supply chain structure for fast-

much smaller role in this supply chain compared with their Indian counterparts. We argue that the difference in supply chain structure can be explained by the impact a distributor has on the supply chain surplus in the two countries.

goods from most manufacturers. This consolidation gives retailers sufficient scale that the intro- duction of an intermediary such as a distributor does little to reduce costs—and may actually increase costs because of an additional transaction. In contrast, India has millions of small retail outlets. The small size of Indian retail outlets limits the amount of inventory they can hold, thus requiring frequent replenishment—an order can be compared with the weekly grocery shopping

low is to bring full truckloads of product close to the market and then distribute locally using “milk runs” with smaller vehicles. The presence of an intermediary that can receive a full truck- load shipment, break bulk, and then make smaller deliveries to the retailers is crucial if trans-

everything from cooking oil to soaps and detergents made by a variety of manufacturers. Besides the convenience provided by one-stop shopping, distributors in India are also able to reduce transportation costs for outbound delivery to the retailer by aggregating products across multiple manufacturers during the delivery runs. Distributors in India also handle collections, because their cost of collection is significantly lower than that of each manufacturer collecting

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from retailers on its own would be. Thus, the important role of distributors in India can be explained by the growth in supply chain surplus that results from their presence. The supply chain surplus argument implies that as retailing in India begins to consolidate, the role of dis- tributors will diminish.

1.3 THE IMPORTANCE OF SUPPLY CHAIN DECISIONS

There is a close connection between the design and management of supply chain flows (product,

Japan are examples of companies that have built their success on superior design, planning, and operation of their supply chain. In contrast, the failure of many online businesses, such as Web- van, can be attributed to weaknesses in their supply chain design and planning. The rise and subsequent fall of the bookstore chain Borders illustrates how a failure to adapt its supply chain to a changing environment and customer expectations hurt its performance. Dell Computer is another example of a company that had to revise its supply chain design in response to changing technology and customer needs. We discuss these examples later in this section.

Walmart has been a leader at using supply chain design, planning, and operation to achieve success. From its beginning, the company invested heavily in transportation and information infrastructure to facilitate the effective flow of goods and information. Walmart designed its sup- ply chain with clusters of stores around distribution centers to facilitate frequent replenishment at its retail stores in a cost-effective manner. Frequent replenishment allows stores to match supply and demand more effectively than the competition. Walmart has been a leader in sharing infor- mation and collaborating with suppliers to bring down costs and improve product availability. The results are impressive. In its 2013 annual report, the company reported a net income of about $17 billion on revenues of about $469 billion. These are dramatic results for a company that reached annual sales of only $1 billion in 1980. The growth in sales represents an annual com- pounded growth rate of more than 20 percent.

design, planning, and operation to drive growth and profitability. It has used a very responsive replenishment system along with an outstanding information system to ensure that products are available when and where customers need them. Its responsiveness allows it to change the mer- chandising mix at each store by time of day to precisely match customer demand. As a result, the company has grown from sales of 1 billion yen in 1974 to almost 1.9 trillion yen in 2013, with profits in 2013 totaling 222 billion yen.

The failure of many online businesses, such as Webvan and Kozmo, can be attributed to their inability to design appropriate supply chains or manage supply chain flows effectively.

could not compete with traditional supermarket supply chains in terms of cost. Traditional super- market chains bring product to a supermarket close to the consumer using full truckloads, result- ing in very low transportation costs. They turn their inventory relatively quickly and let the customer perform most of the picking activity in the store. In contrast, Webvan turned its inven- tory marginally faster than supermarkets but incurred much higher transportation costs for home delivery, as well as high labor costs to pick customer orders. The result was a company that folded in 2001, within two years of a very successful initial public offering.

As the experience of Borders illustrates, a failure to adapt supply chains to a changing environment can significantly hurt performance. Borders, along with Barnes & Noble, domi- nated the selling of books and music in the 1990s by implementing the superstore concept. Compared with small local bookstores that dominated the industry prior to that, Borders was able to offer greater variety (about 100,000 titles at superstores, relative to fewer than 10,000 titles at a local bookstore) to customers at a lower cost by aggregating operations in large stores. This allowed the company to achieve higher inventory turns than local bookstores with lower

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operating costs per dollar of sales. In 2004, Borders achieved sales of almost $4 billion, with profits of $132 million. Its model, however, was already under attack with the growth of Amazon, which offered much greater variety than Borders at lower cost by selling online and stocking its inventories in a few distribution centers. Borders’ inability to adapt its supply chain to compete with Amazon led to a rapid decline. By 2009, sales had dropped to $2.8 billion; the company lost $109 million that year.

Dell is another example of a company that enjoyed tremendous success based on its sup- ply chain design, planning, and operation but then had to adapt its supply chain in response to shifts in technology and customer expectations. Between 1993 and 2006, Dell experienced unprecedented growth of both revenue and profits by structuring a supply chain that provided customers with customized PCs quickly and at reasonable cost. By 2006, Dell had a net income of more than $3.5 billion on revenues of just over $56 billion. This success was based on two key supply chain features that supported rapid, low-cost customization. The first was Dell’s decision to sell directly to the end customer, bypassing distributors and retailers. The second key aspect of Dell’s supply chain was the centralization of manufacturing and inventories in a few locations where final assembly was postponed until the customer order arrived. As a result, Dell was able to provide a large variety of PC configurations while keeping low levels of com- ponent inventories.

Key Point

a firm. To stay competitive, supply chains must adapt to changing technology and customer expectations.

In spite of this tremendous success, the changing marketplace presented some new chal- lenges for Dell. Whereas Dell’s supply chain was well suited for highly customized PCs, the market shifted to lower levels of customization. Given the growing power of hardware, custom- ers were satisfied with a few model types. Dell reacted by adjusting its supply chain with regard to both direct selling and building to order. The company started selling its PCs through retail

– tion of its assembly to low-cost locations, effectively building to stock rather than to customer

times. It remains to be seen whether these changes will improve Dell’s performance. In the next section, we categorize supply chain decision phases based on the frequency

with which they are made and the time frame they take into account.

1.4 DECISION PHASES IN A SUPPLY CHAIN

– tion, product, and funds. Each decision should be made to raise the supply chain surplus. These decisions fall into three categories or phases, depending on the frequency of each decision and the time frame during which a decision phase has an impact. As a result, each category of deci- sions must consider uncertainty over the decision horizon.

1. Supply chain strategy or design: During this phase, a company decides how to structure the supply chain over the next several years. It decides what the chain’s configuration

decisions made by companies include whether to outsource or perform a supply chain function in-house, the location and capacities of production and warehousing facilities, the products to be manufactured or stored at various locations, the modes of transportation to be made available along different shipping legs, and the type of information system to be used. PepsiCo Inc.’s

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decision in 2009 to purchase two of its largest bottlers is a supply chain design or strategic deci- sion. A firm must ensure that the supply chain configuration supports its strategic objectives and

release on August 4, “while the existing model has served the system very well, the fully inte- grated beverage business will enable us to bring innovative products and packages to market faster, streamline our manufacturing and distribution systems and react more quickly to changes

– ter of years) and are expensive to alter on short notice. Consequently, when companies make these decisions, they must take into account uncertainty in anticipated market conditions over the following few years.

2. Supply chain planning: For decisions made during this phase, the time frame considered is a quarter to a year. Therefore, the supply chain’s configuration determined in the strategic phase is fixed. This configuration establishes constraints within which planning must be done. The goal of planning is to maximize the supply chain surplus that can be generated over the planning horizon given the constraints established during the strategic or design phase. Companies start the planning phase with a forecast for the coming year (or a comparable time frame) of demand and other factors, such as costs and prices in different markets. Planning includes making decisions regarding which markets will be supplied from which locations, the subcontracting of manufacturing, the inventory policies to be followed, and the timing and size

markets supplied by a production facility and target production quantities at each location are classified as planning decisions. In the planning phase, companies must include uncertainty in demand, exchange rates, and competition over this time horizon in their decisions. Given a shorter time frame and better forecasts than in the design phase, companies in the planning phase try to incorporate any flexibility built into the supply chain in the design phase and exploit it to optimize performance. As a result of the planning phase, companies define a set of operating policies that govern short-term operations.

3. Supply chain operation: The time horizon here is weekly or daily. During this phase, companies make decisions regarding individual customer orders. At the operational level, supply chain configuration is considered fixed and planning policies are already defined. The goal of supply chain operations is to handle incoming customer orders in the best possible manner. Dur- ing this phase, firms allocate inventory or production to individual orders, set a date by which an order is to be filled, generate pick lists at a warehouse, allocate an order to a particular shipping mode and shipment, set delivery schedules of trucks, and place replenishment orders. Because operational decisions are being made in the short term (minutes, hours, or days), there is less uncertainty about demand information. Given the constraints established by the configuration and planning policies, the goal during the operation phase is to exploit the reduction of uncer- tainty and optimize performance.

The design, planning, and operation of a supply chain have a strong impact on overall prof- itability and success. It is fair to state that a large part of the success of firms such as Walmart and

In later chapters, we develop concepts and present methodologies that can be used at each

design and planning phases.

Key Point

time frame during which the decisions made apply. Design decisions constrain or enable good planning, which in turn constrains or enables effective operation.

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1.5 PROCESS VIEWS OF A SUPPLY CHAIN

A supply chain is a sequence of processes and flows that take place within and between different stages and combine to fill a customer need for a product. There are two ways to view the pro- cesses performed in a supply chain.

1. Cycle View: The processes in a supply chain are divided into a series of cycles, each performed at the interface between two successive stages of the supply chain.

2. Push/Pull View: The processes in a supply chain are divided into two categories, depending on whether they are executed in response to a customer order or in anticipation of customer orders. Pull processes are initiated by a customer order, whereas push processes are initiated and performed in anticipation of customer orders.

Cycle View of Supply Chain Processes

Given the five stages of a supply chain as shown in Figure 1-2, all supply chain processes can be broken down into the following four process cycles, as shown in Figure 1-3:

Each cycle occurs at the interface between two successive stages of the supply chain. Not every supply chain will have all four cycles clearly separated. For example, a grocery supply chain in which a retailer stocks finished-goods inventories and places replenishment orders with a distributor is likely to have all four cycles separated. Dell, in contrast, bypasses the retailer and distributor when it sells servers directly to customers.

Each cycle consists of six subprocesses, as shown in Figure 1-4. Each cycle starts with the supplier marketing the product to customers. A buyer then places an order that is received by

Customer Order Cycle

Replenishment Cycle

Manufacturing Cycle

Procurement Cycle

Customer

Distributor

Manufacturer

Supplier

Retailer

FIGURE 1-3 Supply Chain Process Cycles

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the supplier. The supplier supplies the order, which is received by the buyer. The buyer may return some of the product or other recycled material to the supplier or a third party. The cycle of activities then begins again. The subprocesses in Figure 1-4 can be linked to the source,

– ships between these processes, and a set of metrics to measure process performance. The

discussed in this section. Depending on the transaction in question, the subprocesses in Figure 1-4 can be applied to

the appropriate cycle. When customers shop online at Amazon, they are part of the customer order cycle—with the customer as the buyer and Amazon as the supplier. In contrast, when Amazon orders books from a distributor to replenish its inventory, it is part of the replenishment cycle—with Amazon as the buyer and the distributor as the supplier.

Within each cycle, the goal of the buyer is to ensure product availability and to achieve economies of scale in ordering. The supplier attempts to forecast customer orders and reduce the cost of receiving the order. The supplier then works to fill the order on time and improve efficiency and accuracy of the order fulfillment process. The buyer then works to

environmental objectives. Even though each cycle has the same basic subprocesses, there are a few important dif-

ferences among the cycles. In the customer order cycle, demand is external to the supply chain and thus is uncertain. In all other cycles, order placement is uncertain but can be projected based on policies followed by the particular supply chain stage. For example, in the procure- ment cycle, a tire supplier to an automotive manufacturer can predict tire demand precisely once the production schedule at the manufacturer is known. The second difference across cycles relates to the scale of an order. A customer buys a single car, but the dealer orders mul- tiple cars at a time from the manufacturer, and the manufacturer, in turn, orders an even larger quantity of tires from the supplier. As we move from the customer to the supplier, the number of individual orders declines and the size of each order increases. Thus, sharing of information and operating policies across supply chain stages becomes more important as we move further from the end customer.

The detailed process description of a supply chain in the cycle view is useful when consid- ering operational decisions because it clearly specifies the roles of each member of the supply

chain operations.

Supplier stage markets product

Buyer returns reverse flows to supplier or

third party

Buyer stage places order

Buyer stage receives supply

Supplier stage receives order

Supplier stage supplies order

FIGURE 1-4 Subprocesses in Each Supply Chain Process Cycle

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Push/Pull View of Supply Chain Processes

All processes in a supply chain fall into one of two categories, depending on the timing of their execution relative to end customer demand. With pull processes, execution is initiated in response to a customer order. With push processes, execution is initiated in anticipation of customer orders based on a forecast. Pull processes may also be referred to as reactive pro- cesses because they react to customer demand. Push processes may also be referred to as speculative processes because they respond to speculated (or forecasted), rather than actual, demand. The push/pull boundary in a supply chain separates push processes from pull pro- cesses, as shown in Figure 1-5. Push processes operate in an uncertain environment because customer demand is not yet known. Pull processes operate in an environment in which cus- tomer demand is known. They are, however, often constrained by inventory and capacity deci- sions that were made in the push phase.

Let us compare a make-to-stock environment like that of L. L. Bean and a build-to-order environment like that of Ethan Allen to compare the push/pull view and the cycle view.

L. L. Bean executes all processes in the customer order cycle after the customer order arrives. All processes that are part of the customer order cycle are thus pull processes.

orders. The goal of the replenishment cycle is to ensure product availability when a customer order arrives. All processes in the replenishment cycle are performed in anticipation of demand and are thus push processes. The same holds true for processes in the manufacturing and procurement cycles. In fact, raw material such as fabric is often purchased six to nine months

point of sale. The processes in the L. L. Bean supply chain break up into pull and push processes, as shown in Figure 1-6.

Key Point

A cycle view of the supply chain clearly defines the processes involved and the owners of each process. This view is useful when considering operational decisions because it specifies the roles and responsi- bilities of each member of the supply chain and the desired outcome for each process.

Process N

Process N ! 1

Process k ” 1

Process 3

Push/Pull Boundary

Customer Order Arrives

Push Processes

Process 1

Pull Processes

Process 2

Process k

FIGURE 1-5 Push/Pull View of the Supply Chain

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Ethan Allen makes customized furniture, such as sofas and chairs, for which customers select the fabric and finish. In this case, the arrival of a customer order triggers production of the product. The manufacturing cycle is thus part of the customer order fulfillment process in the customer order cycle. There are effectively only two cycles in the Ethan Allen supply chain for customized furniture: (1) a customer order and manufacturing cycle and (2) a procurement cycle, as shown in Figure 1-7.

All processes in the customer order and manufacturing cycle at Ethan Allen are classified as pull processes because they are initiated by customer order arrival. The company, however, does not place component orders in response to a customer order. Inventory is replenished in anticipation of customer demand. All processes in the procurement cycle for Ethan Allen are thus classified as push processes, because they are in response to a forecast.

Customer Order Cycle

Replenishment and Manufacturing Cycle

Procurement Cycle

Customer

L. L. Bean

Manufacturer

Supplier

Customer Order Cycle

Procurement, Manufacturing, Replenishment Cycles

PULL PROCESSES

PUSH PROCESSES

Customer Order Arrives

FIGURE 1-6 Push/Pull Processes for the L. L. Bean Supply Chain

Customer Order and Manufacturing Cycle

Procurement Cycle

Customer Order and Manufacturing Cycle

Procurement Cycle

PULL PROCESSES

PUSH PROCESSES

Customer Order Arrives

FIGURE 1-7 Push/Pull Processes for Ethan Allen Supply Chain for Customized Furniture

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A push/pull view of the supply chain is very useful when considering strategic decisions relating to supply chain design. The goal is to identify an appropriate push/pull boundary such that the supply chain can match supply and demand effectively.

The paint industry provides another excellent example of the gains from suitably adjusting the push/pull boundary. The manufacture of paint requires production of the base, mixing of suit-

and paint cans were shipped to stores. These qualified as push processes, as they were performed to a forecast in anticipation of customer demand. Given the uncertainty of demand, though, the paint supply chain had great difficulty matching supply and demand. In the 1990s, paint supply chains were restructured so mixing of colors was done at retail stores after customers placed their orders. In other words, color mixing was shifted from the push to the pull phase of the supply chain even though base preparation and packing of cans were still performed in the push phase. The result is that customers are always able to get the color of their choice, whereas total paint inventories across the supply chain have declined.

Supply Chain Macro Processes in a Firm

All supply chain processes discussed in the two process views and throughout this book can be classified into the following three macro processes, as shown in Figure 1-8:

1. Customer Relationship Management (CRM): all processes at the interface between the firm and its customers

2. Internal Supply Chain Management (ISCM): all processes that are internal to the firm 3. Supplier Relationship Management (SRM): all processes at the interface between the

firm and its suppliers

Key Point

A push/pull view of the supply chain categorizes processes based on whether they are initiated in response to a customer order (pull) or in anticipation of a customer order (push). This view is useful when considering strategic decisions relating to supply chain design.

SRM ISCM CRM

CustomerFirmSupplier

Market Price Sell

g

Strate

Fulf

Source Negotiate Buy

Supply

FIGURE 1-8 Supply Chain Macro Processes

Key Point

These three macro processes manage the flow of information, product, and funds required

customer demand and facilitate the placement and tracking of orders. It includes processes such

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as marketing, pricing, sales, order management, and call center management. At an industrial

marketing materials, management of the website, and management of the call center that takes

planning of internal production and storage capacity, preparation of demand and supply plans,

location and size of warehouses; deciding which products to carry at each warehouse; preparing

macro process aims to arrange for and manage supply sources for various goods and services.

processes include the selection of suppliers for various products, negotiation of pricing and delivery terms with suppliers, sharing of demand and supply plans with suppliers, and the place- ment of replenishment orders.

– ply chain to be successful, it is crucial that the three macro processes are well integrated. The importance of this integration is discussed in Chapter 10. The organizational structure of the firm has a strong influence on the success or failure of the integration effort. In many firms, marketing

unusual for marketing and manufacturing to have different forecasts when making their plans. This lack of integration hurts the supply chain’s ability to match supply and demand effectively, leading to dissatisfied customers and high costs. Thus, firms should structure a supply chain organization that mirrors the macro processes and ensures good communication and coordina- tion among the owners of processes that interact with one another.

1.6 EXAMPLES OF SUPPLY CHAINS

In this section, we consider several supply chains and raise questions that must be answered dur- ing their design, planning, and operation phases. In later chapters, we discuss concepts and pres- ent methodologies that can be used to answer these questions.

Gateway and Apple: Two Different Journeys into Retailing

Gateway was founded in 1985 as a direct sales manufacturer of PCs with no retail footprint. In 1996, Gateway was one of the first PC manufacturers to start selling PCs online. After many years of selling its PCs without a retail infrastructure, however, Gateway introduced an aggres-

stores carried no finished-goods inventory and were primarily focused on helping customers select the right configuration to purchase. All PCs were manufactured to order and shipped to the customer from one of the assembly plants.

Initially, investors rewarded Gateway for this strategy and raised the stock price to more than $80 per share in late 1999. However, this success did not last. By November 2002, Gateway shares had dropped to less than $4, and Gateway was losing a significant amount of money. By April 2004, Gateway had closed all its retail outlets and reduced the number of configurations offered to customers. In August 2007, Gateway was purchased by Taiwan’s Acer for $710 million. By 2010, Gateway computers were sold through more than 20 different retail outlets, including Best Buy and Costco. As one can imagine, this was quite a transition for the company to experience.

In contrast, Apple has enjoyed tremendous success since it opened its first retail store in

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Gateway, Apple has always carried product inventory at its stores. Given its product designs, Apple carries relatively little variety in its stores. In 2012, average revenue per Apple retail store was $51.5 million, a 19 percent increase over 2011.

The following questions highlight supply chain decisions that have a bearing on the differ- ence between Apple’s and Gateway’s performance:

1. Why did Gateway choose not to carry any finished-product inventory at its retail stores? Why did Apple choose to carry inventory at its stores?

2. are the characteristics of products that are most suitable to be carried in finished-goods inventory? What characterizes products that are best manufactured to order?

3. How does product variety affect the level of inventory a retail store must carry? 4. Is a direct selling supply chain without retail stores always less expensive than a supply

chain with retail stores? 5. What factors explain the success of Apple retail and the failure of Gateway country stores?

Zara: Apparel Manufacturing and Retail

retailer. In 2012, Inditex reported sales of about 16 billion euros from more than 6,000 retail outlets in about 86 countries. In an industry in which customer demand is fickle, Zara has grown rapidly with a strategy to be highly responsive to changing trends with affordable prices. Whereas design-to-sales cycle times in the apparel industry have traditionally averaged more than six months, Zara has achieved cycle times of four to six weeks. This speed allows Zara to introduce new designs every week and to change 75 percent of its merchandise display every three to four weeks. Thus, Zara’s products on display match customer preferences much more closely than do those of the competition. The result is that Zara sells most of its products at full price and has about half the markdowns in its stores compared with the competition.

Zara manufactures its apparel using a combination of flexible and quick sources in Europe –

ufacturers, who have moved most of their manufacturing to Asia. About 40 percent of the manu- facturing capacity is owned by Inditex, with the rest outsourced. Products with highly uncertain demand are sourced out of Europe, whereas products that are more predictable are sourced from

production occur after the sales season starts. This compares with less than 20 percent produc- tion after the start of a sales season for a typical retailer. This responsiveness, along with the postponement of decisions until after trends are known, allow Zara to reduce inventories and forecast error. Zara has also invested heavily in information technology to ensure that the latest sales data are available to drive replenishment and production decisions.

In 2012, Inditex distributed to stores all over the world from eight distribution centers

stores and up to a maximum of 48 hours for stores in America or Asia from the time the order

from the DCs to stores were made several times a week. This allowed store inventory to closely match customer demand.

The following questions raise supply chain issues that are central to Zara’s strategy and success:

1. What advantage does Zara gain against the competition by having a very responsive supply chain?

2. Why has Inditex chosen to have both in-house manufacturing and outsourced manufactur- ing? Why has Inditex maintained manufacturing capacity in Europe even though manufac- turing in Asia is much cheaper?

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3. Why does Zara source products with uncertain demand from local manufacturers and products with predictable demand from Asian manufacturers?

4. What advantage does Zara gain from replenishing its stores multiple times a week com- pared with a less frequent schedule?

5. Do you think Zara’s responsive replenishment infrastructure is better suited for online sales or retail sales?

W.W. Grainger and McMaster-Carr: MRO Suppliers

Both companies have catalogs and web pages through which orders can be placed. W.W. Grainger

call in an order, or place it via the website. W.W. Grainger orders are either shipped to the cus-

almost all its orders (although a few customers near its DCs do pick up their own orders). W.W.

product. They both primarily serve the role of a distributor or retailer. Their success is largely linked to their supply chain management ability.

Both firms offer several hundred thousand products to their customers. Grainger stocks

also provides many other products that it does not stock directly from its suppliers. Both firms face the following strategic and operational issues:

1. How many DCs should be built, and where should they be located? 2. 3. What products should be carried in inventory and what products should be left with the

supplier to be shipped directly in response to a customer order? 4. What products should W.W. Grainger carry at a store? 5. How should markets be allocated to DCs in terms of order fulfillment? What should be

locations? How should they be selected?

Toyota: A Global Auto Manufacturer

growth in global sales over the past two decades. A key issue facing Toyota is the design of its global production and distribution network. Part of Toyota’s global strategy is to open factories in every market it serves. Toyota must decide what the production capability of each of the facto- ries will be, as this has a significant impact on the desired distribution system. At one extreme, each plant can be equipped only for local production. At the other extreme, each plant is capable of supplying every market. Before 1996, Toyota used specialized local factories for each market. After the Asian financial crisis in 1996–97, Toyota redesigned its plants so it could also export to markets that remain strong when the local market weakens. Toyota calls this strategy “global complementation.”

Whether to be global or local is also an issue for Toyota’s parts plants and product design.

that supply multiple assembly plants? Toyota has worked hard to increase commonality in parts used around the globe. Although this has helped the company lower costs and improve parts availability, common parts caused significant difficulty when one of the parts had to be recalled. In 2009, Toyota had to recall about 12 million cars using common parts across North America, Europe, and Asia, causing significant damage to the brand as well as to the finances.

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Any global manufacturer like Toyota must address the following questions regarding the configuration and capability of the supply chain:

1. Where should the plants be located, and what degree of flexibility should be built into each? What capacity should each plant have?

2. 3. How should markets be allocated to plants and how frequently should this allocation be

revised? 4. How should the investment in flexibility be valued?

Amazon: Online Sales

Amazon sells books, music, and many other items over the Internet and is one of the pioneers of –

chased from a distributor in response to customer orders. As it grew, the company added ware- houses, allowing it to react more quickly to customer orders. In 2013, Amazon had about 40

– bound shipping-related costs at Amazon in 2012 were over $5 billion.

Following the introduction of the Kindle, Amazon has worked hard to increase sales of digital books. The company has also added a significant amount of audio and video content for sale in digital form.

Amazon has continued to expand the set of products that it sells online. Besides books and music, Amazon has added many product categories such as toys, apparel, electronics, jewelry, and shoes. In 2009, one of its largest acquisitions was Zappos, a leader in online shoe sales. This acquisition added a great deal of product variety: According to the Amazon annual report, this required creating 121,000 product descriptions and uploading more than 2.2 million images to

– pos, this acquisition added little variety but considerable shipping volumes.

continues to add:

1. Why is Amazon building more warehouses as it grows? How many warehouses should it have, and where should they be located?

2. 3. What advantage can bricks-and-mortar players derive from setting up an online channel?

How should they use the two channels to gain maximum advantage? 4. What advantages and disadvantages does the online channel enjoy in the sale of shoes and

diapers relative to a retail store? 5. For what products does the online channel offer the greater advantage relative to retail

stores? What characterizes these products?

Macy’s: Omni-Channel Retailing

channel retailing, allowing customers to have a seamless experience between shopping online or at a store. Customers can browse online and then experience the product at a store or order online

that were sold out of a particular item. If customers desire, orders placed online can be picked up at select stores and items purchased online can be returned to stores.

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Any omni-channel retailer must address the following questions:

1. 2. How should store inventories be managed in an omni-channel setting? 3.

1.7 SUMMARY OF LEARNING OBJECTIVES

1. Discuss the goal of a supply chain and explain the impact of supply chain decisions on the success of a firm. The goal of a supply chain should be to grow overall supply chain surplus.

cost incurred across all stages of the supply chain. A focus on the supply chain surplus increases

impact on the success or failure of each firm because they significantly influence both the revenue

and funds to provide a high level of product availability to the customer while keeping costs low.

2. Identify the three key supply chain decision phases and explain the significance of each one. –

– ply chain configuration. These decisions have a long-term impact that lasts for several years. Planning decisions cover a period of a few months to a year and include decisions regarding

decisions define the constraints for planning decisions, and planning decisions define the con- straints for operational decisions.

3. Describe the cycle and push/pull views of a supply chain. The cycle view divides pro- cesses into cycles, each performed at the interface between two successive stages of a supply chain. Each cycle starts with an order placed by one stage of the supply chain and ends when the order is received from the supplier stage. A push/pull view of a supply chain characterizes processes based on their timing relative to that of a customer order. Pull processes are performed in response to a customer order, whereas push processes are performed in anticipation of customer orders.

4. Classify the supply chain macro processes in a firm. All supply chain processes can be classified into three macro processes based on whether they are at the customer or supplier

– face between the firm and the customer that work to generate, receive, and track customer orders.

processes at the interface between the firm and its suppliers that work to evaluate and select sup- pliers and then source goods and services from them.

Discussion Questions 1. Consider the purchase of a can of soda at a convenience

store. Describe the various stages in the supply chain and the different flows involved.

2. Why should a firm such as Dell take into account total sup- ply chain profitability when making decisions?

3. What are some strategic, planning, and operational decisions that must be made by an apparel retailer such as Gap?

4. Consider the supply chain involved when a customer purchases a book at a bookstore. Identify the cycles in

this supply chain and the location of the push/pull boundary.

5. Consider the supply chain involved when a customer orders a book from Amazon. Identify the push/pull boundary and two processes each in the push and pull phases.

6. In what way do supply chain flows affect the success or failure of a firm such as Amazon? List two supply chain decisions that have a significant impact on supply chain profitability.

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Bibliography Supply

Chain Management Review

Product?” Harvard Business Review 83–93.

“Tailored Logistics: The Next Advantage.” Harvard Business Review

Sloan Management Review 2003): 27–34.

Supply Chain Management Review

– tainties.” California Management Review

Supply Chain Management Review

Diagnosing Greatness: Ten Traits of the Best Supply Chains.

The Logistics Handbook. New York: Free Press, 1994.

Harvard Business Review

Harvard Business Review

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In Chapter 1, we discussed what a supply chain is and the importance of supply chain design, planning, and operation to a firm’s success. In this chapter, we define supply chain strategy and explain how creating a strategic fit between a company’s competitive strategy and its supply chain strategy affects performance. We also discuss the importance of expanding the scope of strategic fit from one operation within a company to all stages of the supply chain.

2.1 COMPETITIVE AND SUPPLY CHAIN STRATEGIES

A company’s competitive strategy defines, relative to its competitors, the set of customer needs that it seeks to satisfy through its products and services. For example, Walmart aims to provide high availability of a variety of products of reasonable quality at low prices. Most products sold at Walmart are commonplace (everything from home appliances to clothing) and can be pur- chased elsewhere. What Walmart provides is a low price and product availability. McMaster- Carr sells maintenance, repair, and operations (MRO) products. It offers more than 500,000 products through both a catalog and a website. Its competitive strategy is built around providing the customer with convenience, availability, and responsiveness. With this focus on responsive- ness, McMaster does not compete based on low price. Clearly, the competitive strategy at Walmart is different from that at McMaster.

Supply Chain Performance Achieving Strategic Fit and Scope

C H A P T E R

2

LEARNING OBJECTIVES After reading this chapter, you will be able to

19

1. Explain why achieving strategic fit is critical to a company’s overall success.

2. Describe how a company achieves strategic fit between its supply chain strategy and its competitive strategy.

3. Discuss the importance of expanding the scope of strategic fit across the supply chain.

4. Describe the major challenges that must be overcome to manage a supply chain successfully.

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We can also contrast Blue Nile, with its online retailing model for diamonds, with Zales, which sells diamond jewelry through retail outlets. Blue Nile has emphasized the variety of dia- monds available from its website and the fact that its margins are significantly lower than its bricks-and-mortar competition. Customers, however, have to wait to get their jewelry and do not have any opportunity to touch and see it before purchase (Blue Nile does provide a 30-day return period, though). At Zales, in contrast, a customer can walk into the retail store, be helped by a salesperson, and leave immediately with a diamond ring. The amount of variety available at a Zales store, however, is limited. Whereas Blue Nile offers more than 90,000 stones on its site, a typical Zales store carries fewer than a thousand.

In each case, the competitive strategy is defined based on how the customer prioritizes product cost, delivery time, variety, and quality. A McMaster-Carr customer places greater emphasis on product variety and response time than on cost. A Walmart customer, in contrast, places greater emphasis on cost. A Blue Nile customer, purchasing online, places great emphasis on product variety and cost. A customer purchasing jewelry at Zales is most concerned with fast response time and help in product selection. Thus, a firm’s competitive strategy will be defined based on its customers’ priorities. Competitive strategy targets one or more customer segments and aims to provide products and services that satisfy these customers’ needs.

To see the relationship between competitive and supply chain strategies, we start with the value chain for a typical organization, as shown in Figure 2-1.

The value chain begins with new product development, which creates specifications for the product. Marketing and sales generate demand by publicizing the customer priorities that the products and services will satisfy. Marketing also brings customer input back to new prod- uct development. Operations transforms inputs to outputs to create the product according to new product specifications. Distribution either takes the product to the customer or brings the

are core processes or functions that must be performed for a successful sale. Finance, account- ing, information technology, and human resources support and facilitate the functioning of the value chain.

To execute a company’s competitive strategy, all these functions play a role, and each must develop its own strategy. Here, strategy refers to what each process or function will try to do particularly well.

A product development strategy specifies the portfolio of new products that a company will try to develop. It also dictates whether the development effort will be made internally or outsourced. A marketing and sales strategy specifies how the market will be segmented and how the product will be positioned, priced, and promoted. A supply chain strategy determines the nature of procurement of raw materials, transportation of materials to and from the com- pany, manufacture of the product or operation to provide the service, and distribution of the product to the customer, along with any follow-up service and a specification of whether these

operations, distribution, and service functions, whether performed in-house or outsourced, should do particularly well. Because our focus here is on supply chain strategy, we define it in

New Product Development

Marketing and Sales

Operations Distribution Service

Finance, Accounting, Information Technology, Human Resources

FIGURE 2-1 The Value Chain in a Company

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chain and what many traditionally call “supplier strategy,” “operations strategy,” and “logistics

through resellers, and Cisco’s decision to use contract manufacturers define the broad structure

includes design decisions regarding inventory, transportation, operating facilities, and informa- tion flows. For example, Amazon’s decisions to build warehouses to stock some products and to continue using distributors as a source of other products are part of its supply chain strategy.

its supply chain strategy. For a firm to succeed, all functional strategies must support one another and the competi-

easy access to stores and availability of a wide range of products and services. New product

services that draw customers in and exploit the excellent information infrastructure and the fact

focused on having a high density of stores, being very responsive, and providing an excellent information infrastructure. The result is a virtuous cycle in which supply chain infrastructure is exploited to offer new products and services that increase demand, and the increased demand in turn makes it easier for operations to improve the density of stores, responsiveness in replenish- ment, and the information infrastructure.

Given its competitive strategy, what should a company’s supply chain try to do particularly well?

2.2 ACHIEVING STRATEGIC FIT

Strategic fit requires that both the competitive and supply chain strategies of a company have aligned goals. It refers to consistency between the customer priorities that the competitive strat- egy hopes to satisfy and the supply chain capabilities that the supply chain strategy aims to build.

1. The competitive strategy and all functional strategies must fit together to form a coordi- nated overall strategy. Each functional strategy must support other functional strategies and help a firm reach its competitive strategy goal.

2. The different functions in a company must appropriately structure their processes and resources to be able to execute these strategies successfully.

3. The design of the overall supply chain and the role of each stage must be aligned to support the supply chain strategy.

A company may fail either because of a lack of strategic fit or because its overall supply chain design, processes, and resources do not provide the capabilities to support the desired stra- tegic fit. Consider, for example, a situation in which marketing is publicizing a company’s ability to provide a large variety of products quickly; simultaneously, distribution is targeting the lowest-cost means of transportation. In this situation, it is likely that distribution will delay orders so it can get better transportation economies by grouping orders together or using inexpensive but slow modes of transportation. This action conflicts with marketing’s stated goal of providing

level of variety while carrying low levels of inventory but has selected suppliers and carriers based on their low price and not their responsiveness. In this case, the retailer is likely to end up with unhappy customers because of poor product availability.

To elaborate on strategic fit, let us consider the evolution of Dell and its supply chain between 1993 and the present. Between 1993 and 2006, Dell’s competitive strategy was to provide a large

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variety of customizable products at a reasonable price. Given the focus on customization, Dell’s supply chain was designed to be very responsive. Assembly facilities owned by Dell were designed to be flexible and to easily handle the wide variety of configurations requested by customers. A facility that focused on low cost and efficiency by producing large volumes of the same configura- tion would not have been appropriate in this setting.

designed to use common components and to allow rapid assembly. This design strategy clearly

orders. Dell worked hard to carry this alignment to its suppliers. Given that Dell produced cus- tomized products with low levels of inventory, it was crucial that suppliers and carriers be highly

– ply chain accordingly. With a reduced customer focus on hardware customization, Dell branched

– ited variety of desktops and laptops. It is also essential that monitors and other peripherals be

monitor to show up later. Clearly, the flexible and responsive supply chain that aligns well with customer needs for customization does not necessarily align well when customers no longer want customization but prefer low prices. Given the change in customer priorities, Dell has shifted a greater fraction of its production to a build-to-stock model to maintain strategic fit. Contract manufacturers like Foxconn that are focused on low cost now produce many of Dell’s products well in advance of sale. To maintain strategic fit, Dell’s supply chain has moved from a relentless focus on responsiveness to a greater focus on low cost.

How Is Strategic Fit Achieved?

What does a company need to do to achieve that all-important strategic fit between the supply chain and competitive strategies? A competitive strategy will specify, either explicitly or implic- itly, one or more customer segments that a company hopes to satisfy. To achieve strategic fit, a company must ensure that its supply chain capabilities support its ability to satisfy the needs of the targeted customer segments.

There are three basic steps to achieving this strategic fit, which we outline here and then

1. Understanding the customer and supply chain uncertainty: First, a company must understand the customer needs for each targeted segment and the uncertainty these needs impose on the supply chain. These needs help the company define the desired cost and service requirements. The supply chain uncertainty helps the company identify the extent of the unpredictability of demand and supply that the supply chain must be prepared for.

2. Understanding the supply chain capabilities: Each of the many types of supply chains is designed to perform different tasks well. A company must understand what its supply chain is designed to do well.

3. Achieving strategic fit: If a mismatch exists between what the supply chain does particu- larly well and the desired customer needs, the company will either need to restructure the supply chain to support the competitive strategy or alter its competitive strategy.

STEP 1: UNDERSTANDING THE CUSTOMER AND SUPPLY CHAIN UNCERTAINTY To under- stand the customer, a company must identify the needs of the customer segment being served.

a nearby store and are not necessarily looking for the lowest price. In contrast, low price is very

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even purchase large package sizes as long as the price is low. Even though customers purchase

and are willing to spend time getting it. In general, customer demand from different segments

Quantity of the product needed in each lot: An emergency order for material needed to repair a production line is likely to be small. An order for material to construct a new pro- duction line is likely to be large. Response time that customers are willing to tolerate: The tolerable response time for the emergency order is likely to be short, whereas the allowable response time for the con- struction order is apt to be long. Variety of products needed: A customer may place a high premium on the availability of all parts of an emergency repair order from a single supplier. This may not be the case for the construction order. Service level required: A customer placing an emergency order expects a high level of product availability. This customer may go elsewhere if all parts of the order are not imme- diately available. This is not apt to happen in the case of the construction order, for which a long lead time is likely. Price of the product: The customer placing the emergency order is apt to be much less sensitive to price than the customer placing the construction order. Desired rate of innovation in the product: Customers at a high-end department store expect a lot of innovation and new designs in the store’s apparel. Customers at Walmart may be less sensitive to new product innovation.

Each customer in a particular segment will tend to have similar needs, whereas customers in a different segment can have very different needs.

Although we have described several attributes along which customer demand varies, our goal is to identify one key measure for combining all of these attributes. This single measure then helps define what the supply chain should do particularly well.

Implied Demand Uncertainty. At first glance, it may appear that each of the customer need categories should be viewed differently, but in a fundamental sense, each customer need can be translated into the metric of implied demand uncertainty, which is demand uncertainty imposed on the supply chain because of the customer needs it seeks to satisfy.

We make a distinction between demand uncertainty and implied demand uncertainty. Demand uncertainty reflects the uncertainty of customer demand for a product. Implied demand uncertainty, in contrast, is the resulting uncertainty for only the portion of the demand that the supply chain plans to satisfy based on the attributes the customer desires. For example, a firm supplying only emergency orders for a product will face a higher implied demand uncertainty than a firm that supplies the same product with a long lead time, as the second firm has an oppor- tunity to fulfill the orders evenly over the long lead time.

Another illustration of the need for this distinction is the impact of service level. As a sup- ply chain raises its level of service, it must be able to meet a higher and higher percentage of actual demand, forcing it to prepare for rare surges in demand. Thus, raising the service level increases the implied demand uncertainty even though the product’s underlying demand uncer- tainty does not change.

Both the product demand uncertainty and various customer needs that the supply chain tries to fill affect implied demand uncertainty. Table 2-1 illustrates how various customer needs affect implied demand uncertainty.

As each individual customer need contributes to the implied demand uncertainty, we can use implied demand uncertainty as a common metric with which to distinguish different types of demand.

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Fisher (1997) pointed out that implied demand uncertainty is often correlated with other

1. a result, margins tend to be high.

2. Forecasting is more accurate when demand has less uncertainty. 3. Increased implied demand uncertainty leads to increased difficulty in matching supply

with demand. For a given product, this dynamic can lead to either a stockout or an oversup- ply situation. Increased implied demand uncertainty thus leads to both higher oversupply and a higher stockout rate.

4. Markdowns are high for products with greater implied demand uncertainty because over- supply often results.

First, let us take an example of a product with low implied demand uncertainty—such as

markdowns. These characteristics match well with Fisher’s chart of characteristics for products with highly certain demand.

On the other end of the spectrum, a new cell phone has high implied demand uncertainty. It will likely have a high margin, inaccurate demand forecasts, high stockout rates (if it is suc- cessful), and large markdowns (if it is a failure). This, too, matches well with Table 2-2.

Lee (2002) pointed out that along with demand uncertainty, it is important to consider uncertainty resulting from the capability of the supply chain. For example, when a new compo- nent is introduced in the consumer electronics industry, the quality yields of the production pro- cess tend to be low and breakdowns are frequent. As a result, companies have difficulty delivering

TABLE 2-1 Impact of Customer Needs on Implied Demand Uncertainty Customer Need Causes Implied Demand Uncertainty to . . .

Range of quantity required increases Increase because a wider range of the quantity required implies greater variance in demand.

Lead time decreases Increase because there is less time in which to react to orders.

Variety of products required increases Increase because demand per product becomes less predictable.

Number of channels through which product may be acquired increases

Increase because customer demand per channel becomes less predictable.

Rate of innovation increases Increase because new products tend to have more uncertain demand.

Required service level increases Increase because the firm now has to handle unusual surges in demand.

Low Implied Uncertainty

High Implied Uncertainty

Product margin Low High

Average forecast error 10% 40% to 100%

Average stockout rate 1% to 2% 10% to 40%

Average forced season-end markdown 0% 10% to 25%

Source: Harvard Business Review (March–April 1997), 83–93.

TABLE 2-2 Correlation Between Implied Demand Uncertainty and Other Attributes

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according to a well-defined schedule, resulting in high supply uncertainty for electronics manu- facturers. As the production technology matures and yields improve, companies are able to fol- low a fixed delivery schedule, resulting in low supply uncertainty. Table 2-3 illustrates how various characteristics of supply sources affect the supply uncertainty.

products being introduced have higher supply uncertainty because designs and production pro- cesses are still evolving. In contrast, mature products have less supply uncertainty.

We can create a spectrum of uncertainty by combining the demand and supply uncertainty. This implied uncertainty spectrum is shown in Figure 2-2.

A company introducing a brand-new cell phone based on entirely new components and technology faces high implied demand uncertainty and high supply uncertainty. As a result, the implied uncertainty faced by the supply chain is extremely high. In contrast, a supermarket sell- ing salt faces low implied demand uncertainty and low levels of supply uncertainty, resulting in a low implied uncertainty. Many agricultural products, such as coffee, are examples of supply chains facing low levels of implied demand uncertainty but significant supply uncertainty based on weather. The supply chain thus faces an intermediate level of implied uncertainty.

TABLE 2-3 Impact of Supply Source Capability on Supply Uncertainty Supply Source Capability Causes Supply Uncertainty to . . .

Frequent breakdowns Increase

Unpredictable and low yields Increase

Poor quality Increase

Limited supply capacity Increase

Inflexible supply capacity Increase

Evolving production process Increase

Source: Uncertainties.” California Management Review

Predictable supply and

demand

Predictable supply and uncertain demand, or uncertain supply and predictable demand, or somewhat

uncertain supply and demand

Highly uncertain supply and

demand

Salt at a supermarket

An existing automobile

model

A new communication

device

FIGURE 2-2 The Implied Uncertainty (Demand and Supply) Spectrum

Key Point

The first step in achieving strategic fit between competitive and supply chain strategies is to understand customers and supply chain uncertainty. Uncertainty from the customer and the supply chain can be combined and mapped on the implied uncertainty spectrum.

STEP 2: UNDERSTANDING THE SUPPLY CHAIN CAPABILITIES After understanding the uncer-

uncertain environment? Creating strategic fit is all about designing a supply chain whose respon- siveness aligns with the implied uncertainty it faces.

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We now categorize supply chains based on different characteristics that influence their responsiveness and efficiency.

First, we provide some definitions. Supply chain responsiveness includes a supply chain’s

These abilities are similar to many of the characteristics of demand and supply that led to high implied uncertainty. The more of these abilities a supply chain has, the more responsive it is.

Responsiveness, however, comes at a cost. For instance, to respond to a wider range of quantities demanded, capacity must be increased, which increases costs. This increase in cost

Supply chain efficiency is the inverse of the cost of making and delivering a product to the customer. Increases in cost lower efficiency. For every strategic choice to increase responsiveness, there are additional costs that lower efficiency.

The cost-responsiveness efficient frontier is the curve in Figure 2-3 showing the lowest possible cost for a given level of responsiveness. Lowest cost is defined based on existing tech- nology; not every firm is able to operate on the efficient frontier, which represents the cost- responsiveness performance of the best supply chains. A firm that is not on the efficient frontier can improve both its responsiveness and its cost performance by moving toward the efficient frontier. In contrast, a firm on the efficient frontier can improve its responsiveness only by

efficiency and responsiveness. Of course, firms on the efficient frontier are also continuously improving their processes and changing technology to shift the efficient frontier itself. Given the trade-off between cost and responsiveness, a key strategic choice for any supply chain is the level of responsiveness it seeks to provide.

a goal of producing and supplying at the lowest possible cost. Figure 2-4 shows the responsive- ness spectrum and where some supply chains fall on this spectrum.

High

Low

High Low Cost

Responsiveness

FIGURE 2-3 Cost-Responsiveness Efficient Frontier

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The more capabilities constituting responsiveness a supply chain has, the more responsive it

the afternoon, and dinner items at night. As a result, the available product variety changes by time

chain very responsive. Another example of a responsive supply chain is W.W. Grainger. The com- pany faces both demand and supply uncertainty; therefore, the supply chain has been designed to deal effectively with both to provide customers with a wide variety of MRO products within 24 hours. An efficient supply chain, in contrast, lowers cost by eliminating some of its responsive

The supply chain is capable of low costs, and the focus of this supply chain is clearly on efficiency.

Highly efficient

Somewhat efficient

Somewhat responsive

Highly responsive

Integrated steel mills: Production scheduled weeks

or months in advance with

little variety or flexibility

Hanes apparel: A traditional make-to- stock manufacturer

with production lead time of

several weeks

Most automotive production:

Delivering a large variety of products

in a few weeks

Seven-Eleven Japan: Changing merchandise mix by location and

time of day

FIGURE 2-4 The Responsiveness Spectrum

Key Point

The second step in achieving strategic fit between competitive and supply chain strategies is to under- stand the supply chain and map it on the responsiveness spectrum.

STEP 3: ACHIEVING STRATEGIC FIT After mapping the level of implied uncertainty and under- standing the supply chain position on the responsiveness spectrum, the third and final step is to ensure that the degree of supply chain responsiveness is consistent with the implied uncertainty. The goal is to target high responsiveness for a supply chain facing high implied uncertainty, and efficiency for a supply chain facing low implied uncertainty.

For example, the competitive strategy of McMaster-Carr targets customers that value hav- ing a large variety of MRO products delivered to them within 24 hours. Given the large variety of products and rapid desired delivery, demand from McMaster-Carr customers can be character- ized as having high implied demand uncertainty. If McMaster-Carr designed an efficient supply chain, it may carry less inventory and maintain a level load on the warehouse to lower picking and packing costs. These choices, however, would make it difficult for the company to support the customer’s desire for a wide variety of products that are delivered within 24 hours. To serve its customers effectively, McMaster-Carr carries a high level of inventory and picking and pack- ing capacity. Clearly, a responsive supply chain is better suited to meet the needs of customers targeted by McMaster-Carr even if it results in higher costs.

– –

able. Barilla could design a highly responsive supply chain in which pasta is custom made in small batches in response to customer orders and shipped via a rapid transportation mode such as FedEx. This choice would obviously make the pasta prohibitively expensive, resulting in a loss of customers. Barilla, therefore, is in a much better position if it designs a more efficient supply chain with a focus on cost reduction.

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From the preceding discussion, it follows that increasing implied uncertainty from cus- tomers and supply sources is best served by increasing responsiveness from the supply chain. This relationship is represented by the “zone of strategic fit” illustrated in Figure 2-5. For a high level of performance, companies should move their competitive strategy (and resulting implied uncertainty) and supply chain strategy (and resulting responsiveness) toward the zone of strategic fit.

The next step in achieving strategic fit is to assign roles to different stages of the supply chain that ensure the appropriate level of responsiveness. It is important to understand that the desired level of responsiveness required across the supply chain may be attained by assigning different levels of responsiveness and efficiency to each stage of the supply chain as illustrated by the following examples.

has targeted customers who want stylish furniture at a reasonable cost. The company limits the variety of styles that it sells through modular design. The large scale of each store and the limited variety of furniture (through modular design) decrease the implied uncertainty faced by the sup- ply chain. IKEA stocks all styles in inventory and serves customers from stock. Thus, it uses inventory to absorb all the uncertainty faced by the supply chain. The presence of inventory at large IKEA stores allows replenishment orders to its manufacturers to be more stable and pre- dictable. As a result, IKEA passes along little uncertainty to its manufacturers, who tend to be located in low-cost countries and focus on efficiency. IKEA provides responsiveness in the sup- ply chain, with the stores absorbing most of the uncertainty and being responsive, and the suppli- ers absorbing little uncertainty and being efficient.

In contrast, another approach for responsiveness may involve the retailer holding little inventory. In this case, the retailer does not contribute significantly to supply chain responsive- ness, and most of the implied demand uncertainty is passed on to the manufacturer. For the sup- ply chain to be responsive, the manufacturer now needs to be flexible and have low response times. An example of this approach is England, Inc., a furniture manufacturer located in Tennes- see. Every week, the company makes several thousand sofas and chairs to order, delivering them to furniture stores across the country within three weeks. England, Inc.’s retailers allow custom- ers to select from a wide variety of styles and promise relatively quick delivery. This imposes a high level of implied uncertainty on the supply chain. The retailers, however, do not carry much inventory and pass most of the implied uncertainty on to England, Inc. The retailers can thus be efficient because most of the implied uncertainty for the supply chain is absorbed by England, Inc., with its flexible manufacturing process. England, Inc., itself has a choice of how much

Responsive Supply Chain

Responsiveness Spectrum

Efficient Supply Chain

Certain Demand

Implied Uncertainty Spectrum

Uncertain Demand

Zo ne

of

Str ate

gic Fi

t

FIGURE 2-5 Finding the Zone of Strategic Fit

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uncertainty it passes along to its suppliers. By holding more raw material inventories, the com- pany allows its suppliers to focus on efficiency. If it decreases its raw material inventories, its suppliers must become more responsive.

The preceding discussion illustrates that the supply chain can achieve a given level of responsiveness by adjusting the role of each of its stages. Making one stage more responsive allows other stages to focus on becoming more efficient. The best combination of roles depends on the efficiency and flexibility available at each stage. The notion of achieving a given level of responsiveness by assigning different roles and levels of uncertainty to differ- ent stages of the supply chain is illustrated in Figure 2-6. The figure shows two supply chains that face the same implied uncertainty but achieve the desired level of responsiveness with

I has a very responsive retailer that absorbs most of the uncertainty, allowing (actually requir-

responsive manufacturer that absorbs most of the uncertainty, thus allowing the other stages to focus on efficiency.

To achieve complete strategic fit, a firm must also ensure that all its functions maintain consistent strategies that support the competitive strategy. All functional strategies must support the goals of the competitive strategy. All substrategies within the supply chain—such as manu- facturing, inventory, and purchasing—must also be consistent with the supply chain’s level of responsiveness. Table 2-4 lists some of the major differences in functional strategy between sup- ply chains that are efficient and those that are responsive.

Extent of Implied Uncertainty for the Supply Chain

RetailerManufacturerSupplier

Retailer absorbs most of the implied

uncertainty and must be very responsive.

Manufacturer absorbs less implied

uncertainty and must be somewhat

efficient.

Supplier absorbs the least implied uncertainty and

must be very efficient.

Supply Chain I

Supply Chain II

Retailer absorbs the least implied uncertainty and

must be very efficient.

Manufacturer absorbs most of the implied uncertainty and must be very

responsive.

Supplier absorbs less implied

uncertainty and must be somewhat

efficient.

RetailerManufacturerSupplier

FIGURE 2-6 Different Roles and Allocations of Implied Uncertainty for a Given Level of Supply Chain Responsiveness

Key Point

The final step in achieving strategic fit is to match supply chain responsiveness with the implied uncer- tainty from demand and supply. The supply chain design and all functional strategies within the firm must also support the supply chain’s level of responsiveness.

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Tailoring the Supply Chain for Strategic Fit

Our previous discussion focused on achieving strategic fit when a firm serves a single market segment and the result is a well-defined and narrow strategic position. Although such a scenario holds for firms like IKEA, many firms are required to achieve strategic fit while serving many customer segments with a variety of products across multiple channels. In such a scenario, a “one size fits all” supply chain cannot provide strategic fit, and a tailored supply chain strategy is required. For example, Zara sells trendy items with unpredictable demand along with basics, such as white T-shirts, with a more predictable demand. Whereas Zara uses a responsive supply chain with production in Europe for trendy items, it uses a more efficient supply chain with pro- duction in Asia for the basics. This tailored supply chain strategy provides a better strategic fit for

both customized and standard-sized jeans. Demand for standard-sized jeans has a much lower

supply chain to meet both sets of needs. In each of the previous examples, the products sold and the customer segments served have

different implied demand uncertainty. When devising supply chain strategy in these cases, the key issue for a company is to design a tailored supply chain that is able to be efficient when implied uncertainty is low and responsive when it is high. By tailoring its supply chain, a com- pany can provide responsiveness to fast-growing products, customer segments, and channels while maintaining low cost for mature, stable products and customer segments.

Tailoring the supply chain requires sharing operations for some links in the supply chain, while having separate operations for other links. The links are shared to achieve maximum pos- sible efficiency while providing the appropriate level of responsiveness to each segment. For instance, all products may be made on the same line in a plant, but products requiring a high level

that do not have high responsiveness needs may be sent by slower and less expensive means such as truck, rail, or even ship. In other instances, products requiring high responsiveness may be manufactured using a flexible process, whereas products requiring less responsiveness may be manufactured using a less responsive but more efficient process. The mode of transportation

TABLE 2-4 Comparison of Efficient and Responsive Supply Chains Efficient Supply Chains Responsive Supply Chains

Primary goal Supply demand at the lowest cost Respond quickly to demand

Product design strategy Maximize performance at a minimum product cost

Create modularity to allow postponement of product differentiation

Pricing strategy Lower margins because price is a prime customer driver

Higher margins because price is not a prime customer driver

Manufacturing strategy Lower costs through high utilization

Maintain capacity flexibility to buffer against demand/supply uncertainty

Inventory strategy Minimize inventory to lower cost Maintain buffer inventory to deal with demand/supply uncertainty

Lead-time strategy Reduce, but not at the expense of costs

Reduce aggressively, even if the costs are significant

Supplier strategy Select based on cost and quality Select based on speed, flexibility, reliability, and quality

Source: Harvard Business Review (March–April 1997), 83–93.

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used in both cases, however, may be the same. In other cases, some products may be held at regional warehouses close to the customer, whereas others may be held in a centralized ware- house far from the customer. W.W. Grainger holds fast-moving items with low implied uncer- tainty in its decentralized locations close to the customer. It holds slow-moving items with higher implied demand uncertainty in a centralized warehouse. Appropriate tailoring of the supply chain helps a firm achieve varying levels of responsiveness for a low overall cost. The level of responsiveness is tailored to each product, channel, or customer segment. Tailoring of the supply chain is an important concept that we develop further in subsequent chapters.

The concept of tailoring to achieve strategic fit is important in industries such as high-tech and pharmaceuticals, in which innovation is critical and products move through a life cycle. Let us consider changes in demand and supply characteristics over the life cycle of a product. Toward

1. Demand is very uncertain, and supply may be unpredictable. 2. Margins are often high, and time is crucial to gaining sales. 3. 4. Cost is often a secondary consideration.

Consider a pharmaceutical firm introducing a new drug. Initial demand for the drug is highly uncertain, margins are typically high, and product availability is the key to capturing mar- ket share. The introductory phase of a product’s life cycle corresponds to high implied uncer- tainty, given the high demand uncertainty and the need for a high level of product availability. In such a situation, responsiveness is the most important characteristic of the supply chain.

As the product becomes a commodity product later in its life cycle, the demand and supply

1. Demand has become more certain, and supply is predictable. 2. Margins are lower as a result of an increase in competitive pressure. 3.

In the case of a pharmaceutical company, these changes occur when demand for the drug stabilizes, production technologies are well developed, and supply is predictable. This stage cor- responds to a low level of implied uncertainty. As a result, the supply chain must change. In such a situation, efficiency becomes the most important characteristic of the supply chain. The phar- maceutical industry has reacted by building a mix of flexible and efficient capacity whose use is tailored to the product life cycle. New products are typically introduced using flexible capacity that is more expensive but responsive enough to deal with the high level of uncertainty during the early stages of the life cycle. Mature products with high demand are shifted to dedicated capacity that is highly efficient because it handles low levels of uncertainty and enjoys the advantage of high scale. The tailored capacity strategy has allowed pharmaceutical firms to maintain strategic fit for a wide range of products at different stages of their life cycle.

In the next section, we describe how the scope of the supply chain has expanded when achieving strategic fit. We also discuss why expanding the scope of strategic fit is critical to sup- ply chain success.

Key Point

When supplying multiple customer segments with a wide variety of products through several channels, a firm must tailor its supply chain to achieve strategic fit.

2.3 EXPANDING STRATEGIC SCOPE

A key issue relating to strategic fit is the scope, in terms of supply chain stages, across which the strategic fit applies. Scope of strategic fit refers to the functions within the firm and stages across the supply chain that devise an integrated strategy with an aligned objective. At one extreme,

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every operation within each functional area devises its own independent strategy, with the objec- tive of optimizing its local performance. In this case, the scope of strategic fit is restricted to an operation in a functional area within a stage of the supply chain. At the opposite extreme, all functional areas across all stages of the supply chain devise aligned strategies that maximize sup- ply chain surplus. In this case, the scope of strategic fit extends to the entire supply chain.

In this section, we discuss how expanding the scope of strategic fit improves supply chain performance. For example, IKEA has achieved great success by expanding its scope of strategic fit to include all functions and stages within the supply chain. Its competitive strategy is to offer a reasonable variety of furniture and home furnishings at low prices. Its stores are large and carry all products in inventory. Its products are designed to be modular and easy to assemble. The large stores and modular design allow IKEA to move final assembly and last-mile delivery (two high- cost operations) to the customer. As a result, all functions within the IKEA supply chain focus on efficiency. Its suppliers concentrate on producing large volumes of a few modules at low cost. Its transportation function focuses on shipping large quantities of high-density unassembled mod- ules at low cost to the large stores. The strategy at every stage and function of the IKEA supply chain is aligned to increase the supply chain surplus.

Intraoperation Scope: Minimizing Local Cost

The intraoperation scope has each stage of the supply chain devising its strategy independently. In such a setting, the resulting collection of strategies typically does not align, resulting in con- flict. This limited scope was the dominant practice during the 1950s and 1960s, when each oper- ation within each stage of the supply chain attempted to minimize its own costs. As a result of this narrow scope, the transportation function at many firms may have shipped full truckloads without any regard for the resulting impact on inventories or responsiveness, or the sales function may have offered trade promotions to enhance revenue without any consideration for how those promotions affected production, warehousing, and transportation costs. The resulting lack of alignment diminished the supply chain surplus.

Intrafunctional Scope: Minimizing Functional Cost

Over time, managers recognized the weakness of the intraoperation scope and attempted to align all operations within a function. For example, the use of air freight could be justified only if the resulting savings in inventories and improved responsiveness justified the increased transporta- tion cost. With the intrafunctional view, firms attempted to align all operations within a function. All supply chain functions, including sourcing, manufacturing, warehousing, and transportation, had to align their strategies to minimize total functional cost. As a result, product could be sourced from a higher-cost local supplier because the resulting decrease in inventory and trans- portation costs more than compensated for the higher unit cost.

Interfunctional Scope: Maximizing Company Profit

The key weakness of the intrafunctional view is that different functions within a firm may have conflicting objectives. Over time, companies became aware of this weakness as they saw, for example, marketing and sales focusing on revenue generation, and manufacturing and distribu- tion focusing on cost reduction. Actions the two functions took were often in conflict, hurting the firm’s overall performance. Companies realized the importance of expanding the scope of strate- gic fit and aligning strategy across all functions within the firm. With the interfunctional scope, the goal is to maximize company profit. To achieve this goal, all functional strategies are devel- oped to align with one another and with the competitive strategy.

The goal of aligning strategies across functions results in warehouse operations within McMaster-Carr carrying high inventory and excess capacity to ensure that marketing’s promise of next-day delivery is always met. The company’s profits grow because the increased margin

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that customers are willing to pay for high reliability more than compensates for the higher inven- tory and warehouse expense. The company enjoys high profits because all functions align their strategy around the common objective of customer convenience in the form of next-day delivery of a wide variety of MRO products.

Intercompany Scope: Maximizing Supply Chain Surplus

The goal of only maximizing company profits can sometimes lead to conflict between stages of a supply chain. For example, both the supplier and the manufacturer in a supply chain may prefer to have the other side hold most of the inventory, with the goal of improving their own profits. If the two parties cannot look beyond their own profits, the more powerful party will simply force the other to hold inventories without any regard for where inventories are best held. The result is a decrease in the supply chain surplus—the total pie that both parties get to share.

The intercompany scope proposes a different approach. Instead of just forcing the inven- tory onto the weaker party, the two parties work together to reduce the amount of inventory required. By working together and sharing information, they can reduce inventories and total cost, thus increasing the supply chain surplus. The higher the supply chain surplus, the more competitive the supply chain is.

Key Point

The intercompany scope of strategic fit requires firms to evaluate every action in the context of the entire supply chain. This broad scope increases the size of the surplus to be shared among all stages of the sup- ply chain. The intercompany scope of strategic fit is essential today because the competitive playing field has shifted from company versus company to supply chain versus supply chain. A company’s part- ners in the supply chain may well determine the company’s success, as the company is intimately tied to its supply chain.

jointly. The two companies have a team (with employees from both parties) that works to ensure that the promotion is timed and executed to benefit both sides. Before the initiation of this collab-

at high cost. The result was a decrease in the supply chain surplus because the product was sold at a discount at a time when it was being produced at high marginal cost. The collaborative teams now try to increase the supply chain surplus by timing the promotion to have high sales impact while minimizing the marginal cost increase. They work to ensure that the product is produced in such a manner that all promotion demand is met without generating excess unsold inventories.

Agile Intercompany Scope

Up to this point, we have discussed strategic fit in a static context; that is, the players in a supply chain and the customers’ needs do not change over time. In reality, the situation is much more

of individual customers. A company may have to partner with many firms, depending on the product being produced and the customer being served. Firms’ strategies and operations must be agile enough to maintain strategic fit in a changing environment.

Agile intercompany scope refers to a firm’s ability to achieve strategic fit when partnering with supply chain stages that change over time. Firms must think in terms of supply chains con- sisting of many players at each stage. For example, a manufacturer may interface with a different set of suppliers and distributors depending on the product being produced and the customer being served. Furthermore, as customers’ needs vary over time, firms must have the ability to become part of new supply chains while ensuring strategic fit. This level of agility becomes more important as the competitive environment becomes more dynamic.

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2.4 CHALLENGES TO ACHIEVING AND MAINTAINING STRATEGIC FIT

The key to achieving strategic fit is a company’s ability to find a balance between responsiveness and efficiency that best matches the needs of its target customers. In deciding where this balance should be located on the responsiveness spectrum, companies face many challenges. On one hand, these challenges have made it much more difficult for companies to create the ideal bal- ance. On the other hand, they have afforded companies increased opportunities for improving supply chain management. Managers need a solid understanding of the impact of these chal- lenges because they are critical to a company’s ability to grow its supply chain surplus.

Increasing Product Variety and Shrinking Life Cycles

One of the biggest challenges to maintaining strategic fit is the growth in product variety and the decrease in the life cycle of many products. Greater product variety and shorter life cycles increase uncertainty while reducing the window of opportunity within which the supply chain can achieve fit. The challenge gets magnified when companies continue to increase new products without maintaining the discipline of eliminating older ones. Apple, for example, has had great success limiting its product variety while continuing to introduce new products. This has allowed the company the luxury of dealing only with high-demand products, for which it becomes easier to design an aligned supply chain. In general, however, firms must design product platforms with common components and maintain a tailored supply chain that contains a responsive solution to handle new products and other low-volume products and a low-cost solution to handle successful

customer. This often requires the continual elimination of older products.

Globalization and Increasing Uncertainty

Globalization has increased both the opportunities and risks for supply chains. The twenty-first century has started with significant fluctuations in exchange rates, global demand, and the price of crude oil, all factors that affect supply chain performance. In 2008 alone, the euro peaked in value at about $1.59 and went as low as $1.25. In 2001, the euro went as low as $0.85. After

dropped significantly between November 2007 and October 2008. In October 2008, auto sales in

The drop in sales of larger vehicles was much more significant than the drop for smaller, more

$50 a barrel by November 2008.

that ignored them. For example, Honda built flexible plants that were a great help in 2008 as

operations. In contrast, companies that had built plants dedicated to producing only large trucks

for global risks and uncertainties if they want to maintain strategic fit.

Fragmentation of Supply Chain Ownership

Over the past several decades, most firms have become less vertically integrated. As companies have shed noncore functions, they have been able to take advantage of supplier and customer com- petencies that they themselves did not have. This new ownership structure, however, has also made aligning and managing the supply chain more difficult. With the chain broken into many owners, each with its own policies and interests, the chain is more difficult to coordinate. This problem could potentially cause each stage of a supply chain to work only toward its local objectives rather

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than those of the whole chain, resulting in the reduction of overall supply chain profitability. Align- ing all members of a supply chain has become critical to achieving supply chain fit.

Changing Technology and Business Environment

With a changing environment in terms of customer needs and technology, companies must con- stantly evaluate their supply chain strategy to maintain strategic fit. A strategy that may have been very successful in one environment can easily become a weakness in a changed setting. Dell is an excellent example of this difficulty. For more than a decade, Dell enjoyed tremendous success

were built to order in flexible facilities. By about 2005, though, the market had moved toward laptops, and customers started to place less value on customization. As a result, Dell was forced to

to increase the amount of assembly that was outsourced to low-cost contract manufacturers.

postal system to ship an even greater variety of films at low cost from centralized distribution centers. The growth in bandwidth allowed Netflix to stream digital content directly to customer

rented at low cost. Blockbuster’s inability to adjust to this transformation in technology and the business environment resulted in its bankruptcy in 2010.

The Environment and Sustainability

Issues related to the environment and sustainability have grown in relevance and must be accounted for when designing supply chain strategy. In some instances, regulation has been driv- ing changes; in others, change has been driven by the perception of the lack of sustainability as a risk factor. For example, the Waste Electrical and Electronic Equipment (WEEE) and Restriction

focus on local sustainability of its supply sources because a supply failure, especially for higher- quality coffee, would have significantly affected its ability to grow. The company developed sourcing guidelines to ensure that produced coffee met environmental and social performance criteria at each stage of the supply chain. Environmental issues represent a tremendous opportu- nity to firms that can often add value to customers and lower their own costs along this dimen- sion (for example, with more appropriate packaging). These issues also represent a major challenge because some of the greatest opportunities require coordination across different mem- bers of the supply chain. To be successful, firms will need to design a strategy that engages the entire supply chain to identify and address opportunities for improved sustainability.

Key Point

Many challenges, such as rising product variety and shorter product life cycles, have made it increas- ingly difficult for supply chains to achieve strategic fit. Overcoming these challenges offers a tremen- dous opportunity for firms to use supply chain management to gain competitive advantage.

2.5 SUMMARY OF LEARNING OBJECTIVES

1. Explain why achieving strategic fit is critical to a company’s overall success. A lack of strategic fit between the competitive and supply chain strategies can result in the supply chain tak- ing actions that are not consistent with customer needs, leading to a reduction in supply chain sur-

and stages in the supply chain target the same goal—one that is consistent with customer needs.

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2. Describe how a company achieves strategic fit between its supply chain strategy and its competitive strategy. To achieve strategic fit, a company must first understand the needs of the customers being served, understand the uncertainty of the supply chain, and identify the implied uncertainty. The second step is to understand the supply chain’s capabilities in terms of efficiency and responsiveness. The key to strategic fit is ensuring that supply chain responsive- ness is consistent with customer needs, supply capabilities, and the resulting implied uncertainty. Tailoring the supply chain is essential to achieving strategic fit when supplying a wide variety of customers with many products through different channels.

3. Discuss the importance of expanding the scope of strategic fit across the supply chain. The scope of strategic fit refers to the functions and stages within a supply chain that coordinate strategy and target a common goal. When the scope is narrow, individual functions try to optimize their performance based on their own goals. This practice often results in conflicting actions that reduce the supply chain surplus. As the scope of strategic fit is enlarged to include the entire supply chain, actions are evaluated based on their impact on overall supply chain per- formance, which helps increase supply chain surplus.

4. Describe the major challenges that must be overcome to manage a supply chain suc- cessfully. Globalization, increasing product variety, decreasing product life cycles, fragmenta- tion of supply chains, changing technologies, and an increased focus on sustainability represent significant challenges to achieving strategic fit. They also represent great opportunities for firms that can successfully address these challenges with their supply chain strategies.

Discussion Questions 1. How would you characterize the competitive strategy of a

high-end department store chain such as Nordstrom? What are the key customer needs that Nordstrom aims to fill?

2. Where would you place the demand faced by Nordstrom on the implied demand uncertainty spectrum? Why?

3. What level of responsiveness would be most appropriate for Nordstrom’s supply chain? What should the supply chain be able to do particularly well?

4. How can Nordstrom expand the scope of strategic fit across its supply chain?

5. Reconsider the previous four questions for other companies such as Amazon, a supermarket chain, an auto manufacturer, and a discount retailer such as Walmart.

6. Give arguments to support the statement that Walmart has achieved good strategic fit between its competitive and supply

chain strategies. What challenges does it face as it works to open smaller format stores in urban environments?

7. What are some factors that influence implied uncertainty? How does the implied uncertainty differ between an inte- grated steel mill that measures lead times in months and requires large orders and a steel service center that promises 24-hour lead times and sells orders of any size?

8. What is the difference in implied uncertainty faced by a con- venience store chain such as 7-Eleven, a supermarket chain, and a discount retailer such as Costco?

9. What are some problems that can arise when each stage of a supply chain focuses solely on its own profits when mak- ing decisions? Identify some actions that can help a retailer and a manufacturer work together to expand the scope of strategic fit.

Bibliography Blackwell, Roger D., and Kristina Blackwell. “The Century of

Chains.” Supply Chain Management Review 22–32.

– Supply Chain Management

Review Fine, Charles H. Clock Speed, Winning Industry Control in the

Age of Temporary Advantage. 1999.

– uct?” Harvard Business Review

– Harvard Business

Review Supply Chain

Management Review Markets of One: Creat-

ing Customer Unique Value Through Mass Customization.

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Uncertainties.” California Management Review

Harvard Business Review

to End.” Harvard Business Review

Harvard Business Review

“ with Dell Computer’s Michael Dell.” Harvard Business Review

Supply Chain Man- agement Review

Supply Chain Manage- ment Review

Mass Customization. –

Supply Chain Man- agement Review

Harvard Busi- ness Review

Logistics Strategy: Cases and Concepts. – pany, 1985.

Competing Against Time.

Fit.” Supply Chain Quarterly

CASE STUDY The Demise of Blockbuster

After struggling with debt and strong competition from Netflix and Redbox, Blockbuster, Inc. filed for bank-

– pany that had dominated the movie rental business in the 1990s. Blockbuster Inc. was founded by David Cook in 1985 with its first rental outlet in Dallas. Cook planned to take advantage of a highly fragmented video rental mar- ket, in which most of the stores were relatively modest family operations that carried a small selection of former big hit movies mainly due to the high cost distributors typically charged (about $65 per tape). With 8,000 tapes covering 6,500 titles, Blockbuster had a much broader and deeper inventory compared with that of its nearest competitor. The store operations were also greatly stream- lined by a computerized system for inventory control and checkout. The store was a huge success, which prompted the addition of three more locations by mid-1986.

In 1986, because of liquidity problems, Cook was forced to turn over the whole company to a group of investors led by Wayne Huizenga. Between 1987 and 1993, Huizenga grew Blockbuster into an enormous suc- cess. During this period, Blockbuster opened stores around the globe at the rate of about one every 24 hours. By 1993, Blockbuster was the leading global provider of in-home movie and game entertainment, with more than 3,400 stores throughout the Americas, Europe, Asia, and Australia. Blockbuster stores were a ubiquitous neigh- borhood feature that stayed open 365 days a year, gener- ally from 10 a.m. to midnight. Merchandise selection,

quantity, and formats were customized at the store level to meet the needs and preferences of local customers.

In the early 2000s, though, Blockbuster began to see real competition from the burgeoning online rental

– petitor was Netflix, launched in 1997. In addition to

suited for shipping by mail because they were less expensive to ship and less fragile than tapes.

Netflix challenged Blockbuster on two key dimensions—variety and late fees. Whereas Blockbuster stores generally carried about 3,000 titles, Netflix ini- tially offered more than ten times that amount. In addi- tion, Netflix did not charge Blockbuster’s greatly disliked “late fees,” instead allowing customers to keep titles as long as they wanted. Netflix’s monthly subscrip- tion plan offered unlimited mail-order rentals for $9, the cost of two rentals at a Blockbuster store.

Meanwhile, Redbox, a unit of Coinstar Inc., oper-

$1 a night. Despite its best efforts, Blockbuster’s brick- and-mortar stores could not match the low-cost operat- ing models of Netflix and Redbox, leading to its bankruptcy (see financial results in Table 2-5).

Netflix

Netflix was founded in 1997 by Reed Hastings as a pay- per-rental mail-order video rental company. After

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TABLE 2-5 Financial Results for Blockbuster, Netflix, and Coinstar in 2009 (in millions of dollars) Blockbuster Netflix Coinstar

Revenue 4,062 1,670 1,145

Cost of revenue 1,884 1,079 793

Gross profit 2,178 591 351

Operating expenses

Sales, general, and administrative 2,020 289 150

Total operating expenses 2,533 399 267

Operating income (355) 192 84

Net income from continuing operations (518) 116 29

Net income (558) 116 54

ASSETS

Receivables 79 — 61

Inventories 639 37 104

Total current assets 1,060 411 391

Property and equipment at cost 2,374 266 759

Accumulated depreciation (2,125) (134) (358)

Net property, plant, and equipment 249 132 400

Total assets 1,538 680 1,223

experimenting with both pay-per-rental and subscrip- tion, the company settled on a subscription-based strat- egy by the end of 1999. By 2010, Netflix had 13 million members and was the world’s largest subscription ser-

television episodes over the Internet. For $8.99 a month, Netflix members could have any of more than 100,000

watch a smaller set of television episodes and movies streamed to their televisions and computers. Netflix

Netflix focused its strategy around offering a large variety of titles, helping customers navigate titles with a sophisticated recommendation engine, and ensuring that titles reached customers quickly. Whereas a bricks-and- mortar rental store typically carried about 3,000 titles, in 2010 Netflix offered its customers a selection of more

by Netflix were titles with release dates older than thir- teen weeks.

In 2010, Netflix had about 60 regional distribu-

– tion center processes were linked to the recommendation

software, movies that were likely to be in stock were rec- ommended to customers. When the distribution center

one from the customer’s rental queue was shipped out. These distribution centers were highly automated for rapid processing and were located within driving distance

estimated that it would spend about $600 million in 2010 on shipping expenses.

Netflix’s ability to rent older titles was very appeal- ing to studios that had historically seen little revenue

– dios at cost and, in turn, provided them a percentage of the subscription revenue based on utilization for rentals over a specified period (typically 6–12 months). For newer content, Netflix did not attempt to serve the entire initial rush of rental demand. Given the higher initial cost of purchase, the company purchased only a limited num-

weeks and buy the bulk of its supply at lower cost. Cus- tomers could put new titles into their queues and receive

Between 2005 and 2009, Netflix delivered excel- lent financial results and grew revenues by 150 percent and profits by about 175 percent. Despite the strong

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company was focused on increasing the fraction of digi- tal content it delivered. Its streaming service, launched in 2007, allowed customers to watch select movies and

the Netflix service offered more than 17,000 titles (although most new releases were not included in the selection) streamed through a variety of devices. By 2013, the streaming service contributed majority of Netflix’s revenue, although most of the profits still

Redbox

The concept of Redbox originated in 2002 within –

tify new ways to drive traffic to its restaurants and pro- vide added convenience and relevance to customers. Redbox’s first kiosk was launched in 2004 in Denver. Coinstar, Inc. purchased Redbox in early 2009.

Redbox’s strategy was based on targeting the budget-conscious movie renter who wanted to quickly

placing its automated red kiosks at easily accessible locations, where customers could rent movies for $1 per night. Movies could be returned to any Redbox machine and no membership was required.

By early 2010, Redbox had approximately 23,000 kiosks nationwide, including in select McDonald’s res- taurants, leading grocery stores, and Walmart, Wal- greens, and 7-Eleven stores. Redbox planned to more than double the number of its kiosks by 2012. Retailers, who were struggling to keep people shopping, realized

some cases, retailers even offered discounts that essen- tially made it free for Redbox to install a kiosk.

Each Redbox kiosk carried about 630 discs, com- prising 200 of the newest movie titles. A Redbox kiosk

– able for sale to customers for $7.

By mid-2010, Redbox accounted for 25 percent of –

pany was on course to generate more than $1 billion in annual sales, faster than Netflix was able to achieve that milestone.

Study Questions

1. In what ways did Blockbuster achieve better strategic fit than local stores?

2. How did Netflix and Redbox achieve better strategic fit than Blockbuster?

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3

In this chapter, our goal is to link key financial measures of firm performance to supply chain performance. We introduce the three logistical drivers—facilities, inventory, and transportation—and the three cross-functional drivers—information, sourcing, and pricing—that determine the performance of any supply chain. We discuss how these drivers are used in the design, planning, and operation of the supply chain. We define several met- rics that can be used to gauge the performance of each driver and its impact on financial performance.

3.1 FINANCIAL MEASURES OF PERFORMANCE

In Chapter 1, we discussed how growing the supply chain surplus is the ultimate goal of a supply chain. Our premise was that increasing the surplus allows for a growth of supply chain profitability, which facilitates an improvement in the financial performance of each member of the supply chain. In this section, we define important financial measures that are reported by a firm and affected by supply chain performance. In later sections, we link sup- ply chain drivers and associated metrics to the various financial measures. The definitions of financial measures in this section are taken from Dyckman, Magee, and Pfeiffer (2011). To illustrate the various financial measures, we use the financial results reported in 2013 by Amazon.com and Nordstrom Inc. and assume a tax rate of 0.35.

1. Describe key financial measures of firm performance.

2. Identify the major drivers of supply chain performance.

3. Discuss the role of each driver in creating strategic fit between the supply chain strategy and the competitive strategy.

4. Define the key metrics that track the performance of the supply chain in terms of each driver.

LEARNING OBJECTIVES After reading this chapter, you will be able to

Supply Chain Drivers and Metrics

C H A P T E R

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From a shareholder perspective, return on equity (ROE) is the main summary measure of a firm’s performance.

ROE = Net Income

Average Shareholder Equity

Whereas ROE measures the return on investment made by a firm’s shareholders, return on assets (ROA) measures the return earned on each dollar invested by the firm in assets.

ROA = Earnings before Interest

Average Total Assets =

Net Income + 3Interest Expense * (1 – Tax Rate)4 Average Total Assets

Consider the financial performance shown in Table 3-1 for Amazon.com and Nordstrom Inc. In 2013, Amazon {Nordstrom} achieved ROE = 274>9,746 = 2.81 percent {613>1,913 = 32.04 percent} and ROA = [274 + 141 * (1 – 0.35)]>40,159 = 0.91 percent{[613 + 160 * (1 – 0.35)]>8,089 = 8.86 percent}. The difference between ROE and ROA is referred to as return on financial leverage (ROFL). In 2013, Amazon {Nordstrom} had ROFL = 2.81 − 0.91 = 1.90 percent {32.04 – 8.86 = 23.18 percent}. ROFL captures the amount of ROE that can be attrib- uted to financial leverage (such as accounts payable and debt). In Amazon’s case, a significant portion of the financial leverage in 2013 came from accounts payable rather than debt. Thus, an important ratio that defines financial leverage is accounts payable turnover (APT).

APT = Cost of Goods Sold Accounts Payable

In Amazon’s {Nordstrom’s} case, in 2013 APT = 54,181>21,821= 2.48 {7,432>1,415 = 5.25}. The small APT indicates that Amazon was able to use the money it owed suppliers to finance a considerable fraction of its operations. In 2013, Amazon effectively financed its own operations for about 52>2.48 = 20.97 weeks with its suppliers’ money.

ROA can be written as the product of two ratios—profit margin and asset turnover—as shown below:

ROA = Earnings before Interest

Sales Revenue * Sales Revenue

Total Assets = Profit Margin * Asset Turnover

Thus, a firm can increase ROA by growing the profit margin and/or increasing the asset turnover. In 2013, Amazon {Nordstrom} achieved a profit margin of 647>74,452 = 0.87 percent {1,345>12,148 = 11.07 percent} and an asset turnover of 74,452>40,159 = 1.85 {12,148>8,089 = 1.50}. Despite a lower asset turnover than Amazon, Nordstrom had a better ROA because it achieved much higher profit margins. Profit margin can be improved by receiving better prices or by reducing the various expenses incurred. Although Nordstrom’s higher profit margin can be explained partly by its customers’ willingness to pay for the greater responsiveness that Nord- strom provides, good supply chain management also allows a firm to decrease the expenses incurred to serve customer demand. In Amazon’s case, a significant expense is outbound shipping cost. In its 2013 annual report, the company reported outbound shipping costs of $5.13 billion. After accounting for shipping revenue, the net loss on outbound shipping was reported to be $2.85 billion, about ten times its net income. Clearly, a reduction in outbound shipping costs can have a significant impact on Amazon’s profit margin.

The key components of asset turnover are accounts receivable turnover (ART); inventory turnover (INVT); and property, plant, and equipment turnover (PPET). These are defined as follows:

ART = Sales Revenue

Accounts Receivable ; INVT =

Cost of Goods Sold Inventories

; PPET = Sales Revenue

PP&E

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Amazon {Nordstrom} achieved accounts receivable turnover of 74,452>4,767 = 15.62 {12,148>2,356 = 5.16} in 2013. Amazon collected its money from sales relatively quickly (in about 52>15.62 = 3.33 weeks on average in 2013) after it made a sale, whereas Nordstrom took longer (about 10 weeks). Amazon {Nordstrom} turned its inventory about 54,181>7,411 = 7.31 {7,432>1,360 = 5.46} times and had PPET = 74,452>10,949 = 6.80 {12,148>2,579 = 4.71} in 2013. Thus, inventory sat with Amazon {Nordstrom} in 2013 for about 52>7.31 = 7.11 {52>5.46 = 9.52} weeks on average, and each dollar invested in PP&E supported about $6.80 {$4.71} of

TABLE 3-1 Selected Financial Data for Amazon.com and Nordstrom Inc. Amazon.com Nordstrom Inc.

Period Ending 31-Dec-13 2-Feb-13

Total Revenue 74,452,000 12,148,000

Cost of Goods Sold 54,181,000 7,432,000

Gross Profit 20,271,000 4,716,000

Selling, General, and Administrative 19,526,000 3,371,000

Operating Income or Loss 745,000 1,345,000

Total Other Income/Expenses Net -98,000 – Earnings Before Interest and Taxes 647,000 1,345,000

Interest Expense 141,000 160,000

Income Before Tax 506,000 1,185,000

Income Tax Expense 161,000 450,000

Minority Interest – –

Net Income 274,000 613,000

Assets

Cash and Cash Equivalents 8,658,000 1,285,000

Short-Term Investments 3,789,000 –

Net Receivables 4,767,000 2,356,000

Inventory 7,411,000 1,360,000

Other Current Assets – 80,000

Total Current Assets 24,625,000 5,081,000

Property, Plant, and Equipment (PP&E) 10,949,000 2,579,000

Goodwill 2,655,000 175,000

Other Assets 1,930,000 254,000

Total Assets 40,159,000 8,089,000

Liabilities and Stockholder Equity

Accounts Payable 21,821,000 1,415,000

Short-/Current Long-Term Debt – 7,000

Other Current Liabilities 1,159,000 804,000

Long-Term Debt 3,191,000 3,124,000

Other Liabilities 4,242,000 341,000

Deferred Long-Term Liability Charges – 485,000

Total Liabilities 30,413,000 6,176,000

Total Stockholder Equity 9,746,000 1,913,000

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sales in 2013. Amazon achieved a higher asset turnover than Nordstrom by turning its inventory faster and generating higher revenue per dollar invested in PP&E. Nordstrom, however, achieved a much higher ROA compared with Amazon because it had a much higher profit margin. A com- pany can improve its asset turnover by turning its inventory more quickly or using its existing warehousing and technology infrastructure to support a higher level of sales (or decreasing the warehousing and technology infrastructure needed to support the existing level of sales). A com- pany can improve its profit margin by increasing a customer’s willingness to pay or decreasing operating expense.

Another useful metric is the cash-to-cash (C2C) cycle, which roughly measures the aver- age amount of time from when cash enters the process as cost to when it returns as collected revenue.

C2C = -Weeks Payable 11>APT2 + Weeks in Inventory 11> INVT2 + Weeks Receivable 11>ART2

In Amazon’s case, we obtain C2C = -20.97 + 7.11 + 3.33 = -10.53 in 2013. In 2013, Amazon collected its money from the sale of products more than 10 weeks before it had to pay its suppliers. Table 3-2 shows selected financial metrics across industries. It is interesting to observe that the consumer electronics industry has an average C2C cycle of only 9.3 days, whereas medical device manufacturers average more than 200 days.

There are two important measures, however, that are not explicitly part of a firm’s finan- cial statements: markdowns and lost sales. Markdowns represent the discounts required to convince customers to buy excess inventory. Financial statements show only the revenue received from sales, not the revenue that “could” have been received. For General Motors (GM), one of the biggest problems in the early part of the twenty-first century was the dis- counts required to move excess inventory from dealer lots. These discounts significantly hurt financial performance. In 2010, one of the biggest improvements in financial performance for GM was its ability to sell its cars with much smaller discounts because the supply chain had far less excess inventory. Lost sales represent customer sales that did not materialize because of the absence of products the customer wanted to buy. Every lost sale corresponds to product margin that is lost. Both markdowns and lost sales reduce net income and arguably represent the biggest impact of supply chain performance on the financial performance of a firm. Firms such as Walmart and Zara have achieved strong financial performance in large part because their supply chains allow a better matching of supply and demand, thereby reducing mark- downs and lost sales.

TABLE 3-2 Selected Financial Metrics Across Industries, 2000–2012

Industry

Average Operating Margin

Average C2C Cycle

Average Inventory Turns

Average SG&A Cost/ Revenue

Pharmaceutical 0.25 190.3 2.0 0.31

Medical device manufacturers 0.18 211.6 2.2 0.36

Consumer packaged goods 0.17 28.3 5.6 0.31

Food 0.16 37.4 6.2 0.23

Consumer electronics 0.12 9.3 43.8 0.14

Apparel 0.10 127.7 3.2 0.35

Chemical 0.09 78.1 5.3 0.09

Automotive 0.04 75.9 9.9 0.13

Source: (2000–2012).” Supply Chain Insights LLC report, November 11, 2013.

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In the next section, we identify key drivers of supply chain performance that influence the financial performance of a firm. Our goal is to understand how these drivers may explain the dif- ference in financial performance between firms such as Amazon and Nordstrom.

3.2 DRIVERS OF SUPPLY CHAIN PERFORMANCE

The strategic fit discussed in Chapter 2 requires that a company’s supply chain achieve the bal- ance between responsiveness and efficiency that best supports the company’s competitive strat- egy. A supply chain’s performance in terms of responsiveness and efficiency is based on the interaction between the following logistical and cross-functional drivers of supply chain perfor- mance: facilities, inventory, transportation, information, sourcing, and pricing. The structure of

the drivers to achieve the desired level of responsiveness at the lowest possible cost, thus improv- ing the supply chain surplus and the firm’s financial performance.

First we define each driver and discuss its impact on the performance of the supply chain.

1. Facilities are the actual physical locations in the supply chain network where product is stored, assembled, or fabricated. The two major types of facilities are production sites and stor- age sites. Decisions regarding the role, location, capacity, and flexibility of facilities have a sig- nificant impact on the supply chain’s performance. For example, in 2013, Amazon increased the number of warehousing facilities (and, as a result, experienced an increase in PP&E) located close to customers to improve its responsiveness. In contrast, Best Buy tried to improve its effi- ciency in 2013 by shutting down retail facilities even though it reduced responsiveness. Facility costs show up under PP&E if facilities are owned by the firm or under selling, general, and administrative if they are leased.

2. Inventory encompasses all raw materials, work in process, and finished goods within a supply chain. The inventory belonging to a firm is reported under assets. Changing inventory policies can dramatically alter the supply chain’s efficiency and responsiveness. For example, W.W. Grainger makes itself responsive by stocking large amounts of inventory and satisfying

practice makes sense for Grainger because its products hold their value for a long time. A strat- egy using high inventory levels can be dangerous in the fashion apparel business, though, in which inventory loses value relatively quickly with changing seasons and trends. Rather than

– uct and replenishment lead times. As a result, the company is very responsive but carries low levels of inventory.

3. Transportation entails moving inventory from point to point in the supply chain. Transportation can take the form of many combinations of modes and routes, each with its own performance characteristics. Transportation choices have a large impact on supply chain respon- siveness and efficiency. For example, a mail-order catalog company can use a faster mode of transportation such as FedEx to ship products, thus making its supply chain more responsive— but also less efficient, given the high costs associated with using FedEx. McMaster-Carr and W.W. Grainger, however, have structured their supply chains to provide next-day service to most of their customers using ground transportation. They are providing a high level of responsiveness at lower cost. Outbound transportation costs of shipping to the customer are typically included in selling, general, and administrative expense, whereas inbound transportation costs are typically included in the cost of goods sold.

4. Information consists of data and analysis concerning facilities, inventory, transporta- tion, costs, prices, and customers throughout the supply chain. Information is potentially the biggest driver of performance in the supply chain because it directly affects each of the other drivers. Information presents management with the opportunity to make supply chains more

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responsive and match supply and demand while achieving production and distribution economies. The result is a high level of responsiveness to customer demand while production and replenishment costs are lowered. Information technology-related expenses are typically included under either operating expense (typically under selling, general, and administrative expense) or assets. For example, in 2012, Amazon included $4.54 billion in technology expense under operating expense and another $454 million under fixed assets to be depreciated.

5. Sourcing is the choice of who will perform a particular supply chain activity, such as production, storage, transportation, or the management of information. At the strategic level, these decisions determine what functions a firm performs and what functions the firm outsources.

– ola outsourced much of its production to contract manufacturers in China, for instance, it saw its efficiency improve but its responsiveness suffer because of the long lead times. To make up for the drop in responsiveness, Motorola started flying in some of its cell phones from China even though this choice increased transportation cost. Flextronics, an electronics contract manufac- turer, is hoping to offer both responsive and efficient sourcing options to its customers. It is try- ing to make its production facilities in high-cost locations very responsive while keeping its facilities in low-cost countries efficient. Flextronics hopes to become an effective source for all

and monies owed to suppliers are recorded under accounts payable.

6. Pricing determines how much a firm will charge for the goods and services that it makes available in the supply chain. Pricing affects the behavior of the buyer of the good or ser- vice, thus affecting demand and supply chain performance. For example, if a transportation com- pany varies its charges based on the lead time provided by the customers, it is likely that customers who value efficiency will order early and customers who value responsiveness will be willing to wait and order just before they need a product transported. Differential pricing pro- vides responsiveness to customers that value it and low cost to customers that do not value responsiveness as much. Any change in pricing affects revenues directly but could also affect costs based on the impact of this change on the other drivers.

Our definitions of these drivers attempt to delineate logistics and supply chain manage-

increase the supply chain surplus. Cross-functional drivers have become increasingly important in raising the supply chain surplus in recent years. Although logistics remains a major part, sup- ply chain management is increasingly becoming focused on the three cross-functional drivers.

It is important to realize that these drivers do not act independently but interact to deter- mine the overall supply chain performance. Good supply chain design and operation recognize this interaction and make the appropriate trade-offs to deliver the desired level of responsive- ness. Consider, for example, the sale of furniture at IKEA. The primary goal of this supply chain is to deliver a low price and acceptable quality. Modular design and unassembled furni- ture allows IKEA to carry components in inventory at its stores. The low component variety and stable replenishment orders allow IKEA’s suppliers to focus on efficiency. Given the avail- able inventory, low-cost modes of transportation are used to ship densely packed components. In this instance, relatively low-cost inventory at IKEA allows the supply chain to become

makers have chosen to focus on providing variety. Given the high variety and high prices, keeping inventory of all variants at a retailer would be very expensive. In this case, the supply chain has been designed so the retailer carries little inventory. Customers place their orders with the retailer by seeing one variant of the furniture and selecting among the various options. The supply chain is made responsive by using information technology to convey order infor- mation effectively, structuring flexible manufacturing facilities to be able to produce in small lots, and using responsive transportation to deliver the furniture to the customer. In this

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instance, responsive facilities, transportation, and information are used to lower inventory costs. As the rest of this chapter will illustrate, the key to achieving strategic fit and strong financial performance across the supply chain is to structure the supply chain drivers appropri- ately to provide the desired level of responsiveness at the lowest possible cost.

Doheny et al. (2010) point out that supply chain performance affects nearly 35 percent of the financial performance of apparel retailers. As a percentage of sales, they state that mark- downs, representing 10 to 30 percent of sales, and lost sales, representing 5 to 10 percent of sales, are the dominant drivers of retailers’ financial performance. They further state that trans- portation represents 2 to 5 percent, warehousing 1 to 3 percent, store product handling 3 to 5 percent, and inventory costs 2 to 5 percent of sales. Although the precise fraction will vary for different supply chains, it is evident that supply chain performance along the six drivers has a significant influence on a firm’s financial performance.

Before we discuss each of the six drivers in detail, we put these drivers into a framework that helps clarify the role of each in improving supply chain performance.

3.3 FRAMEWORK FOR STRUCTURING DRIVERS

We provide a visual framework for supply chain decision making in Figure 3-1. Most companies begin with a competitive strategy and then decide what their supply chain strategy ought to be. The supply chain strategy determines how the supply chain should perform with respect to effi- ciency and responsiveness. The supply chain must then use the three logistical and three cross- functional drivers to reach the performance level the supply chain strategy dictates and maximize the supply chain profits. Although this framework is generally viewed from the top down, in

Competitive Strategy

Efficiency Responsiveness Supply Chain Structure

Logistical Drivers

Cross-Functional Drivers

Supply Chain Strategy

Facilities Inventory Transportation

Information Sourcing Pricing

FIGURE 3-1 Supply Chain Decision-Making Framework

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many instances a study of the six drivers may indicate the need to change the supply chain strat- egy and, potentially, even the competitive strategy.

Consider this framework using Walmart as an example. Walmart’s competitive strategy is to be a reliable, low-cost retailer for a wide variety of mass-consumption goods. This strat- egy dictates that the ideal supply chain will emphasize efficiency but also maintain an ade- quate level of responsiveness in terms of product availability. Walmart uses the three logistical and three cross-functional drivers effectively to achieve this type of supply chain performance. With the inventory driver, Walmart maintains an efficient supply chain by keeping low levels of inventory. For instance, Walmart pioneered cross-docking, a system in which inventory is not stocked in a warehouse but rather is shipped to stores from the manufacturer with a brief stop at a distribution center (DC), where product is transferred from inbound trucks from the supplier to outbound trucks to the retail store. This lowers inventory significantly because products are stocked only at stores, not at both stores and warehouses. With respect to inven- tory, Walmart favors efficiency over responsiveness. On the transportation front, Walmart runs its own fleet, to keep responsiveness high. This increases transportation cost, but the benefits in terms of reduced inventory and improved product availability justify this cost in Walmart’s case. In the case of facilities, Walmart uses centrally located DCs within its network of stores to decrease the number of facilities and increase efficiency at each DC. Walmart builds retail stores only where the demand is sufficient to justify having several of them supported by a DC, thereby increasing efficiency of its transportation assets. Walmart has invested significantly more than its competitors in information technology, allowing the company to feed demand information across the supply chain to suppliers that manufacture only what is being demanded. As a result, Walmart is a leader in its use of the information driver to improve responsiveness and decrease inventory investment. With regard to the sourcing driver, Walmart identifies effi- cient sources for each product it sells. Walmart feeds them large orders, allowing them to be efficient by exploiting economies of scale. Finally, for the pricing driver, Walmart practices “everyday low pricing” (EDLP) for its products. This ensures that customer demand stays steady and does not fluctuate with price variations. The entire supply chain then focuses on meeting this demand in an efficient manner. Walmart uses all the supply chain drivers to achieve the right balance between responsiveness and efficiency so its competitive strategy and supply chain strategy are in harmony.

We devote the next six sections to a detailed discussion of each of the three logistical and three cross-functional drivers, their roles in the supply chain, and their impact on financial per- formance.

3.4 FACILITIES

In this section, we discuss the role that facilities play in the supply chain and critical facility- related decisions that supply chain managers need to make.

Role in the Supply Chain

Firms can increase responsiveness by increasing the number of facilities, making them more flexible, or increasing capacity. Each of these actions, however, comes at a cost. Increasing the number of facilities increases facility and inventory costs but decreases transportation costs and reduces response time. Increasing the flexibility or capacity of a facility increases facility costs but decreases inventory costs and response time. Thus, each supply chain must find the appropri- ate tradeoff when designing its facilities network. Whereas IKEA has become profitable by

– dreds per city) to provide responsiveness. Both companies are successful because the facility decisions are aligned with the supply chain strategy.

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EXAMPLE 3-1 Toyota and Honda

Both Toyota and Honda use facilities decisions to be more responsive to their customers. These companies have an end goal of opening manufacturing facilities in every major market that they enter. Although there are other benefits to opening local facilities, such as protection from cur- rency fluctuation and trade barriers, the increase in responsiveness plays a large role in Toyota’s and Honda’s decision to place facilities in their local markets. The flexibility of Honda facilities

maintained a high level of utilization.

Components of Facilities Decisions

Decisions regarding facilities are a crucial part of supply chain design. We now identify compo- nents of facilities decisions that companies must analyze.

ROLE Firms must decide whether production facilities will be flexible, dedicated, or a combi- nation of the two. Flexible capacity can be used for many types of products but is often less efficient, whereas dedicated capacity can be used for only a limited number of products but is more efficient. Firms must also decide whether to design a facility with a product focus or a functional focus. A product-focused facility performs all functions (e.g., fabrication and assem- bly) needed for producing a single type of product. A functional-focused facility performs a given set of functions (e.g., fabrication or assembly) on many types of products. A product focus tends to result in more expertise about a particular type of product at the expense of the func- tional expertise that comes from a functional methodology.

For warehouses and DCs, firms must decide whether they will be primarily cross-docking facilities or storage facilities. At cross-docking facilities, inbound trucks from suppliers are unloaded; the product is broken into smaller lots and is quickly loaded onto store-bound trucks. Each store-bound truck carries a variety of products, some from each inbound truck. For storage facilities, firms must decide on the products to be stored at each facility.

LOCATION Deciding where a company will locate its facilities constitutes a large part of the design of a supply chain. A basic trade-off here is whether to centralize to gain economies of scale or to decentralize to become more responsive by being closer to the customer. Companies must also consider a host of issues related to the various characteristics of the local area in which the facility is situated. These include macroeconomic factors, quality of workers, cost of work- ers, cost of facility, availability of infrastructure, proximity to customers, the location of that firm’s other facilities, tax effects, and other strategic factors.

CAPACITY Companies must also determine a facility’s capacity to perform its intended func- tion or functions. A large amount of excess capacity allows the facility to respond to wide swings in the demands placed on it. Excess capacity, however, costs money and therefore can decrease efficiency. A facility with little excess capacity will likely be more efficient per unit of product it produces than one with a lot of unused capacity. The high-utilization facility, however, will have difficulty responding to demand fluctuations. Therefore, a company must make a trade-off to determine the right amount of capacity to have at each of its facilities.

FACILITY-RELATED METRICS Facility-related decisions affect both the financial performance of the firm and the supply chain’s responsiveness to customers. On the financial side, facilities decisions have an impact on the cost of goods sold, assets in PP&E (if facilities are owned), and

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selling, general, and administrative expense (if facilities are leased). A manager should track the following facility-related metrics that influence supply chain performance:

Capacity measures the maximum amount a facility can process. Utilization measures the fraction of capacity that is currently being used in the facility. Utilization affects both the unit cost of processing and the associated delays. Unit costs tend to decline (PPET increases) and delays increase with increasing utilization. Processing/setup/down/idle time measures the fraction of time that the facility was pro- cessing units, being set up to process units, unavailable because it was down, or idle because it had no units to process. Ideally, utilization should be limited by demand and not setup or downtime. Production cost per unit measures the average cost to produce a unit of output. These costs may be measured per unit, per case, or per pound, depending on the product. Quality losses measure the fraction of production lost as a result of defects. Quality losses hurt both financial performance and responsiveness. Theoretical flow/cycle time of production measures the time required to process a unit if there are absolutely no delays at any stage. Actual average flow/cycle time measures the average actual time taken for all units pro- cessed over a specified duration, such as a week or a month. The actual flow/cycle time includes the theoretical time and any delays. This metric should be used when setting due dates for orders. Flow time efficiency is the ratio of the theoretical flow time to the actual average flow time. Low values for flow time efficiency indicate that a large fraction of time is spent waiting. Product variety measures the number of products or product families processed in a facility. Processing costs and flow times are likely to increase with product variety. Volume contribution of top 20 percent SKUs and customers measures the fraction of

– tomers. An 80/20 outcome, in which the top 20 percent contribute 80 percent of volume, indicates likely benefits from focusing the facility so separate processes are used to process the top 20 percent and the remaining 80 percent. Average production batch size measures the average amount produced in each produc- tion batch. Large batch sizes will decrease production cost but increase inventories. Production service level measures the fraction of production orders completed on time and in full.

3.5 INVENTORY

In this section, we discuss the role that inventory plays in the supply chain and how managers use inventory to drive supply chain performance.

Role in the Supply Chain

Inventory exists in the supply chain because of a mismatch between supply and demand. This mismatch is intentional at a steel manufacturer, where it is economical to manufacture in large lots that are then stored for future sales. The mismatch is also intentional at a retail store where inventory is held in anticipation of future demand or when the retail store builds up inventory to prepare for a surge in sales during the holiday season. In these instances, inventory is held to reduce cost or increase the level of product availability.

Inventory affects the assets held, the costs incurred, and responsiveness provided in the supply chain. High levels of inventory in an apparel supply chain improve responsiveness but also leave the supply chain vulnerable to the need for markdowns, lowering profit margins. A higher level of inventory also facilitates a reduction in production and transportation costs

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because of improved economies of scale in both functions. This choice, however, increases inventory holding cost. Low levels of inventory improve inventory turns but may result in lost sales if customers are unable to find products they are ready to buy. In general, managers should aim to reduce inventory in ways that do not increase cost or reduce responsiveness.

Inventory also has a significant impact on the material flow time in a supply chain. Mate- rial flow time is the time that elapses between the point at which material enters the supply chain to the point at which it exits. For a supply chain, throughput is the rate at which sales occur. If inventory is represented by I, flow time by T, and throughput by D, the three can be related using Little’s law as follows:

I = DT (3.1)

For example, if an Amazon warehouse holds 100,000 units in inventory and sells 1,000 units daily, Little’s law tells us that the average unit will spend 100,000>1,000 = 100 days in inventory. If Amazon were able to reduce flow time to 50 days while holding throughput constant, it would reduce inventory to 50,000 units. Note that in this relationship, inventory and throughput must have consistent units.

EXAMPLE 3-2 Amazon.com

Amazon attempts to provide a wide variety of books (among other products) to its customers. Best-selling books are stocked in many regional warehouses close to customers for high respon-

– tory but are obtained from the publisher/distributor or printed on demand when requested by a customer. Amazon changes the form, location, and quantity of inventory it holds by the level of sales of a book to provide the right balance of responsiveness and efficiency.

Components of Inventory Decisions

We now identify major inventory-related decisions that supply chain managers must make to effectively create more responsive and more efficient supply chains.

CYCLE INVENTORY Cycle inventory is the average amount of inventory used to satisfy demand between receipts of supplier shipments. The size of the cycle inventory is a result of the produc- tion, transportation, or purchase of material in large lots. Companies produce or purchase in large lots to exploit economies of scale in the production, transportation, or purchasing process. With the increase in lot size, however, comes an increase in carrying costs. As an example of a cycle inventory decision, consider an online book retailer. This e-retailer’s sales average around 10 truckloads of books a month. The cycle inventory decisions the retailer must make are how much to order for replenishment and how often to place these orders. The e-retailer could order 10 truckloads once each month or it could order one truckload every three days. The basic trade- off supply chain managers face is the cost of holding larger lots of inventory (when cycle inven- tory is high) versus the cost of ordering more frequently (when cycle inventory is low).

SAFETY INVENTORY Safety inventory is inventory held in case demand exceeds expectation; it is held to counter uncertainty. If the world were perfectly predictable, only cycle inventory would be needed. Because demand is uncertain and may exceed expectations, however, compa- nies hold safety inventory to satisfy an unexpectedly high demand. Managers face a key deci- sion when determining how much safety inventory to hold. For example, a toy retailer such as Toys “R” Us must calculate its safety inventory for the holiday buying season. If it has too much

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safety inventory, toys will go unsold and may have to be discounted after the holidays. If the company has too little safety inventory, however, then Toys “R” Us will lose sales, along with the margin those sales would have brought. Therefore, choosing safety inventory involves mak- ing a trade-off between the costs of having too much inventory and the costs of losing sales owing to not having enough inventory.

SEASONAL INVENTORY Seasonal inventory is built up to counter predictable seasonal vari- ability in demand. Companies using seasonal inventory build up inventory in periods of low demand and store it for periods of high demand, when they will not have the capacity to pro- duce all that is demanded. Managers face key decisions in determining whether to build sea- sonal inventory and, if they do build it, in deciding how much to build. If a company has volume flexibility and can rapidly change the rate of its production system at very low cost, then it may not need seasonal inventory. However, if changing the rate of production is expensive (e.g., when workers must be hired or fired), then a company would be wise to establish a smooth production rate and build up its inventory during periods of low demand. Therefore, the basic trade-off supply chain managers face in determining how much seasonal inventory to build is the cost of carrying the additional seasonal inventory versus the cost of having a more flexible production rate.

LEVEL OF PRODUCT AVAILABILITY Level of product availability is the fraction of demand that is served on time from product held in inventory. A high level of product availability provides a high level of responsiveness but increases cost because much inventory is held but rarely used. In contrast, a low level of product availability lowers inventory holding cost but results in a higher fraction of customers who are not served on time. The basic trade-off when determining the level of product availability is between the cost of inventory to increase product availability and the loss from not serving customers on time.

INVENTORY-RELATED METRICS Inventory-related decisions affect the cost of goods sold, the C2C cycle, the assets held by the supply chain, and its responsiveness to customers. A manager should track the following inventory-related metrics that influence supply chain performance:

C2C cycle time is a high-level metric that includes inventories, accounts payable, and receivables. Average inventory measures the average amount of inventory carried. Average inventory should be measured in units, days of demand, and financial value. Inventory turns measure the number of times inventory turns over in a year. It is the ratio of average inventory to either the cost of goods sold or sales. Products with more than a specified number of days of inventory identifies the prod- ucts for which the firm is carrying a high level of inventory. This metric can be used to identify products that are in oversupply or to identify reasons that justify the high inven- tory, such as price discounts or a product being a very slow mover. Average replenishment batch size measures the average amount in each replenishment

demand. It can be estimated by averaging over time the difference between the maximum and the minimum inventory (measured in each replenishment cycle) on hand. Average safety inventory measures the average amount of inventory on hand when a

units and days of demand. It can be estimated by averaging over time the minimum inven- tory on hand in each replenishment cycle. Seasonal inventory measures the amount by which the inflow of product exceeds its

anticipated spikes in demand.

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Fill rate (order/case) measures the fraction of orders/demand that were met on time from inventory. Fill rate should be averaged not over time but over a specified number of units of demand (say, every thousand or million). Fraction of time out of stock zero inventory. This fraction can be used to estimate the lost sales during the stockout period. Obsolete inventory measures the fraction of inventory older than a specified obsoles- cence date.

3.6 TRANSPORTATION

In this section, we discuss the role that transportation plays in the supply chain and key transpor- tation-related decisions that supply chain managers must make.

Role in the Supply Chain

Transportation moves product between different stages in a supply chain and affects both responsiveness and efficiency. Faster transportation is more expensive but allows a supply chain to be more responsive. As a result, the supply chain may carry lower inventories and have fewer facilities.

The appropriate choice of transportation allows a firm to adjust the location of its facilities and inventory to find the right balance between responsiveness and efficiency. A firm selling high-value items such as pacemakers may use rapid transportation to be responsive while cen- tralizing its facilities and inventory to lower cost. In contrast, a firm selling low-value, high- demand items like light bulbs may carry a fair amount of inventory close to the customer but then use low-cost transportation such as sea, rail, and full trucks to replenish this inventory from plants located in low-cost countries.

EXAMPLE 3-3 Blue Nile

Blue Nile is an online retailer of diamonds that has used responsive transportation with FedEx to

Asia. Given the high value of diamonds, Blue Nile offers free shipping for overnight delivery. Responsive shipping, however, allows Blue Nile to centralize its inventory of diamonds and eliminate the need for expensive storefronts. In spite of the high transportation costs, Blue Nile has very low costs compared with those of bricks-and-mortar retailers because of the low facility and inventory expenses. Blue Nile is thus able to offer significantly lower prices than its bricks- and-mortar competition.

Components of Transportation Decisions

We now identify key components of transportation that companies must analyze when designing and operating a supply chain.

DESIGN OF TRANSPORTATION NETWORK The transportation network is the collection of transportation modes, locations, and routes along which product can be shipped. A company must decide whether transportation from a supply source will be direct to the demand point or will go through intermediate consolidation points. Design decisions also include whether or not multiple supply or demand points will be included in a single run.

CHOICE OF TRANSPORTATION MODE The mode of transportation is the manner in which a product is moved from one location in the supply chain network to another. Companies can choose

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among air, truck, rail, sea, and pipeline as modes of transport for products. Today, information goods can also be sent via the Internet. Each mode has different characteristics with respect to the speed, size of shipments (individual parcels to pallets to full trucks to entire ships), cost of ship- ping, and flexibility that lead companies to choose one particular mode over the others.

TRANSPORTATION-RELATED METRICS Inbound transportation decisions affect the cost of goods sold, whereas outbound transportation costs are part of the selling, general, and adminis- trative expenses. Thus, transportation costs affect the profit margin. A manager should track the following transportation-related metrics that influence supply chain performance:

Average inbound transportation cost typically measures the cost of bringing product into a facility. Ideally, this cost should be measured per unit brought in, but it is often mea-

supplier. Average incoming shipment size measures the average number of units or dollars in each incoming shipment at a facility. Average inbound transportation cost per shipment measures the average transportation cost of each incoming delivery. Along with the incoming shipment size, this metric identi- fies opportunities for greater economies of scale in inbound transportation. Average outbound transportation cost measures the cost of sending product out of a facility to the customer. Ideally, this cost should be measured per unit shipped, but it is often measured as a percentage of sales. It is useful to separate this metric by customer. Average outbound shipment size measures the average number of units or dollars on each outbound shipment at a facility. Average outbound transportation cost per shipment measures the average transporta- tion cost of each outgoing delivery. Along with the outgoing shipment size, this metric identifies opportunities for greater economies of scale in outbound transportation. Fraction transported by mode measures the fraction of transportation (in units or dol- lars) using each mode of transportation. This metric can be used to estimate whether cer- tain modes are overused or underused.

3.7 INFORMATION

In this section, we discuss the role that information plays in the supply chain, as well as key information-related decisions that supply chain managers must make.

Role in the Supply Chain

Good information can help improve the utilization of supply chain assets and the coordination of –

mation to improve product availability while decreasing inventories. Walmart uses information on shipments from suppliers to facilitate cross-docking and lower inventory and transportation expense. Li & Fung, a global trading group supplying time-sensitive consumer goods such as apparel, uses information on its third-party manufacturers to source each order from the most appropriate supplier. Airlines routinely use information to offer the right number of seats at a discount price, leaving sufficient seats for business customers who make reservations at the last minute and are willing to pay a higher price. Each of these examples illustrates the importance of information as a key driver that can be used to provide higher responsiveness while simultane- ously improving efficiency.

Even though the sharing of information can help a supply chain better meet customer needs at lower cost, there is a danger in the assumption that more information is always better. As more information is shared across a supply chain, the complexity and cost of both the required

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infrastructure and the follow-up analysis grow exponentially. The marginal value provided by the information shared, however, diminishes as more and more information becomes available. It is thus important to evaluate the minimum information required to accomplish the desired objec- tives. For example, it may often be enough if aggregate sales, rather than detailed point-of-sale data, are shared between a retailer and a manufacturer. Aggregate information is cheaper to share and provides most of the value with regard to better production planning. The trade-off between complexity and value is important to consider when setting up the information infrastructure. The following examples illustrate how information can be used to provide customized products and improve supply chain performance.

EXAMPLE 3-4 Andersen Windows

Andersen Windows, a major manufacturer of residential wood windows located in Bayport, Minnesota, has invested in an information system that enables the company to bring customized products to the market rapidly. This system, called “Window of Knowledge,” allows distributors and customers to design windows to custom-fit their needs. Users can select from a library of more than 50,000 components that can be combined in any number of ways. The system imme- diately gives the customer price quotes and automatically sends the order to the factory if the customer decides to buy. This information investment not only gives the customer a much wider variety of products, but also allows Andersen to be much more responsive to the customer, as it gets the customer’s order to the factory as soon as the order is placed.

EXAMPLE 3-5 Sunsweet Growers

Demand is also seasonal, with peak times occurring during the Christmas period. Good planning

function should operate with the same data, and an early warning capability should alert planners and managers about any potential mismatches between supply and demand. After the implemen-

accuracy improved by 15 to 20 percent. The early warning system alerts allowed planners to react as much as two to three weeks earlier than before the implementation.

Components of Information Decisions

We now consider key components of information that a company must analyze to increase effi- ciency and improve responsiveness within its supply chain.

PUSH VERSUS PULL When designing processes of the supply chain, managers must determine whether these processes are part of the push or pull phase in the chain. We discussed this distinc- tion in Chapter 1, but we mention it again because different types of systems require different types of information. Push systems start with forecasts that are used to build the master produc- tion schedule and roll it back, creating schedules for suppliers with part types, quantities, and delivery dates. Pull systems require information on actual demand to be transmitted extremely quickly throughout the entire chain so production and distribution of products can reflect the real demand accurately.

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COORDINATION AND INFORMATION SHARING Supply chain coordination occurs when all stages of a supply chain work toward the objective of maximizing total supply chain profitability based on shared information. Lack of coordination can result in a significant loss of supply chain surplus. Coordination among different stages in a supply chain requires each stage to share appropri- ate information with other stages. For example, if a supplier is to produce the right parts in a timely manner for a manufacturer in a pull system, the manufacturer must share demand and production information with the supplier. Information sharing is thus crucial to the success of a supply chain.

SALES AND OPERATIONS PLANNING Sales and operations planning of creating an overall supply plan (production and inventories) to meet the anticipated level of

the supply chain, which, in turn, communicates to sales and marketing whether the needs can be

and inventory plan that can be used to plan supply chain needs and project revenues and profits. The sales and operations plan becomes a critical piece of information to be shared across the sup- ply chain because it affects both the demand on a firm’s suppliers and the supply to its customers.

ENABLING TECHNOLOGIES Many technologies exist to share and analyze information in the supply chain. Managers must decide which technologies to use and how to integrate them into

1. Electronic data interchange (EDI) was developed in the 1970s to facilitate the placement of instantaneous, paperless purchase orders with suppliers. Its proprietary nature, however, required significant upfront investment and often some translation between the communicating parties. It did, however, make transactions faster and more accurate than when they were paper based.

2. Relative to EDI, the Internet conveys much more information using a standard infra- structure allowing supply chains to improve both efficiency and responsiveness. The beginning of the twenty-first century has seen the Internet become the dominant medium of communication

, discussed in Chapter 1) that link the sup- ply chain from suppliers to customers.

3. Enterprise resource planning (ERP) systems provide the transactional tracking and global visibility of information from within a company and across its supply chain. This real- time information helps a supply chain improve the quality of its operational decisions. ERP sys- tems keep track of the information, whereas the Internet provides one method with which to view this information.

4. – vide analytical decision support in addition to the visibility of information. ERP systems show a

5. Radio frequency identification (RFID) consists of an active or passive radio frequency (RF) tag, applied to the item being tracked, and an RF reader/emitter. A passive tag draws energy from the reader, whereas an active tag has its own battery and draws power from it. RFID has many potential uses. It can be used in manufacturing to check availability of the entire bill of materials. The technology can make the receiving of a truck much faster and cheaper. Full imple- mentation of RFID could eliminate the need for manual counting and bar-code scanning at the receiving dock. It can also be used to get an exact count of incoming items and items in storage.

INFORMATION-RELATED METRICS A manager should track the following information-related metrics that influence supply chain performance:

Forecast horizon identifies how far in advance of the actual event a forecast is made. The forecast horizon must be greater than or equal to the lead time of the decision that is driven by the forecast.

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Frequency of update identifies how frequently each forecast is updated. The forecast should be updated somewhat more frequently than a decision will be revisited, so large changes can be flagged and corrective action taken. Forecast error measures the difference between the forecast and actual demand. The forecast error is a measure of uncertainty and drives all responses to uncertainty, such as safety inventory or excess capacity. Seasonal factors measure the extent to which the average demand in a season is above or below the average in the year. Variance from plan identifies the difference between the planned production/inventories and the actual values. These variances can be used to raise flags that identify shortages and surpluses. Ratio of demand variability to order variability measures the standard deviation of incoming demand and supply orders placed. A ratio less than 1 potentially indicates the existence of the bullwhip effect, which is discussed in Chapter 10.

3.8 SOURCING

In this section, we discuss the role that sourcing plays in the supply chain and key sourcing- related decisions that managers need to make.

Role in the Supply Chain

Sourcing is the set of business processes required to purchase goods and services. Managers must first decide whether each task will be performed by a responsive or efficient source and

should be made to increase the size of the total surplus to be shared across the supply chain. Out- sourcing to a third party is meaningful if the third party raises the supply chain surplus more than the firm can on its own. In contrast, a firm should keep a supply chain function in-house if the third party cannot increase the supply chain surplus or if the risk associated with outsourcing is significant. For example, W.W. Grainger outsources package delivery to a third party because it is very expensive to build this capability in-house. In contrast, Grainger owns and operates its

to provide the appropriate level of responsiveness at the lowest cost. The following example illustrates how Zara has sourced appropriately to be efficient for

basic products and responsive for trendy products.

EXAMPLE 3-6 Zara

Zara has a sourcing strategy that varies by product type. For basic products such as white T-shirts, Zara aims for efficiency because demand is predictable. These products are sourced from suppli- ers in low cost countries. For trendy products for which demand is unpredictable, in contrast, Zara sources from company-owned factories in Europe. These factories are not low cost, but they are flexible and responsive to the rapidly evolving needs of the trendy market.

Components of Sourcing Decisions

We now consider key sourcing decisions that are made within a firm.

IN-HOUSE OR OUTSOURCE The most significant sourcing decision for a firm is whether to perform a task in-house or outsource it to a third party. Within a task such as transportation, man- agers must decide whether to outsource all of it, outsource only the responsive component, or

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outsource only the efficient component. This decision should be driven in part by its impact on the total supply chain surplus. It is best to outsource if the growth in total supply chain surplus is significant with little additional risk.

SUPPLIER SELECTION Managers must decide on the number of suppliers they will have for a particular activity. They must then identify the criteria along which suppliers will be evaluated and how they will be selected.

PROCUREMENT Procurement is the process of obtaining goods and services within a supply chain. Managers must structure procurement with a goal of increasing supply chain surplus. For example, a firm should set up procurement for direct materials to ensure good coordination between the supplier and buyer. In contrast, the procurement of MRO products should be struc- tured to ensure that transaction costs are low.

SOURCING-RELATED METRICS sold and accounts payable. The performance of the source also affects quality, inventories, and inbound transportation costs. A manager should track the following sourcing-related metrics that influence supply chain performance:

Days payable outstanding measures the number of days between when a supplier per- formed a supply chain task and when it was paid for. Average purchase price measures the average price at which a good or service was pur- chased during the year. The average should be obtained by weighting each price by the quantity purchased at that price. Range of purchase price measures the fluctuation in purchase price during a specified period. The goal is to identify if the quantity purchased correlated with the price. Average purchase quantity measures the average amount purchased per order. The goal is to identify whether a sufficient level of aggregation is occurring across locations when placing an order. Supply quality measures the quality of product supplied. Supply lead time measures the average time between when an order is placed and when the product arrives. Long lead times reduce responsiveness and add to the inventory the supply chain must carry. Percentage of on-time deliveries measures the fraction of deliveries from the supplier that were on time. Supplier reliability measures the variability of the supplier’s lead time as well as the delivered quantity relative to plan. Poor supplier reliability hurts responsiveness and adds to the amount of inventory the supply chain must carry.

3.9 PRICING

In this section, we discuss the role that pricing plays in the supply chain.

Role in the Supply Chain

Pricing is the process by which a firm decides how much to charge customers for its goods and services. Pricing affects the customer segments that choose to buy the product, as well as influ- encing the customer’s expectations. This directly affects the supply chain in terms of the level of responsiveness required as well as the demand profile that the supply chain attempts to serve. Pricing is also a lever that can be used to match supply and demand, especially when the supply

decrease seasonal demand spikes by moving some of the demand forward. All pricing decisions should be made with the objective of increasing firm profits. This requires an understanding of

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the cost structure of performing a supply chain activity and the value this activity brings to the

policy that prices are kept steady but low. The steady prices ensure that demand stays relatively stable. The Costco supply chain exploits the relative stability of demand to be efficient. In con- trast, some manufacturing and transportation firms use pricing that varies with the response time desired by the customer. Through their pricing, these firms are targeting a broader set of custom- ers, some of whom need responsiveness while others need efficiency. In this case, it becomes important for these firms to structure a supply chain that can meet the two divergent needs. Ama- zon uses a menu of shipping options and prices to identify customers who value responsiveness and those who value low cost. This identification allows the company to serve both effectively, as shown in the following example.

EXAMPLE 3-7 Amazon.com

Amazon offers its customers a large menu of prices for products that are purchased from the

standard shipping (3 to 5 business days) at a cost of $4.98, two-day shipping at a cost of $14.97, one-day shipping at a cost of $24.97, or free shipping (5 to 8 business days). The pricing menu allows Amazon to attract customers with varying levels of desired responsiveness. Whereas cus- tomers paying for one-day shipping impose a high degree of uncertainty on Amazon, customers opting for free shipping can be used to level out the workload at the warehouse over time. Ama- zon can thus use its pricing to provide responsiveness to those who value it while using custom- ers who want a low price to help it improve its efficiency.

Components of Pricing Decisions

We now describe key components of pricing decisions that affect supply chain performance.

PRICING AND ECONOMIES OF SCALE Most supply chain activities display economies of scale. Changeovers make small production runs more expensive per unit than large production runs. Loading and unloading costs make it cheaper to deliver a truckload to one location than to four. In each case, the provider of the supply chain activity must decide how to price it appropri- ately to reflect these economies of scale. A commonly used approach is to offer quantity dis- counts. Care must be taken to ensure that quantity discounts offered are consistent with the economies of scale in the underlying process. Otherwise, there is a danger of customer orders being driven primarily by the quantity discounts, even though the underlying process does not have significant economies of scale.

EVERYDAY LOW PRICING VERSUS HIGH–LOW PRICING A firm such as Costco practices EDLP at its warehouse stores, keeping prices steady over time. Costco will go to the extent of not offering any discount on damaged books to ensure its EDLP strategy. In contrast, most super- markets practice high–low pricing and offer steep discounts on a subset of their product every week. The Costco pricing strategy results in relatively stable demand. The high–low pricing strategy results in a peak during the discount week, often followed by a steep drop in demand during the following weeks. The two pricing strategies lead to different demand profiles that the supply chain must serve.

FIXED PRICE VERSUS MENU PRICING A firm must decide whether it will charge a fixed price for its supply chain activities or have a menu with prices that vary with some other attribute, such

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as the response time or location of delivery. If marginal supply chain costs or the value to the customer vary significantly along some attribute, it is often effective to have a pricing menu. We have already discussed Amazon as an example of a firm offering a menu that is somewhat con- sistent with the cost of providing the particular supply chain service. An example of when the pricing menu is somewhat inconsistent is seen at many MRO suppliers, which often allow cus- tomers to have their order shipped to them or to be picked up in person. A customer pays an additional shipping fee for home delivery, but pays nothing for a personal pickup. The pick, pack, and deliver cost at the warehouse, however, is higher in the case of a personal pickup compared with home delivery. The pricing policy thus can lead to customer behavior that has a negative impact on profits.

PRICING-RELATED METRICS Pricing directly affects revenues but can also affect production costs and inventories, depending on its impact on consumer demand. A manager should track the following pricing-related metrics. With menu pricing, each metric should be tracked separately for each segment in the menu:

Profit margin measures profit as a percentage of revenue. A firm needs to examine a wide variety of profit margin metrics to optimize its pricing, including dimensions such as

type, and others. Days sales outstanding measures the average time between when a sale is made and when the cash is collected. Incremental fixed cost per order measures the incremental costs that are independent of the size of the order. These include changeover costs at a manufacturing plant or order processing or transportation costs that are incurred independent of shipment size at a mail- order firm. Incremental variable cost per unit measures the incremental costs that vary with the size of the order. These include picking costs at a mail-order firm or variable production costs at a manufacturing plant. Average sale price measures the average price at which a supply chain activity was per- formed in a given period. The average should be obtained by weighting the price with the quantity sold at that price. Average order size measures the average quantity per order. The average sale price, order size, incremental fixed cost per order, and incremental variable cost per unit help estimate the contribution from performing the supply chain activity. Range of sale price measures the maximum and the minimum of sale price per unit over a specified time horizon. Range of periodic sales measures the maximum and minimum of the quantity sold per period (day/week/month) during a specified time horizon. The goal is to understand any correlation between sales and price and any potential opportunity to shift sales by chang- ing price over time.

3.10 SUMMARY OF LEARNING OBJECTIVES

1. Describe key financial measures of firm performance. The key financial measures of firm performance include return on equity; return on assets; accounts payable turnover; profit margin; asset turnover and accounts receivable turnover; inventory turns; property, plant, and equipment turns; and cash-to-cash cycle.

2. Identify the major drivers of supply chain performance. The major drivers of supply chain performance are facilities, inventory, transportation, information, sourcing, and pricing.

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3. Discuss the role of each driver in creating strategic fit between the supply chain strategy and the competitive strategy. A company achieving strategic fit has found the right balance between responsiveness and efficiency. Each driver affects this balance. Hav- ing more facilities generally makes a chain more responsive, whereas having fewer, central facilities creates higher efficiency. Holding higher levels of inventory increases the respon- siveness of a supply chain, whereas keeping inventory low increases the chain’s efficiency. Using faster modes of transportation increases a chain’s responsiveness, whereas using slower modes generally increases efficiency. Investing in information can vastly improve the supply chain performance on both dimensions. This investment, however, must be made based on the strategic position supported by the other drivers. Appropriate sourcing deci- sions raise supply chain profits by assigning supply chain functions to the right party, which brings higher economies of scale or a higher level of aggregation of uncertainty. Pricing can be used to attract the right target customer segment. Differential pricing can be used to attract customers who value responsiveness as well as customers who want effi- ciency. The supply chain can then be structured to provide responsiveness to some custom- ers while improving overall efficiency.

4. Define the key metrics that track the performance of the supply chain in terms of each driver. Facility-related metrics are capacity, utilization, theoretical flow/cycle time of production, actual flow/cycle time, flow time efficiency, product variety, volume

average production batch size. Inventory-related metrics are average inventory, products with more than a specified number of days of inventory, average replenishment batch size, average safety inventory, seasonal inventory, fill rate, and fraction of time out of stock. Transportation-related metrics are average inbound transportation cost, average incoming shipment size, average inbound transportation cost per shipment, average outbound trans- portation cost, average outbound shipment size, average outbound transportation cost per shipment, and fraction transported by mode. Information-related metrics are forecast hori- zon, forecast error, seasonal factors, variance from plan, and ratio of demand variability to

price, range of purchase price, average purchase quantity, fraction on-time deliveries, sup- ply quality, and supply lead time. Pricing-related metrics are profit margin, days sales outstanding, incremental fixed cost per order, incremental variable cost per unit, average sale price, average order size, range of sale price, and range of periodic sales. Each of these metrics directly or indirectly affects the financial metrics and the responsiveness to customers.

Discussion Questions 1. How could a grocery retailer use inventory to increase the

responsiveness of the company’s supply chain? 2. How could an auto manufacturer use transportation to increase

the efficiency of its supply chain? 3. How could a bicycle manufacturer increase responsiveness

through its facilities? 4. How could an industrial supplies distributor use information

to increase its responsiveness? 5. Motorola has gone from manufacturing all its cell phones in-

house to almost completely outsourcing the manufacturing. What are the pros and cons of the two approaches?

6. How can a home-delivery company like Peapod use pricing of its delivery services to improve its profitability?

7. What are some industries in which products have proliferated and life cycles have shortened? How have the supply chains in these industries adapted?

8. How can the full set of logistical and cross-functional drivers be used to create strategic fit for a cell phone manufacturer targeting both time-sensitive and price-conscious customers?

9. On which supply chain drivers should a firm trying to shrink its cash-to-cash cycle focus?

10. Would you expect a brick-and-mortar retailer or an online retailer to have a higher asset turnover? Which supply chain drivers impact asset turnover?

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Bibliography

Supply Chain Management Review Doheny, Mike, Karl-Hendrik Magnus, Paulo Marchesan, Brian

Ruwadi, Chris Turner, and Nursen Ulker. “Driving Productiv- –

able at https://operations-extranet.mckinsey.com/html/ knowledge/article/20101213_apparel_supply_chain.asp

Dyckman, Thomas R., Robert P. Magee, and Glenn M. Pfeiffer. Financial Accounting. Westmont, IL: Cambridge Business Publishers, 2011.

Sup- ply Chain Management Review

Supply Chain Management Review (March–April 2000): 60–68.

at the Cash-to-Cash Cycle (2000–2012).” Supply Chain Insights LLC report (November 11, 2013).

Chain–Finance Link.” Supply Chain Management Review

The New Supply Chain Agenda: The Five Steps that Drive Real Value. Boston: Harvard Business Press, 2010.

CASE STUDY Seven-Eleven Japan Co.

set up its first store in Koto-ku, Tokyo, in May 1974. The

Holdings Co. Ltd., was established as the holding com-

– nal growth between 1985 and 2013. During that period,

more than 16,000. Globally, the firm had more than

world’s largest chain in terms of retail outlets. Global –

tions were 1,899 billion yen in 2013 with an operating income of 221.7 billion yen. The firm was present in 42

outlets averaged more than 1,000 per store per day in 2013.

Company History and Profile

by Masatoshi Ito. He started his retail empire after World War II, when he joined his mother and elder brother and began to work in a small clothing store in Tokyo. By 1960, he was in sole control, and the single store had grown into a $3 million company. After a trip to the

was still dominated by mom-and-pop stores. Ito’s chain of superstores in the Tokyo area was instantly popular and soon constituted the core of Ito-Yokado’s retail operations.

– –

in Tokyo.

continued (Table 3-3), resulting in 16,086 stores by 2014.

Ito-Yokado’s help, and on March 5, 1991, IYG Holding

– land’s common stock for a total price of $430 million.

subsidiaries in North America and China contributed

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The Convenience Store Industry and Seven-Eleven in Japan

The Seven-Eleven Japan Franchise System

TABLE 3-3 Stores and Annual Sales for Seven-Eleven Japan

Year Number of Stores

Annual Sales (billion yen)

1974 15 0.7

1979 801 109.8

1984 2,299 386.7

1989 3,954 780.3

1994 5,905 1,392.3

1999 8,153 1,963.9

2004 10,826 2,440.8

2005 11,310 2,498.7

2006 11,735 2,533.5

2007 12,034 2,574.3

2008 12,298 2,762.5

2009 12,753 2,784.9

2010 13,232 2,947.6

2011 14,005 3,280.5

2012 15,072 3,508.4

2013 16,086 3,781.2

Source: Business

TABLE 3-4 Financial Figures for Seven & i (2011–2013) For Fiscal Years Ending February 28/29 2011 2012 2013

Total revenues (billion yen) 5,119.7 4,786.3 4,991.6

Total operating income (billion yen) 243.3 292.1 295.7

Convenience store revenues (billion yen) 1,662.7 1,899.5

Convenience store operating income (billion yen) 215.9 221.7

Source:

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Seven-Eleven Japan responsibilities:

Franchise owner responsibilities:

Store Information and Contents

 

TABLE 3-5 Sales by Product Category in 2012 Percentage of Total Sales

Processed foods 26.6

Fast foods 26.0

Fresh/daily foods 12.3

Nonfoods 35.1

Source:

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included soft drinks, nutritional drinks, alcoholic bever- ages such as beer and wine, game software, music CDs, and magazines.

– ber of original items that were available only at their stores. In 2004, original items accounted for roughly 52

Private brand products were sold across all store for- mats and were viewed by the company as an important part of the expansion of synergies across its various retail formats.

Store Services

a variety of services that customers could obtain at its stores. The first service, added in October 1987, was the in-store payment of Tokyo Electric Power bills. The company later expanded the set of utilities for which customers could pay their bills in the stores to include gas, insurance, and telephone. With more convenient operating hours and locations than banks or other financial institutions, the bill payment service attracted millions of additional customers every year. In April

payments on behalf of credit companies. It started sell- ing ski-lift pass vouchers in November 1994. In 1995, it began to accept payment for mail-order purchases. This was expanded to include payment for Internet shopping in November 1999. In August 2000, a meal

ATMs. The company averaged 111 transactions per ATM per day.

Other services offered at stores include photo- copying, ticket sales (including baseball games, express buses, and music concerts), using multifunc- tional copiers, and being a pick-up location for parcel delivery companies that typically did not leave the parcel outside if the customer was not at home. In 2010, the convenience stores also started offering some

government services, such as providing certificates of residence. The major thrust for offering these services was to take advantage of the convenient locations of

– tional revenue, the services also got customers to visit

(described later) in the store.

7dream.com, an e-commerce company. The goal was to exploit the existing distribution system and the fact that

a publisher) discovered that 92 percent of its customers preferred to pick up their online purchases at the local convenience store, rather than have them delivered to their homes. This was understandable, given the fre-

convenience store; 7dream hoped to build on this prefer- ence along with the synergies from the existing distribu- tion system.

“Otoriyose-bin,” or Internet shopping. The service enabled customers to buy products that were typically not available at the retail stores. Customers were allowed to order on the Internet with both pick-up and

group’s stores and Internet services. In April 2007,

Eleven stores. The service allowed customers to prepay and use a card or cell phone to make payments. The service was offered as a convenience to customers making small purchases and was also a reward system offering one yen’s worth of points for every 100 yen spent by the customer. By 2013, 21.45 million nanaco accounts had been issued.

Eleven estimated that in 2009, more than 70 percent of

Eleven wanted to exploit its “close-by convenient stores” to better serve its customers. The company attempted to do this by increasing the number of high daily consump- tion rate products from 500 to 900 and by bolstering its

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Seven-Eleven Japan’s Integrated Store Information System

its operations by using advanced information technol-

in every outlet and linked to headquarters, suppliers,

online network linking the head office, stores, and vendors was established in 1979, though the company

cash registers and terminal control equipment. In 1985, the company, jointly with NEC, developed per- sonal computers using color graphics that were

– isters. These computers were also on the network link- ing the store to the head office, as well as the vendors.

centers, and suppliers were linked only by a traditional analog network. At that time, an integrated services

than 5,000 stores, it became one of the world’s largest

store by 11:00 p.m. were processed and ready for anal- ysis the next morning.

included the following:

Graphic order terminal: This was a handheld device with a wide-screen graphic display, used by the store owner or manager to place orders. The store manager/owner walked down the aisles and placed orders by item. When placing an order, the store manager had access (from the

related to the particular item. This included sales

time, analysis of waste, 10-week sales trends by

for new products, sales analysis by day and time, list of slow-moving items, analysis of sales and number of customers over time, contribution of product to sections in store display, and sales growth by product categories. The store manager used this information when placing an order, which was entered directly into the terminal. Once all the orders were placed, the terminal was

returned to its slot, at which point the orders were relayed by the store computer to both the appro-

center. Scanner terminal: These scanners read bar codes and recorded inventory. They were used to receive products coming in from a distribution center. This was automatically checked against a previously placed order, and the two were recon- ciled. Before the scanner terminals were intro- duced, truck drivers waited in the store until the delivery was checked. Once they were introduced, the driver simply dropped the delivery in the store, and a store clerk received it at a suitable time when there were few customers. The scanner terminals were also used when examining inven- tory at stores. Store computer:

order terminal, and the scanner terminal. It com- municated among the various input sources, tracked store inventory and sales, placed orders,

– tained and regulated store equipment. POS register: As soon as a customer purchased

other data (such as the age and sex of the customer) were stored and transmitted to headquarters through the store computer.

The analyzed and updated data were then sent

each morning. All this information was available on the graphic order terminal with the objective of improving order placement.

could adjust the merchandising mix on the shelves according to consumption patterns throughout the day. For example, popular breakfast items were stocked ear- lier during the day, and popular dinner items were stocked later in the evening. The identification of slow and nonmoving items allowed a store to convert shelf space to introduce new items. About 70 percent of the

course of a year. About 100 new products were intro- duced each week. When a new product was introduced, the decision whether to continue stocking it was made within the first three weeks. Each item on the shelf

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Seven-Eleven’s Distribution System

combined delivery system. At the

7-Eleven in the United States

TABLE 3-6 Global Store Distribution for Seven-Eleven in December 2013

Country Stores

Japan 16,020

United States 8,155

Taiwan 4,919

Thailand 7,429

South Korea 7,085

China 2,001

Malaysia 1,557

Mexico 1,690

Canada 486

Australia 595

Singapore 537

Philippines 1,009

Norway 157

Sweden 190

Denmark 196

Indonesia 149

Total 52,175

Source:

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With the goal of introducing “fresh” products in

combined distribution centers (CDCs) around 2000. By 2003, 7-Eleven had 23 CDCs located throughout North America, supporting about 80 percent of the store network. CDCs delivered fresh items such as sand- wiches, bakery products, bread, produce, and other per- ishables once a day. A variety of fresh-food suppliers sent product to the CDC throughout the day, where they were sorted for delivery to stores at night. Requests from store managers were sent to the nearest CDC, and by 10:00 p.m., the products were en route to the stores.

especially hot food such as wings and pizza, was pre- pared in the store. Fresh-food sales in North America exceeded $450 million in 2003. During this period,

stores also continued. This was a period when 7-Eleven worked very

hard to introduce new fresh-food items, with a goal of

than with traditional gas station food marts. 7-Eleven in

from non-gasoline products compared with the rest of the industry, for which this number was closer to 35 percent. The goal was to continue to increase sales in the fresh-food and fast-food categories with a special focus on hot foods.

totaled $16.0 billion, with about 63 percent coming from merchandise and the rest from the sale of gasoline. The North American inventory turnover rate in 2004 was

– ever, represented a significant improvement in North

American performance, where inventory turns in 1992 were around 12.

Study Questions

1. A convenience store chain attempts to be responsive and provide customers with what they need, when they need it, where they need it. What are some different ways that a convenience store supply chain can be responsive? What are some risks in each case?

2. described as attempting to micro-match supply and demand using rapid replenishment. What are some risks associated with this choice?

3. – tion, inventory management, transportation, and informa- tion infrastructure to develop capabilities that support its

4. but has all products flow through its distribution center.

When is direct store delivery more appropriate? 5.

6.

pros and cons of this approach? Keep in mind that

manufacturers. 7.

replenish convenience stores. What are the pros and cons to having a distributor replenish convenience stores versus

– tion function?

CASE STUDY Financial Statements for Walmart Stores Inc. and Macy’s Inc.

Table 3-7 contains the financial results for Walmart and Macy’s for 2012. Evaluate the financial performance of each company based on the various metrics discussed in

turns, APT, C2C, ART, INVT, and PPET. Can you explain the differences you see in their performance

based on their supply chain strategy and structure? Com- pare the metrics for each company with similar metrics for Amazon and Nordstrom from Table 3-1. Which met- rics does each company perform better on? What supply chain drivers and metrics might explain this difference in performance?

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TABLE 3-7 Selected Financial Data for Walmart Stores Inc. and Macy’s Inc. Year ended January 31, 2013 ($ millions) Walmart Macy’s

Net operating revenues 469,162 27,686 Cost of goods sold 352,488 16,538 Gross profit 116,674 11,148 Selling, general, and administrative expense 88,873 8,482 Operating income 27,801 2,661 Interest expense 2,251 425 Other income (loss)—net 187 (134) Income before income taxes 25,737 2,102 Income taxes 7,981 767 Net income 17,756 1,198

Assets Cash and cash equivalents 7,781 1,836 Net receivables 6,768 371 Inventories 43,803 5,308 Total current assets 59,940 7,876 Property, plant, and equipment 116,681 8,196 Goodwill 20,497 3,743 Other assets 5,987 615 Total assets 203,105 20,991

Liabilities and Stockholder Equity Accounts payable 59,099 4,951 Short-term debt 12,719 124 Total current liability 71,818 5,075 Long-term debt 41,417 6,806 Total liabilities 126,243 14,940 Stockholder equity 76,343 6,051

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In this chapter, we provide an understanding of the role of distribution within a supply chain and identify factors that should be considered when designing a distribution network. We identify several potential designs for distribution networks and evaluate the strengths and weaknesses of each option. We apply these ideas to discuss the evolution of distribution net- works in various industries since the advent of online sales. Our goal is to provide managers with a logical framework for selecting the appropriate distribution network given product, competi- tive, and market characteristics.

4.1 THE ROLE OF DISTRIBUTION IN THE SUPPLY CHAIN

Distribution refers to the steps taken to move and store a product from the supplier stage to a customer stage in the supply chain. Distribution occurs between every pair of stages in the supply chain. Raw materials and components are moved from suppliers to manufacturers, whereas finished products are moved from the manufacturer to the end consumer. Distribution is a key driver of the overall profitability of a firm because it affects both the supply chain cost and the customer value directly. In the apparel retail industry, for example, distribution affects about 35 percent of the revenue (including its influence on markdowns and lost sales). In India, the outbound distribution cost of cement is about 30 percent of the cost of producing and selling it.

Designing Distribution Networks and Applications

to Online Sales

C H A P T E R

4

LEARNING OBJECTIVES After reading this chapter, you will be able to

69

1. Identify the key factors to be considered when designing a distribution network.

2. Discuss the strengths and weaknesses of various distribution options.

3. Understand how online sales have affected the design of distribution networks in different industries.

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It would be no exaggeration to state that two of the world’s most profitable companies, –

ing distribution design and operation. In the case of Walmart, distribution allows the company to provide high availability levels of relatively common products at a very low cost. In the case of

at a reasonable cost. The process of designing a distribution network has two broad phases. In the first phase,

the broad structure of the supply chain network is visualized. This phase decides the number of stages in the supply chain and the role of each stage. The second phase then takes the broad structure and converts it into specific locations and their capability, capacity, and demand alloca- tion. This chapter focuses on issues that affect the design of the broad distribution network. Chapters 5 and 6 focus on the second phase, which starts with the broad network and results in a specific supply chain network.

The appropriate distribution network can be used to achieve a variety of supply chain

highlight the variety of distribution network choices and the issues that arise when selecting among these options.

Until 2007, Dell distributed its PCs directly to end consumers, whereas companies such as HP distributed through resellers. Dell customers waited several days to get a PC,

2007, Dell also started selling its PCs through retailers such as Walmart. In the late 1990s, Gateway opened Gateway Country stores, wherein customers could examine the products and have salespeople help them configure a PC that suited their needs. Gateway, however, chose to sell no products at the stores; all PCs were shipped directly from the factory to the

– ers. These companies have chosen different distribution models. How can we evaluate this wide range of distribution choices? Which ones serve the companies and their customers better?

P&G has chosen to distribute directly to large supermarket chains while obligating smaller players to buy P&G products from distributors. Products move directly from P&G to the larger chains but move through an additional stage when going to smaller supermarkets. Texas Instruments, which once used only direct sales, now sells about 30 percent of its vol- ume to 98 percent of its customers through distributors, while serving the remaining 2 per- cent of customers with 70 percent of the volume directly (Raman and Rao, 1997). What value do these distributors provide? When should a distribution network include an additional stage, such as a distributor? Distributors play a much more significant role for consumer

this be the case?

order placement. The remaining slower-moving products are not stocked but instead are shipped directly from the manufacturer when a customer places an order. It takes several days for the

can they be justified?

profitability of the firm, as is evident in the failure of companies such as Blockbuster and Webvan. The appropriate choice of distribution network grows the supply chain surplus by satisfying cus- tomer needs at the lowest possible cost.

In the next section, we identify performance measures that must be considered when designing the distribution network.

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4.2 FACTORS INFLUENCING DISTRIBUTION NETWORK DESIGN

dimensions:

1. Value provided to the customer 2. Cost of meeting customer needs

Thus, as it compares different distribution network options, a firm must evaluate the impact on customer service and cost. The customer needs that are met influence the company’s reve- nues, which, along with cost, decide the profitability of the delivery network.

– enced by the structure of the distribution network:

Response time is the amount of time it takes for a customer to receive an order. Product variety is the number of different products or configurations that are offered by the distribution network. Prod- uct availability is the probability of having a product in stock when a customer order arrives. Customer experience includes the ease with which customers can place and receive orders and the extent to which this experience is customized. It also includes purely experiential aspects, such as the possibility of getting a cup of coffee and the value that the sales staff provides. Time to market is the time it takes to bring a new product to the market. Order visibility is the ability of customers to track their orders from placement to delivery. Returnability is the ease with which a customer can return unsatisfactory merchandise and the ability of the network to handle such returns.

It may seem, at first, that a customer always wants the highest level of performance along all these dimensions. In practice, however, this is not the case. Customers ordering a book at

response times for high levels of variety. Firms that target customers who can tolerate a long response time require only a few loca-

tions that may be far from the customer. These companies can focus on increasing the capacity of each location. In contrast, firms that target customers who value short response times need to locate facilities close to them. These firms must have many facilities, each with a low capacity. Thus, a decrease in the desired response time increases the number of facilities required in the

books on the same day but requires hundreds of stores to achieve this goal for most of the United

only about forty locations to store its books. Changing the distribution network design affects the following supply chain costs (notice

that these are four of the six supply chain drivers we discussed earlier):

The other two drivers, sourcing and pricing, also affect the choice of the distribution sys- tem; these links will be discussed when relevant.

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(see Chapter 12), as shown in Figure 4-2. To decrease inventory costs, firms try to consolidate

Inbound transportation costs are the costs incurred in bringing material into a facility. Outbound transportation costs are the costs of sending material out of a facility. Outbound trans- portation costs per unit tend to be higher than inbound costs because inbound lot sizes are typi-

the inbound side, but ships out small packages with only a few books per customer on the out- bound side. Increasing the number of warehouse locations decreases the average outbound dis- tance to the customer and makes outbound transportation distance a smaller fraction of the total distance traveled by the product. Thus, as long as inbound transportation economies of scale are maintained, increasing the number of facilities decreases total transportation cost, as shown in Figure 4-3. If the number of facilities is increased to a point at which inbound lot sizes are also very small and result in a significant loss of economies of scale in inbound transportation, increasing the number of facilities increases total transportation cost, as shown in Figure 4-3.

Required Number of Facilities

Desired Response Time

FIGURE 4-1 Relationship Between Desired Response Time and Number of Facilities

Inventory Costs

Number of Facilities

FIGURE 4-2 Relationship Between Number of Facilities and Inventory Costs

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Facility costs decrease as the number of facilities is reduced, as shown in Figure 4-4, because a consolidation of facilities allows a firm to exploit economies of scale.

Total logistics costs are the sum of inventory, transportation, and facility costs for a supply

at least the number of facilities that

logistics costs (and improve response time). If a firm wants to reduce the response time to its customers further, it may have to increase the number of facilities beyond the point that mini-

– agers are confident that the increase in revenues because of better responsiveness will be greater than the increase in costs because of the additional facilities.

Transportation Cost

Number of Facilities

FIGURE 4-3 Relationship Between Number of Facilities and Transportation Cost

Facility Costs

Number of Facilities

FIGURE 4-4 Relationship Between Number of Facilities and Facility Costs

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The customer service and cost components listed earlier are the primary measures used to evaluate different delivery network designs. In general, no distribution network will outperform others along all dimensions. Thus, it is important to ensure that the strengths of the distribution network fit with the strategic position of the firm.

In the next section, we discuss various distribution networks and their relative strengths and weaknesses.

4.3 DESIGN OPTIONS FOR A DISTRIBUTION NETWORK

In this section, we discuss distribution network choices from the manufacturer to the end con- sumer. When considering distribution between any other pair of stages, such as supplier to manu- facturer or even a service company serving its customers through a distribution network, many of the same options still apply. Managers must make two key decisions when designing a distribu- tion network:

1. Will product be delivered to the customer location or picked up from a prearranged site? 2. Will product flow through an intermediary (or intermediate location)?

Based on the firm’s industry and the answers to these two questions, one of six distinct distribution network designs may be used to move products from factory to customer. These designs are classified as follows:

1. Manufacturer storage with direct shipping 2. Manufacturer storage with direct shipping and in-transit merge 3. Distributor storage with carrier delivery 4. Distributor storage with last-mile delivery 5. Manufacturer/distributor storage with customer pickup 6. Retail storage with customer pickup

Manufacturer Storage with Direct Shipping

In this option, product is shipped directly from the manufacturer to the end customer, bypassing the retailer (who takes the order and initiates the delivery request). This option is also referred to as drop-shipping. The retailer carries no inventory. Information flows from the customer, via the

Response Time

Number of Facilities

Total Logistics Cost

FIGURE 4-5 Variation in Logistics Cost and Response Time with Number of Facilities

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retailer, to the manufacturer, and product is shipped directly from the manufacturer to customers,

products in inventory and uses the drop-ship model for slow-moving footwear. W.W. Grainger also uses drop-shipping to deliver slow-moving items to customers.

The biggest advantage of drop-shipping is the ability to centralize inventories at the manu-

issue with regard to drop-shipping is the ownership structure of the inventory at the manufac- turer. If specified portions of inventory at the manufacturer are allocated to individual retailers, there is little benefit of aggregation even though the inventory is physically aggregated. Benefit of aggregation is achieved only if the manufacturer can allocate at least a portion of the available inventory across retailers on an as-needed basis. The benefits from centralization are highest for

items with predictable demand and low value. Thus, drop-shipping does not offer a significant inventory advantage to an online grocer selling a staple item such as detergent. For slow-moving items, inventory turns can increase by a factor of six or higher if drop-shipping is used instead of storage at retail stores.

Drop-shipping also offers the manufacturer the opportunity to postpone customization until after a customer has placed an order. Postponement, if implemented, further lowers inven- tories by aggregating to the component level. For example, a publisher may drop-ship books that have been printed on demand, thus reducing the value of inventory held.

high because manufacturers are farther from the end consumer. With drop-shipping, a customer order including items from several manufacturers will involve multiple shipments to the cus- tomer. This loss in aggregation of outbound transportation also increases cost.

inventories are centralized at the manufacturer. This eliminates the need for other warehousing space in the supply chain. There can be some savings of handling costs as well, because the transfer from manufacturer to retailer no longer occurs. Handling cost savings must be evaluated carefully, however, because the manufacturer is now required to transfer items to the factory warehouse in full cases and then ship out from the warehouse in single units. The inability of a manufacturer to develop single-unit delivery capabilities can have a significant negative effect on handling cost and response time. Handling costs can be reduced significantly if the manufacturer has the capability to ship orders directly from the production line.

Manufacturers

Customers

Retailer

Product Flow Information Flow

FIGURE 4-6 Manufacturer Storage with Direct Shipping

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the retailer can provide product availability information to the customer, even though the inven- tory is located at the manufacturer. The customer should also have visibility into order process- ing at the manufacturer, even with the order being placed with the retailer. Drop-shipping generally requires significant investment in information infrastructure.

Response times tend to be long when drop-shipping is used because the order must be transmitted from the retailer to the manufacturer and shipping distances are generally longer from the manufacturer’s centralized site. eBags, for example, states that order processing may take from 1 to 5 days and ground transportation after that may take from 3 to 11 business days. This implies that customer response time at eBags will be 4 to 16 days using ground transporta- tion and drop-shipping.

part of a customer order. Given an order containing products from several sources, the customer will receive multiple partial shipments over time, making receiving more complicated for the customer.

Manufacturer storage allows a high level of product variety to be available to the customer. With a drop-shipping model, every product at the manufacturer can be made available to the customer without any limits imposed by shelf space. W.W. Grainger is able to offer hundreds of thousands of slow-moving items from thousands of manufacturers using drop-shipping. This would be impossible if each product had to be stored by W.W. Grainger. Drop-shipping allows a new product to be available to the market on the day the first unit is produced.

Drop-shipping provides a good customer experience in the form of delivery to the cus- tomer location. The experience, however, suffers when a single order containing products from several manufacturers is delivered in partial shipments.

Order visibility is important in the context of manufacturer storage, because two stages in the supply chain are involved in every customer order. Failure to provide this capability is likely to have a significant negative effect on customer satisfaction. Order tracking, however, becomes harder to implement in a drop-ship system because it requires complete integration of informa- tion systems at both the retailer and the manufacturer.

– tomer satisfaction. The handling of returns is more expensive under drop-shipping because each order may involve shipments from more than one manufacturer. Returns can be handled in two ways. One is for the customer to return the product directly to the manufacturer. The second approach is for the retailer to set up a separate facility (across all manufacturers) to handle returns. The first approach incurs high transportation and coordination costs, whereas the second approach requires investment in a facility to handle returns.

The performance characteristics of drop-shipping along various dimensions are summa- rized in Table 4-1.

Given its performance characteristics, manufacturer storage with direct shipping is best suited for a large variety of low-demand, high-value items for which customers are willing to wait for delivery and accept several partial shipments. Manufacturer storage is also suitable if it allows the manufacturer to postpone customization, thus reducing inventories. It is thus ideal for direct sellers that are able to build to order. For drop-shipping to be effective, there should be few sourcing locations per order.

Manufacturer Storage with Direct Shipping and In-Transit Merge

Unlike pure drop-shipping, under which each product in the order is sent directly from its manu- facturer to the end customer, in-transit merge combines pieces of the order coming from different locations so the customer gets a single delivery. Information and product flows for the in-transit merge network are shown in Figure 4-7. In-transit merge has been used by Dell and can be used by companies implementing drop-shipping. When a customer ordered a PC from Dell along with

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making a single delivery to the customer. –

hold all their inventories at the factory. This approach has the greatest benefits for products with high value whose demand is difficult to forecast, particularly if product customization can be postponed.

costs relative to drop-shipping by aggregating the final delivery. Facility and processing costs for the manufacturer and the retailer are similar to those for

drop-shipping. The party performing the in-transit merge has higher facility costs because of the merge capability required. Receiving costs at the customer are lower because a single delivery is received. Overall supply chain facility and handling costs are somewhat higher than with drop-shipping.

TABLE 4-1 Performance Characteristics of Manufacturer Storage with Direct Shipping Network Cost Factor Performance

Inventory Lower costs because of aggregation. Benefits of aggregation are highest for low-demand, high-value items. Benefits are large if product customization can be postponed at the manufacturer.

Transportation Higher transportation costs because of increased distance and disaggregate shipping.

Facilities and handling

Lower facility costs because of aggregation. Some saving on handling costs if manufacturer can manage small shipments or ship from production line.

Information Significant investment in information infrastructure to integrate manufacturer and retailer.

Service Factor Performance

Response time Long response time of one to two weeks because of increased distance and two stages for order processing. Response time may vary by product, thus complicating receiving.

Product variety Easy to provide a high level of variety.

Product availability Easy to provide a high level of product availability because of aggregation at manufacturer.

Customer experience

Good in terms of home delivery but can suffer if order from several manufacturers is sent as partial shipments.

Time to market Fast, with the product available as soon as the first unit is produced.

Order visibility More difficult but also more important from a customer service perspective.

Returnability Expensive and difficult to implement.

Factories

Customers

In-Transit Merge by CarrierRetailer

Product Flow Information Flow

FIGURE 4-7 In-Transit Merge Network

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to information, operations at the retailer, manufacturers, and the carrier must be coordinated. The investment in information infrastructure is higher than that for drop-shipping.

Response times, product variety, availability, and time to market are similar to those for drop-shipping. Response times may be higher if the shipments from the various sources are not coordinated. Customer experience is likely to be better than with drop-shipping, because the customer receives only one delivery for an order instead of many partial shipments. Order visi-

– gration of manufacturer, carrier, and retailer, tracking itself becomes easier given the merge that occurs at the carrier hub.

handling returns are likely, and the reverse supply chain will continue to be expensive and diffi- cult to implement.

The performance of factory storage with in-transit merge is compared with that of drop- shipping in Table 4-2. The main advantages of in-transit merge over drop-shipping are lower transportation cost and improved customer experience. The major disadvantage is the additional effort during the merge itself. Given its performance characteristics, manufacturer storage with in-transit merge is best suited for low- to medium-demand, high-value items the retailer is sourc- ing from a limited number of manufacturers. Compared with drop-shipping, in-transit merge requires a higher demand from each manufacturer (not necessarily each product) to be effective. When there are too many sources, in-transit merge can be difficult to coordinate and implement. In-transit merge is best implemented if there are no more than four or five sourcing locations.

was high, but there were few sourcing locations with relatively large total demand from each sourcing location.

Distributor Storage with Carrier Delivery

Under this option, inventory is held not by manufacturers at the factories, but by distributors/ retailers in intermediate warehouses, and package carriers are used to transport products from the

TABLE 4-2 Performance Characteristics of In-Transit Merge Cost Factor Performance

Inventory Similar to drop-shipping.

Transportation Somewhat lower transportation costs than drop-shipping.

Facilities and handling

Handling costs higher than drop-shipping at carrier; receiving costs lower at customer.

Information Investment is somewhat higher than for drop-shipping.

Service Factor Performance

Response time Similar to drop-shipping; may be marginally higher.

Product variety Similar to drop-shipping.

Product availability

Similar to drop-shipping.

Customer experience

Better than drop-shipping because only a single delivery is received.

Time to market Similar to drop-shipping

Order visibility Similar to drop-shipping.

Returnability Similar to drop-shipping.

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Grainger and McMaster-Carr have used this approach combined with drop-shipping from a man- ufacturer (or distributor). Information and product flows when using distributor storage with delivery by a package carrier are shown in Figure 4-8.

Relative to manufacturer storage, distributor storage requires a higher level of inventory because of a loss of aggregation. From an inventory perspective, distributor storage makes sense

W.W. Grainger. They stock only the slow- to fast-moving items at their warehouses, with very- slow-moving items stocked farther upstream. In some instances, postponement of product dif- ferentiation can be implemented with distributor storage, but it does require that the warehouse develop some assembly capability. Distributor storage, however, requires much less inventory

Transportation costs are somewhat lower for distributor storage compared with those for manufacturer storage because an economic mode of transportation (e.g., truckloads) can be employed for inbound shipments to the warehouse, which is closer to the customer. Unlike man- ufacturer storage, under which multiple shipments may need to go out for a single customer order with multiple items, distributor storage allows outbound orders to the customer to be bun- dled into a single shipment, further reducing transportation cost. Distributor storage provides savings on the transportation of faster-moving items relative to manufacturer storage.

Compared with those for manufacturer storage, facility costs (of warehousing) are some- what higher with distributor storage because of a loss of aggregation. Processing and handling costs are comparable to those of manufacturer storage unless the factory is able to ship to the end customer directly from the production line. In that case, distributor storage has higher processing costs. From a facility cost perspective, distributor storage is not appropriate for extremely slow- moving items.

The information infrastructure needed with distributor storage is significantly less com- plex than that needed with manufacturer storage. The distributor warehouse serves as a buffer between the customer and the manufacturer, decreasing the need to coordinate the two com- pletely. Real-time visibility between customers and the warehouse is needed, whereas real-time visibility between the customer and the manufacturer is not. Visibility between the distributor warehouse and manufacturer can be achieved at a much lower cost than real-time visibility between the customer and manufacturer.

Response time under distributor storage is better than under manufacturer storage because distributor warehouses are, on average, closer to customers, and the entire order is aggregated at

items within a day and then it takes three to five business days using ground transportation for the order to reach the customer. W.W. Grainger processes customer orders on the same day and

Warehouse Storage by Distributor/ Retailer

Factories

Customers

Product Flow Information Flow

FIGURE 4-8 Distributor Storage with Carrier Delivery

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has enough warehouses to deliver most orders the next day using ground transport. Warehouse storage limits to some extent the variety of products that can be offered. W.W. Grainger does not store very-low-demand items at its warehouse, relying on manufacturers to drop-ship those prod- ucts to the customer. Customer convenience is high with distributor storage because a single shipment reaches the customer in response to an order. Time to market under distributor storage is somewhat higher than that under manufacturer storage because of the need to stock another stage in the supply chain. Order visibility becomes easier than with manufacturer storage because there is a single shipment from the warehouse to the customer and only one stage of the supply chain is directly involved in filling the customer order. Returnability is better than it is with manufacturer storage because all returns can be processed at the warehouse itself. The customer also has to return only one package, even if the items are from several manufacturers.

The performance of distributor storage with carrier delivery is summarized in Table 4-3. Distributor storage with carrier delivery is well suited for slow- to fast-moving items. Distributor storage also makes sense when customers want delivery faster than is offered by manufacturer storage but do not need delivery immediately. Distributor storage can handle somewhat lower variety than manufacturer storage but can handle a much higher level of variety than a chain of retail stores.

Distributor Storage with Last-Mile Delivery

Last-mile delivery refers to the distributor/retailer delivering the product to the customer’s home

networks for a variety of products, but they failed to survive. The automotive spare parts industry is one in which distributor storage with last-mile delivery is the dominant model. It is too expen- sive for dealers to carry all spare parts in inventory. Thus, original equipment manufacturers

than a couple of hours’ drive from their dealers and often managed by a third party. The local

TABLE 4-3 Performance Characteristics of Distributor Storage with Carrier Delivery Cost Factor Performance

Inventory Higher than manufacturer storage. Difference is not large for faster-moving items but can be large for very-slow-moving items.

Transportation Lower than manufacturer storage. Reduction is highest for faster-moving items.

Facilities and handling

Somewhat higher than manufacturer storage. The difference can be large for very-slow-moving items.

Information Simpler infrastructure compared with manufacturer storage.

Service Factor Performance

Response time Faster than manufacturer storage.

Product variety Lower than manufacturer storage.

Product availability

Higher cost to provide the same level of availability as manufacturer storage.

Customer experience

Better than manufacturer storage with drop-shipping.

Time to market Higher than manufacturer storage.

Order visibility Easier than manufacturer storage.

Returnability Easier than manufacturer storage.

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distribution center is responsible for delivering needed parts to a set of dealers and makes multi- ple deliveries per day. Unlike package carrier delivery, last-mile delivery requires the distributor warehouse to be much closer to the customer. Given the limited radius that can be served with last-mile delivery, more warehouses are required compared to when package delivery is used. The warehouse storage with last-mile delivery network is as shown in Figure 4-9.

Distributor storage with last-mile delivery requires higher levels of inventory than the other options (except for retail stores) because it has a lower level of aggregation. From an inventory perspective, warehouse storage with last-mile delivery is suitable for relatively fast-moving items

by car dealers fit this description. –

ery, especially when delivering to individuals. This is because package carriers aggregate deliv- ery across many retailers and are able to obtain better economies of scale than are available to a distributor/retailer attempting last-mile delivery. Delivery costs (including transportation and processing) can be more than $20 per home delivery in the grocery industry. Last-mile delivery may be somewhat less expensive in large, dense cities, especially if the distributor has very large

product categories, seems better equipped for last-mile delivery than Peapod, which carries only

mile delivery effectively, given its ability to amortize its distribution costs across a large stream of deliveries. Transportation costs may also be justifiable for bulky products for which the cus- tomer is willing to pay for home delivery. Home delivery of water and large bags of rice has proved quite successful in China, where the high population density has helped decrease deliv- ery costs. Transportation costs of last-mile delivery are best justified in settings where the cus- tomer is purchasing in large quantities. This is rare for individual customers, but businesses such as auto dealerships purchase large quantities of spare parts on a daily basis and can thus justify daily delivery. Home delivery to individual customers can be justified for bulky items such as

last-mile delivery is cheaper and more convenient than customers picking up their own bottles or bags.

Using this option, facility costs are somewhat lower than those for a network with retail stores but much higher than for either manufacturer storage or distributor storage with package carrier delivery. Processing costs, however, are much higher than those for a network of retail

performs all the processing until the product is delivered to the customer’s home, unlike a super- market, where the customer does much more work.

The information infrastructure with last-mile delivery is similar to that for distributor stor- age with package carrier delivery. However, it requires the additional capability of scheduling deliveries.

Distributor/Retailer Warehouse

Factories

Customers

Product Flow Information Flow

FIGURE 4-9 Distributor Storage with Last-Mile Delivery

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Urbanfetch tried to provide same-day delivery, whereas online grocers typically provide next- day delivery. Product variety is generally lower than for distributor storage with carrier delivery. The cost of providing product availability is higher than for every option other than retail stores. The customer experience can be good using this option, particularly for bulky, hard-to-carry items. Time to market is even higher than for distributor storage with package carrier delivery because the new product has to penetrate deeper before it is available to the customer. Order vis- ibility is less of an issue, given that deliveries are made within 24 hours. The order-tracking fea- ture does become important to handle exceptions in case of incomplete or undelivered orders. Of all the options discussed, returnability is best with last-mile delivery, because trucks making deliveries can also pick up returns from customers. Returns are still more expensive to handle in this manner than at a retail store, where a customer can bring the product back.

The performance characteristics of distributor storage with last-mile delivery are summa- rized in Table 4-4. In areas with high labor costs, it is hard to justify last-mile delivery to indi- vidual consumers on the basis of efficiency or improved margin. Last-mile delivery may be justifiable if customer orders are large enough to provide some economies of scale and custom- ers are willing to pay for this convenience. Peapod has changed its pricing policies to reflect this idea. Its minimum order size is $60 (with a delivery charge of $9.95), and delivery charges drop to $6.95 for orders totaling more than $100. Peapod offers discounts for deliveries during slower periods based on what its schedule looks like. Last-mile delivery is easier to justify when the customer is a business like an auto dealer purchasing large quantities.

Manufacturer or Distributor Storage with Customer Pickup

In this approach, inventory is stored at the manufacturer or distributor warehouse, but customers place their orders online or on the phone and then travel to designated pickup points to collect their

TABLE 4-4 Performance Characteristics of Distributor Storage with Last-Mile Delivery Cost Factor Performance

Inventory Higher than distributor storage with package carrier delivery.

Transportation Very high cost, given minimal scale economies. Higher than any other distribution option.

Facilities and handling

Facility costs higher than manufacturer storage or distributor storage with package carrier delivery, but lower than a chain of retail stores.

Information Similar to distributor storage with package carrier delivery.

Service Factor Performance

Response time Very quick. Same-day to next-day delivery.

Product variety Somewhat less than distributor storage with package carrier delivery but larger than retail stores.

Product availability More expensive to provide availability than any other option except retail stores.

Customer experience

Very good, particularly for bulky items.

Time to market Slightly higher than distributor storage with package carrier delivery.

Order visibility Less of an issue and easier to implement than manufacturer storage or distributor storage with package carrier delivery.

Returnability Easier to implement than other previous options. Harder and more expensive than a retail network.

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to pick up online orders at a designated store. Tesco has implemented such a service in the United –

business (B2B) example is W.W. Grainger, whose customers can pick up their orders at one of the

come from a central location. In the case of 7dream.com, the order is delivered from a manufacturer –

vice, which allows customers to order thousands of products online at Walmart.com and have them shipped free to a local Walmart store. Items arrive in stores 7 to 10 business days after the order is processed, and customers receive an e-mail notification when their order is ready for pickup.

Inventory costs using this approach can be kept low, with either manufacturer or distributor storage to exploit aggregation. W.W. Grainger keeps its inventory of fast-moving items at pickup locations, whereas slow-moving items are stocked at a central warehouse or in some cases at the manufacturer.

Transportation cost is lower than for any solution using package carriers because signifi- cant aggregation is possible when delivering orders to a pickup site. This allows the use of truck- load or less-than-truckload carriers to transport orders to the pickup site. For a company such as

trucks are already making deliveries to the stores, and their utilization can be improved by includ-

orders without a shipping fee.

lower the additional facility costs. This, for example, is the case with 7dream.com, Walmart, and W.W. Grainger, for which the stores already exist. Processing costs at the manufacturer or the warehouse are comparable to those of other solutions. Processing costs at the pickup site are high because each order must be matched with a specific customer when he or she arrives. Creating this capability can increase processing costs significantly if appropriate storage and information

Cross-Dock DC

Factories

Pickup Sites

Customers

Retailer

Product Flow Information Flow

Customer Flow

FIGURE 4-10 Manufacturer or Distributor Warehouse Storage with Consumer Pickup

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systems are not provided. Increased processing cost and potential errors at the pickup site are the biggest hurdle to the success of this approach.

customer picks it up. Good coordination is needed among the retailer, the storage location, and the pickup location.

In this case, a response time comparable to that using package carriers can be achieved. Vari- ety and availability comparable to any manufacturer or distributor storage option can be provided. There is some loss of customer experience, because unlike the other options discussed, customers must pick up their own orders. On the other hand, customers who do not want to pay online can pay

– lets, it can be argued that the loss of customer convenience is small, because most customers are close to a pickup site and can collect an order at their convenience. In some cases, this option is considered more convenient because it does not require the customer to be at home at the time of delivery. Time to market for new products can be as short as with manufacturer storage.

Order visibility is extremely important for customer pickups. The customer must be informed when the order has arrived, and the order should be easily identified once the customer arrives to

supply chain. Returns can potentially be handled at the pickup site, making it easier for customers. From a transportation perspective, return flows can be handled using the delivery trucks.

The performance characteristics of manufacturer or distributor storage with consumer pickup sites are summarized in Table 4-5. The main advantages of a network with consumer pickup sites are that it can lower the delivery cost and expand the set of products sold and cus- tomers served online. The major hurdle is the increased handling cost and complexity at the

pickup sites, because this type of network improves the economies from existing infrastructure.

W.W. Grainger, which have both a network of stores and an online business. Unfortunately, such

TABLE 4-5 Performance Characteristics of Network with Consumer Pickup Sites Cost Factor Performance

Inventory Can match any other option, depending on the location of inventory.

Transportation Lower than the use of package carriers, especially if using an existing delivery network.

Facilities and handling

Facility costs can be high if new facilities have to be built. Costs are lower if existing facilities are used. The increase in handling cost at the pickup site can be significant.

Information Significant investment in infrastructure is required.

Service Factor Performance

Response time Similar to package carrier delivery with manufacturer or distributor storage. Same-day delivery is possible for items stored locally at pickup site.

Product variety Similar to other manufacturer or distributor storage options.

Product availability

Similar to other manufacturer or distributor storage options.

Customer experience

Lower than other options because of the lack of home delivery. Experience is sensitive to capability of pickup location.

Time to market Similar to manufacturer storage options.

Order visibility Difficult but essential.

Returnability Somewhat easier, given that pickup location can handle returns.

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retail sites are typically designed to allow the customer to do the picking and need to develop the capability of picking a customer-specific order.

Retail Storage with Customer Pickup

In this option, often viewed as the most traditional type of supply chain, inventory is stored locally at retail stores. Customers walk into the retail store or place an order online or by phone

placement include Walmart and Tesco. In either case, customers can walk into the store or order

and pick up their order at one of W.W. Grainger’s retail outlets. Local storage increases inventory costs because of the lack of aggregation. For fast- to very-

fast-moving items, however, there is marginal increase in inventory, even with local storage. Walmart uses local storage for its fast-moving products while delivering a wider variety of products from its

items at pickup locations, whereas slow-moving items are stocked at a central warehouse. Transportation cost is much lower than with other solutions because inexpensive modes of

transport can be used to replenish product at the retail store. Facility costs are high because many

into the store and place orders. For online orders, however, a significant information infrastruc- ture is needed to provide visibility of the order until the customer picks it up.

Good response times can be achieved with this system because of local storage. For example, both Tesco and W.W. Grainger offer same-day pickup from their retail locations. Product variety stored locally is lower than that under other options. It is more expensive than with all other options to provide a high level of product availability. Customer experience depends on whether or not the customer likes to shop. Time to market is the highest with this option because the new product must penetrate through the entire supply chain before it is available to customers. Order visibility is extremely important for customer pickups when orders are placed online or by phone. Returns can be handled at the pickup site. Overall, returnability is fairly good using this option.

The performance characteristics of a network with customer pickup sites and local retail storage are summarized in Table 4-6. The main advantage of a network with retail storage is that

TABLE 4-6 Performance Characteristics of Retail Storage at Consumer Pickup Sites Cost Factor Performance

Inventory Higher than all other options.

Transportation Lower than all other options.

Facilities and handling

Higher than other options. The increase in handling cost at the pickup site can be significant for online and phone orders.

Information Some investment in infrastructure required for online and phone orders.

Service Factor Performance

Response time Same-day (immediate) pickup possible for items stored locally at pickup site.

Product variety Lower than all other options.

Product availability More expensive to provide than all other options.

Customer experience

Related to whether shopping is viewed as a positive or negative experience by customer.

Time to market Highest among distribution options.

Order visibility Trivial for in-store orders. Difficult, but essential, for online and phone orders.

Returnability Easier than other options because retail store can provide a substitute.

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it can lower delivery costs and provide a faster response than other networks. The major disad-

items or items for which customers value rapid response.

Selecting a Distribution Network Design

when deciding on the appropriate delivery network. The various networks considered earlier have different strengths and weaknesses. In Table 4-7, the various delivery networks are ranked

performance along a given dimension; as the relative performance worsens, the ranking number increases.

Only niche companies end up using a single distribution network. Most companies are best served by a combination of delivery networks. The combination used depends on product char- acteristics and the strategic position that the firm is targeting. The suitability of different delivery designs (from a supply chain perspective) in various situations is shown in Table 4-8.

aforementioned options in its distribution network. The network, however, is tailored to match the characteristics of the product and the needs of the customer. Fast-moving and emergency items are stocked locally, and customers can either pick them up or have them shipped, depend-

– tomer within a day or two. Very-slow-moving items are typically drop-shipped from the

stocks fast-moving items at most of its warehouses and slower-moving items at fewer ware- houses; very-slow-moving items may be drop-shipped from suppliers.

We can now revisit the examples from the computer industry discussed at the beginning of the chapter. Gateway’s decision to create a network of retail stores without exploiting any of the

TABLE 4-7 Comparative Performance of Delivery Network Designs

Retail Storage with

Customer Pickup

Manufacturer

Storage with Direct Shipping

Manufacturer Storage with

In-Transit Merge

Distributor Storage with

Package Carrier

Delivery

Distributor

Storage with Last-Mile Delivery

 

Manufacturer Storage with

Pickup

Response time 1 4 4 3 2 4

Product variety 4 1 1 2 3 1

Product availability

4 1 1 2 3 1

Customer experience

Varies from 1 to 5

4 3 2 1 5

Time to market 4 1 1 2 3 1

Order visibility 1 5 4 3 2 6

Returnability 1 5 5 4 3 2

Inventory 4 1 1 2 3 1

Transportation 1 4 3 2 5 1

Facility and handling

6 1 2 3 4 5

Information 1 4 4 3 2 5

Key: 1 corresponds to the strongest performance and 6 the weakest performance.

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supply chain advantages such a network offers was flawed. To fully exploit the benefits of the retail network, Gateway should have stocked its standard configurations (likely to have high demand) at the retail stores, with all other configurations drop-shipped from the factory (perhaps with local pickup at the retail stores if that was economical). Instead, it drop-shipped all configu-

4.4 ONLINE SALES AND THE DISTRIBUTION NETWORK

In this section, we use ideas discussed earlier in the chapter to see how the Internet has affected the structure and performance of various distribution networks. The goal is to understand what drove the successful introduction of online sales in some networks and not others, and how these networks are likely to evolve.

online sales affect a supply chain’s ability to meet customer needs and the cost of meeting those needs. We now detail the contents of each scorecard category.

Impact of Online Sales on Customer Service

customer service elements such as response time, product variety, availability, customer experi- ence, time to market, visibility, and returnability. We also look at factors such as direct sales and the ability to offer flexible pricing that help companies selling online.

TABLE 4-8 Performance of Delivery Networks for Different Product/Customer Characteristics

 

Retail Storage with

Customer Pickup

Manufacturer Storage

with Direct Shipping

Manufacturer Storage with

In-Transit Merge

Distributor Storage with

Package Carrier

Delivery

Distributor Storage with

Last-Mile Delivery

Manufacturer Storage with

Pickup

High-demand product

+2 -2 -1 0 +1 -1

Medium-demand product

+1 -1 0 +1 0 0

Low-demand product

-1 +1 0 +1 -1 +1

Very-low-demand product

-2 +2 +1 0 -2 +1

Many product sources

+1 -1 -1 +2 +1 0

High product value

-1 +2 +1 +1 0 +2

Quick desired response

+2 -2 -2 -1 +1 -2

High product variety

-1 +2 0 +1 0 +2

Low customer effort

-2 +1 +2 +2 +2 -1

Key: +2 = very suitable; +1 = somewhat suitable; 0 = neutral; -1 = somewhat unsuitable; -2 = very unsuitable.

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RESPONSE TIME TO CUSTOMERS When selling physical products that cannot be downloaded, customer requests take longer to fulfill through online sales than in a retail store because of the shipping time involved. Thus, customers who require a short response time may not use the Internet to order a product. There is no such delay, however, for information goods. The Internet has facilitated almost instantaneous access to movies, music, and books in digital form.

PRODUCT VARIETY – ucts than a bricks-and-mortar store. For example, Walmart.com offers a much larger selection of products than Walmart stores do. Offering the same selection at a store would require a huge location with a correspondingly large amount of inventory.

PRODUCT AVAILABILITY By aggregating its inventory, a company selling online improves product availability. Better information on customer preferences also allows firms selling online to improve availability.

CUSTOMER EXPERIENCE Online sales affect customer experience in terms of access, custom- ization, and convenience. Unlike most retail stores that are open only during business hours, the Internet allows a customer to place an order at any convenient time. In fact, W.W. Grainger has observed a surge in online orders after its bricks-and-mortar stores close. Online sales also allow a firm to access customers who are geographically distant. Using the Internet, a small specialty

The Internet offers an opportunity to create a personalized buying experience for each cus-

purchased or browsed. Firms that focus on mass customization can use the Internet to help cus- tomers select a product that suits their needs. For example, Pella allows customers to design their windows on the Pella website.

For both consumers and companies, online sales can increase the ease with which one does business. Customers do not have to leave home or work to make a purchase. For many compa- nies selling online, such as Peapod, information from past purchases is used to significantly speed up order placement for the customer.

FASTER TIME TO MARKET

channels must produce enough units to stock the shelves at its distributors and retailers before it –

uct available online as soon as the first unit is ready to be produced. This is evident at Walmart. com, where larger new TVs go on sale well before they are sold at Walmart stores.

ORDER VISIBILITY The Internet makes it possible to provide visibility of order status. From a customer’s perspective, it is crucial to provide this visibility because an online order has no physical equivalent to a customer shopping for an item at a retail store.

RETURNABILITY Returnability is harder with online orders, which typically arrive from a cen- tralized location. It is much easier to return a product purchased at a retail store. The proportion of returns is also likely to be much higher for online orders because customers are unable to touch and feel the product before their purchase. Going online thus increases the cost of reverse flows.

DIRECT SALES TO CUSTOMERS The Internet allows manufacturers and other members of the supply chain that do not have direct contact with customers in traditional channels to get cus-

Facebook and Twitter allow firms to pitch products and promotions directly to customers.

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FLEXIBLE PRICING, PRODUCT PORTFOLIO, AND PROMOTIONS Given the ease of changing prices and assortments online, the Internet allows a company selling online to manage revenues from its available product portfolio much more effectively than do traditional channels. Promo- tion information can be conveyed to customers quickly and inexpensively using the Internet as long as the business has access to its customer network. Groupon is one company that has used social networking online to push promotions to customers.

EFFICIENT FUNDS TRANSFER The Internet and cell phones can enhance the convenience and lower the cost of revenue collection, especially in small amounts. For example, after the earth- quake in Haiti in 2010, Mercy Corps transferred $40 automatically into each Haitian person’s account, allowing him or her to buy food at local merchants. This was much more efficient than handing out cash or vouchers.

Impact of Online Sales on Cost

Online sales affect the cost of inventory, facilities, transportation, and information. It is important to observe that the impact in each case is not necessarily positive.

INVENTORY Online sales can lower inventory levels by aggregating inventories far from cus- –

note is that the relative benefit of aggregation is small for high-demand items with low variability but large for low-demand items with high variability.

Online sales can lower a firm’s inventories if it can postpone the introduction of variety until after the customer order is received. The time lag between when a customer places the order and when he or she expects delivery offers a company selling online a window of opportunity to imple- ment postponement. For example, for its online business, Dell keeps its inventory as components and assembles its servers after receiving the customer order. The amount of component inventory required is much lower than it would be if Dell kept its inventories in the form of assembled servers.

FACILITIES Two basic types of facilities costs must be included in the analysis: (1) costs related to the number and location of facilities in a network and (2) costs associated with the operations

centralizing operations, thereby decreasing the number of facilities required. For example,

needed thousands of retail outlets to serve customers. With regard to ongoing operating costs, customer participation in selection and order

placement allows a company selling online to lower its resource costs relative to staffing retail stores. Online sales can also lower a firm’s order fulfillment costs because it does not have to fill

staffing during peak periods. With online sales, if a reasonable buffer of unfilled orders is main- tained, the rate of order fulfillment can be made significantly smoother than the rate at which orders arrive, which reduces the peak load for order fulfillment and thus reduces resource require- ments and cost.

On the downside, however, for some products, such as groceries, online sales require the firm to perform tasks currently performed by the customer at retail stores, affecting both han- dling and transportation costs. In such situations, companies selling online will incur higher handling and delivery costs than a retail store. For example, whereas a customer picks out the required items at a grocery store, an online seller such as Peapod incurs higher handling costs

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because its employees must pick a customer’s order from the warehouse shelves and deliver it to the customer’s home.

TRANSPORTATION goods in digital form, such as movies, music, and books. For nondigital products, aggregating inven- tories increases outbound transportation relative to inbound transportation. Compared to a business with many retail outlets, an online seller with aggregated inventories tends to have higher transporta- tion costs (across the entire supply chain) per unit because of the increased outbound costs.

INFORMATION improve visibility. The Internet may also be used to share planning and forecasting information within the supply chain, further improving coordination. This helps reduce overall supply chain costs and better match supply and demand. Here we see that information is an enabler of many of the benefits of online sales discussed so far.

about 120,000 product descriptions and more than 2 million photographs to its website. The blank B2C online sales scorecard shown in Table 4-9 can be used by a firm to sum-

marize the impact of online sales on each of the areas identified earlier.

– cers have gone out of business. The scorecard in Table 4-9 can be used to understand how online sales affect the performance of different supply chain networks. In the next section, we apply the online sales scorecard to several examples.

Using Online Sales to Sell Computer Hardware: Dell

sell its consumer products through retail stores such as Walmart in 2007. However, it continued

TABLE 4-9 The Online Sales Scorecard Area Impact

Response time

Product variety

Product availability

Customer experience

Time to market

Order visibility

Direct sales

Flexible pricing, portfolio, promotions

 

Efficient funds transfer

 

Inventory

Facilities

Transportation

Information

Key: +2 = very positive; +1 = positive; 0 = neutral; -1 = negative; -2 = very negative.

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phones and computers through retail stores. This raises the question of the relative value of the online channel and retail stores for selling computer hardware.

To make this comparison, we compare Dell’s supply chain for servers and laptops. Prior to 2007, both servers and laptops were configured to order in Dell factories. By 2014, servers were configured to order, but laptops, whether sold through retail stores or online, were typically assembled well in advance of the final sale. We use our framework to understand why servers and laptops are better handled through different channels.

IMPACT OF ONLINE SALES ON CUSTOMER SERVICE FOR COMPUTER HARDWARE One dis- advantage for Dell of selling hardware over the Internet is the delay in fulfilling the customer request. Whereas a longer response time is not a big negative for customized servers, it is a dis- advantage for Dell when trying to sell its standardized laptops online.

Dell is able to exploit most of the responsiveness-enhancing opportunities offered by the Internet for customized servers. The company uses the Internet to offer a wide variety of custom- ized server configurations with the desired chassis, processor, memory, and operating system. Customization allows Dell to satisfy customers by giving them a product that is close to their specific requirements. The customization options are easy to display over the Internet, allowing Dell to attract customers who value this choice. Clearly, all these capabilities are not as valuable for standardized laptops.

quickly. This is particularly important in the computer and cell phone industries, in which many products have short life cycles of a few months. Whereas the Internet allows a new product to be offered as soon as it is produced, the retail channel requires the entire supply chain to be stocked before customers can access the product.

The Internet channel has allowed companies like Dell to make price changes quickly and efficiently based on product availability and demand. By being available all day, the online chan- nel allows Dell to serve customers at a much lower cost than retail stores.

IMPACT OF ONLINE SALES ON COST IN THE HARDWARE INDUSTRY Inventory Costs. Its online sale of servers offers Dell the ability to reduce its invento-

ries by aggregating them in a few geographic locations. Dell is able to further reduce inventories by postponing assembly of servers until after the order arrives. This allows Dell to hold invento- ries of components instead of finished goods.

Observe that inventory reduction through aggregation and postponement is much more valuable for customized servers with low and unpredictable demand compared with standardized laptops with large and predictable demand.

Facility Costs. The online channel allows the Dell supply chain to lower facility costs relative to the retail channel because Dell incurs only the cost of the manufacturing facility and

– tion warehouses and retail stores as well.

Dell is also able to take advantage of customer participation and save on the cost of call center representatives because customers do all the work when they place an order online. Once again, the overall savings are greater for customized servers compared with standardized laptops.

Transportation Costs. supply chain are higher than in a supply chain selling hardware through distributors and retailers. Whereas the transportation cost increase is a small fraction of the cost of a high-end customized server, it can be a large fraction of the cost of a low-end standardized laptop.

Information Costs. technology (IT) to implement its build-to-order model, the majority of these IT costs would be

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incurred regardless of Dell’s online sales status. Therefore, online sales do add incrementally to Dell’s information costs, but this is not a significant factor given the benefits. The value of the information infrastructure, however, is greater for a customized server relative to a standardized laptop.

IMPACT OF ONLINE SALES ON PERFORMANCE AT DELL scorecard in Table 4-10, online sales allow Dell to significantly improve its performance for cus- tomized servers in terms of both responsiveness and cost. For standardized laptops, however, the

become more significant for standardized configurations.

A TAILORED SUPPLY CHAIN NETWORK FOR HARDWARE USING RETAIL STORES AND THE INTERNET It may seem, at first glance, that selling hardware online has significant advantages.

channel can be very effective. The online channel is most effective for selling new products or customized hardware configurations whose demand is hard to forecast, with the retail channel

– ized products, manufacturers should introduce new models on the Internet; as demand for some

recommended configurations of new models at retail stores, while selling all customized con- figurations on the Internet. The manufacturer is thus able to decrease inventories by aggregating all high-variability production and satisfying that demand online. These models should be built to order using as many common components as is feasible. The standard models can be produced

through distributors and retail stores allows the supply chain to be more responsive and save on transportation costs, which are more significant for these configurations.

TABLE 4-10 Impact of Online Sales on Performance at Dell Area

Impact for Customized Servers

Impact for Standard Laptops

Response time -1 -2 Product variety +2 0 Product availability +1 +1 Customer experience +2 +1 Time to market +2 +1 Order visibility +1 0 Direct sales +2 +1 Flexible pricing, portfolio, promotions

+2 +1

Efficient funds transfer +2 +2 Inventory +2 +1 Facilities +2 +1 Transportation -1 -2 Information +1 0

Key: +2 = very positive; +1 = positive; 0 = neutral; -1 = negative; -2 = very negative.

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of the strengths of both online sales and traditional retail and distribution channels. Gateway failed in its effort with retail stores because it did not use any of the supply chain strengths of the bricks-and-mortar channel. Instead of just helping people with configuration at its retail stores, Gateway would have served its customers better by also carrying recommended configurations of its computers in the stores. This would have immediately satisfied customers who wanted the recommended configuration, while allowing Gateway to produce the more customized configu-

sells a relatively low variety of standardized hardware in large volumes at its stores. Dell has also started using the tailored approach with customized hardware such as servers built to order, whereas standardized hardware, such as laptops, is produced in low-cost countries and sold through retail stores such as Walmart. In the long run, a tailored approach is likely to prevail in the computer hardware and cell phone industry.

Using Online Sales to Sell Books: Amazon

Book supply chains have been transformed with the advent of online sales and the launching of –

ings, including music, toys, electronics, software, and home improvement equipment. Whereas

has magnified with the growth in electronic books (e-books).

IMPACT OF ONLINE SALES ON CUSTOMER SERVICE IN THE BOOK INDUSTRY Online sales have not helped profits for traditional books to the same extent as in the customized hardware industry. Unlike the hardware industry, in which online sales facilitate direct sales by manufac- turers, the Internet has not shortened supply chains in the book industry.

books. The company tries to counter this problem by providing reviews and other information on books to allow customers to get a feel for the book online.

quickly introduced and made available online, whereas in a bricks-and-mortar bookstore chain, all retail stores must be stocked.

– tomers who value this convenience and are willing to wait for delivery.

– ple, customers can download a book in seconds without having to leave home. For people who value time, this experience is superior to buying a traditional book either online or at a bookstore. Product availability is never an issue with e-books, and variety can be added at low marginal cost. In fact, the Internet has allowed the availability of books that are not guaranteed a high enough demand to make them viable for traditional publishers. For very-low-volume books, there is no better channel than online as e-books.

COST IMPACT OF ONLINE SALES ON THE BOOK INDUSTRY lower its inventory and some of its facility costs. For traditional books, transportation costs increase as a result of selling books online. For e-books, however, transportation cost is not a factor given that they can be downloaded efficiently on the Internet.

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Inventory Costs. –

titles are carried at every store. The reduction of inventories from aggregation is most significant for low-demand books with high demand uncertainty. The benefit is less significant for best sellers, for

but it purchases low-demand titles from publishers in response to a customer order. In some

Facility Costs.

– umes were low, the distributor was a better location to carry inventories because it aggregated

although they are still much lower than the facility costs for a bookstore chain. For e-books,

capacity is likely to be cheaper than that for the warehousing required to serve physical demand.

Transportation Costs. than does a bookstore chain selling through retail stores. Local bookstores do not have the cost of

significant fraction of the cost of a book (it can be even higher than 100 percent for an inexpen-

closer to customers, decrease its transportation costs, and improve response time. Transportation

the net loss on outbound transportation was $2.85 billion, a very significant amount. In contrast, the cost of delivering e-books and other digital content to customers is negligible in comparison.

Information Costs. in IT, but is not prohibitively expensive. The cost of IT infrastructure to support download of e-books, however, is more expensive.

Impact of Online Sales on Performance at Amazon. –

offer far greater advantages when selling customized computer hardware than when selling physical books. This fact is explained by the following key differences between the two prod- ucts: (1) product differentiation in hardware can be postponed until after the customer has placed an order, whereas physical books are currently published well in advance of a sale and (2) trans- portation cost represents a much higher portion of the cost of books and a relatively small portion of the cost of hardware. For e-books, however, the Internet offers tremendous advantage relative

encourage customers to buy books online.

instance, the Internet channel offers tremendous advantage relative to physical distribution. With

formats of music had a difficult time surviving, with most closing by 2010. In the movie busi- ness, large DVD retailers like Walmart have continued to do well, but smaller retail formats such as Blockbuster have not survived selling and renting physical DVDs.

A TAILORED SUPPLY CHAIN NETWORK FOR BOOKS USING RETAIL STORES AND THE INTERNET

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themselves at the expense of mom-and-pop bookstores primarily through aggregation. Large retail footprints allowed the two chains to carry a greater variety of books while often achieving

– ety but charged full price for low-demand books, while they offered a discount on best sellers.

Internet to sell low-volume books much more efficiently than either bookstore chain. With the growth in e-books and other retail formats such as Walmart and Costco selling best sellers, the large bookstore chains are stuck in the middle without any area of dominance. Large book- store chains are being squeezed from both ends: other retail formats for best-selling books and online sales for other low-volume books and e-books. Borders was shut down and liquidated in

Using the Internet to Sell Groceries: Peapod

The grocery industry saw a spurt in new online sellers in 1998 and l999, although virtually all have gone out of business. Peapod, one of the oldest online grocers, is one of the few left. Given this industry’s poor track record, one might surmise that this is an industry not well suited for online

with our scorecard to see where, if at all, the Internet offers an advantage in this industry. Peapod started by using employees at grocery stores to pick and deliver orders. The com-

pany has now moved to supplying orders from centralized fulfillment centers in Chicago and

fulfillment center is much larger than a supermarket and is comparable to a warehouse. The Peapod and supermarket supply chains are comparable except that Peapod must deliver goods to the customer, whereas the customer comes to a supermarket.

IMPACT OF ONLINE SALES ON CUSTOMER SERVICE IN THE GROCERY INDUSTRY Peapod and other online groceries have tried to sell convenience and time savings to potential custom- ers. For many people, grocery shopping is a chore that is time consuming and rarely enjoyable.

TABLE 4-11 Impact of Online Sales on Performance at Amazon Area Physical books e-books

Response time -1 11 Product variety +2 +2 Product availability +1 +2 Customer experience +1 +1 Time to market +1 +2 Order visibility 0 0

Direct sales 0 +1 Flexible pricing, portfolio, promotions

+1 +1

Efficient funds transfer 0 0

Inventory +1 +2 Facilities +1 +1 Transportation -2 +1 Information -1 -1

Key: +2 = very positive; +1 = positive; 0 = neutral; -1 = negative; -2 = very negative.

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Peapod allows customers to place orders at any time and have them delivered at home, eliminat- ing a trip to the supermarket. This can be a significant convenience, especially in urban areas, where customers must walk to a supermarket and carry all their groceries home. In a suburban area, the benefit is smaller because people tend to batch their shopping and can drive to super- markets with relative ease. The convenience of saving time, however, remains quite valuable.

The convenience factor related to access is even more significant if a specialty-food pro-

people often drive long distances to reach them. Offering these foods on the Internet provides easy access to customers and saves a long drive. Most large supermarkets offer sufficiently large variety to cover the needs of most households. Peapod, however, offers less variety than a typical supermarket.

Peapod is able to increase revenues by creating a personalized shopping experience for customers and delivering customized, one-to-one advertising and promotions. This is done using extensive member profiles that Peapod creates based on online shopping behavior, purchase his- tories, and surveys. Unlike a supermarket, in which the store does not know what customers have selected until they check out, Peapod can guide online customers based on what they purchase. For example, if a customer buys some pasta, Peapod can suggest a type of pasta sauce or some Parmesan cheese. Over longer periods, Peapod can collect shopping patterns and suggest prod-

– tomers’ impulse purchases.

Peapod also adds to its revenues by giving consumer goods companies a forum for targeted interactive advertising and electronic coupons. Consumer choice data available to an online gro- cer is more valuable than scanner data from a supermarket because scanner data reveals only the

process by, for example, recording a customer’s substitution patterns for items that are out of stock. With scanner data, a supermarket cannot record substitutions because it has no way of finding out whether the customer looked for something that is out of stock.

IMPACT OF ONLINE SALES ON COSTS IN THE GROCERY INDUSTRY Peapod and other online grocers use online sales to lower some facility costs and, to an extent, inventory costs. Picking costs and transportation costs, however, are much higher than for traditional supermarkets.

Inventory Costs. Compared with a supermarket chain, an online grocer such as Peapod can lower inventories by aggregating the inventory in a few large replenishment centers. The

– ware, because Peapod needs fulfillment centers in every urban area it serves to get food to cus- tomers in acceptable condition.

The benefits of aggregation are further diminished by the fact that the majority of products sold at a supermarket are staple items with steady demand. Thus, aggregation provides a small benefit in terms of improved forecast accuracy and reduced inventories (see Chapter 12). The benefits of aggregation are higher for specialty, low-demand items with high demand uncer- tainty. These products constitute a small fraction of overall sales at a supermarket. Thus, aggre- gation allows e-grocers to lower their inventory costs only marginally compared with a typical supermarket. If online grocers focused primarily on specialty items such as ethnic foods, the inventory benefits of aggregation would be larger.

Facility Costs. Peapod’s online sales allow it to lower facility costs because it needs only warehouse facilities and can save on the cost of retail outlets such as supermarkets. Processing costs at Peapod to fulfill an order, however, are significantly higher than those for a supermarket and overwhelm the savings from needing fewer facilities. Peapod saves on checkout clerks com- pared with a supermarket but must pick the customer order, a task the customer performs at a supermarket and one that is much more time consuming than checkout. Thus, online sales result in a loss of customer participation compared to a supermarket and raise overall facility costs.

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Transportation. –

transportation cost for products, with customers providing transportation from the supermarket to their homes. Inbound transportation costs tend to be low because supermarkets have large deliveries that enable them to exploit economies of scale in transportation. Peapod, in contrast, must bear inbound transportation cost to its fulfillment centers and then outbound delivery costs from the fulfillment centers to customer homes. Outbound delivery costs are high, because indi- vidual orders must be delivered to each customer’s home. The task becomes all the more prob- lematic given the different temperature requirements for different types of food.

Compared with computers and even books, groceries have a low value-to-weight/volume ratio. For example, paper towels and bathroom tissues have very low value but occupy a lot of space in a truck. Thus, transportation costs are a significant fraction of the cost incurred by online grocers. This makes it difficult for an online grocer to compete with a supermarket on prices.

Information Costs. costs. In the case of an online grocer, this is somewhat more significant than with the other online channels, because an online grocer takes on a wider range of functions that shoppers do them-

costs are not a deal breaker for this business model.

IMPACT OF ONLINE SALES ON PERFORMANCE AT PEAPOD Online sales offer some reve- nue-enhancement opportunities in the grocery industry. Costs, however, are significantly higher

Tables 4-10, 4-11, and 4-12 shows that online sales offer fewer benefits when selling groceries

the inventory benefits that aggregation offers, without having the additional delivery cost incurred by an online grocer. Online grocers cannot compete with supermarkets on price and can succeed only if there are enough people willing to pay a premium for the convenience of home delivery. Online grocers, however, can provide some cost advantage when selling specialty groceries, whose demand tends to be low and uncertain.

TABLE 4-12 Impact of Online Sales on Performance at Peapod Area Impact

Response time -1 Product variety 0

Product availability 0

Customer experience +1 Time to market 0

Order visibility -1 Direct sales 0

Flexible pricing, portfolio, promotions +1 Efficient funds transfer 0

Inventory 0

Facilities -1 Transportation -2 Information -1

Key: +2 = very positive; +1 = positive; 0 = neutral; -1 = negative; -2 = very negative.

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A TAILORED SUPPLY CHAIN FOR GROCERIES Traditional supermarket chains can benefit by using the online channel to complement the strengths of their existing networks. The online

– kets can be used to target customers who value lower prices.

at differing prices based on the amount of work the customer does. The cheapest service involves customers walking into the supermarket and shopping for the products they want. In this case, the customer picks the order from the shelves and provides outbound transportation for it. For an addi- tional charge, a supermarket might allow customers to place orders online to be picked up at a later time. The supermarket personnel would pick the order from the shelves, but the customer would provide outbound transportation. The most expensive service involves the customer placing orders online for home delivery. In this case, the supermarket chain is responsible for both picking the order from the shelves and delivering it to the customer’s home. The varying services and prices would allow supermarket chains to efficiently satisfy the needs of a variety of customers.

physical supermarkets to serve customers in a variety of ways. Customers could shop at a super- market, order online for home delivery, or pick up from a designated location. Traditionally, Tesco picked groceries at existing supermarkets for home delivery. By 2012, however, Tesco had opened

online orders. Rather than open large warehouses, Tesco preferred to serve online orders from these dark stores. Tesco intended to open more dark stores in London and other cities. Tesco had

The glass walls of subway stations were covered with pictures of products laid out as they would

smartphone to build up a shopping basket quickly. If the train arrived before shopping was com- plete, commuters could continue to fill their shopping basket using a traditional mobile app. Deliveries were arranged to arrive in time for the commuter to cook dinner that evening.

Using the Internet to Rent Movies: Netflix

world’s largest subscription service sending DVDs by mail and streaming movies and television episodes over the Internet. For $7.99 a month, customers could obtain unlimited streaming of

factors that drove DVD rental chain Blockbuster into bankruptcy in 2010.

IMPACT OF ONLINE SALES ON CUSTOMER SERVICE FOR NETFLIX with its staggering selection and an excellent recommendation engine that allowed customers to access a list of titles they were likely to enjoy. Whereas a typical Blockbuster store offered 3,000

a program that made recommendations based on a customer’s rental history and preferences coupled

from members, with about 4 million movies being rated per day. The rating system had proved to be –

tions tailored to their individual tastes.1 The company used its recommendation technology to keep the DVD shipments moving and a greater number of its older DVD titles in circulation.

1 New York Times

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considerable rate. It was estimated that 48 percent of customers watched more than 15 minutes of streaming content in the fourth quarter of 2009, up from 28 percent the previous year.2 This proportion was likely to grow in the future.

than from rentals, they had negotiated a four-week delay from when the DVD was first available

sales through outlets like Walmart.

IMPACT OF ONLINE SALES ON COSTS AT NETFLIX lower its facility and inventory costs relative to Blockbuster.

Inventory Costs.

most of its inventory at thousands of retail stores. In 2009, about 70 percent of the DVDs shipped 3 Movie studios were happy that

customers could view their older catalogs (which otherwise provided little revenue) and thus

$37 million in inventories (on sales of $1.67 billion), whereas Blockbuster carried $639 million in inventories (on sales of $4.06 billion).

Facility Costs. aggregated its operations in fewer than 60 distribution centers, whereas Blockbuster had thou-

– ported $1.67 billion of sales in 2009, Blockbuster required $2.37 billion in property and equipment to support $4.06 billion of sales.

Transportation Costs. –

– ings in transportation costs as subscribers moved toward watching more content online.

Information Costs. – tive to Blockbuster. With the growth in digital streaming, information costs are likely to increase.

IMPACT OF ONLINE SALES ON PERFORMANCE AT NETFLIX – tages in renting movies compared with physical distribution channel of Blockbuster as shown in Table 4-13. These advantages are most pronounced for the wide selection of older movies that studios have in their catalogs. These advantages could become bigger as more content is

streaming supply chain is sourcing content because the supply chain has relatively low invest-

unlike its competition with Blockbuster.

2

3Ibid.

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A TAILORED SUPPLY CHAIN FOR RENTING MOVIES through its centralized model to supply a wide variety of older movies (either by DVD or stream- ing), Redbox has used DVD vending machines to provide a low-cost channel for recent releases. These vending machines carry only a few hundred titles, consisting of new movies and popular children’s videos. They allow customers to go online and reserve movies at specific machines using a credit card. The result is a virtual aggregation of inventories, which improves the match- ing of supply and demand and reduces inventory expense. The vending machines are typically installed in existing retail infrastructure, such as grocery stores. Thus, the marginal increase in

provide both recent releases and a wide assortment at a much lower cost than Blockbuster’s try- ing to do both at its stores.

4.5 DISTRIBUTION NETWORKS IN PRACTICE

1. The ownership structure of the distribution network can have as big an impact as the type of distribution network. The bulk of this chapter deals with different types of physical net- works and subsequent flows to distribute products successfully. However, equally important is who owns each stage in the distribution network. Distribution networks that have exactly the same physical flow but different ownership structures can have vastly different performance. For exam- ple, a manufacturer that owns its distribution network can control the network’s actions. However, if the manufacturer does not own the distribution network, as is more often the case, a wide variety of issues must be taken into account to optimize over the network. Obviously, an independent

– ing to optimize over a distribution network with multiple enterprises requires great skill in coordi- nating the incentives of each of the players and in creating the right relationships.

2. It is important to have adaptable distribution networks. Distribution networks must

TABLE 4-13 Impact of Online Sales on Netflix Performance Relative to Blockbuster Area Impact for DVDs Impact for Digital Content

Response time –1 +2 Product variety +2 +2 Product availability +1 +2 Customer experience +1 +1 Time to market –1 –1

Order visibility 0 0

Direct sales 0 0

Flexible pricing, portfolio, promotions

+1 +1

Efficient funds transfer 0 0

Inventory +2 +2 Facilities +1 +1 Transportation –2 0

Information –1 –1

Key: +2 = very positive; +1 = positive; 0 = neutral; –1 = negative; –2 = very negative.

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damaging in these times of rapid change. For example, Blockbuster in the movie rental business and Borders in the bookselling business had great success with a network of retail stores. Their

advantage of the Internet to create a tailored distribution network, it can be argued that they could have continued their dominance. Walmart is an example of a company that, through trial and error, adapted its distribution network to take advantage of the Internet along with its existing retail store network.

3. Product price, commoditization, and criticality affect the type of distribution sys- tem preferred by customers. Interactions between a buyer and a seller take time and

that can deliver a full line of products. For high-value, specialized, or critical products, cus- tomers are willing to have a relationship solely around that particular product. For low-value, commoditized products like office supplies, however, most customers prefer a one-stop shop.

unlikely that a stapler manufacturer could succeed without distributing through general sta- –

chase to locations that are not manufacturer specific even for products such as computers and smartphones.

4. Integrate the Internet with the existing physical network. To extract maximum ben- efit from the online channel for physical goods, firms should integrate it with their existing sup-

chain. They should be coupled in a tailored manner that exploits the strengths of each channel. Tesco’s use of its physical assets to satisfy both online orders and people who want to shop

example of an effective tailored strategy is Walmart, which allows customers to pick up online orders at its retail stores. The Internet is used to expand the variety available to customers at a Walmart store. Walmart stores stock popular items, whereas customers can order online the col- ors or sizes that may not be available in the store. This allows Walmart to centralize low-demand

products it handles best.

4.6 SUMMARY OF LEARNING OBJECTIVES

1. Identify the key factors to be considered when designing a distribution network. manager must consider the customer needs to be met and the cost of meeting these needs when

time, product variety/availability, convenience, order visibility, and returnability. Important costs that managers must consider include inventories, transportation, facilities and handling, and information. Increasing the number of facilities decreases the response time and transportation cost but increases inventory and facility cost.

2. Discuss the strengths and weaknesses of various distribution options. Distribution networks that ship directly to the customer are better suited for a large variety of high-value products that have low and uncertain demand. These networks carry low levels of inventory but incur high transportation cost and provide a slow response time. Distribution networks that carry local inventory are suitable for products with high demand, especially if transportation is a large fraction of total cost. These networks incur higher inventory cost but lower transportation cost and provide a faster response time.

3. Understand how online sales have affected the design of distribution networks in dif- ferent industries. The rise of online sales has affected both customer service and costs in sup- ply chains. Online sales allow a firm to offer greater product variety and improve product

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availability by centralizing inventories. This is especially beneficial for low-volume, high-variety products. The online channel also improves the customer experience by providing 24-hour access

no significant loss of customer participation. Transportation costs increase, however; this is par- ticularly significant for low-value products with predictable demand. Online sales have been most effective for high-value products with uncertain demand, when customers are willing to wait some time before delivery. The Internet is particularly effective for products such as music, movies, and books that can be digitized because the two major disadvantages of distributing

Discussion Questions 1. What differences in the retail environment may justify the

fact that the fast-moving consumer goods supply chain in India has far more distributors than it has in the United

2. operations into Brazil, where five companies dominate the consumption of specialty chemicals. What sort of distribu- tion network should this company use?

3. from which it buys is considering going direct to the con- sumer. What can the distributor do about this? What advan- tages can it offer the manufacturer that the manufacturer is unlikely to be able to reproduce?

4. What types of distribution networks are typically best suited for commodity items?

5. What type of network is best suited to highly differentiated products?

6. In the future, do you see the value added by distributors decreasing, increasing, or staying about the same?

7. Why has the online channel been more successful in the computer hardware industry compared with the grocery industry? In the future, how valuable is the online channel likely to be in the computer hardware industry?

8. Is the online channel likely to be more beneficial in the early part or the mature part of a product’s life cycle? Why?

9. Consider the sale of home improvement products at Home Depot or a chain of hardware stores such as True Value. Which can extract the greater benefit from going online? Why?

10. home improvement products online. In which product cate- gory does going online offer the greatest advantage com- pared with a retail store chain? In which product category does the online channel offer the smallest advantage (or a potential cost disadvantage) compared with a retail store chain? Why?

11. warehouses as its sales volume grows?

Bibliography

Transportation Research, Part E (2003): 39, 123–140.

507, 2010.

Supply Chain Management Review

– Harvard Business Review

December 1999): 84–94. Internet Marketing and

e-Commerce. 2007.

Sloan Management Review 54–62.

Supply Chain Manage- ment Review

Supply Chain Management Review (Fall 1999): 52–58. A Tale of Two Electronic

Component Suppliers. 064, 1997.

– Supply Chain Management

Review (Fall 1999): 60–70. Supply

Chain Management Review (Winter 2000): 63–70. Willcocks, Leslie P., and Robert Plant. “Pathways to e-Business

Sloan Manage- ment Review

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Information Rules: A Strategic Guide to the Network Economy. Boston: Harvard Business

Supply Chain Management Review (Fall 1999).

Electronic Commerce: A Managerial Perspective. –

CASE STUDY Blue Nile and Diamond Retailing1

the largest online retailer of diamonds. The list price for the customer’s desired diamond is only $100 above your total cost for a stone of the same characteristics. Do you let the customer walk, or come down in price to compete?2

argue that jewelers should lower prices on stones to keep the customer. Future sales and add-on sales such as cus- tom designs, mountings, and repairs can then be used to make additional margins. Others argue that cutting prices to compete sends a negative signal to loyal cus- tomers from the past who may be upset by the fact that they were not given the best price.

– son of 2007, the differences in performance between

sales during its fourth quarter. For the same quarter, Tif- fany posted a 2 percent drop in domestic same-store

“This business is all about taking market share. We look

The Diamond Retailing Industry

For both wholesalers and retailers in the diamond indus- try, 2008 was a very difficult year. It was so bad at the supply end that the dealers’ trade association, the World Federation of Diamond Bourses, issued an appeal for the diamond producers to reduce the supply of new gems entering the market in an effort to reduce supply.

However, the world’s largest producer, De Beers, appeared unmoved, refusing to give any commitment to curtail production. The company had recently opened

operational, could add 800,000 carats a year into an

already oversupplied market. Historically, De Beers had exerted tremendous control over the supply of diamonds, going so far as to purchase large quantities of rough dia-

Commission forced De Beers to phase out its agreement

largest diamond producer, which accounted for most of the diamond production in Russia. Russia was the sec- ond largest producer of diamonds in the world after Botswana.

Costco continued to thrive, the situation was difficult for traditional jewelry retailers. Friedman’s filed for Chapter 11

bankruptcy, Friedman was the third largest jewelry chain

100 stores that year. This shakeup offered an opportunity for other players to move in and try to gain market share.

With the weakening economy, the third and fourth quarters of 2008 were particularly hard on diamond

– ened their belts and cut back on discretionary spending, high-cost purchases such as diamond jewelry were often the first to be postponed. The situation worsened as competition for the shrinking number of customers became fiercer. In such a difficult environment, it was hard to judge which factors could best help different jewelry retailers succeed.

Blue Nile

In December 1998, Mark Vadon, a young consultant, was shopping for an engagement ring and stumbled

1

2 Professional Jeweller Magazine

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across a company called Internet Diamonds, run by

ring but also went into business with Williams in early

the end of 1999 because the new name “sounded elegant

– phy as follows: “Offer high-quality diamonds and fine jewelry at outstanding prices. When you visit our web- site, you’ll find extraordinary jewelry, useful guidance, and easy-to-understand jewelry education that’s perfect

Many customers (especially men) liked the low- pressure selling tactics that focused on education.

could determine ranges along each of the four Cs and

that fit the customer’s desired profile. Customers selected the stone of their choice, followed by the setting

have their questions resolved on the phone by sales reps who did not work on commission. This low-pressure selling approach had great appeal to a segment of the population. In a BusinessWeek article in 2008, Internet

shopping for his engagement ring (for which he spent

3

The company focused on providing good value to its customers. Whereas retail jewelers routinely marked

could afford the lower markup because of lower inven- tory and warehousing expense. Unlike jewelry retailers

stocked its entire inventory. The company strategy was not without hurdles

because some customers did not care as much about underpricing the competition. For example, some cus- tomers preferred “a piece of fine jewelry in a robin’s egg

4 to getting a price discount.

willing to spend thousands of dollars on an item they had

offered a 30-day money back guarantee on items in orig- inal condition.

In 2007, the company launched websites in

in Dublin with local customer service and fulfillment operations. The Dublin office offered free shipping to

handled international shipping to some countries in the

from $17 million in 2007 to more than $62 million in 2012.

rings larger than a carat, with 25 orders totaling more

diamond for $1.5 million. Forbes called it perhaps “the

5 The stone, larger than 10 carats, had a

have the stone in inventory, but its network of suppliers quickly located one on a plane en route from a dealer in

– cess took only three days.

140,000 diamonds on its site. Of these diamonds, more than 50,000 were one carat or larger, with prices up to

website were priced higher than $2,500. In 2010, com-

discounter. We are selling a very high-end product but

that “we aim to limit our diamond offerings to those pos-

In 2012, the company had sales of about $400 mil-

had risen, income had dropped relative to 2011. The company had also started to offer a broad range of non- engagement products, including rings, wedding bands, necklaces, pendants, bracelets, and gifts and accessories containing precious metals, diamonds, gemstones, or pearls. The company, however, maintained that the engagement category was its core business.

3 BusinessWeek e.biz, May 2000. 4

5 Forbes.com, October 2007.

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Zales

Their marketing strategy was to offer a credit plan of “a

them to grow to twelve stores in Oklahoma and Texas by 1941. Over the next four decades, the company grew to hundreds of stores by buying up other stores and smaller chains.

In 1986, the company was purchased in a lever-

into Chapter 11 bankruptcy for a year. It became a public company again in that decade and operated nearly 2,400 stores by 2005. The company’s divisions included Pierc- ing Pagoda, which ran mall-based kiosks selling jewelry

– elry for working-class mall shoppers; and the upscale

offered even pricier products out of fancier malls, was

Three years of declining market share, lost mostly to discounters such as Walmart and Costco, put pressure

its lower-value 10-karat gold jewelry and modest-quality diamonds. The goal was to make the jeweler more upscale and fashion conscious, moving away from its promotion-driven, lower-end reputation. Unfortunately, the move was a disaster. There were delays in bringing in new merchandise, and same-store sales dropped. The company lost many of its traditional customers without winning the new ones it desired. It was soon passed by

resign by early 2006.

transition to return to its role as a promotional retailer focused on diamond fashion jewelry and diamond rings. The transition involved selling nearly $50 million in dis- continued inventory from its upscale strategy and an

The company had some success with its new strat- egy but was hurt by the rise in fuel prices and falling home prices in 2007 that made its middle-class custom- ers feel less secure. Its core customers hesitated to buy jewelry as they battled higher prices for food and fuel.

– –

cent. In February 2008, the company announced a plan to close approximately 105 stores, reduce inventory by $100 million, and reduce staff in company headquarters by about 20 percent. The goal of this plan was to enhance the company’s profitability and improve its overall effec-

reported a profit for 2012 (see Table 4-14).

Tiffany

Tiffany opened in 1837 as a stationery and fancy goods

in 1845. The company enjoyed tremendous success, with its silver designs in particular becoming popular all over the world. In 1886, Tiffany introduced its now

The Tiffany brand was so strong that it helped set dia- mond and platinum purity standards used all over the world. In 1950, Truman Capote published his best seller Breakfast at Tiffany’s, which was released as a success-

– dous success with its jewelry and other products, the company went public in 1987.

Tiffany’s high-end products included diamond rings, wedding bands, gemstone jewelry, and gemstone bands with diamonds as the primary gemstone. The com- pany also sold non-gemstone gold, platinum, and ster- ling silver jewelry. Other products included watches and high-end items for the home, such as crystal and sterling silver serving trays. Besides its own designs, Tiffany also

By 2012, Tiffany had 275 stores and boutiques all over the world, with about 90 of them in the United

with an average of 7,100 square feet. Its flagship store in

sales in 2007. Besides retail outlets, Tiffany also sold products through a website and catalogs. The company, however, did not offer any engagement jewelry through its website as of 2012. Its high-end products, including jewelry, were sold primarily through the retail stores. The direct channel focused on what Tiffany referred to

sterling silver jewelry with an average price of $200 in 2007. Category D sales represented about 58 percent of

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total sales for the direct channel. In contrast, more than half the retail sales came from high-end products such as diamond rings and gemstone jewelry with an average sale price in 2007 higher than $3,000.

Tiffany maintained its own manufacturing facili-

source from third parties. In 2007, the company sourced almost 60 percent of its jewelry from internal manufac- turing facilities. Tiffany had a retail service center in

over the world and replenishing its retail stores. The company had a separate customer fulfillment center for processing direct-to-customer orders.

Until 2003, the company did not purchase any rough diamonds, focusing entirely on the purchase of

– nam. In 2007, approximately 40 percent of the dia- monds used by Tiffany were produced from rough

diamonds could be cut and polished to the quality

standards of Tiffany. Diamonds that failed to meet Tif- fany’s standards were then sold to third parties at mar- ket price, sometimes at a loss.

In 2012, 90 percent of Tiffany’s net sales came from jewelry, with approximately 48 percent of net sales coming from products containing diamonds of various sizes.6 Products containing one or more diamonds of one carat or larger accounted for more than 10 percent of net

– mance in 2012 are shown in Table 4-14.

The Tiffany brand’s association with quality, lux- ury, and exclusivity was an important part of its success.

anywhere near those enjoyed by Tiffany. In its annual reports, the company listed the strong brand as a major risk factor because any dilution in its brand image would have a significant negative impact on its margins.

Study Questions

1. What are some key success factors in diamond retailing?

dimensions?

TABLE 4-14 Select Financial Data for Blue Nile, Inc., Zale Corporation, and Tiffany & Co. (in millions of dollars) for 2012

Blue Nile Zale Tiffany

Net sales 400.0 1,888.0 3,794.2

Cost of sales 325.0 903.6 1,631.0

Gross profit 75.1 984.4 2,163.2

Selling, general, and administrative expenses

62.8 915.5 1,466.1

Operating income 12.3 35.1 697.2

Net income 8.4 10.0 416.1

Cash and cash equivalents 87.0 17.1 504.8

Net receivables 4.4 – 253.5

Inventories 33.3 767.5 2,234.3

Total current assets 125.9 837.2 3,151.6

Property and equipment 7.9 108.9 818.8

Other assets 89 35.7 354.0

Total assets 145.9 1,187.3 4,630.9

Accounts payable 128.6 327.6 325.9

Stockholder equity 14.1 183.3 2,598.7

6

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2. stones priced at $2,500 or higher, whereas a large fraction of the products sold from the Tiffany website are priced at around $200? Which of the two product categories is bet- ter suited to the strengths of the online channel?

3. What do you think of Tiffany’s decision to not sell engage-

growth into the non-engagement category?

4. Given that Tiffany stores have thrived with their focus on selling high-end jewelry, what do you think caused the

5. Which of the three companies do you think is best struc- tured to deal with weak economic times?

6. What advice would you give to each of the three compa- nies regarding its strategy and structure?

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In this chapter, we start with the broad supply chain design discussed in Chapter 4 and focus on the fundamental questions of facility location, capacity allocation, and market allocation when designing a supply chain network. We identify and discuss the various factors that influence the facility location, capacity, and market allocation decisions. We then establish a framework and discuss various solution methodologies for network design deci- sions in a supply chain.

5.1 THE ROLE OF NETWORK DESIGN IN THE SUPPLY CHAIN

Supply chain network design decisions include the assignment of facility role; location of manu- facturing-, storage-, or transportation-related facilities; and the allocation of capacity and mar- kets to each facility. Supply chain network design decisions are classified as follows:

1. Facility role: What role should each facility play? What processes are performed at each facility?

2. Facility location: Where should facilities be located? 3. Capacity allocation: How much capacity should be allocated to each facility? 4. Market and supply allocation: What markets should each facility serve? Which supply

sources should feed each facility?

Network design decisions have a significant impact on performance because they deter- mine the supply chain configuration and set constraints within which the other supply chain

Network Design in the Supply Chain

C H A P T E R

5

LEARNING OBJECTIVES After reading this chapter, you will be able to

108

1. Understand the role of network design in a supply chain.

2. Identify factors influencing supply chain network design decisions.

3. Develop a framework for making network design decisions.

4. Use optimization for facility location and capacity allocation decisions.

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drivers can be used either to decrease supply chain cost or to increase responsiveness. All net- work design decisions affect one another and must be made taking this fact into consideration. Decisions concerning the role of each facility are significant because they determine the amount of flexibility the supply chain has in changing the way it meets demand. For example, Toyota has plants located worldwide, in each market that it serves. Before 1997, each plant was capable of serving only its local market. This hurt Toyota when the Asian economy went into a recession in the late 1990s. The local plants in Asia had idle capacity that could not be used to serve other markets that were experiencing excess demand. Toyota has added flexibility to each plant to be able to serve markets other than the local one. This additional flexibility helps Toyota deal more effectively with changing global market conditions. Similarly, the flexibility of Honda’s U.S. plants to produce both SUVs and cars in the same plant was helpful in 2008 when SUV demand dropped but small-car demand did not.

Facility location decisions have a long-term impact on a supply chain’s performance because it is expensive to shut down a facility or move it to a different location. A good location decision can help a supply chain be responsive while keeping its costs low. Toyota, for example, built its first U.S. assembly plant in Lexington, Kentucky, in 1988, and has continued to build new plants in the United States since then. The U.S. plants proved profitable for Toyota when the yen strengthened and cars produced in Japan were too expensive to be cost competitive with cars produced in the United States. Local plants allowed Toyota to be responsive to the U.S. market while keeping costs low.

Capacity allocation can be altered more easily than location, but capacity decisions do tend to stay in place for several years. Allocating too much capacity to a location results in poor utili- zation and, as a result, higher costs. Allocating too little capacity results in poor responsiveness if demand is not satisfied or high cost if demand is filled from a distant facility.

The allocation of supply sources and markets to facilities has a significant impact on per- formance because it affects total production, inventory, and transportation costs incurred by the supply chain to satisfy customer demand. This decision should be reconsidered on a regular basis so the allocation can be changed as production and transportation costs, market conditions, or plant capacities change. Of course, the allocation of markets and supply sources can be changed only if the facilities are flexible enough to serve different markets and receive supply from differ- ent sources.

Network design decisions must be revisited as market conditions change or when two companies merge. For example, as its subscriber base grew, Netflix had 58 DCs by 2010 across the United States to lower transportation cost and improve responsiveness. With the growth in video streaming and the corresponding drop in DVD rentals, Netflix closed almost 20 DCs by the end of 2013. In contrast, Amazon increased the number of DCs in the United States from about 20 in 2009 to about 40 in 2013. Changing the number, location, and demand allocation of DCs with changing demand has been critical to maintaining low cost and responsiveness at both Netflix and Amazon.

Following a merger, consolidating some facilities and changing the location and role of others can often help reduce cost and improve responsiveness because of the redundancies and differences in markets served by either of the two separate firms. Network design decisions may also need to be revisited if factor costs such as transportation have changed significantly. In 2008, P&G announced that it would rethink its distribution network, which was implemented when the “cost of oil was $10 per barrel.”

We focus on developing a framework as well as methodologies that can be used for net- work design in a supply chain.

5.2 FACTORS INFLUENCING NETWORK DESIGN DECISIONS

In this section we examine a wide variety of factors that influence network design decisions in supply chains.

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Strategic Factors

A firm’s competitive strategy has a significant impact on network design decisions within the supply chain. Firms that focus on cost leadership tend to find the lowest cost location for their manufacturing facilities, even if that means locating far from the markets they serve. Electronic manufacturing service providers such as Foxconn and Flextronics have been successful in pro- viding low-cost electronics assembly by locating their factories in low-cost countries such as China. In contrast, firms that focus on responsiveness tend to locate facilities closer to the market and may select a high-cost location if this choice allows the firm to react quickly to changing market needs. Zara, the Spanish apparel manufacturer, has a large fraction of its production capacity in Portugal and Spain despite the higher cost there. The local capacity allows the com- pany to respond quickly to changing fashion trends. This responsiveness has allowed Zara to become one of the largest apparel retailers in the world.

Convenience store chains aim to provide easy access to customers as part of their competi- tive strategy. Convenience store networks thus include many stores that cover an area, with each store being relatively small. In contrast, discount stores such as Sam’s Club or Costco use a com- petitive strategy that focuses on providing low prices. Thus, their networks have large stores, and customers often have to travel many miles to get to one. The geographic area covered by one Sam’s Club store may include dozens of convenience stores.

Global supply chain networks can best support their strategic objectives with facilities in different countries playing different roles. For example, Zara has production facilities in Europe as well as Asia. Its production facilities in Asia focus on low cost and produce primarily stan- dardized, low-value products that sell in large amounts. The European facilities focus on being responsive and produce primarily trendy designs whose demand is unpredictable. This combina- tion of facilities allows Zara to produce a wide variety of products in the most profitable manner.

Technological Factors

Characteristics of available production technologies have a significant impact on network design decisions. If production technology displays significant economies of scale, a few high-capacity locations are most effective. This is the case in the manufacture of computer chips, for which factories require a large investment and the output is relatively inexpensive to transport. As a result, most semiconductor companies build a few high-capacity facilities.

In contrast, if facilities have lower fixed costs, many local facilities are preferred because this helps lower transportation costs. For example, bottling plants for Coca-Cola do not have a high fixed cost. To reduce transportation costs, Coca-Cola sets up many bottling plants all over the world, each serving its local market.

Macroeconomic Factors

Macroeconomic factors include taxes, tariffs, exchange rates, and shipping costs that are not internal to an individual firm. As global trade has increased, macroeconomic factors have had a significant influence on the success or failure of supply chain networks. Thus, it is imperative that firms take these factors into account when making network design decisions.

TARIFFS AND TAX INCENTIVES Tariffs refer to any duties that must be paid when products and/or equipment are moved across international, state, or city boundaries. Tariffs have a strong influence on location decisions within a supply chain. If a country has high tariffs, companies either do not serve the local market or set up manufacturing plants within the country to save on duties. High tariffs lead to more production locations within a supply chain network, with each location having a lower allocated capacity. As tariffs have decreased with the World Trade Orga- nization and regional agreements such as the North American Free Trade Agreement (NAFTA), the European Union, and Mercosur (South America), global firms have consolidated their global production and distribution facilities.

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Tax incentives are a reduction in tariffs or taxes that countries, states, and cities often pro- vide to encourage firms to locate their facilities in specific areas. Many countries vary incentives from city to city to encourage investments in areas with lower economic development. Such incentives are often a key factor in the final location decision for many plants. BMW, for instance, built its U.S. factory in Spartanburg, South Carolina, mainly because of the tax incentives offered by that state.

Developing countries often create free trade zones in which duties and tariffs are relaxed as long as production is used primarily for export. This creates a strong incentive for global firms to set up plants in these countries to be able to exploit their low labor costs. In China, for example, the establishment of a free trade zone near Guangzhou led to many global firms locating facili- ties there in the 1990s.

A large number of developing countries also provide additional tax incentives based on training, meals, transportation, and other facilities offered to the workforce. Tariffs may also vary based on the product’s level of technology. China, for example, waived tariffs entirely for high- tech products in an effort to encourage companies to locate there and bring in state-of-the-art technology. Motorola located a large chip manufacturing plant in China to take advantage of the reduced tariffs and other incentives available to high-tech products.

Many countries also place minimum requirements on local content and limits on imports to help develop local manufacturers. Such policies lead global companies to set up local facilities and source from local suppliers. For example, the Spanish company Gamesa was a dominant supplier of wind turbines to China, owning about a third of the market share in 2005. In that year, China declared that wind farms had to buy equipment in which at least 70 percent of content was local. This forced players such as Gamesa and GE, which wanted a piece of the Chinese market, to train local suppliers and source from them. In 2009, China revoked the local content require- ments. By then, Chinese suppliers had sufficiently large scale to achieve some of the lowest costs in the world. These suppliers also sold parts to Gamesa’s Chinese competitors, which developed into dominant global players.

EXCHANGE-RATE AND DEMAND RISK Fluctuations in exchange rates are common and have a significant impact on the profits of any supply chain serving global markets. For example, the dollar fluctuated between a high of 124 yen in 2007 and a low of 81 yen in 2010, then back to over 100 yen in 2014. A firm that sells its product in the United States with production in Japan is exposed to the risk of appreciation of the yen. The cost of production is incurred in yen, whereas revenues are obtained in dollars. Thus, an increase in the value of the yen increases the production cost in dollars, decreasing the firm’s profits. In the 1980s, many Japanese manufac- turers faced this problem when the yen appreciated in value, because most of their production capacity was located in Japan. The appreciation of the yen decreased their revenues (in terms of yen) from large overseas markets, and they saw their profits decline. Most Japanese manufactur- ers responded by building production facilities all over the world. The dollar fluctuated between 0.63 and 1.15 euros between 2002 and 2008, dropping to 0.63 euro in July 2008. The drop in the dollar was particularly negative for European automakers such as Daimler, BMW, and Porsche, which export many vehicles to the United States. It was reported that every one-cent rise in the euro cost BMW and Mercedes roughly $75 million each per year.

Exchange-rate risks may be handled using financial instruments that limit, or hedge against, the loss due to fluctuations. Suitably designed supply chain networks, however, offer the opportunity to take advantage of exchange-rate fluctuations and increase profits. An effective way to do this is to build some overcapacity into the network and make the capacity flexible so it can be used to supply different markets. This flexibility allows the firm to react to exchange-rate fluctuations by altering production flows within the supply chain to maximize profits.

Companies must also take into account fluctuations in demand caused by changes in the economies of different countries. For example, 2009 was a year in which the economies of the United States and Western Europe shrank (real GDP in the United States decreased by 2.4 percent),

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while that in China grew by more than 8 percent and in India by about 7 percent. During this period, global companies with presence in China and India and the flexibility to divert resources from shrinking to growing markets did much better than those that did not have either presence in these markets or the flexibility. As the economies of Brazil, China, and India continue to grow, global supply chains will have to build more local presence in these countries along with the flexibility to serve multiple markets.

FREIGHT AND FUEL COSTS Fluctuations in freight and fuel costs have a significant impact on the profits of any global supply chain. For example, in 2010 alone, the Baltic Dry Index, which measures the cost to transport raw materials such as metals, grains, and fossil fuels, peaked at 4,187 in May and hit a low of 1,709 in July. Crude oil prices were as low as about $31 per barrel in February 2009 and increased to about $90 per barrel by December 2010. It can be difficult to deal with this extent of price fluctuation even with supply chain flexibility. Such fluctuations are best dealt with by hedging prices on commodity markets or signing suitable long-term contracts. During the first decade of the twenty-first century, for example, a significant fraction of South- west Airlines’ profits were attributed to fuel hedges it had purchased at good prices.

When designing supply chain networks, companies must account for fluctuations in exchange rates, demand, and freight and fuel costs.

Political Factors

The political stability of the country under consideration plays a significant role in location choice. Companies prefer to locate facilities in politically stable countries where the rules of commerce and ownership are well defined. While political risk is hard to quantify, there are some indices, such as the Global Political Risk Index (GPRI), that companies can use when investing in emerging markets. The GPRI is evaluated by a consulting firm (Eurasia Group) and aims to measure the capacity of a country to withstand shocks or crises along four categories: govern- ment, society, security, and economy.

Infrastructure Factors

The availability of good infrastructure is an important prerequisite to locating a facility in a given area. Poor infrastructure adds to the cost of doing business from a given location. In the 1990s, global companies located their factories in China near Shanghai, Tianjin, or Guangzhou—even though these locations did not have the lowest labor or land costs—because these locations had good infrastructure. Key infrastructure elements to be considered during network design include availability of sites and labor, proximity to transportation terminals, rail service, proximity to airports and seaports, highway access, congestion, and local utilities.

Competitive Factors

Companies must consider competitors’ strategy, size, and location when designing their supply chain networks. A fundamental decision firms make is whether to locate their facilities close to or far from competitors. The form of competition and factors such as raw material or labor avail- ability influence this decision.

POSITIVE EXTERNALITIES BETWEEN FIRMS Positive externalities occur when the collocation of multiple firms benefits all of them. Positive externalities lead to competitors locating close to each other. For example, retail stores tend to locate close to each other because doing so increases overall demand, thus benefiting all parties. By locating together in a mall, competing retail stores make it more convenient for customers, who need drive to only one location to find everything they are looking for. This increases the total number of customers who visit the mall, increasing demand for all stores located there.

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Another example of positive externality occurs when the presence of a competitor leads to the development of appropriate infrastructure in a developing area. In India, Suzuki was the first foreign auto manufacturer to set up a manufacturing facility. The company went to considerable effort and built a local supplier network. Given the well-established supplier base in India, Suzuki’s competitors have also built assembly plants there, because they now find it more effective to build cars in India rather than import them to the country.

LOCATING TO SPLIT THE MARKET When there are no positive externalities, firms locate to be able to capture the largest possible share of the market. A simple model first proposed by Hotel- ling explains the issues behind this decision (Tirole, 1997).

When firms do not control price but compete on distance from the customer, they can maximize market share by locating close to each other and splitting the market. Consider a situ- ation in which customers are uniformly located along the line segment between 0 and 1 and two firms compete based on their distance from the customer as shown in Figure 5-1. A customer goes to the closer firm and customers who are equidistant from the two firms are evenly split between them.

If total demand is 1, Firm 1 locates at point a, and Firm 2 locates at point 1– b, the demand at the two firms, d1 and d2, is given by

d1 = a + 1 – b – a

2 and d2 =

1 + b – a 2

Both firms maximize their market share if they move closer to each other and locate at a = b = 1>2.

Observe that when both firms locate in the middle of the line segment (a = b = 1>2), the average distance that customers have to travel is 1>4. If one firm locates at 1>4 and the other at 3>4, the average distance customers have to travel drops to 1>8 (customers between 0 and 1>2 come to Firm 1, located at 1>4, whereas customers between 1>2 and 1 come to Firm 2, located at 3>4). This set of locations, however, is not an equilibrium because it gives both firms an incen- tive to try to increase market share by moving to the middle (closer to 1>2). The result of compe- tition is for both firms to locate close together, even though doing so increases the average distance to the customer.

If the firms compete on price and the customer incurs the transportation cost, it may be optimal for the two firms to locate as far apart as possible, with Firm 1 locating at 0 and Firm 2 locating at 1. Locating far from each other minimizes price competition and helps the firms split the market and maximize profits.

Customer Response Time and Local Presence

Firms that target customers who value a short response time must locate close to them. Custom- ers are unlikely to come to a convenience store if they have to travel a long distance to get there. It is thus best for a convenience store chain to have many stores distributed in an area so most people have a convenience store close to them. In contrast, customers shop for larger quantity of goods at supermarkets and are willing to travel longer distances to get to one. Thus, supermarket chains tend to have stores that are larger than convenience stores and not as densely distributed. Most towns have fewer supermarkets than convenience stores. Discounters such as Sam’s Club target customers who are even less time sensitive. These stores are even larger than supermarkets

a 1 − b

0 1 FIGURE 5-1 Two Firms Locating on a Line

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and there are fewer of them in an area. W.W. Grainger uses about 400 facilities all over the United States to provide same-day delivery of maintenance and repair supplies to many of its customers. McMaster-Carr, a competitor, targets customers who are willing to wait for next-day delivery. McMaster-Carr has only five facilities throughout the United States and is able to pro- vide next-day delivery to a large number of customers.

If a firm is delivering its product to customers, use of a rapid means of transportation allows it to build fewer facilities and still provide a short response time. This option, however, increases transportation cost. Moreover, there are many situations in which the presence of a facility close to a customer is important. A coffee shop is likely to attract customers who live or work nearby. No faster mode of transport can serve as a substitute and be used to attract custom- ers who are far away from the coffee shop.

Logistics and Facility Costs

Logistics and facility costs incurred within a supply chain change as the number of facilities, their location, and capacity allocation change. Companies must consider inventory, transporta- tion, and facility costs when designing their supply chain networks.

Inventory and facility costs increase as the number of facilities in a supply chain increases. Transportation costs decrease as the number of facilities increases. If the number of facilities increases to the point at which inbound economies of scale are lost, then transportation costs increase. For example, with few facilities, Amazon has lower inventory and facility costs than Barnes & Noble, which has hundreds of stores. Barnes & Noble, however, has lower transporta- tion costs.

The supply chain network design is also influenced by the transformation occurring at each facility. When there is a significant reduction in material weight or volume as a result of processing, it may be better to locate facilities closer to the supply source rather than the cus- tomer. For example, when iron ore is processed to make steel, the amount of output is a small fraction of the amount of ore used. Locating the steel factory close to the supply source is pre- ferred because it reduces the distance that the large quantity of ore has to travel.

Total logistics costs are the sum of the inventory, transportation, and facility costs. The facilities in a supply chain network should at least equal the number that minimizes total logistics cost. A firm may increase the number of facilities beyond this point to improve the response time to its customers. This decision is justified if the revenue increase from improved response out- weighs the increased cost from additional facilities.

In the next section we discuss a framework for making network design decisions.

5.3 FRAMEWORK FOR NETWORK DESIGN DECISIONS

The goal when designing a supply chain network is to maximize the firm’s profits while satisfy- ing customer needs in terms of demand and responsiveness. To design an effective network, a manager must consider all the factors described in Section 5.2 and those discussed in Chapter 4. Global network design decisions are made in four phases, as shown in Figure 5-2. We describe each phase in greater detail.

Phase I: Define a Supply Chain Strategy/Design

The objective of the first phase of network design is to define a firm’s broad supply chain design. This includes determining the stages in the supply chain and whether each supply chain function will be performed in-house or outsourced (see Chapter 4).

Phase I starts with a clear definition of the firm’s competitive strategy as the set of cus- tomer needs that the supply chain aims to satisfy. The supply chain strategy then specifies what capabilities the supply chain network must have to support the competitive strategy (see Chapter 2). Next, managers must forecast the likely evolution of global competition and whether competitors

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in each market will be local or global players. Managers must also identify constraints on avail- able capital and whether growth will be accomplished by acquiring existing facilities, building new facilities, or partnering.

Based on the competitive strategy of the firm, its resulting supply chain strategy, an analy- sis of the competition, any economies of scale or scope, and any constraints, managers must determine the broad supply chain design for the firm.

Phase II: Define the Regional Facility Configuration

The objective of the second phase of network design is to identify regions where facilities will be located, their potential roles, and their approximate capacity.

An analysis of Phase II starts with a forecast of the demand by country or region. Such a forecast must include a measure of the size of the demand and a determination of the homogene- ity or variability of customer requirements across different regions. Homogeneous requirements favor large consolidated facilities, whereas requirements that vary across countries favor flexible facilities or smaller, localized, dedicated facilities.

The next step is for managers to identify whether economies of scale or scope can play a significant role in reducing costs, given available production technologies. If economies of scale or scope are significant, it may be better to have a few facilities serving many markets. For

COMPETITIVE STRATEGY

INTERNAL CONSTRAINTS Capital, growth strategy,

existing network

PRODUCTION METHODS Skill needs, response time

FACTOR COSTS Labor, materials, site specific

PHASE I Supply Chain

Strategy

PHASE II Regional Facility

Configuration

PHASE III Desirable Sites

PHASE IV Location Choices

GLOBAL COMPETITION

TARIFFS AND TAX INCENTIVES

REGIONAL DEMAND Size, growth, homogeneity,

local specifications

POLITICAL, EXCHANGE RATE,

AND DEMAND RISK

AVAILABLE INFRASTRUCTURE

LOGISTICS COSTS Transport, inventory,

coordination

PRODUCTION TECHNOLOGIES

Cost, scale/scope impact, support required, flexibility

COMPETITIVE ENVIRONMENT

AGGREGATE FACTOR AND LOGISTICS COSTS

FIGURE 5-2 Framework for Network Design Decisions

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example, semiconductor manufacturers such as Advanced Micro Devices have few plants for their global markets, given the economies of scale in production. If economies of scale or scope are not significant, it may be better for each market to have its own facility.

Next, managers must identify demand risk, exchange-rate risk, and political risk associated with regional markets. They must also identify regional tariffs, any requirements for local pro- duction, tax incentives, and any export or import restrictions for each market. The goal is to design a network that maximizes after-tax profits.

Managers must identify competitors in each region and make a case for whether a facility needs to be located close to or far from a competitor’s facility. The desired response time for each market and logistics costs at an aggregate level in each region must also be identified.

Based on all this information, managers identify the regional facility configuration for the supply chain network using network design models discussed in the next section. The regional configuration defines regions where facilities will be set up, the approximate number of facilities in the network, and whether a facility will produce all products for a given market or a few prod- ucts for all markets in the network.

Phase III: Select a Set of Desirable Potential Sites

The objective of Phase III is to select a set of desirable potential sites within each region where facilities are to be located. Sites should be selected based on an analysis of infrastructure avail- ability to support the desired production methodologies. Hard infrastructure requirements include the availability of suppliers, transportation services, communication, utilities, and ware- housing facilities. Soft infrastructure requirements include the availability of a skilled workforce, workforce turnover, and the community receptivity to business and industry.

Phase IV: Location Choices

The objective of Phase IV is to select, from among the potential sites, a precise location and capacity allocation for each facility. The network is designed to maximize total profits, taking into account the expected margin and demand in each market, various logistics and facility costs, and the taxes and tariffs at each location.

In the next section, we discuss methodologies for making facility location and capacity allocation decisions during Phases II through IV.

5.4 MODELS FOR FACILITY LOCATION AND CAPACITY ALLOCATION

A manager’s goal when locating facilities and allocating capacity should be to maximize the overall profitability of the resulting supply chain network while providing customers with the appropriate responsiveness. Revenues come from the sale of product, whereas costs arise from facilities, labor, transportation, material, and inventories. The profits of the firm are also affected by taxes and tariffs. Ideally, profits after tariffs and taxes should be maximized when designing a supply chain network.

A manager must consider many trade-offs during network design. For example, building many facilities to serve local markets reduces transportation cost and provides a fast response time, but it increases the facility and inventory costs incurred by the firm.

Managers use network design models in two situations. First, these models are used to decide on locations where facilities will be established and determine the capacity to be assigned to each facility. Managers must make this decision considering a time horizon over which locations and capacities will not be altered (typically in years). Second, these models are used to assign current demand to the available facilities and identify lanes along which product will be transported. Managers must consider this decision at least on an annual basis as demand, prices, exchange rates, and tariffs change. In both cases, the goal is to maximize

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the profit while satisfying customer needs. The following information ideally is available in making the design decision:

Given this information, either gravity models or network optimization models may be used to design the network. We organize the models according to the phase of the network design framework at which each model is likely to be useful.

Phase II: Network Optimization Models

During Phase II of the network design framework (see Figure 5-2), a manager considers regional demand, tariffs, economies of scale, and aggregate factor costs to decide the regions where facil- ities are to be located. As an example, consider SunOil, a manufacturer of petrochemical prod- ucts with worldwide sales. The vice president of supply chain is considering several options to meet demand. One possibility is to set up a facility in each region. The advantage of such an approach is that it lowers transportation cost and also helps avoid duties that may be imposed if product is imported from other regions. The disadvantage of this approach is that plants are sized to meet local demand and may not fully exploit economies of scale. An alternative approach is to consolidate plants in just a few regions. This improves economies of scale but increases transpor- tation cost and duties. During Phase II, the manager must consider these quantifiable trade-offs along with nonquantifiable factors such as the competitive environment and political risk.

Network optimization models are useful for managers considering regional configuration during Phase II. The first step is to collect the data in a form that can be used for a quantitative model. For SunOil, the vice president of supply chain decides to view the worldwide demand in terms of five regions—North America, South America, Europe, Africa, and Asia. The data col- lected are shown in Figure 5-3.

Annual demand for each of the five regions is shown in cells B9:F9. Cells B4:F8 contain the variable production, inventory, and transportation cost (including tariffs and duties) of pro- ducing in one region to meet demand in each individual region. All costs are in thousands of dollars. For example, as shown in cell C4, it costs $92,000 (including duties) to produce 1 mil- lion units in North America and sell them in South America. As shown in cell G4, it costs $6 million in annualized fixed cost to build a low-capacity plant in North America. Observe that the data collected at this stage are at a fairly aggregate level.

FIGURE 5-3 Cost Data (in Thousands of Dollars) and Demand Data (in Millions of Units) for SunOil

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There are fixed as well as variable costs associated with facilities, transportation, and inventories at each facility. Fixed costs are those that are incurred no matter how much is pro- duced or shipped from a facility. Variable costs are those that are incurred in proportion to the quantity produced or shipped from a given facility. Facility, transportation, and inventory costs generally display economies of scale, and the marginal cost decreases as the quantity produced at a facility increases. In the models we consider, however, all variable costs grow linearly with the quantity produced or shipped.

SunOil is considering two plant sizes in each location. Low-capacity plants can produce 10 million units a year, whereas high-capacity plants can produce 20 million units a year, as shown in cells H4:H8 and J4:J8, respectively. High-capacity plants exhibit some economies of scale and have fixed costs that are less than twice the fixed costs of a low-capacity plant, as shown in cells I4:I8. All fixed costs are annualized. The vice president wants to know what the lowest-cost network should look like. To answer this question, we next discuss the capacitated plant location model, which can be used in this setting.

THE CAPACITATED PLANT LOCATION MODEL The capacitated plant location network optimi- zation model requires the following inputs:

n = number of potential plant locations/capacity (each level of capacity will count as a separate location)

m = number of markets or demand points Dj = annual demand from market j Ki = potential capacity of plant i fi = annualized fixed cost of keeping plant i open cij = cost of producing and shipping one unit from plant i to market j (cost includes pro-

duction, inventory, transportation, and tariffs)

The supply chain team’s goal is to decide on a network design that maximizes profits after taxes. For the sake of simplicity, however, we assume that all demand must be met and taxes on earnings are ignored. The model thus focuses on minimizing the cost of meeting global demand. It can be modified, however, to include profits and taxes. Define the following decision variables:

yi = 1 if plant i is open, 0 otherwise xij = quantity shipped from plant i to market j

The problem is then formulated as the following mixed integer program:

Min a n

i = 1 fi yi + a

n

i = 1 a m

j = 1 cijxij

subject to

a n

i = 1 xij = Dj for j = 1, c, m (5.1)

a m

j = 1 xij … Ki yi for i = 1, c, n (5.2)

yi ∈ 50, 16 for i = 1, c, n, xij Ú 0 (5.3) The objective function minimizes the total cost (fixed + variable) of setting up and operat-

ing the network. The constraint in Equation 5.1 requires that the demand at each regional market be satisfied. The constraint in Equation 5.2 states that no plant can supply more than its capacity. (Clearly, the capacity is 0 if the plant is closed and Ki if it is open. The product of terms, Kiyi, captures this effect.) The constraint in Equation 5.3 enforces that each plant is either open (yi = 1)

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or closed (yi = 0). The solution identifies the plants that are to be kept open, their capacity, and the allocation of regional demand to these plants.

The model is solved using the Solver tool in Excel (see spreadsheet Figures 5-3 to 7). Given the data, the next step in Excel is to identify cells corresponding to each decision variable, as shown in Figure 5-4. Cells B14:F18 correspond to the decision variables xij and determine the amount produced in a supply region and shipped to a demand region. Cells G14:G18 contain the decision variables yi corresponding to the low-capacity plants, and cells H14:H18 contain the decision variables yi corresponding to the high-capacity plants. Initially, all decision variables are set to be 0.

The next step is to construct cells for the constraints in Equations 5.1 and 5.2 and the objective function. The constraint cells and objective function are shown in Figure 5-5. Cells B22:B26 contain the capacity constraints in Equation 5.2, and cells B28:F28 contain the demand constraints in Equation 5.1. The objective function is shown in cell B31 and measures the total fixed cost plus the variable cost of operating the network.

The next step is to use Data to invoke Solver, as shown in Figure 5-6. Within Solver, the goal is to minimize the total cost in cell B31. The variables are in cells B14:H18. The constraints are as follows:

B14:H18 Ú 0 5All decision variables are nonnegative6 B22:B26 Ú 0 eKi yi – am

j = 1 xij Ú 0 for i = 1, c, 5 f

B28:F28 = 0 eDj – an i = 1

xij = 0 for j = 1, c, 5 f G14:H18 binary 5Location variables yi are binary; that is, 0 or 16

Within the Solver Parameters dialog box, select Simplex LP and then click on Solve to obtain the optimal solution, as shown in Figure 5-7. From Figure 5-7, the supply chain team concludes that the lowest-cost network will have facilities located in South America (cell H15 = 1), Asia (cell H17 = 1), and Africa (cell H18 = 1). Further, a high-capacity plant should be planned in each region. The plant in South America meets the North American demand (cell B15), whereas the European demand is met from plants in Asia (cell D17) and Africa (cell D18).

The model discussed earlier can be modified to account for strategic imperatives that require locating a plant in some region. For example, if SunOil decides to locate a plant in

FIGURE 5-4 Spreadsheet Area for Decision Variables for SunOil

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Europe for strategic reasons, we can modify the model by adding a constraint that requires one plant to be located in Europe. At this stage, the costs associated with a variety of options incor- porating different combinations of strategic concerns such as local presence should be evaluated. A suitable regional configuration is then selected.

Next we consider a model that can be useful during Phase III.

Phase III: Gravity Location Models

During Phase III (see Figure 5-2), a manager identifies potential locations in each region where the company has decided to locate a plant. As a preliminary step, the manager needs to identify the geographic location where potential sites may be considered. Gravity location models can be useful when identifying suitable geographic locations within a region. Gravity models are used to find locations that minimize the cost of transporting raw materials from suppliers and finished goods to the markets served. Next, we discuss a typical scenario in which gravity mod- els can be used.

—Objective Function

=SUMPRODUCT(B14:F18,B4:F8) + SUMPRODUCT(G14:G18,G4:G8) + SUMPRODUCT(H14:H18,I4:I8)

B31

B23:B265.2=G14*H4 + H14*J4 – SUM(B14:F14)B22

C28:F285.1=B9 – SUM(B14:B18)B28

Copied toEquationCell FormulaCell

FIGURE 5-5 Spreadsheet Area for Constraints and Objective Function for SunOil

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Consider, for example, Steel Appliances (SA), a manufacturer of high-quality refrigerators and cooking ranges. SA has one assembly factory located near Denver, from which it has supplied the entire United States. Demand has grown rapidly and the CEO of SA has decided to set up another factory to serve its eastern markets. The supply chain manager is asked to find a suitable location for the new factory. Three parts plants, located in Buffalo, Memphis, and St. Louis, will supply parts to the new factory, which will serve markets in Atlanta, Boston, Jacksonville, Philadel- phia, and New York. The coordinate location, the demand in each market, the required supply from each parts plant, and the shipping cost for each supply source or market are shown in Table 5-1.

Gravity models assume that both the markets and the supply sources can be located as grid points on a plane. All distances are calculated as the geometric distance between two points on the plane. These models also assume that the transportation cost grows linearly with the quantity shipped. We discuss a gravity model for locating a single facility that receives raw material from supply sources and ships finished product to markets. The basic inputs to the model are as follows:

xn, yn: coordinate location of either a market or supply source n

Fn: cost of shipping one unit (a unit could be a piece, pallet, truckload or ton) for one mile between the facility and either market or supply source n

Dn: quantity to be shipped between facility and market or supply source n

If (x, y) is the location selected for the facility, the distance dn between the facility at loca- tion (x, y) and the supply source or market n is given by

dn = 21x – xn22 + 1y – yn22 (5.4)

FIGURE 5-6 Using Solver to Set Regional Configuration for SunOil

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and the total transportation cost (TC) is given by

TC = a k

n = 1 dnDnFn (5.5)

The optimal location is one that minimizes the total TC in Equation 5.5. The optimal solution for SA is obtained using the Solver tool in Excel (see spreadsheet Figure 5-8), as shown in Figure 5-8.

FIGURE 5-7 Optimal Regional Network Configuration for SunOil

TABLE 5-1 Locations of Supply Sources and Markets for Steel Appliances

Sources/Markets Transportation

Cost $/Ton Mile (Fn) Quantity in Tons (Dn)

Coordinates xn yn

Supply Sources

Buffalo 0.90 500 700 1,200

Memphis 0.95 300 250 600

St. Louis 0.85 700 225 825

Markets

Atlanta 1.50 225 600 500

Boston 1.50 150 1,050 1,200

Jacksonville 1.50 250 800 300

Philadelphia 1.50 175 925 975

New York 1.50 300 1,000 1,080

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The first step is to enter the problem data as shown in cells B5:F12. Next, we set the decision variables (x, y) corresponding to the location of the new facility in cells B16 and B17, respectively. In cells G5:G12, we then calculate the distance dn from the facility location (x, y) to each source or market, using Equation 5.4. The total TC is then calculated in cell B19 using Equation 5.5.

The next step is to to invoke Solver (Data | Solver). Within the Solver Parameters dialog box (see Figure 5-8), the following information is entered to represent the problem:

Set Cell: B19

Equal To: Select Min

By Changing Variable Cells: B16:B17

Select GRG Nonlinear and click on the Solve button. The optimal solution is returned in cells B16 and B17 to be 681 and 882, respectively.

The manager thus identifies the coordinates (x, y) = (681, 882) as the location of the fac- tory that minimizes total cost TC. From a map, these coordinates are close to the border between North Carolina and Virginia. The precise coordinates provided by the gravity model may not correspond to a feasible location, though. The manager should look for desirable sites close to

—5.2=SUMPRODUCT(G5:G12,D5:D12,C5: C12)

B19

G6:G125.1=SQRT(($B$16-E5)^2+($B$17-F5)^2)G5

Copied toEquationCell FormulaCell

FIGURE 5-8 Using Solver to Optimize Location for Steel Appliances

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the optimal coordinates that have the required infrastructure as well as the appropriate worker skills available.

The gravity model can also be solved using the following iterative procedure.

1. For each supply source or market n, evaluate dn as defined in Equation 5.4. 2. Obtain a new location (x9, y9) for the facility, where

x′ = a

k

n = 1

DnFn xn dn

a k

n = 1

DnFn dn

and y′ = a

k

n = 1

DnFn yn dn

a k

n = 1

DnFn dn

3. If the new location (x9, y9) is almost the same as (x, y) stop. Otherwise, set (x, y) = (x9, y9) and go back to step 1.

Phase IV: Network Optimization Models

During Phase IV (see Figure 5-2), a manager decides on the location and capacity allocation for each facility. Besides locating the facilities, a manager also decides how markets are allocated to facilities. This allocation must account for customer service constraints in terms of response time. The demand allocation decision can be altered on a regular basis as costs change and mar- kets evolve. When designing the network, both location and allocation decisions are made jointly.

We illustrate the relevant network optimization models using the example of TelecomOne and HighOptic, two manufacturers of telecommunication equipment. TelecomOne has focused on the eastern half of the United States. It has manufacturing plants located in Baltimore, Mem- phis, and Wichita and serves markets in Atlanta, Boston, and Chicago. HighOptic has targeted the western half of the United States and serves markets in Denver, Omaha, and Portland from plants located in Cheyenne and Salt Lake City.

Plant capacities, market demand, variable production and transportation cost per thousand units shipped, and fixed costs per month at each plant are shown in Table 5-2.

ALLOCATING DEMAND TO PRODUCTION FACILITIES From Table 5-2 we calculate that Tele- comOne has a total production capacity of 71,000 units per month and a total demand of 32,000 units per month, whereas HighOptic has a production capacity of 51,000 units per month and a demand of 24,000 units per month. Each year, managers in both companies must decide how to allocate the demand to their production facilities as demand and costs change.

TABLE 5-2 Capacity, Demand, and Cost Data for TelecomOne and HighOptic

Supply City

Demand City Production and Transportation

Cost per Thousand Units (Thousand $)

Monthly Capacity

(Thousand Units), K

Monthly Fixed Cost (Thousand

$) fAtlanta Boston Chicago Denver Omaha Portland

Baltimore 1,675 400 685 1,630 1,160 2,800 18 7,650

Cheyenne 1,460 1,940 970 100 495 1,200 24 3,500

Salt Lake City 1,925 2,400 1,425 500 950 800 27 5,000

Memphis 380 1,355 543 1,045 665 2,321 22 4,100

Wichita 922 1,646 700 508 311 1,797 31 2,200

Monthly demand (thousand units), Dj

10 8 14 6 7 11

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n = m = Dj = j Ki = i cij = i j

xij = i j

Min a n

i = 1 a m

j = 1 cij xij

a n

i = 1 xij = Dj r j = 1 c m (5.6)

a m

j = 1 xij … Ki r i = 1 c n (5.7)

– Figures 5-9 to 12

LOCATING PLANTS: THE CAPACITATED PLANT LOCATION MODEL

TABLE 5-3 Optimal Demand Allocation for TelecomOne and HighOptic

Atlanta Boston Chicago Denver Omaha Portland

TelecomOne Baltimore 0 8 2

Memphis 10 0 12

Wichita 0 0 0

HighOptic Salt Lake 0 0 11

Cheyenne 6 7 0

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supply chain team to study the network for the combined company and identify the plants that could be shut down.

The problem of selecting the optimal location and capacity allocation is very similar to the regional configuration problem we have already studied in Phase II. The only difference is that instead of using costs and duties that apply over a region, we now use location-specific costs and duties. The supply chain team thus decides to use the capacitated plant location model discussed earlier to solve the problem in Phase IV.

Ideally, the problem should be formulated to maximize total profits, taking into account costs, taxes, and duties by location. Given that taxes and duties do not vary among locations, the supply chain team decides to locate factories and then allocate demand to the open factories to minimize the total cost of facilities, transportation, and inventory. Define the following decision variables:

yi = 1 if factory i is open, 0 otherwise xij = quantity shipped from factory i to market j

Recall that the problem is then formulated as the following mixed integer program:

Min a n

i = 1 fi yi + a

n

i = 1 a m

j = 1 cij xij

subject to x and y satisfying the constraints in Equations 5.1, 5.2, and 5.3. The capacity and demand data, along with production, transportation, and inventory costs

at different factories for the merged firm TelecomOptic, are given in Table 5-2. The supply chain team decides to solve the plant location model using the Solver tool in Excel.

The first step in setting up the Solver model is to enter the cost, demand, and capacity information as shown in Figure 5-9 (see sheet Figure 5-12 in spreadsheet Figures 5-9 to 12). The fixed costs fi for the five plants are entered in cells H4 to H8. The capacities Ki of the five plants are entered in cells I4 to I8. The variable costs from each plant to each demand city, cij, are entered in cells B4 to G8. The demands Dj of the six markets are entered in cells B9 to G9. Next, corresponding to decision variables xij and yi, cells B14 to G18 and H14 to H18, respectively, are assigned as shown in Figure 5-9. Initially, all variables are set to be 0.

The next step is to construct cells for each of the constraints in Equations 5.1 and 5.2. The constraint cells are as shown in Figure 5-10. Cells B22 to B26 contain the capacity constraints in Equation 5.1, whereas cells B29 to G29 contain the demand constraints in Equation 5.2. The cell

FIGURE 5-9 Spreadsheet Area for Decision Variables for TelecomOptic

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B29 corresponds to the demand constraint for the market in Atlanta. The constraint in cell B22 corresponds to the capacity constraint for the factory in Baltimore. The capacity constraints require that the cell value be greater than or equal to (Ú ) 0, whereas the demand constraints require the cell value be equal to 0.

The objective function measures the total fixed and variable cost of the supply chain net- work and is evaluated in cell B32. The next step is to invoke Solver, as shown in Figure 5-11.

Within Solver, the goal is to minimize the total cost in cell B32. The variables are in cells B14:H18. The constraints are as follows:

B14:G18 Ú 0 5All decision variables are nonnegative6 B22:B26 Ú 0 eKiyi – am

j = 1 xij Ú 0 for i = 1, c, 5 f

B29:G29 = 0 eDj – an i = 1

xij = 0 for j = 1, c, 6 f H14:H18 binary 5Location variables yi are binary; that is, 0 or 16

Within the Solver Parameters dialog box, select Simplex LP and click on Solve to obtain the optimal solution, as shown in Figure 5-12. From Figure 5-12, the supply chain team concludes

—Objective function

= SUMPRODUCT(B4:G8, B14:G18) + SUMPRODUCT(H4:H8, H14:H18)

B32

C29:G295.2= B9 – SUM(B14:B18)B29

B23:B265.1= I4*H14 – SUM(B14:G14)B22

Copied toEquationFormulaCell

FIGURE 5-10 Spreadsheet Area for Constraints for TelecomOptic

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FIGURE 5-11 Solver Dialog Box for TelecomOptic

FIGURE 5-12 Optimal Network Design for TelecomOptic

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that it is optimal for TelecomOptic to close the plants in Salt Lake City and Wichita, while keeping the plants in Baltimore, Cheyenne, and Memphis open. The total monthly cost of this network and operation is $47,401,000. This cost represents savings of about $3 million per month compared with the situation in which TelecomOne and HighOptic operate separate supply chain networks.

LOCATING PLANTS: THE CAPACITATED PLANT LOCATION MODEL WITH SINGLE SOURCING In some cases, companies want to design supply chain networks in which a market is supplied from only one factory, referred to as a single source. Companies may impose this constraint because it lowers the complexity of coordinating the network and requires less flexibility from each facility. The plant location model discussed earlier needs some modification to accommo- date this constraint. The decision variables are redefined as follows:

yi = 1 if factory is located at site i, 0 otherwise xij = 1 if market j is supplied by factory i, 0 otherwise

The problem is formulated as the following integer program:

Min a n

i = 1 fi yi + a

n

i = 1 a m

j = 1 Dj cij xij

subject to

a n

i = 1 xij = 1 for j = 1, c, m (5.8)

a m

j = 1 Djxij … Kiyi for i = 1, c, n (5.9)

xij,yi ∈ 50, 16 (5.10) The constraints in Equations 5.8 and 5.10 enforce that each market is supplied by exactly

one factory. We do not describe the solution of the model in Excel because it is very similar to the

model discussed earlier. The optimal network with single sourcing for TelecomOptic is shown in Table 5-4 (see sheet Table 5-4 Single Sourcing in spreadsheet Figures 5-9 to 12).

If single sourcing is required, it is optimal for TelecomOptic to close the factories in Balti- more and Cheyenne. This is different from the result in Figure 5-12, in which factories in Salt Lake City and Wichita were closed. The monthly cost of operating the network in Table 5-4 is $49,717,000. This cost is about $2.3 million higher than the cost of the network in Figure 5-12, in which single sourcing was not required. The supply chain team thus concludes that single sourcing adds about $2.3 million per month to the cost of the supply chain network, although it makes coordination easier and requires less flexibility from the plants.

LOCATING PLANTS AND WAREHOUSES SIMULTANEOUSLY A much more general form of the plant location model needs to be considered if the entire supply chain network from the

TABLE 5-4 Optimal Network Configuration for TelecomOptic with Single Sourcing

Open/Closed Atlanta Boston Chicago Denver Omaha Portland

Baltimore Closed 0 0 0 0 0 0

Cheyenne Closed 0 0 0 0 0 0

Salt Lake Open 0 0 0 6 0 11

Memphis Open 10 8 0 0 0 0

Wichita Open 0 0 14 0 7 0

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supplier to the customer is to be designed. We consider a supply chain in which suppliers send material to factories that supply warehouses that supply markets, as shown in Figure 5-13. Loca- tion and capacity allocation decisions must be made for both factories and warehouses. Multiple warehouses may be used to satisfy demand at a market, and multiple factories may be used to replenish warehouses. It is also assumed that units have been appropriately adjusted such that one unit of input from a supply source produces one unit of the finished product. The model requires the following inputs:

m = number of markets or demand points n = number of potential factory locations l = number of suppliers t = number of potential warehouse locations Dj = annual demand from customer j Ki = potential capacity of factory at site i Sh = supply capacity at supplier h We = potential warehouse capacity at site e Fi = fixed cost of locating a plant at site i fe = fixed cost of locating a warehouse at site e chi = cost of shipping one unit from supply source h to factory i cie = cost of producing and shipping one unit from factory i to warehouse e cej = cost of shipping one unit from warehouse e to customer j

The goal is to identify plant and warehouse locations, as well as quantities shipped between various points, that minimize the total fixed and variable costs. Define the following decision variables:

yi = 1 if factory is located at site i, 0 otherwise ye = 1 if warehouse is located at site e, 0 otherwise xej = quantity shipped from warehouse e to market j xie = quantity shipped from factory at site i to warehouse e xhi = quantity shipped from supplier h to factory at site i

The problem is formulated as the following integer program:

Min a n

i = 1 Fi yi + a

t

e = 1 feye + a

l

h = 1 a

n

i = 1 chi xhi + a

n

i = 1 a

t

e = 1 cie xie + a

t

e = 1 a m

j = 1 cej xej

Suppliers Plants Warehouses Markets

FIGURE 5-13 Stages in a Supply Network

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The objective function minimizes the total fixed and variable costs of the supply chain network subject to the following constraints:

a n

i = 1 xhi … Sh for h = 1, c, l (5.11)

The constraint in Equation 5.11 specifies that the total amount shipped from a supplier cannot exceed the supplier’s capacity.

a l

h = 1 xhi – a

t

e = 1 xie Ú 0 for i = 1, c, n (5.12)

The constraint in Equation 5.12 states that the amount shipped out of a factory cannot exceed the quantity of raw material received.

a t

e = 1 xie … Kiyi for i = 1, c, n (5.13)

The constraint in Equation 5.13 enforces that the amount produced in the factory cannot exceed its capacity.

a n

i = 1 xie – a

m

j = 1 xej Ú 0 for e = 1, c, t (5.14)

The constraint in Equation 5.14 specifies that the amount shipped out of a warehouse can- not exceed the quantity received from the factories.

a m

j = 1 xej … Weye for e = 1, c, t (5.15)

The constraint in Equation 5.15 specifies that the amount shipped through a warehouse cannot exceed its capacity.

a t

e = 1 xej = Dj for j = 1, c, m (5.16)

The constraint in Equation 5.16 specifies that the amount shipped to a customer must cover the demand.

yi, ye ∈ 50, 16, xej, xie, xhi Ú 0 (5.17) The constraint in Equation 5.17 enforces that each factory or warehouse is either open or

closed. The model discussed earlier can be modified to allow direct shipments between factories

and markets. All the models discussed previously can also be modified to accommodate econo- mies of scale in production, transportation, and inventory costs. However, these requirements make the models more difficult to solve.

Accounting for Taxes, Tariffs, and Customer Requirements

Network design models should be structured such that the resulting supply chain network maxi- mizes profits after tariffs and taxes while meeting customer service requirements. The models discussed earlier can be modified to maximize profits accounting for taxes, even when revenues are in different currencies. If rj is the revenue from selling one unit in market j, the objective function of the capacitated plant location model can be modified to be

Max a m

j = 1 rja

n

i = 1 xij – a

n

i = 1 Fi yi – a

n

i = 1 a m

j = 1 cij xij

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This objective function maximizes profits for the firm. When using a profit maximization objec- tive function, a manager should modify the constraint in Equation 5.1 to be

a n

i = 1 xij … Dj for j = 1, c, m (5.18)

The constraint in Equation 5.18 is more appropriate than the constraint in Equation 5.1 because it allows the network designer to identify the demand that can be satisfied profitably and the demand that is satisfied at a loss to the firm. The plant location model with Equation 5.18 instead of Equation 5.1 and a profit maximization objective function will serve only that portion of demand that is profitable to serve. This may result in some markets in which a portion of the demand is dropped, unless constrained otherwise, because it cannot be served profitably.

Customer preferences and requirements may be in terms of desired response time and the choice of transportation mode or transportation provider. Consider, for example, two modes of transportation available between plant location i and market j. Mode 1 may be sea and mode 2 may be air. The plant location model is modified by defining two distinct decision variables x1ij and x2ij corresponding to the quantity shipped from location i to market j using modes 1 and 2, respectively. The desired response time using each transportation mode is accounted for by allowing shipments only when the time taken is less than the desired response time. For example, if the time from location i to market j using mode 1 (sea) is longer than would be acceptable to the customer, we simply drop the decision variable x1ij from the plant location model. The option among several transportation providers can be modeled similarly.

5.5 MAKING NETWORK DESIGN DECISIONS IN PRACTICE

Managers should keep the following issues in mind when making network design decisions for a supply chain.

Do not underestimate the life span of facilities. It is important to think through the long- term consequences of facility decisions because facilities last a long time and have an enduring impact on a firm’s performance. Managers must consider not only future demand and costs but also scenarios in which technology may change. Otherwise, facilities may become useless within a few years. For example, an insurance company moved its clerical labor from a metropolitan location to a suburban location to lower costs. With increasing automation, however, the need for clerical labor decreased significantly, and within a few years the facility was no longer needed. The company found it difficult to sell the facility, given its distance from residential areas and airports (Harding, 1988). Within most supply chains, production facilities are harder to change than storage facilities. Supply chain network designers must consider that any factories that they put in place will stay there for an extended period of a decade or more. Warehouses or storage facilities, though, particularly those that are not owned by the company, can be changed within a year of making the decision.

Do not gloss over the cultural implications. Network design decisions regarding facility location and facility role have a significant impact on the culture of each facility and the firm. The culture at a facility will be influenced by other facilities in its vicinity. Network designers can use this fact to influence the role of the new facility and the focus of people working there. For example, when Ford Motor Company introduced the Lincoln Mark VIII model, management was faced with a dilemma. At that time, the Mark VIII shared a platform with the Mercury Cou- gar. However, the Mark VIII was part of Ford’s luxury Lincoln division. Locating the Mark VIII line with the Cougar would have obvious operational advantages because of shared parts and processes. However, Ford decided to locate the Mark VIII line in the Wixom, Michigan, plant, where other Lincoln cars were produced. The primary reason for doing so was to ensure that the focus on quality for the Mark VIII would be consistent with that of other Ford luxury cars that were produced in Wixom.

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Do not ignore quality-of-life issues. The quality of life at selected facility locations has a significant impact on performance because it influences the available workforce and its morale. In many instances, a firm may be better off selecting a higher-cost location if it provides a much bet- ter quality of life. Failure to do so can have dire consequences. For example, an aerospace supplier decided to relocate an entire division to an area with a lower standard of living in order to reduce costs. Most of the marketing team, however, refused to relocate. As a result, customer relations deteriorated, and the company had a very difficult transition. The effort to save costs hurt the com- pany and effectively curtailed the firm’s status as a major player in its market (Harding, 1988).

Focus on tariffs and tax incentives when locating facilities. Managers making facility location decisions should consider tariffs and tax incentives carefully. When considering interna- tional locations, it is astounding how often tax incentives drive the choice of location, often overcoming all of the other cost factors combined. For instance, Ireland has developed a large high-tech industry by enticing companies with low taxes. Even within nations, local govern- ments may offer generous packages of low to no taxes and free land when firms decide to locate facilities within their jurisdiction. Toyota, BMW, and Mercedes have all chosen their facility locations in the United States due in large part to tax incentives offered by different states.

5.6 SUMMARY OF LEARNING OBJECTIVES

1. Understand the role of network design in a supply chain. Network design deci- sions include identifying facility roles, locations, and capacities and allocating markets to be served by different facilities. These decisions define the physical constraints within which the network must be operated as market conditions change. Good network design decisions increase supply chain profits.

2. Identify factors influencing supply chain network design decisions. Broadly speaking, network design decisions are influenced by strategic, technological, macroeconomic, political, infrastructure, competitive, and operational factors.

3. Develop a framework for making network design decisions. The goal of network design is to maximize the supply chain’s long-term profitability. The process starts by defining the supply chain strategy, which must be aligned with the competitive strategy of the firm. The supply chain strategy, regional demand, costs, infrastructure, and competitive environment are used to define a regional facility configuration. For regions where facilities are to be located, potentially attractive sites are then selected based on available infrastructure. The optimal configuration is determined from the potential sites using demand, logistics cost, factor costs, taxes, and margins in different markets.

4. Use optimization for facility location and capacity allocation decisions. Gravity location models identify a location that minimizes inbound and outbound transportation costs. They are simple to implement but do not account for other important costs. Network optimiza- tion models can include contribution margins, taxes, tariffs, production, transportation, and inventory costs and are used to maximize profitability. These models are useful when locating facilities, allocating capacity to facilities, and allocating markets to facilities.

Discussion Questions 1. How do the location and size of warehouses affect the perfor-

mance of a firm such as Amazon? What factors should Ama- zon take into account when deciding where and how big its warehouses should be?

2. How do import duties and exchange rates affect the location decision in a supply chain?

3. How is the rise in transportation costs likely to affect global supply chain networks?

4. Amazon has built new warehouses as it has grown. How does this change affect various cost and response times in the Amazon supply chain?

5. McMaster-Carr sells MRO equipment from five warehouses in the United States. W.W. Grainger sells products from more than 350 retail locations, supported by several warehouses. In both cases, customers place orders using the Internet or on the phone. Discuss the pros and cons of the two strategies.

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6. Consider a firm such as Apple or Dell, with few production facilities worldwide. List the pros and cons of this approach and why it may or may not be suitable for the computer industry.

7. Consider a firm such as Ford, with more than 150 facilities worldwide. List the pros and cons of having many facilities and why this model may or may not be suitable for the auto- mobile industry.

Exercises 1. SC Consulting, a supply chain consulting firm, must decide

on the location of its home offices. Its clients are located pri- marily in the 16 states listed in Table 5-5. There are four potential sites for home offices: Los Angeles, Tulsa, Denver, and Seattle. The annual fixed cost of locating an office in Los Angeles is $165,428, Tulsa is $131,230, Denver is $140,000, and Seattle is $145,000. The expected number of trips to each state and the travel costs from each potential site are shown in Table 5-5. Each consultant is expected to take at most 25 trips each year. a. If there are no restrictions on the number of consultants at

a site and the goal is to minimize costs, where should the home offices be located and how many consultants should be assigned to each office? What is the annual cost in terms of the facility and travel?

b. If 10 consultants are to be assigned to a home office, at most, where should the offices be set up? How many con- sultants should be assigned to each office? What is the annual cost of this network?

c. What do you think of a rule by which all consulting proj- ects out of a given state are assigned to one home office? How much is this policy likely to add to cost compared to allowing multiple offices to handle a single state?

2. DryIce, Inc., is a manufacturer of air conditioners that has seen its demand grow significantly. The company anticipates nationwide demand for the next year to be 180,000 units in the South, 120,000 units in the Midwest, 110,000 units in the East, and 100,000 units in the West. Managers at DryIce are designing the manufacturing network and have selected four potential sites—New York, Atlanta, Chicago, and San Diego. Plants could have a capacity of either 200,000 or 400,000 units. The annual fixed costs at the four locations are shown in Table 5-6, along with the cost of producing and shipping an air conditioner to each of the four markets. Where should DryIce build its factories and how large should they be?

3. Sunchem, a manufacturer of printing inks, has five manufac- turing plants worldwide. Their locations and capacities are shown in Table 5-7 along with the cost of producing 1 ton of

TABLE 5-5 Travel Costs and Number of Trips for SC Consulting

Travel Costs ($) Number of TripsState Los Angeles Tulsa Denver Seattle

Washington 150 250 200 25 40

Oregon 150 250 200 75 35

California 75 200 150 125 100

Idaho 150 200 125 125 25

Nevada 100 200 125 150 40

Montana 175 175 125 125 25

Wyoming 150 175 100 150 50

Utah 150 150 100 200 30

Arizona 75 200 100 250 50

Colorado 150 125 25 250 65

New Mexico 125 125 75 300 40

North Dakota 300 200 150 200 30

South Dakota 300 175 125 200 20

Nebraska 250 100 125 250 30

Kansas 250 75 75 300 40

Oklahoma 250 25 125 300 55

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TABLE 5-6 Production and Transport Costs for DryIce, Inc.

New York Atlanta Chicago San Diego

Annual fixed cost of 200,000 plant

$6 million $5.5 million $5.6 million $6.1 million

Annual fixed cost of 400,000 plant

$10 million $9.2 million $9.3 million $10.2 million

East $211 $232 $238 $299

South $232 $212 $230 $280

Midwest $240 $230 $215 $270

West $300 $280 $270 $225

TABLE 5-7 Capacity, Demand, Production, and Transportation Costs for Sunchem

North America Europe Japan

South America Asia

Capacity Tons/Year

Production Cost/Ton

United States $600 $1,300 $2,000 $1,200 $1,700 185 $10,000

Germany $1,300 $600 $1,400 $1,400 $1,300 475 15,000 euro

Japan $2,000 $1,400 $300 $2,100 $900 50 1,800,000 yen

Brazil $1,200 $1,400 $2,100 $800 $2,100 200 13,000 real

India $2,200 $1,300 $1,000 $2,300 $800 80 400,000 rupees

Demand (tons/year)

270 200 120 190 100

TABLE 5-8 Anticipated Exchange Rates for the Next Year

US$ Euro Yen Real Rupee

US$ 1.000 1.993 107.7 1.78 43.55

Euro 0.502 1 54.07 0.89 21.83

Yen 0.0093 0.0185 1 0.016 0.405

Real 0.562 1.124 60.65 1 24.52

Rupee 0.023 0.046 2.47 0.041 1

ink at each facility. The production costs are in the local cur- rency of the country where the plant is located. The major markets for the inks are North America, South America, Europe, Japan, and the rest of Asia. Demand at each market is shown in Table 5-7. Transportation costs from each plant to each market in U.S. dollars are shown in Table 5-7. Manage- ment must come up with a production plan for the next year. a. If exchange rates are expected as in Table 5-8, and no

plant can run below 50 percent of capacity, how much should each plant produce and which markets should each plant supply?

b. If there are no limits on the amount produced in a plant, how much should each plant produce?

c. Can adding 10 tons of capacity in any plant reduce costs? d. How should Sunchem account for the fact that exchange

rates fluctuate over time? 4. Sleekfon and Sturdyfon are two major cell phone manufac-

turers that have recently merged. Their current market sizes are as shown in Table 5-9. All demand is in millions of units. Sleekfon has three production facilities in Europe (EU), North America, and South America. Sturdyfon also has three production facilities in Europe (EU), North America, and the rest of Asia/Australia. The capacity (in millions of units), annual fixed cost (in millions of $), and variable production costs ($ per unit) for each plant are as shown in Table 5-10. Transportation costs between regions are as shown in Table 5-11. All transportation costs are shown in dollars per unit. Duties are applied on each unit based on the fixed cost per unit capacity, variable cost per unit, and transportation cost. Thus, a unit currently shipped from North America to Africa has a fixed cost per unit of capacity of $5.00, a vari- able production cost of $5.50, and a transportation cost of $2.20. The 25 percent import duty is thus applied on $12.70 (5.00 + 5.50 + 2.20) to give a total cost on import of $15.88. For the questions that follow, assume that market demand is as in Table 5-9. The merged company has estimated that scaling back a 20-million-unit plant to 10 million units saves 30 percent in fixed costs. Variable costs at a scaled-back plant are

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TABLE 5-9 Global Demand and Duties for Sleekfon and Sturdyfon

Market N. America S. America Europe

(EU) Europe

(Non-EU) Japan Rest of Asia/

Australia Africa

Sleekfon demand 10 4 20 3 2 2 1

Sturdyfon demand 12 1 4 8 7 3 1

Import duties (%) 3 20 4 15 4 22 25

TABLE 5-10 Plant Capacities and Costs for Sleekfon and Sturdyfon

Capacity Fixed Cost/Year Variable Cost/Unit

Sleekfon Europe (EU) 20 100 6.0

N. America 20 100 5.5

S. America 10 60 5.3

Sturdyfon Europe (EU) 20 100 6.0

N. America 20 100 5.5

Rest of Asia 10 50 5.0

unaffected. Shutting a plant down (either 10 million or 20 million units) saves 80 percent in fixed costs. Fixed costs are only partially recovered because of severance and other costs associated with a shutdown. a. What is the lowest cost achievable for the production and

distribution network prior to the merger? Which plants serve which markets?

b. What is the lowest cost achievable for the production and distribution network after the merger if none of the plants is shut down? Which plants serve which markets?

c. What is the lowest cost achievable for the production and distribution network after the merger if plants can be scaled back or shut down in batches of 10 million units of capacity? Which plants serve which markets?

d. How is the optimal network configuration affected if all duties are reduced to 0?

e. How should the merged network be configured?

5. Return to the Sleekfon and Sturdyfon data in Exercise 4. Management has estimated that demand in global markets is likely to grow. North America, Japan, and Europe (EU) are relatively saturated and expect no growth. South America, Africa, and Europe (Non-EU) markets expect a growth of 20 percent. The rest of Asia/Australia anticipates a growth of 200 percent. a. How should the merged company configure its network to

accommodate the anticipated growth? What is the annual cost of operating the network?

b. There is an option of adding capacity at the plant in the rest of Asia/Australia. Adding 10 million units of capacity incurs an additional fixed cost of $40 million per year. Adding 20 million units of additional capacity incurs an additional fixed cost of $70 million per year. If shutdown costs and duties are as in Exercise 4, how should the merged company configure its network to accommodate

TABLE 5-11 Transportation Costs Between Regions ($ per Unit)

N. America S. America Europe

(EU) Europe

(Non-EU) Japan Rest of Asia/

Australia Africa

N. America 1.00 1.50 1.50 1.80 1.70 2.00 2.20

S. America 1.50 1.00 1.70 2.00 1.90 2.20 2.20

Europe (EU) 1.50 1.70 1.00 1.20 1.80 1.70 1.40

Europe (Non-EU) 1.80 2.00 1.20 1.00 1.80 1.60 1.50

Japan 1.70 1.90 1.80 1.80 1.00 1.20 1.90

Rest of Asia/Australia 2.00 2.20 1.70 1.60 1.20 1.00 1.80

Africa 2.20 2.20 1.40 1.50 1.90 1.80 1.00

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anticipated growth? What is the annual cost of operating the new network?

c. If all duties are reduced to 0, how does your answer to Exercise 5(b) change?

d. How should the merged network be configured given the option of adding to the plant in the rest of Asia/ Australia?

6. StayFresh, a manufacturer of refrigerators in India, has two plants—one in Mumbai and the other in Chennai. Each plant has a capacity of 300,000 units. The two plants serve the entire country, which is divided into four regional markets: the north, with a demand of 100,000 units; the west, with a demand of 150,000 units; the south, with a demand of 150,000 units; and the east, with a demand of 50,000 units. Two other potential sites for plants include Delhi and Kolk- ata. The variable production and transport costs (in thousands of rupees; 1 U.S. dollar is worth about 65 rupees) per refrig- erator from each potential production site to each market are as shown in Table 5-12. StayFresh is anticipating a compounded growth in demand of 20 percent per year for the next five years and must plan its network investment decisions. Demand is anticipated to stabilize after five years of growth. Capacity can be added in increments of either 150,000 or 300,000 units. Adding 150,000 units of capacity incurs a one-time cost of 2 billion rupees, whereas adding 300,000 units of capacity incurs a one-time cost of 3.4 billion rupees. Assume that StayFresh plans to meet all demand (prices are sufficiently high) and that capacity for each year must

be in place by the beginning of the year. Also assume that the cost for the fifth year will continue for the next 10 years—that is, years 6 to 15. The problem can now be solved for different discount factors. To begin with, assume a discount factor of 0.2—that is, 1 rupee spent next year is worth 1 – 0.2 = 0.8 rupee this year. a. How should the production network for the company

evolve over the next five years? b. How does your answer change if the anticipated growth is

15 percent? 25 percent? c. How does your decision change for a discount factor of

0.25? 0.15? d. What investment strategy do you recommend for the

company? 7. Blue Computers, a major server manufacturer in the United

States, currently has plants in Kentucky and Pennsylvania. The Kentucky plant has a capacity of 1 million units a year, and the Pennsylvania plant has a capacity of 1.5 million units a year. The firm divides the United States into five markets: northeast, southeast, midwest, south, and west. Each server sells for $1,000. The firm anticipates a 50 per- cent growth in demand (in each region) this year (after which demand will stabilize) and wants to build a plant with a capacity of 1.5 million units per year to accommo- date the growth. Potential sites being considered are in North Carolina and California. Currently the firm pays fed- eral, state, and local taxes on the income from each plant. Federal taxes are 20 percent of income, and all state and local taxes are 7 percent of income in each state. North Car- olina has offered to reduce taxes for the next 10 years from 7 percent to 2 percent. Blue Computers would like to take the tax break into consideration when planning its network. Consider income over the next 10 years in your analysis. Assume that all costs remain unchanged over the 10 years. Use a discount factor of 0.1 for your analysis. Annual fixed costs, production and shipping costs per unit, and current regional demand (before the 50 percent growth) are shown in Table 5-13. a. If Blue Computers sets an objective of minimizing total

fixed and variable costs, where should it build the new plant? How should the network be structured?

TABLE 5-12 Production and Transport Cost (Thousands of Rupees) per Refrigerator

North East West South

Chennai 20 19 17 15

Delhi 15 18 17 20

Kolkata 18 15 20 19

Mumbai 17 20 15 17

TABLE 5-13 Variable Production and Shipping Costs for Blue Computers

Variable Production and Shipping Cost ($/Unit) Annual Fixed

Cost (Million $)Northeast Southeast Midwest South West

Kentucky 185 180 175 175 200 150

Pennsylvania 170 190 180 200 220 200

N. Carolina 180 180 185 185 215 150

California 220 220 195 195 175 150

Demand (thousands of units/month) 700 400 400 300 600

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b. If Blue Computers sets an objective of maximizing after- tax profits, where should it build the new plant? How should the network be structured?

8. Hot&Cold and CaldoFreddo are two European manufactur- ers of home appliances that have merged. Hot&Cold has plants in France, Germany, and Finland, whereas Caldo- Freddo has plants in the United Kingdom and Italy. The European market is divided into four regions: north, east, west, and south. Plant capacities (millions of units per year), annual fixed costs (millions of euros per year), regional demand (millions of units), and variable produc- tion and shipping costs (euros per unit) are as shown in Table 5-14. Each appliance sells for an average price of 300 euros. All plants are currently treated as profit centers, and the

company pays taxes separately for each plant. Tax rates in the various countries are as follows: France, 0.25; Germany, 0.25; Finland, 0.3; UK, 0.2; and Italy, 0.35. a. Before the merger, what is the optimal network for each

of the two firms if their goal is to minimize costs? What is the optimal network if the goal is to maximize after-tax profits?

b. After the merger, what is the minimum cost configuration if none of the plants is shut down? What is the configura- tion that maximizes after-tax profits if none of the plants is shut down?

c. After the merger, what is the minimum cost configuration if plants can be shut down (assume that a shutdown saves 100 percent of the annual fixed cost of the plant)? What is the configuration that maximizes after-tax profits?

TABLE 5-14 Capacity, Cost, and Demand Data for Hot&Cold and CaldoFreddo

Variable Production and Shipping Costs Annual

Fixed CostNorth East South West Capacity

Hot&Cold France 100 110 105 100 50 1,000

Germany 95 105 110 105 50 1,000

Finland 90 100 115 110 40 850

Demand 30 20 20 35

CaldoFreddo U.K. 105 120 110 90 50 1,000

Italy 110 105 90 115 60 1,150

Demand 15 20 30 20

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Bovet, David. “Good Time to Rethink European Distribution.” Supply Chain Management Review (July–August 2010): 6–7.

Daskin, Mark S. Network and Discrete Location. New York: Wiley, 1995.

Drezner, Z., and H. Hamacher. Facility Location: Applications and Theory. Berlin: Springer Verlag, 2004.

Ferdows, Kasra. “Making the Most of Foreign Factories.” Har- vard Business Review (March–April 1997): 73–88.

Harding, Charles F. “Quantifying Abstract Factors in Facility- Location Decisions.” Industrial Development (May–June 1988): 24.

Korpela, Jukka, Antti Lehmusvaara, and Markku Tuominen. “Customer Service Based Design of the Supply Chain.”

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MacCormack, Alan D., Lawrence J. Newman III, and Donald B. Rosenfield. “The New Dynamics of Global Manufacturing Site Location.” Sloan Management Review (Summer 1994): 69–79.

Mentzer, Joseph. “Seven Keys to Facility Location.” Supply Chain Management Review (May–June 2008): 25–31.

Murphy, Sean. “Will Sourcing Come Closer to Home?” Supply Chain Management Review (September 2008): 33–37.

Note on Facility Location. Harvard Business School Note 9–689– 059, 1989.

Robeson, James F., and William C. Copacino, eds. The Logistics Handbook. New York: Free Press, 1994.

Tayur, Sridhar, Ram Ganeshan, and Michael Magazine, eds. Quantitative Models for Supply Chain Management. Boston: Kluwer Academic Publishers, 1999.

Tirole, Jean. The Theory of Industrial Organization. Cambridge, MA: MIT Press, 1997.

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CASE STUDY Managing Growth at SportStuff.com

In December 2008, Sanjay Gupta and his management team were busy evaluating the performance at Sport- Stuff.com over the previous year. Demand had grown by 80 percent. This growth, however, was a mixed blessing. The venture capitalists supporting the company were very pleased with the growth in sales and the resulting increase in revenue. Sanjay and his team, however, could clearly see that costs would grow faster than revenues if demand continued to grow and the supply chain network was not redesigned. They decided to analyze the perfor- mance of the current network to see how it could be redesigned to best cope with the rapid growth antici- pated over the next three years.

SportStuff.com

Sanjay Gupta founded SportStuff.com in 2004 with a mission of supplying parents with more affordable sports equipment for their children. Parents complained about having to discard expensive skates, skis, jackets, and shoes because children outgrew them rapidly. Sanjay’s initial plan was for the company to purchase used equip- ment and jackets from families and surplus equipment from manufacturers and retailers and sell these over the Internet. The idea was well received in the marketplace, demand grew rapidly, and, by the end of 2004, the com- pany had sales of $0.8 million. By this time, a variety of new and used products were being sold, and the com- pany received significant venture capital support.

In June 2004, Sanjay leased part of a warehouse in the outskirts of St. Louis to manage the large amount of product being sold. Suppliers sent their product to the warehouse. Customer orders were packed and shipped by UPS from there. As demand grew, SportStuff.com leased more space within the warehouse. By 2007, SportStuff.com leased the entire warehouse and orders were being shipped to customers all over the United States. Management divided the United States into six

customer zones for planning purposes. Demand from each customer zone in 2007 was as shown in Table 5-15. Sanjay estimated that the next three years would see a growth rate of about 80 percent per year, after which demand would level off.

The Network Options

Sanjay and his management team could see that they needed more warehouse space to cope with the antici- pated growth. One option was to lease more warehouse space in St. Louis itself. Other options included leasing warehouses all over the country. Leasing a warehouse involved fixed costs based on the size of the warehouse and variable costs that depended on the quantity shipped through the warehouse. Four potential locations for warehouses were identified in Denver, Seattle, Atlanta, and Philadelphia. Leased warehouses could be either small (about 100,000 sq. ft.) or large (200,000 sq. ft.). Small warehouses could handle a flow of up to 2 million units per year, whereas large warehouses could handle a flow of up to 4 million units per year. The current ware- house in St. Louis was small. The fixed and variable costs of small and large warehouses in different loca- tions are shown in Table 5-16.

Sanjay estimated that the inventory holding costs at a warehouse (excluding warehouse expense) was about $600 1F, where F is the number of units flowing through the warehouse per year. This relationship is based on the theoretical observation that the inventory held at a facility (not across the network) is proportional to the square root of the throughput through the facility. As a result, aggregating throughput through a few facili- ties reduces the inventory held as compared with disag- gregating throughput through many facilities. Thus, a warehouse handling 1 million units per year incurred an inventory holding cost of $600,000 in the course of the year. If your version of Excel has problems solving the

TABLE 5-15 Regional Demand at SportStuff.com for 2007

Zone Demand in 2007 Zone Demand in 2007

Northwest 320,000 Lower Midwest 220,000

Southwest 200,000 Northeast 350,000

Upper Midwest 160,000 Southeast 175,000

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nonlinear objective function, use the following inventory costs:

Range of F Inventory Cost

0–2 million $250,000Y + 0.310F 2–4 million $530,000Y + 0.170F 4–6 million $678,000Y + 0.133F More than 6 million $798,000Y + 0.113F

If you can handle only a single linear inventory cost, you should use $475,000Y + 0.165F. For each facility, Y = 1 if the facility is used, 0 otherwise.

SportStuff.com charged a flat fee of $3 per ship- ment sent to a customer. An average customer order con- tained four units. SportStuff.com, in turn, contracted

with UPS to handle all its outbound shipments. UPS charges were based on both the origin and the destina- tion of the shipment and are shown in Table 5-17. Man- agement estimated that inbound transportation costs for shipments from suppliers were likely to remain unchanged, no matter what warehouse configuration was selected.

Study Questions

1. What is the cost SportStuff.com incurs if all warehouses leased are in St. Louis?

2. What supply chain network configuration do you recom- mend for SportStuff.com? Why?

3. How would your recommendation change if transportation costs were twice those shown in Table 5-17?

TABLE 5-16 Fixed and Variable Costs of Potential Warehouses

Small Warehouse Large Warehouse

Location Fixed Cost

($/year) Variable Cost ($/Unit Flow)

Fixed Cost ($/year)

Variable Cost ($/Unit Flow)

Seattle 300,000 0.20 500,000 0.20

Denver 250,000 0.20 420,000 0.20

St. Louis 220,000 0.20 375,000 0.20

Atlanta 220,000 0.20 375,000 0.20

Philadelphia 240,000 0.20 400,000 0.20

TABLE 5-17 UPS Charges per Shipment (Four Units)

Northwest Southwest Upper Midwest Lower Midwest Northeast Southeast

Seattle $2.00 $2.50 $3.50 $4.00 $5.00 $5.50

Denver $2.50 $2.50 $2.50 $3.00 $4.00 $4.50

St. Louis $3.50 $3.50 $2.50 $2.50 $3.00 $3.50

Atlanta $4.00 $4.00 $3.00 $2.50 $3.00 $2.50

Philadelphia $4.50 $5.00 $3.00 $3.50 $2.50 $4.00

CASE STUDY Designing the Production Network at CoolWipes

Matt O’Grady, vice president of supply chain at Cool- Wipes, thought that his current production and distribu- tion network was not appropriate, given the significant

increase in transportation costs over the past few years. Compared to when the company had set up its produc- tion facility in Chicago, transportation costs had

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increased by a factor of more than four and were expected to continue growing in the next few years. A quick decision on building one or more new plants could save the company significant amounts in transportation expense in the future.

CoolWipes

CoolWipes was founded in the late 1980s and produced baby wipes and diaper ointment. Demand for the two products was as shown in Table 5-18. The company cur- rently had one factory in Chicago that produced both products for the entire country. The wipes line in the Chicago facility had a capacity of 5 million units, an annualized fixed cost of $5 million a year, and a variable cost of $10 per unit. The ointment line in the Chicago facility had a capacity of 1 million units, an annualized fixed cost of $1.5 million a year, and a variable cost of $20 per unit. The current transportation costs per unit (for both wipes and ointment) are shown in Table 5-19.

New Network Options

Matt had identified Princeton, New Jersey; Atlanta; and Los Angeles as potential sites for new plants. Each new plant could have a wipes line, an ointment line, or both. A new wipes line had a capacity of 2 million units, an

annual fixed cost of $2.2 million, and a variable produc- tion cost of $10 per unit. A new ointment line had a capacity of 1 million units, an annual fixed cost of $1.5 million, and a variable cost of $20 per unit. The current transportation costs per unit are shown in Table 5-19. Matt had to decide whether to build a new plant and if so, which production lines to put into the new plant.

Study Questions

1. What is the annual cost of serving the entire nation from Chicago?

2. Do you recommend adding any plant(s)? If so, where should the plant(s) be built and what lines should be included? Assume that the Chicago plant will be main- tained at its current capacity but could be run at lower uti- lization. Would your decision be different if transportation costs are half of their current value? What if they were double their current value?

3. If Matt could design a new network from scratch (assume he did not have the Chicago plant but could build it at the cost and capacity specified in the case), what production network would you recommend? Assume that any new plants built besides Chicago would be at the cost and capacity specified under the new network options. Would your decision be different if transportation costs were half of their current value? What if they were double their cur- rent value?

TABLE 5-18 Regional Demand at CoolWipes (in thousands)

Zone Wipes

Demand Ointment Demand Zone

Wipes Demand

Ointment Demand

Northwest 500 50 Lower Midwest 800 65

Southwest 700 90 Northeast 1,000 120

Upper Midwest 900 120 Southeast 600 70

TABLE 5-19 Transportation Costs per Unit

Northwest Southwest Upper

Midwest Lower

Midwest Northeast Southeast

Chicago $6.32 $6.32 $3.68 $4.04 $5.76 $5.96

Princeton $6.60 $6.60 $5.76 $5.92 $3.68 $4.08

Atlanta $6.72 $6.48 $5.92 $4.08 $4.04 $3.64

Los Angeles $4.36 $3.68 $6.32 $6.32 $6.72 $6.60

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Globalization has offered tremendous opportunity, as well as increased risk, in the devel-opment of supply chains. High-performance supply chains such as Samsung and Zara have taken full advantage of globalization. In contrast, several supply chains have found themselves unprepared for the increased risk that has accompanied globalization. As a result, managers must account for both opportunities and uncertainties over the long term when design- ing a global supply chain network. In this chapter, we identify sources of risk for global supply chains, discuss risk mitigation strategies, detail the methodologies used to evaluate network design decisions under uncertainty, and show how they improve global supply chain decisions.

6.1 THE IMPACT OF GLOBALIZATION ON SUPPLY CHAIN NETWORKS

Globalization offers companies opportunities to simultaneously increase revenues and decrease costs. In its 2008 annual report, P&G reported that more than a third of the company’s sales growth was from developing markets with a profit margin that was comparable to developed mar- ket margins. By 2010, sales for the company in developing markets represented almost 34 percent of global sales. Most of Samsung’s sales were outside its home country of Korea. In 2012, over- seas revenue represented 86 percent of sales for Samsung. While maintaining a dominant position in developed markets like the United States, it had also penetrated effectively into emerging mar- kets such as China and India. By 2012, Samsung was the leading vendor of smartphones in both

Designing Global Supply Chain Networks

C H A P T E R

6

LEARNING OBJECTIVES After reading this chapter, you will be able to

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1. Identify factors that need to be included in total cost when making global sourcing decisions.

2. Define uncertainties that are particularly relevant when designing global supply chains.

3. Explain different strategies that may be used to mitigate risk in global supply chains.

4. Understand decision tree methodologies used to evaluate supply chain design decisions under uncertainty.

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markets. Clearly, globalization has offered both P&G and Samsung a significant revenue enhance- ment opportunity.

Apparel and consumer electronics are two industries for which globalization has offered significant cost reduction opportunities. Consumer electronics focuses on small, lightweight, high-value items that are relatively easy and inexpensive to ship. Companies have exploited large economies of scale by consolidating production of standardized electronics components in a single location for use in multiple products across the globe. Contract manufacturers such as Foxconn and Flextronics have become giants with facilities in low-cost countries. Apparel manu- facture has high labor content, and the product is relatively lightweight and cost effective to transport. Companies have exploited globalization by shifting much apparel manufacturing to low-labor-cost countries, especially China. In the first half of 2009, about 33 percent of U.S. apparel imports were from China. The net result is that both industries have benefited tremen- dously from cost reduction as a result of globalization.

One must keep in mind, however, that the opportunities from globalization are often accom- panied by significant additional risk. In a survey conducted by the consulting company Accenture in 2006, more than 50 percent of the executives surveyed believed that supply chain risk had increased as a result of their global operations strategy. For example, in 2005, hurricane damage to 40,000 acres of plantations decreased Dole’s global banana production by about 25 percent. Com- ponent shortage when Sony introduced the PlayStation 3 game console hurt revenues and Sony’s stock price. The ability to incorporate suitable risk mitigation into supply chain design has often been the difference between global supply chains that have succeeded and those that have not.

The Accenture survey categorized risk in global supply chains, as shown in Table 6-1, and asked respondents to indicate the factors that affected them. More than a third of the respondents were affected by natural disasters, volatility of fuel prices, and the performance of supply chain partners.

Crude oil spot price and exchange rate fluctuations in 2008 illustrate the extreme volatility that global supply chains must deal with. Crude started 2008 at about $90 per barrel, peaked in July at more than $140 per barrel, and plummeted to below $40 per barrel in December. The euro started 2008 at about $1.47, peaked in July at almost $1.60, dropped to about $1.25 at the end of October, and then rose back to $1.46 toward the end of December. One can only imagine the

TABLE 6-1 Results of Accenture Survey on Sources of Risk That Affect Global Supply Chain Performance Risk Factors Percentage of Supply Chains Affected

Natural disasters 35

Shortage of skilled resources 24

Geopolitical uncertainty 20

Terrorist infiltration of cargo 13

Volatility of fuel prices 37

Currency fluctuation 29

Port operations/custom delays 23

Customer/consumer preference shifts 23

Performance of supply chain partners 38

Logistics capacity/complexity 33

Forecasting/planning accuracy 30

Supplier planning/communication issues 27

Inflexible supply chain technology 21

Source: Adapted from Jaume Ferre, Johann Karlberg, and Jamie Hintlian, “Integration: The Key to Global Success.” Supply Chain Management Review (March 2007): 24–30.

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havoc such fluctuation played on supply chain performance in 2008! Similar fluctuations in exchange rates and crude prices have continued since then.

The only constant in global supply chain management seems to be uncertainty. Over the life of a supply chain network, a company experiences fluctuations in demand, prices, exchange rates, and the competitive environment. A decision that looks good under the current environ- ment may be quite poor if the situation changes. Between 2000 and 2008, the euro fluctuated from a low of $0.84 to a high of almost $1.60. Clearly, supply chains optimized to $0.84 per euro would have difficulty performing well when the euro reached $1.60.

Uncertainty of demand and price drives the value of building flexible production capacity at a plant. If price and demand do vary over time in a global network, flexible production capac- ity can be reconfigured to maximize profits in the new environment. Between 2007 and 2008, auto sales in the United States dropped by more than 30 percent. Whereas all vehicle categories were affected, the drop in SUV sales was much more significant than the drop in sales of small cars and hybrids. SUV sales dropped by almost 35 percent, but small car sales actually increased by about 1 percent. Honda dealt with this fluctuation more effectively than its competitors because its plants were flexible enough to produce both vehicle types. This flexibility to produce both SUVs and cars in the same facility kept Honda plants operating at reasonably high levels of utilization. In contrast, companies with plants dedicated to SUV production had no option but to leave a lot of idle capacity. In the late 1990s, Toyota made its global assembly plants more flex- ible so each plant could supply multiple markets. One of the main benefits of this flexibility is that it allowed Toyota to react to fluctuations in demand, exchange rates, and local prices by altering production to maximize profits. Thus, supply, demand, and financial uncertainty must be considered when making global network design decisions.

6.2 THE OFFSHORING DECISION: TOTAL COST

This importance of comparative advantage in global supply chains was recognized by Adam Smith in The Wealth of Nations when he stated, “If a foreign country can supply us with a commodity cheaper than we ourselves can make it, better buy it of them with some part of the produce of our own industry, employed in a way in which we have some advantage.” Cost reduction by moving production to low-cost countries is typically mentioned among the top reasons for a supply chain to become global. The challenge, however, is to quantify the benefits (or comparative advantage) of offshore production along with the reasons for this comparative advantage. Whereas many compa- nies have taken advantage of cost reduction through offshoring, others have found the benefits of offshoring to low-cost countries to be far less than anticipated—and, in some cases, nonexistent. The increases in transportation costs between 2000 and 2011 have had a significant negative impact on the perceived benefits of offshoring. Companies have failed to gain from offshoring for two primary reasons: (1) focusing exclusively on unit cost rather than total cost when making the off- shoring decision and (2) ignoring critical risk factors. In this section, we focus on dimensions along which total landed cost needs to be evaluated when making an offshoring decision.

The significant dimensions of total cost can be identified by focusing on the complete sourcing process when offshoring. It is important to keep in mind that a global supply chain with offshoring increases the length and duration of information, product, and cash flows. As a result, the complexity and cost of managing the supply chain can be significantly higher than antici- pated. Table 6-2 identifies dimensions along which each of the three flows should be analyzed for the impact on cost and product availability.

Ferreira and Prokopets (2009) suggest that companies should evaluate the impact of off- shoring on the following key elements of total cost:

1. Supplier price: should link to costs from direct materials, direct labor, indirect labor, man- agement, overhead, capital amortization, local taxes, manufacturing costs, and local regu- latory compliance costs.

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2. Terms: costs are affected by net payment terms and any volume discounts. 3. Delivery costs: include in-country transportation, ocean/air freight, destination transport,

and packaging. 4. Inventory and warehousing: include in-plant inventories, in-plant handling, plant ware-

house costs, supply chain inventories, and supply chain warehousing costs. 5. Cost of quality: includes cost of validation, cost of performance drop due to poorer quality,

and cost of incremental remedies to combat quality drop. 6. Customer duties, value-added taxes, local tax incentives. 7. Cost of risk, procurement staff, broker fees, infrastructure (IT and facilities), and tooling

and mold costs. 8. Exchange rate trends and their impact on cost.

It is important to both quantify these factors carefully when making the offshoring deci- sion and track them over time. As Table 6-2 indicates, unit cost reduction from low labor and

TABLE 6-2 Dimensions to Consider When Evaluating Total Cost from Offshoring Performance Dimension

Activity Affecting Performance

Impact of Offshoring

Order communication Order placement More difficult communication

Supply chain visibility Scheduling and expediting Poorer visibility

Raw material costs Sourcing of raw material Could go either way depending on raw material sourcing

Unit cost Production, quality (production and transportation)

Labor/fixed costs decrease; quality may suffer

Freight costs Transportation modes and quantity

Higher freight costs

Taxes and tariffs Border crossing Could go either way

Supply lead time Order communication, supplier production scheduling, production time, customs, transportation, receiving

Lead time increase results in poorer forecasts and higher inventories

On-time delivery/lead time uncertainty

Production, quality, customs, transportation, receiving

Poorer on-time delivery and increased uncertainty resulting in higher inventory and lower product availability

Minimum order quantity Production, transportation Larger minimum quantities increase inventory

Product returns Quality Increased returns likely

Inventories Lead times, inventory in transit and production

Increase

Working capital Inventories and financial reconciliation

Increase

Hidden costs Order communication, invoicing errors, managing exchange rate risk

Higher hidden costs

Stockouts Ordering, production, transportation with poorer visibility

Increase

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fixed costs, along with possible tax advantages, are likely to be the major benefit from offshor- ing, with almost every other factor being negative. In some instances, the substitution of labor for capital can provide a benefit when offshoring. For example, auto and auto parts plants in India are designed with much greater labor content than similar manufacturing in developed countries to lower fixed costs. The benefit of lower labor cost, however, is unlikely to be significant for a manufactured product if labor cost is a small fraction of total cost. It is also the case that in sev- eral low-cost countries, such as China and India, labor costs have escalated significantly. As mentioned by Goel et al. (2008), wage inflation in China averaged 19 percent in dollar terms between 2003 and 2008 compared to around 3 percent in the United States. During the same period, transportation costs increased by a significant amount (ocean freight costs increased 135 percent between 2005 and 2008) and the Chinese yuan strengthened relative to the dollar (by about 18 percent between 2005 and 2008). The net result was that offshoring manufactured prod- ucts from the United States to China looked much less attractive in 2008 than in 2003.

In general, offshoring to low-cost countries is likely to be most attractive for products with high labor content, large production volumes, relatively low variety, and low transportation costs relative to product value. For example, a company producing a large variety of pumps is likely to find that offshoring the production of castings for a common part across many pumps is likely to be much more attractive than the offshoring of highly specialized engineered parts.

Given that global sourcing tends to increase transportation costs, it is important to focus on reducing transportation content for successful global sourcing. Suitably designed components can facilitate much greater density when transporting products. IKEA has designed modular products that are assembled by the customer. This allows the modules to be shipped flat in high

– nents so that they can be packed more tightly when shipping. The use of supplier hubs can be effective if several components are being sourced globally from different locations. Many manu- facturers have created supplier hubs in Asia that are fed by each of their Asian suppliers. This allows for a consolidated shipment to be sent from the hub rather than several smaller shipments being sent from each supplier. More sophisticated flexible policies that allow for direct shipping from the supplier when volumes are high, coupled with consolidated shipping through a hub when volumes are low, can be effective in lowering transportation cost.

It is also important to perform a careful review of the production process to decide which parts are to be offshored. For example, a small American jewelry manufacturer wanted to off- shore manufacturing to Hong Kong for a piece of jewelry. Raw material in the form of gold sheet was sourced in the United States. The first step in the manufacturing process was the stamping of the gold sheet into a suitable-sized blank. This process generated about 40 percent waste, which could be recycled to produce more gold sheet. The manufacturer faced the choice of stamping in the United States or Hong Kong. Stamping in Hong Kong would incur lower labor cost but higher transportation cost and would require more working capital because of the delay before the waste gold could be recycled. A careful analysis indicated that it was cheaper for the stamp- ing tools to be installed at the gold sheet supplier in the United States. Stamping at the gold sheet supplier reduced transportation cost because only usable material was shipped to Hong Kong. More importantly, this decision reduced working capital requirement because the waste gold from stamping was recycled within two days.

One of the biggest challenges with offshoring is the increased risk and its potential impact on cost. This challenge is exacerbated if a company uses an offshore location that is primarily targeting low costs to absorb all the uncertainties in its supply chain. In such a context, it is often much more effective to use a combination of an offshore facility that is given predictable, high-volume work along with an onshore or near-shore facility that is designed specifically to handle most of the fluc- tuation. Companies using only an offshore facility often find themselves carrying extra inventory and resorting to air freight because of the long and variable lead times. The presence of a flexible onshore facility that absorbs all the variation can often lower total landed cost by eliminating expensive freight and significantly reducing the amount of inventory carried in the supply chain.

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6.3 RISK MANAGEMENT IN GLOBAL SUPPLY CHAINS

Global supply chains today are subject to more risk factors than localized supply chains of the past. These risks include supply disruption, supply delays, demand fluctuations, price fluctua- tions, and exchange rate fluctuations. As was evident in the financial crisis of 2008, underesti- mating risks in global supply chains and not having suitable mitigation strategies in place can result in painful outcomes. For example, contamination at one of the two suppliers of flu vaccine to the United States led to a severe shortage at the beginning of the 2004 flu season. This short- age led to rationing in most states and severe price gouging in some cases. Similarly, the signifi- cant strengthening of the euro in 2008 hurt firms that had most of their supply sources located in Western Europe. In another instance, failure to buffer supply uncertainty with sufficient inven- tory resulted in high costs rather than savings. An automotive component manufacturer had hoped to save $4 million to $5 million a year by sourcing from Asia instead of Mexico. As a result of port congestion in Los Angeles–Long Beach, the company had to charter aircraft to fly the parts in from Asia because it did not have sufficient inventory to cover the delays. A charter that would have cost $20,000 per aircraft from Mexico ended up costing the company $750,000. The anticipated savings turned into a $20 million loss.

It is thus critical for global supply chains to be aware of the relevant risk factors and build in suitable mitigation strategies. Table 6-3 contains a categorization of supply chain risks and their drivers that must be considered during network design.

Good network design can play a significant role in mitigating supply chain risk. For instance, having multiple suppliers mitigates the risk of disruption from any one supply source.

– trast, Ericsson had no backup source in its network and was unable to react. Ericsson estimated that it lost revenues of $400 million as a result. Similarly, having flexible capacity mitigates the risks of global demand, price, and exchange rate fluctuations. For example, Hino Trucks uses flexible capacity at its plants to change production levels for different products by shifting work- force among lines. As a result, the company keeps a constant workforce in the plant even though the production at each line varies to best match supply and demand. As illustrated by these examples, designing mitigation strategies into the network significantly improves a supply chain’s ability to deal with risk.

Every mitigation strategy comes at a price, however, and may increase other risks. For example, increasing inventory mitigates the risk of delays but increases the risk of obsolescence. Acquiring multiple suppliers mitigates the risk of disruption but increases costs because each supplier may have difficulty achieving economies of scale. Thus, it is important to develop tai- lored mitigation strategies during network design that achieve a good balance between the amount of risk mitigated and the increase in cost. Some tailored mitigation strategies are outlined in Table 6-4. Most of these strategies are discussed in greater detail later in the book.

Global supply chains should generally use a combination of mitigation strategies designed into the supply chain along with financial strategies to hedge uncovered risks. A global supply chain strategy focused on efficiency and low cost may concentrate global production in a few

Key Point

It is critical that offshoring decisions be made while accounting for total cost. Offshoring typically low- ers labor and fixed costs but increases risk, freight costs, and working capital. Before offshoring, product design and process design should be carefully evaluated to identify steps that may lower freight content and the need for working capital. Including an onshore option can lower the cost associated with cover- ing risk from an offshore facility.

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low-cost countries. Such a supply chain design, however, is vulnerable to the risk of supply dis- ruption along with fluctuations in transportation prices and exchange rates. In such a setting, it is crucial that the firm hedge fuel costs and exchange rates because the supply chain design itself has no built-in mechanisms to deal with these fluctuations. In contrast, a global supply chain designed with excess, flexible capacity allows production to be shifted to whatever location is most effective in a given set of macroeconomic conditions. The ability of such a flexible design to react to fluctuations decreases the need for financial hedges. Operational hedges such as flex- ibility are more complex to execute than financial hedges, but they have the advantage of being reactive because the supply chain can be reconfigured to best react to the macroeconomic state of the world.

It is important to keep in mind that any risk mitigation strategy is not always “in the money.” For example, flexibility built into Honda plants proved effective only when demand for vehicles shifted in an unpredictable manner in 2008. If there had been no fluctuation in demand,

TABLE 6-3 Supply Chain Risks to Be Considered During Network Design Category Risk Drivers

Disruptions Natural disaster, war, terrorism

Labor disputes

Supplier bankruptcy

Delays High capacity utilization at supply source

Inflexibility of supply source

Poor quality or yield at supply source

Systems risk Information infrastructure breakdown

System integration or extent of systems being networked

Forecast risk Inaccurate forecasts due to long lead times, seasonality, product variety, short life cycles, small customer base

Information distortion

Intellectual property risk

Vertical integration of supply chain

Global outsourcing and markets

Procurement risk Exchange rate risk

Price of inputs

Fraction purchased from a single source

Industrywide capacity utilization

Receivables risk Number of customers

Financial strength of customers

Inventory risk Rate of product obsolescence

Inventory holding cost

Product value

Demand and supply uncertainty

Capacity risk Cost of capacity

Capacity flexibility

Source: Adapted from Sunil Chopra and Manmohan S. Sodhi, “Managing Risk to Avoid Supply Chain Breakdown.” Sloan Management Review (Fall 2004): 53–61.

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the flexibility would have gone unutilized. Flexibility in the form of the intelligent body assem-

the state of the automotive markets was relatively stable at that time. Similarly, the use of fuel hedges that made billions for Southwest Airlines cost it money toward the end of 2008 when crude oil prices dropped significantly.

It is thus critical that risk mitigation strategies be evaluated rigorously as real options in terms of their expected long-term value before they are implemented. In the following sections, we discuss methodologies that allow for the financial evaluation of risk mitigation strategies designed into a global supply chain.

Flexibility, Chaining, and Containment

Flexibility plays an important role in mitigating different risks and uncertainties faced by a global supply chain. Flexibility can be divided into three broad categories—new product flexibility, mix flexibility, and volume flexibility. New product flexibility refers to a firm’s ability to introduce

– bility may result from the use of common architectures and product platforms with the goal of providing a large number of distinct models using as few unique platforms as possible. The con- sumer electronics industry has historically followed this approach to introduce a continuous

capacity is flexible enough to be able to produce any product. This approach has been used in the pharmaceutical industry, in which a fraction of the capacity is very flexible with all new products first manufactured there. Only once the product takes off is it moved to a dedicated capacity with lower variable costs.

TABLE 6-4 Tailored Risk Mitigation Strategies During Network Design Risk Mitigation Strategy

Tailored Strategies

Increase capacity Focus on low-cost, decentralized capacity for predictable demand. Build centralized capacity for unpredictable demand. Increase decentralization as cost of capacity drops.

Get redundant suppliers

More redundant supply for high-volume products, less redundancy for low- volume products. Centralize redundancy for low-volume products in a few flexible suppliers.

Increase responsiveness

Favor cost over responsiveness for commodity products. Favor responsiveness over cost for short–life cycle products.

Increase inventory Decentralize inventory of predictable, lower-value products. Centralize inventory of less predictable, higher-value products.

Increase flexibility Favor cost over flexibility for predictable, high-volume products. Favor flexibility for unpredictable, low-volume products. Centralize flexibility in a few locations if it is expensive.

Pool or aggregate demand

Increase aggregation as unpredictability grows.

Increase source capability

Prefer capability over cost for high-value, high-risk products. Favor cost over capability for low-value commodity products. Centralize high capability in flexible source if possible.

Source: Adapted from Sunil Chopra and Manmohan S. Sodhi, “Managing Risk to Avoid Supply Chain Breakdown.” Sloan Management Review (Fall 2004): 53–61.

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Mix flexibility refers to the ability to produce a variety of products within a short period of time. Mix flexibility is critical in an environment in which demand for individual products is small or highly unpredictable, supply of raw materials is uncertain, and technology is evolving rapidly. The consumer electronics industry is a good example for which mix flexibility is essen- tial in production environments, especially as more production has moved to contract manufac- turers. Modular design and common components facilitate mix flexibility. Zara’s European facilities have significant mix flexibility, allowing the company to provide trendy apparel with highly unpredictable demand.

Volume flexibility refers to a firm’s ability to operate profitably at different levels of output. Volume flexibility is critical in cyclical industries. Firms in the automotive industry that lacked volume flexibility were badly hurt in 2008 when demand for automobiles in the United States shrank significantly. The steel industry is an example in which some volume flexibility and con- solidation have helped performance. Prior to 2000, firms had limited volume flexibility and did not adjust production volumes when demand started to fall. The result was a buildup of invento- ries and a significant drop in the price of steel. In the early 2000s, a few large firms consolidated and developed some volume flexibility. As a result, they were able to cut production as demand fell. The result has been less buildup of inventory and smaller drops in price during downturns, followed by a quicker recovery for the steel industry.

Given that some form of flexibility is often used to mitigate risks in global supply chains, it is important to understand the benefits and limitations of this approach. When dealing with demand uncertainty, Jordan and Graves (1995) make the important observation that as flexibility is increased, the marginal benefit derived from the increased flexibility decreases. They suggest operationalizing this idea in the concept of chaining, which is illustrated as follows. Consider a firm that sells four distinct products. A dedicated supply network with no flexibility would have four plants, each dedicated to producing a single product, as shown in Figure 6-1. A fully flexible network configuration would have each plant capable of producing all four products. The pro- duction flexibility of plants is beneficial when demand for each of the four products is unpredict- able. With dedicated plants, the firm is not able to meet demand in excess of plant capacity. With flexible plants, the firm is able to shift excess demand for a product to a plant with excess capac- ity. Jordan and Graves define a chained network with one long chain (limited flexibility), config- ured as shown in Figure 6-1. In a chained configuration, each plant is capable of producing two products with the flexibility organized so that the plants and their products form a chain. Jordan and Graves show that a chained network mitigates the risk of demand fluctuation almost as effec- tively as a fully flexible network. Given the higher cost of full flexibility, the results of Jordan and Graves indicate that chaining is an excellent strategy to lower cost while gaining most of the benefits of flexibility.

The desired length of chains is an important question to be addressed when designing chained networks. When dealing with demand uncertainty, longer chains have the advantage of effectively pooling available capacity to a greater extent. Long chains, however, do have a few disadvantages. The fixed cost of building a single long chain can be higher than the cost of multiple smaller chains. With a single long chain, the effect of any fluctuation ripples to all facilities in the chain, making coordination more difficult across the network. It has also been observed by several researchers that flexibility and chaining are effective when dealing with demand fluctuation but less effective when

Dedicated Network

Chained Network with One Long

Chain

Chained Network with Two Short

Chains

Fully Flexible Network

FIGURE 6-1 Different Flexibility Configurations in Network

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dealing with supply disruption. In the presence of supply disruption, Lim et al. (2008) have observed that designing smaller chains that contain or limit the impact of a disruption can be more effective than designing a network with one long chain. An example of containment is shown in the last example in Figure 6-1, which shows four plants with the flexibility to produce the four products in the form of two short chains. In this design, any disruption in one of the chains does not affect the other chain. A simple example of containment is hog farming: The farms are large to gain econo- mies of scale, but the hogs are kept separated in small groups to ensure that the risk of disease is contained within a group and does not spread to the entire farm.

Key Point

Appropriate flexibility is an effective approach for a global supply chain to deal with a variety of risks and uncertainties. Whereas some flexibility is valuable, too much flexibility may not be worth the cost. Strategies such as chaining and containment should be used to maximize the benefit from flexibility while keeping costs low.

6.4 DISCOUNTED CASH FLOWS

Global supply chain design decisions should be evaluated as a sequence of cash flows over the duration of time they will be in place. This requires the evaluation of future cash flows account- ing for risks and uncertainties likely to arise in the global supply chain. In this section, we dis- cuss the basics of analysis to evaluate future cash flows before introducing uncertainty in the next section.

The present value of a stream of cash flows is what that stream is worth in today’s dollars. Discounted cash flow (DCF) analysis evaluates the present value of any stream of future cash flows and allows management to compare two streams of cash flows in terms of their financial value. DCF analysis is based on the fundamental premise that “a dollar today is worth more than a dollar tomorrow” because a dollar today may be invested and earn a return in addition to the dollar invested. This premise provides the basic tool for comparing the relative value of future cash flows that will arrive during different time periods.

The present value of future cash flow is found by using a discount factor. If a dollar today can be invested and earn a rate of return k over the next period, an investment of $1 today will result in 1 + k dollars in the next period. An investor would therefore be indifferent between obtaining $1 in the next period or $1>(1 + k) in the current period. Thus, $1 in the next period is discounted by the

Discount factor = 1

1 + k (6.1)

to obtain its present value. The rate of return k is also referred to as the discount rate, hurdle rate, or opportunity cost

of capital. Given a stream of cash flows C0, C1, … , CT over the next T periods, and a rate of return k

PV = C0 + a T

t = 1 a 1

1 + kb tCt (6.2)

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EXAMPLE 6-1

Trips Logistics, a third-party logistics firm that provides warehousing and other logistics ser- vices, is facing a decision regarding the amount of space to lease for the upcoming three-year period. The general manager has forecast that Trips Logistics will need to handle a demand of 100,000 units for each of the next three years. Historically, Trips Logistics has required 1,000 square feet of warehouse space for every 1,000 units of demand. For the purposes of this discus- sion, the only cost Trips Logistics faces is the cost for the warehouse.

Trips Logistics receives revenue of $1.22 for each unit of demand. The general manager must decide whether to sign a three-year lease or obtain warehousing space on the spot market each year. The three-year lease will cost $1 per square foot per year, and the spot market rate is expected to be $1.20 per square foot per year for each of the three years. Trips Logistics has a discount rate of k = 0.1.

Analysis

square feet of warehouse space with obtaining the space from the spot market each year. If the general manager obtains warehousing space from the spot market each year, Trips Logistics will earn $1.22 for each unit and pay $1.20 for one square foot of warehouse space required. The expected annual profit for Trips Logistics in this case is given by the following:

Expected annual profit if warehousing = (100,000 * $1.22) space is obtained from spot market – (100,000 * $1.20) = $2,000

Obtaining warehouse space from the spot market provides Trips Logistics with an expected positive

PV1 o lease2 = C0 + C11 + k + C211 + k22 = 2,000 + 2,0001.1 + 2,0001.12 = $5,471 If the general manager leases 100,000 sq. ft. of warehouse space for the next three years, Trips Logistics pays $1 per square foot of space leased each year. The expected annual profit for Trips Logistics in this case is given by the following:

Expected annual profit with three@year lease = (100,000 * $1.22) – (100,000 * $1.00) = $22,000

Signing a lease for three years provides Trips Logistics with a positive cash flow of $22,000 in

PV1Lease2 = C0 + C11 + k + C211 + k22 = 22,000 + 22,0001.1 + 22,0001.12 = $60,182 – $5,471 = $54,711 higher than obtaining warehousing

space on the spot market.

Based on this simple analysis, a manager may opt to sign the lease. However, this does not tell the whole story because we have not yet included the uncertainty in spot prices and valued the greater flexibility to adjust to uncertainty that the spot market provides the man- ager. In the next section, we introduce methodology that allows for uncertainty and discuss how the inclusion of uncertainty of future demand and costs may cause the manager to rethink the decision.

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6.5 EVALUATING NETWORK DESIGN DECISIONS USING DECISION TREES

In any global supply chain, demand, prices, exchange rates, and several other factors are uncer- tain and are likely to fluctuate during the life of any supply chain decision. In an uncertain envi- ronment, the problem with using a simple DCF analysis is that it typically undervalues flexibility. The result is often a supply chain that performs well if everything goes according to plan but becomes terribly expensive if something unexpected happens. A manager makes several differ- ent decisions when designing a supply chain network. For instance:

market as needed? –

tion capacity?

be flexible?

If uncertainty is ignored, a manager will always sign long-term contracts (because they are typically cheaper) and avoid all flexible or backup capacity (because it is more expensive). Such decisions can hurt the firm, however, if future demand or prices are not as forecast at the time of the decision. Executives participating in the Accenture 2013 Global Manufacturing Study “cited a variety of volatility-related factors as potential impediments to their ability to grow—including global currency instability, unpredictable commodities costs, uncertainty about customer demand, political or social unrest in key markets, and potential changes in government regula- tions.” It is thus important to provide a methodology that allows managers to incorporate this uncertainty into their network design process. In this section, we describe such a methodology and show that accounting for uncertainty can have a significant impact on the value of network design decisions.

The Basics of Decision Tree Analysis

A decision tree is a graphic device used to evaluate decisions under uncertainty. Decision trees with DCFs can be used to evaluate supply chain design decisions given uncertainty in prices, demand, exchange rates, and inflation.

The first step in setting up a decision tree is to identify the number of time periods into the future that will be considered when making the decision. The decision maker should also iden- tify the duration of a period—a day, a month, a quarter, or any other time period. The duration of a period should be the minimum period of time over which factors affecting supply chain deci- sions may change by a significant amount. “Significant” is hard to define, but in most cases it is appropriate to use as a period the duration over which an aggregate plan holds. If planning is done monthly, for example, we set the duration of a period at a month. In the following discus- sion, T will represent the number of time periods over which the supply chain decision is to be evaluated.

The next step is to identify factors that will affect the value of the decision and are likely to fluctuate over the next T periods. These factors include demand, price, exchange rate, and infla- tion, among others. Having identified the key factors, the next step is to identify probability dis- tributions that define the fluctuation of each factor from one period to the next. If, for instance, demand and price are identified as the two key factors that affect the decision, the probability of moving from a given value of demand and price in one period to any other value of demand and price in the next period must be defined.

The next step is to identify a periodic discount rate k to be applied to future cash flows. It is not essential that the same discount rate apply to each period or even at every node in a period. The discount rate should take into account the inherent risk associated with the investment. In general, a higher discount rate should apply to investments with higher risk.

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The decision is now evaluated using a decision tree, which contains the present and T future periods. Within each period, a node must be defined for every possible combination of factor values (say, demand and price) that can be achieved. Arrows are drawn from origin nodes in Period i to end nodes in Period i + 1. The probability on an arrow is referred to as the transi- tion probability and is the probability of transitioning from the origin node in Period i to the end node in Period i + 1.

The decision tree is evaluated starting from nodes in Period T and working back to Period 0. For each node, the decision is optimized, taking into account current and future values of vari- ous factors. The analysis is based on Bellman’s principle, which states that for any choice of strategy in a given state, the optimal strategy in the next period is the one that is selected if the entire analysis is assumed to begin in the next period. This principle allows the optimal strategy to be solved in a backward fashion starting at the last period. Expected future cash flows are discounted back and included in the decision currently under consideration. The value of the node in Period 0 gives the value of the investment, as well as the decisions made during each time period. (Tools such as Treeplan are available that help solve decision trees on spreadsheets.)

The decision tree analysis methodology is summarized as follows:

1. Identify the duration of each period (month, quarter, etc.) and the number of periods T over which the decision is to be evaluated.

2. Identify factors such as demand, price, and exchange rate whose fluctuation will be con- sidered over the next T periods.

3. Identify representations of uncertainty for each factor; that is, determine what distribution to use to model the uncertainty.

4. Identify the periodic discount rate k for each period. 5. Represent the decision tree with defined states in each period, as well as the transition

probabilities between states in successive periods. 6. Starting at period T, work back to Period 0, identifying the optimal decision and the

expected cash flows at each step. Expected cash flows at each state in a given period should be discounted back when included in the previous period.

Evaluating Flexibility at Trips Logistics

We illustrate the decision tree analysis methodology by using the lease decision facing the general manager at Trips Logistics. The manager must decide whether to lease warehouse space for the com- ing three years and the quantity to lease. The manager anticipates uncertainty in demand and spot prices for warehouse space over the coming three years. The long-term lease is cheaper but the space could go unused if demand is lower than anticipated. The long-term lease may also end up being more expensive if future spot market prices come down. The manager is considering three options:

1. Get all warehousing space from the spot market as needed. 2. Sign a three-year lease for a fixed amount of warehouse space and get additional require-

ments from the spot market. 3. Sign a flexible lease with a minimum charge that allows variable usage of warehouse space

up to a limit, with additional requirements from the spot market.

We now discuss how the manager can evaluate each decision, taking uncertainty into account. One thousand square feet of warehouse space is required for every 1,000 units of demand,

and the current demand at Trips Logistics is for 100,000 units per year. The manager forecasts that from one year to the next, demand may go up by 20 percent, with a probability of 0.5, or go down by 20 percent, with a probability of 0.5. The probabilities of the two outcomes are indepen- dent and unchanged from one year to the next.

The general manager can sign a three-year lease at a price of $1 per square foot per year. Warehouse space is currently available on the spot market for $1.20 per square foot per year.

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From one year to the next, spot prices for warehouse space may go up by 10 percent, with prob- ability 0.5, or go down by 10 percent, with probability 0.5. The probabilities of the two outcomes are independent and unchanged from one year to the next.

The general manager believes that prices of warehouse space and demand for the product fluctuate independently. Each unit Trips Logistics handles results in revenue of $1.22, and Trips Logistics is committed to handling all demand that arises. Trips Logistics uses a discount rate of k = 0.1 for each of the three years.

The general manager assumes that all costs are incurred at the beginning of each year and thus constructs a decision tree with T = 2. The decision tree is shown in Figure 6-2, with each node representing demand (D) in thousands of units and price (p) in dollars. The probability of each transition is 0.5 3 0.5 5 0.25 because price and demand fluctuate independently.

Evaluating the Spot Market Option

Using the decision tree in Figure 6-2, the manager first analyzes the option of not signing a lease and obtaining all warehouse space from the spot market. He starts with Period 2 and evaluates

Period 0 Period 1 Period 2

D = 100 p = $1.20

D = 120 p = $1.32

D = 120 p = $1.08

D = 80 p = $1.32

D = 80 p = $1.08

D = 144 p = $1.45

D = 144 p = $1.19

D = 96 p = $1.45

D = 144 p = $0.97

D = 96 p = $1.19

D = 96 p = $0.97

D = 64 p = $1.45

D = 64 p = $1.19

D = 64 p = $0.97

0.25

0.25

0.25

0.25

0.25

0.25

0.25

0.25

FIGURE 6-2 Decision Tree for Trips Logistics, Considering Demand and Price Fluctuation

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TABLE 6-5 Period 2 Calculations for Spot Market Option Revenue Cost C(D 5, p 5, 2) Profit P(D 5, p 5, 2)

D = 144, p = 1.45 144,000 : 1.22 144,000 : 1.45 -$33,120 D = 144, p = 1.19 144,000 : 1.22 144,000 : 1.19 $4,320 D = 144, p = 0.97 144,000 : 1.22 144,000 : 0.97 $36,000 D = 96, p = 1.45 96,000 : 1.22 96,000 : 1.45 -$22,080 D = 96, p = 1.19 96,000 : 1.22 96,000 : 1.19 $2,880 D = 96, p = 0.97 96,000 : 1.22 96,000 : 0.97 $24,000 D = 64, p = 1.45 64,000 : 1.22 64,000 : 1.45 -$14,720 D = 64, p = 1.19 64,000 : 1.22 64,000 : 1.19 $1,920 D = 64, p = 0.97 64,000 : 1.22 64,000 : 0.97 $16,000

the profit for Trips Logistics at each node. At the node D = 144, p = $1.45, Trips Logistics must satisfy a demand of 144,000 and faces a spot price of $1.45 per square foot for warehouse space in Period 2. The cost incurred by Trips Logistics in Period 2 at the node D = 144, p = $1.45 is rep- resented by C(D = 144, p =1.45, 2) and is given by

C1D = 144, p = 1.45, 22 = 144,000 * 1.45 = $208,800 The profit at Trips Logistics in Period 2 at the node D = 144, p = $1.45 is represented by

P(D = 144, p = 1.45, 2) and is given by

P1D = 144, p = 1.45, 22 = 144,000 * 1.22 – C1D = 144, p = 1.45, 22 = 175,680 – 208,800 = -$33,120

The profit for Trips Logistics at each of the other nodes in Period 2 is evaluated similarly, as shown in Table 6-5.

The manager next evaluates the expected profit at each node in Period 1 to be the profit during Period 1 plus the present value (in Period 1) of the expected profit in Period 2. The expected profit EP(D =, p =, 1) at a node is the expected profit over all four nodes in Period 2 that may result from this node. PVEP(D =, p =, 1) represents the present value of this expected profit; P(D =, p =, 1), the total expected profit, is the sum of the profit in Period 1 and the pres- ent value of the expected profit in Period 2. From the node D = 120, p = $1.32 in Period 1, there are four possible states in Period 2. The manager thus evaluates the expected profit in Period 2 over all four states possible from the node D = 120, p = $1.32 in Period 1 to be EP(D = 120, p = 1.32, 1), where

EP1D = 120, p = 1.32, 12 = 0.25 * 3P1D = 144, p = 1.45, 22 + P1D = 144, p = 1.19, 2) + P1D = 96, p = 1.45, 22 + P1D = 96, p = 1.19, 224 = 0.25 * 3-33,120 + 4,320 – 22,080 + 2,8804 = -$12,000

The present value of this expected value in Period 1 is given by

PVEP1D = 120, p = 1.32, 12 = EP1D = 120, p = 1.32, 12 > 11 + k2 = -12,000 > 1.1 = -$10,909

The manager obtains the total expected profit P(D = 120, p = 1.32, 1) at node D = 120, p = 1.32 in Period 1 to be the sum of the profit in Period 1 at this node and the present value of future expected profits.

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P1D = 120, p = 1.32, 12 = (120,000 * 1.22) – (120,000 * 1.32) + PVEP(D = 120, p = 1.32, 1) = -$12,000 – $10,909 = -$22,909

The total expected profit for all other nodes in Period 1 is evaluated as shown in Table 6-6. For Period 0, the total profit P(D = 100, p = 1.20, 0) is the sum of the profit at Period 0 and

the present value of the expected profit over the four nodes in Period 1.

EP1D = 100, p = 1.20, 02 = 0.25 * 3P1D = 120, p = 1.32, 12 + P1D = 120, p = 1.08, 12 + P1D = 80, p = 1.32, 12 + P1D = 80, p = 1.08, 124 = 0.25

* 3-22,909 + 32,073 – 15,273 + 21,3824 = $3,818 PVEP1D = 100, p = 1.20, 02 = EP1D = 100, p = 1.20, 02 >11 + k2

= 3,818 > 1.1 = $3,471 P1D = 100, p = 1.20, 02 = (100,000 * 1.22) – (100,000 * 1.20) + PVEP(D = 100,

p = 1.20, 0) = $2,000 + $3,471 = $5,471

spot market is

PV1Spot Market2 = $5,471 Evaluating the Fixed Lease Option

The manager next evaluates the alternative whereby the lease for 100,000 sq. ft. of warehouse space is signed. The evaluation procedure is very similar to that for the previous case, but the outcome changes in terms of profit. For example, at the node D = 144, p = 1.45, the manager will require 44,000 square feet of warehouse space from the spot market at $1.45 per square foot because only 100,000 square feet have been leased at $1 per square foot. If demand happens to be less than 100,000 units, Trips Logistics still has to pay for the entire 100,000 square feet of leased space. For Period 2, the manager obtains the profit at each of the nine nodes, as shown in Table 6-7.

The manager next evaluates the total expected profit for each node in Period 1. Again, the expected profit EP(D =, p =, 1) at a node is the expected profit of all four nodes in Period 2 that may result from this node (see Figure 6-2), and P(D =, p =, 1) is the total expected profit from both Periods 1 and 2. The manager thus obtains the results in Table 6-8.

For Period 0, the expected profit EP(D = 100, p = 1.20, 0) over the four nodes in Period 1 is given by

EP1D = 100, p = 1.20, 02 = 0.25 * 3P1D = 120, p = 1.32, 12 + P1D = 120, p = 1.08, 12 + P1D = 80, p = 1.32, 12 + P1D = 80, p = 1.08, 124 = 0.25 * 335,782 + 45,382 – 4,582 – 4,5824 = $18,000

TABLE 6-6 Period 1 Calculations for Spot Market Option Node

EP(D 5, p 5, 1)

p(D 5, p 5, 1) 5 D 3 1.22 2 D 3 p 1 EP(D 5, p 5, 1)/(1 1 k)

D = 120, p = 1.32 -$12,000 -$22,909 D = 120, p = 1.08 $16,800 $32,073 D = 80, p = 1.32 -$8,000 -$15,273 D = 80, p = 1.08 $11,200 $21,382

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The present value of the expected profit in Period 0 is given by

PVEP1D = 100, p = 1.20, 02 = EP1D = 100, p = 1.20, 02 > 11 + k2 = 18,000 > 1.1 = $16,364

The total expected profit is obtained as the sum of the profit in Period 0 and the present value of the expected profit over all four nodes in Period 1. It is

P1D = 100, p = 1.20, 02 = (100,000 * 1.22) – (100,000 * 1) + PVEP(D = 100, p = 1.20, 0) = $22,000 + $16,364 = $38,364

PV1Lease2 = $38,364 when uncertainty is ignored ($60,182 from Example 6-1). This is because the lease is a fixed decision, and Trips Logistics is unable to react to market conditions by leasing less space if demand is lower. Rigid contracts are less attractive in the presence of uncertainty.

TABLE 6-7 Period 2 Profit Calculations at Trips Logistics for Fixed Lease Option Node

 

Leased Space

Warehouse Space at Spot Price (S)

Profit P(D 5, p 5, 2) 5 D : 1.22 2 (100,000 : 1 1 S 3 p)

D = 144, p = 1.45 100,000 sq. ft. 44,000 sq. ft. $11,880 D = 144, p = 1.19 100,000 sq. ft. 44,000 sq. ft. $23,320 D = 144, p = 0.97 100,000 sq. ft. 44,000 sq. ft. $33,000 D = 96, p = 1.45 100,000 sq. ft. 0 sq. ft. $17,120 D = 96, p = 1.19 100,000 sq. ft. 0 sq. ft. $17,120 D = 96, p = 0.97 100,000 sq. ft. 0 sq. ft. $17,120 D = 64, p = 1.45 100,000 sq. ft. 0 sq. ft. -$21,920 D = 64, p = 1.19 100,000 sq. ft. 0 sq. ft. -$21,920 D = 64, p = 0.97 100,000 sq. ft. 0 sq. ft. -$21,920

TABLE 6-8 Period 1 Profit Calculations at Trips Logistics for Fixed Lease Option Node

 

EP(D 5, p 5, 1)

Warehouse Space at Spot

Price (S)

P(D 5, p 5, 1) 5 D 3 1.22 2 (100,000 3 1 1 S 3 p) 1 EP(D 5, p 5, 1)(1 1 k)

D = 120, p = 1.32 0.25 : [P(D = 144, p = 1.45, 2) + P(D = 144, p = 1.19, 2) + P(D = 96, p = 1.45, 2) + P(D = 96, p = 1.19, 2)] = 0.25 : (11,880 + 23,320 + 17,120 +17,120) = $17,360

20,000 $35,782

D = 120, p = 1.08 0.25 : [23,320 + 33,000 + 17,120 + 17,120] = $22,640

20,000 $45,382

D = 80, p = 1.32 0.25 : [17,120 + 17,120 – 21,920 – 21,920] = -$2,400

0 -$4,582

D = 80, p = 1.08 0.25 : [17,120 + 17,120 -21,920 -21,920] = -$2,400

0 -$4,582

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Evaluating the Flexible Lease Option

The general manager at Trips Logistics has been offered a contract in which, for an upfront pay- ment of $10,000, Trips Logistics will have the flexibility of using between 60,000 square feet and 100,000 square feet of warehouse space at $1 per square foot per year. Trips Logistics must pay $60,000 per year for the first 60,000 square feet and can then use up to another 40,000 square feet on demand at $1 per square foot. The general manager decides to use decision trees to evalu- ate whether this flexible contract is preferable to a fixed contract for 100,000 square feet.

The underlying decision tree for evaluating the flexible contract is exactly as in Figure 6-2. The profit at each node, however, changes because of the flexibility in space used as shown in Table 6-9. If demand is larger than 100,000 units, Trips Logistics uses all 100,000 square feet of warehouse space at $1 and gets the rest at the spot price. If demand is between 60,000 and 100,000 units, Trips Logis- tics uses and pays $1 only for the exact amount of warehouse space required. The profit at all nodes where demand is 100,000 or higher remains the same as in Table 6-7. The profit in Period 2 at all nodes where demand is less than 100,000 units increases as shown in Table 6-9.

The general manager evaluates the expected profit EP(D =, p =, 1) from Period 2 and the total expected profit for each node in Period 1, as discussed earlier. The results are shown in Table 6-10.

The total expected profit in Period 0 is the sum of the profit in Period 0 and the present value of the expected profit in Period 1. The manager thus obtains

EP1D = 100, p = 1.20, 02 = 0.25 * 3P1D = 120, p = 1.32, 12 + P1D = 120, p = 1.08, 12 + P1D = 80, p = 1.32, 12 + P1D = 80, p = 1.08, 124 = 0.25 * 337,600 + 47,200 + 33,600 + 33,6004 = $38,000

PVEP1D = 100, p = 1.20, 12 = EP1D = 100, p = 1.20, 02 > 11 + k2 = 38,000 > 1.1 = $34,545

P1D = 100, p = 1.20, 02 = (100,000 * 1.22) – (100,000 * 1) + PVEP(D = 100, p = 1.20, 0) = $22,000 + $34,545 = $56,545

Key Point

Uncertainty in demand and economic factors should be included in the financial evaluation of supply chain design decisions. The inclusion of uncertainty typically decreases the value of rigidity and increases the value of flexibility.

TABLE 6-9 Period 2 Profit Calculations at Trips Logistics with Flexible Lease Contract Node

Warehouse Space at $1 (W)

Warehouse Space at Spot Price (S)

Profit P(D 5, p 5, 2) 5 D 3 1.22 2 (W 3 1 1 S 3 p)

D = 144, p = 1.45 100,000 sq. ft. 44,000 sq. ft. $11,880 D = 144, p = 1.19 100,000 sq. ft. 44,000 sq. ft. $23,320 D = 144, p = 0.97 100,000 sq. ft. 44,000 sq. ft. $33,000 D = 96, p = 1.45 96,000 sq. ft. 0 sq. ft. $21,120 D = 96, p = 1.19 96,000 sq. ft. 0 sq. ft. $21,120 D = 96, p = 0.97 96,000 sq. ft. 0 sq. ft. $21,120 D = 64, p = 1.45 64,000 sq. ft. 0 sq. ft. $14,080 D = 64, p = 1.19 64,000 sq. ft. 0 sq. ft. $14,080 D = 64, p = 0.97 64,000 sq. ft. 0 sq. ft. $14,080

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With an upfront payment of $10,000, the net expected profit is $46,545 under the flexible lease. Accounting for uncertainty, the manager at Trips Logistics values the three options as shown in Table 6-11.

The flexible contract is thus beneficial for Trips Logistics because it is $8,181 more valu- able than the rigid contract for three years.

6.6 TO ONSHORE OR TO OFFSHORE: EVALUATION OF GLOBAL SUPPLY CHAIN DESIGN DECISIONS UNDER UNCERTAINTY

In this section, we discuss a supply chain design decision at D-Solar, a German manufacturer of solar panels, to illustrate the power of the decision tree analysis methodology for designing global supply chain networks while accounting for uncertainty. D-Solar faces a plant location decision in a global network with fluctuating exchange rates and demand uncertainty.

TABLE 6-10 Period 1 Profit Calculations at Trips Logistics with Flexible Lease Contract Node

 

EP(D5, p 5, 1)

Warehouse

Space at $1 (W)

Warehouse Space at Spot Price (S)

P(D 5, p 5, 1) 5 D 3 1.22 2 (W 3 1 1 S 3 p)

1 EP(D 5, p 5, 1) (1 1 k)

D = 120, p = 1.32 0.25 : [11,880 + 23,320 + 21,120 + 21,120] = $19,360

100,000 20,000 $37,600

D = 120, p = 1.08 0.25 : [23,320 + 33,000 + 21,120 + 21,120] = $24,640

100,000 20,000 $47,200

D = 80, p = 1.32 0.25 : [21,120 + 21,120 + 14,080 + 14,080] = $17,600

80,000 0 $33,600

D = 80, p = 1.08 0.25 : [21,120 + 21,120 + 14,080 + 14,200] = $17,600

80,000 0 $33,600

TABLE 6-11 Comparison of Different Lease Options for Trips Logistics Option Value

All warehouse space from the spot market $5,471

Lease 100,000 sq. ft. for three years $38,364

Flexible lease to use between 60,000 and 100,000 sq. ft. $46,545

Key Point

Flexibility should be valued by taking into account uncertainty in demand and economic factors. In general, the value of flexibility increases with an increase in uncertainty.

D-Solar sells its products primarily in Europe. Demand in the Europe market is cur- rently 100,000 panels per year, and each panel sells for €70. Although panel demand is expected to grow, there are some downside risks if the economy slides. From one year to the next, demand may increase by 20 percent, with probability 0.8, or decrease by 20 percent, with probability 0.2.

D-Solar has to decide whether to build a plant in Europe or China. In either case, D-Solar plans to build a plant with a rated capacity of 120,000 panels. The fixed and variable costs of the

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two plants are shown in Table 6-12. Observe that the fixed costs are given per year rather than as a one-time investment. The European plant is more expensive but will also have greater volume flexibility. The plant will be able to increase or decrease production anywhere in the range of 60,000 to 150,000 panels while maintaining its variable cost. In contrast, the Chinese plant is cheaper (at the current exchange rate of 9 yuan/euro) but will have limited volume flexibility and can produce only between 100,000 and 130,000 panels. If the Chinese plant is built, D-Solar will have to incur variable cost for 100,000 panels even if demand drops below that level and will lose sales if demand increases above 130,000 panels. Exchange rates are volatile; each year, the yuan is expected to rise 10 percent, with a probability of 0.7, or drop 10 percent, with a probability of 0.3. We assume that the sourcing decision will be in place over the next three years and the dis- count rate used by D-Solar is k = 0.1. All costs and revenues are assumed to accrue at the begin- ning of the year, allowing us to consider the first year as period 0 and the following two years as periods 1 and 2.

Evaluating the Options Using Expected Demand and Exchange Rate

A simplistic approach often taken is to consider the expected movement of demand and exchange rates in future periods when evaluating discounted cash flows. The weakness of such an approach is that it averages the trends while ignoring the uncertainty. We start by considering such a sim- plistic approach for the onshoring and offshoring options. On average, demand is expected to increase by 12 percent [(20 : 0.8) – (20 : 0.2) = 12], whereas the yuan is expected to strengthen by 4 percent [(10 : 0.7) – (10 : 0.3) = 4] each year. The expected demand and exchange rates in the two future periods are shown in Table 6-13.

We now evaluate the discounted cash flows for both options assuming the average expected change in demand and exchange rates over the next two periods.

For the onshoring option, we have the following:

Period 0 profits = (100,000 * 70) – 1,000,000 – (100,000 * 40) = ;2,000,000 Period 1 profits = (112,000 * 70) – 1,000,000 – (112,000 * 40) = ;2,360,000 Period 2 profits = (125,440 * 70) – 1,000,000 – (125,440 * 40) = ;2,763,200

Thus, the DCF for the onshoring option is obtained as follows:

Expected profit from onshoring = 2,000,000 + 2,360,000 > 1.1 + 2,763,200 > 1.21 = ;6,429,091

TABLE 6-12 Fixed and Variable Production Costs for D-Solar European Plant Chinese Plant

Fixed Cost (euro) Variable Cost (euro) Fixed Cost (yuan) Variable Cost (yuan)

1 million/year 40/panel 8 million/year 340/panel

TABLE 6-13 Expected Future Demand and Exchange Rate Period 1 Period 2

Demand Exchange Rate Demand Exchange Rate

112,000 8.64 yuan/ euro 125,440 8.2944 yuan /euro

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For the offshoring option, we have the following:

Period 0 profits = (100,000 * 70) – (8,000,000 > 9) – (100,000 * 340 > 9) = ;2,333,333 Period 1 profits = (112,000 * 70) – (8,000,000 > 8.64) – (112,000 * 340 > 8.64) = ; 2,506,667

Period 2 profits = (125,440 * 70) – (8,000,000 >8.2944) – (125,440 * 340>8.2944) = ; 2,674,319 Thus, the DCF for the offshoring option is obtained as follows:

Expected profit from offshoring = 2,333,333 + 2,506,667 > 1.1 + 2,674,319 > 1.21 = ;6,822,302

Based on performing a simple DCF analysis and assuming the expected trend of demand and exchange rates over the next two periods, it seems that offshoring should be preferred to onshoring because it is expected to provide additional profits of almost €393,000.

The problem with the preceding analysis is that it has ignored uncertainty. For example, even though the demand is expected to grow, there is some probability that it will decrease. If demand drops below 100,000 panels, the offshore option could end up costing more because of the lack of flexibility. Similarly, if demand increases more than expected (for example, if it grows by 20 percent in each of the two years), the offshore facility will not be able to keep up with the increase. An accurate analysis must reflect the uncertainties and should ideally be performed using a decision tree.

Evaluating the Options Using Decision Trees

For this analysis we construct a decision tree, as shown in Figure 6-3. Each node in a given period leads to four possible nodes in the next period because demand and the exchange rate may go up or down. The detailed links and transition probabilities are shown in Figure 6-3. Demand is in thousands and is represented by D. The exchange rate is represented by E, where E is the number of yuan to a euro. For example, starting with the node D = 100, E = 9.00 in Period 0, one can transition to any of four nodes in Period 1. The transition to the node D = 120, E = 9.90 in Period 1 occurs if demand increases (probability of 0.8) and the yuan weakens (probability of 0.3). Thus, the transition from node D = 100, E = 9.00 in Period 0 to node D = 120, E = 9.90 in Period 1 occurs with probability 0.8 : 0.3 = 0.24. All other transition probabilities in Figure 6-3 are calculated in a similar manner. The main advantage of using a decision tree is that it allows for the true evaluation of profits in each scenario that D-Solar may find itself.

Evaluating the Onshore Option

Recall that the onshore option is flexible and can change production levels (and thus variable costs) to match demand levels between 60,000 and 150,000. In the following analysis, we cal- culate the expected profits at each node in the decision tree (represented by the corresponding values of D and E) starting in Period 2 and working back to the present (Period 0). With the onshore option, exchange rates do not affect profits in euro because both revenue and costs are in euro.

PERIOD 2 EVALUATION We provide a detailed analysis for the node D = 144 (solar panel demand of 144,000), E = 10.89 (exchange rate of 10.89 yuan per euro). Given its flexibility, the onshore facility is able to produce the entire demand of 144,000 panels at a variable cost of €40 and sell each panel for revenue of €70. Revenues and costs are evaluated as follows:

Revenue from the manufacture and sale of 144,000 panels

= 144,000 * 70 = ;10,080,000 Fixed + variable cost of onshore plant = 1,000,000 + 1144,000 * 402

= ;6,760,000

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In Period 2, the total profit for D-Solar at the node D = 144, E = 10.89 for the onshore option is thus given by

P1D = 144, E = 10.89, 22 = 10,080,000 – 6,760,000 = ;3,320,000 Using the same approach, we can evaluate the profit in each of the nine states (represented by the corresponding value of D and E) in Period 2, as shown in Table 6-14.

PERIOD 1 EVALUATION Period 1 contains four outcome nodes to be analyzed. A detailed anal- ysis for one of the nodes, D = 120, E = 9.90, is presented here. In addition to the revenue and cost at this node, we also need to consider the present value of the expected profit in Period 2 from the four nodes that may result. The transition probability into each of the four nodes is as shown in Figure 6-3. The expected profit in Period 2 for the four potential outcomes resulting from the node D = 120, E = 9.90 is thus given by

D = 100 E = 9.00

D = 120 E = 9.90

D = 120 E = 8.10

D = 80 E = 9.90

D = 80 E = 8.10

D = 144 E = 10.89

D = 144 E = 8.91

D = 96 E = 10.89

D = 96 E = 8.91

D = 144 E = 7.29

D = 96 E = 7.29

D = 64 E = 10.89

D = 64 E = 8.91

D = 64 E = 7.29

0.8 ×

0.3

0.8 × 0.3

0.2 × 0.3

0.2 × 0.3

0.8 ×

0.3 0.8 ×

0.7

0.2 × 0.7

0.8 × 0.

7

0.8 × 0.7

0.2 × 0.7

0.8 ×

0.3

0.2 × 0.3

0.2 × 0.7

0.2 × 0.7

0.8 × 0

.3

0.8 × 0.7

0.8 × 0.7

0.2 × 0.3

0.2 × 0.7

0.2 × 0.3

Period 0 Period 1 Period 2

FIGURE 6-3 Decision Tree for D-Solar

EP1D = 120, E = 9.90, 12 = 0.24 * P1D = 144, E = 10.89, 22 + 0.56 * P1D = 144, E = 8.91, 22 + 0.06 * P1D = 96, E = 10.89, 22 + 0.14 * P1D = 96, E = 8.91, 2)

= (0.24 * 3,320,000) + (0.56 * 3,320,000) + (0.06 * 1,880,000) + (0.14 * 1,880,000) = ;3,032,000

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The present value of the expected profit in Period 2 discounted to Period 1 is given by

PVEP1D = 120, E = 9.90, 12 = EP1D = 120, E = 9.90, 12 > 11 + k2 = 3, 032,000 > 1.1 = ;2, 756,364

D = 120, E = 9.90 from its opera- tions in Period 1, in which the onshore plant produces 120,000 panels at a variable cost of €40 and obtains revenue of €70 per panel. Revenues and costs are evaluated as follows:

Revenue from manufacture and sale of 120,000 panels

= 120,000 * 70 = ; 8,400,000 Fixed + variable cost of onshore plant = 1,000,000 + 1120,000 * 402

= ;5,800,000 The expected profit for D-Solar at the node D = 120, E = 9.90 is obtained by adding the opera- tional profits at this node in Period 1 and the discounted expected profits from the four nodes that may result in Period 2. The expected profit at this node in Period 1 is given by

P1D = 120, E = 9.90, 12 = 8,400,000 – 5,800,000 + PVEP1D = 120, E = 9.90, 12 = 2,600,000 + 2,756,364 = ; 5,356,364

The expected profits for all nodes in Period 1 are calculated similarly and shown in Table 6-15.

PERIOD 0 EVALUATION In Period 0, the demand and exchange rate are given by D = 100, E = 9. In addition to the revenue and cost at this node, we also need to consider the discounted expected profit from the four nodes in Period 1. The expected profit is given by

TABLE 6-14 Period 2 Profits for Onshore Option D

E

 

Sales

Production Cost

Quantity

Revenue

(euro)

 

Cost (euro)

Profit (euro)

144 10.89 144,000 144,000 10,080,000 6,760,000 3,320,000

144 8.91 144,000 144,000 10,080,000 6,760,000 3,320,000

96 10.89 96,000 96,000 6,720,000 4,840,000 1,880,000

96 8.91 96,000 96,000 6,720,000 4,840,000 1,880,000

144 7.29 144,000 144,000 10,080,000 6,760,000 3,320,000

96 7.29 96,000 96,000 6,720,000 4,840,000 1,880,000

64 10.89 64,000 64,000 4,480,000 3,560,000 920,000

64 8.91 64,000 64,000 4,480,000 3,560,000 920,000

64 7.29 64,000 64,000 4,480,000 3,560,000 920,000

EP1D = 100, E = 9.00, 02 = 0.24 * P1D = 120, E = 9.90, 12 + 0.56 * P1D = 120, E = 8.10, 12 + 0.06 * P1D = 80, E = 9.90, 12 + 0.14 * P1D = 80, E = 8.10, 12

= (0.24 * 5,356,364) + (0.56 * 5,356,364) + (0.06 * 2,934,545) + (0.14 * 2,934,545) = ;4,872,000

The present value of the expected profit in Period 1 discounted to Period 0 is given by

PVEP1D = 100, E = 9.00, 02 = EP1D = 100, E = 9.00, 02 > 11 + k2 = 4,872,000 > 1.1 = ;4,429,091

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and sale of 100,000 panels.

Revenue from manufacture and sale of 100,000 panels

= 100,000 * 70 = ;7,000,000 Fixed + variable cost of onshore plant = 1,000,000 + 1100,000 * 402

= ; 5,000,000 The expected profit for D-Solar at the node D = 100, E = 9.00 in Period 0 is given by

P1D = 100, E = 9.00, 02 = 7,000,000 – 5,000,000 + PVEP1D = 100, E = 9.00, 02 = 2,000,000 + 4,429,091 = ;6,429,091

Thus, building the onshore plant has an expected payoff of €6,429,091 over the evaluation period. This number accounts for uncertainties in demand and exchange rates and the ability of the onshore facility to react to these fluctuations.

Evaluating the Offshore Option

As with the onshore option, we start by evaluating profits at each node in Period 2 and then back our evaluation up to Periods 1 and 0. Recall that the offshore option is not fully flexible and can change production levels (and thus variable costs) only between 100,000 and 130,000 panels. Thus, if demand falls below 100,000 panels, D-Solar still incurs the variable production cost of 100,000 panels. If demand increases above 130,000 panels, the offshore facility can meet demand only up to 130,000 panels. At each node, given the demand, we calculate the expected profits accounting for the exchange rate that influences offshore costs evaluated in euro.

PERIOD 2 EVALUATION The detailed analysis for the node D = 144 (solar panel demand of 144,000), E = 10.89 (exchange rate of 10.89 yuan per euro) is as follows. Even though demand is for 144,000 panels, given its lack of volume flexibility, the offshore facility is able to produce only 130,000 panels at a variable cost of 340 yuan each and sell each panel for a revenue of €70. Revenues and costs are evaluated as follows:

Revenue from manufacture and sale of 130,000 panels

= 130,000 * 70 = ;9,100,000 Fixed + variable cost of offshore plant = 8,000,000 + 1130,000 * 3402

= 52,200,000 yuan

The total profit for D-Solar at the node D = 144, E = 10.89 for the offshore option (evaluated in euro), is thus given by

P1D = 144, E = 10.89, 22 = 9, 100, 000 – 152, 200, 00>10.892 = ;4,306,612

TABLE 6-15 Period 1 Profits for Onshore Option D

E

Sales

Production Cost Quantity

Revenue (euro)

Cost (euro)

Expected Profit (euro)

120 9.90 120,000 120,000 8,400,000 5,800,000 5,356,364

120 8.10 120,000 120,000 8,400,000 5,800,000 5,356,364

80 9.90 80,000 80,000 5,600,000 4,200,000 2,934,545

80 8.10 80,000 80,000 5,600,000 4,200,000 2,934,545

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Using the same approach, we can evaluate the profit in each of the nine states (represented by the corresponding values of D and E) in Period 2 as shown in Table 6-16. Observe that the lack of flexibility at the offshore facility hurts D-Solar whenever demand is above 130,000 (lost margin) or below 100,000 (higher costs). For example, when the demand drops to 64,000 panels, the offshore facility continues to incur variable production costs for 100,000 panels. Profits are also hurt when the yuan is stronger than expected.

PERIOD 1 EVALUATION In Period 1, there are four outcome nodes to be analyzed. As with the onshore option, a detailed analysis for the node D = 120, E = 9.90 is presented here. In addition to the revenue and cost from operations at this node, we also need to consider the present value of the expected profit in Period 2 from the four nodes that may result. The transition probability into each of the four nodes is as shown in Figure 6-3. The expected profit in Period 2 from the node D = 120, E = 9.90 is thus given by

TABLE 6-16 Period 2 Profits for Offshore Option D

E

 

Sales

Production Cost

Quantity

Revenue

(euro)

Cost

(yuan)

Profit (euro)

144 10.89 130,000 130,000 9,100,000 52,200,000 4,306,612

144 8.91 130,000 130,000 9,100,000 52,200,000 3,241,414

96 10.89 96,000 100,000 6,720,000 42,000,000 2,863,251

96 8.91 96,000 100,000 6,720,000 42,000,000 2,006,195

144 7.29 130,000 130,000 9,100,000 52,200,000 1,939,506

96 7.29 96,000 100,000 6,720,000 42,000,000 958,683

64 10.89 64,000 100,000 4,480,000 42,000,000 623,251

64 8.91 64,000 100,000 4,480,000 42,000,000 -233,805 64 7.29 64,000 100,000 4,480,000 3,560,000 -1,281,317

EP1D = 120, E = 9.90, 12 = 0.24 * P1D = 144, E = 10.89, 22 + 0.56 * P1D = 144, E = 8.91, 22 + 0.06 * P1D = 96, E = 10.89, 22 + 0.14 * P1D = 96, E = 8.91, 22

= (0.24 * 4,306,612) + (0.56 * 3,241,414) + (0.06 * 2,863,251) + (0.14 * 2,006,195) = ;3,301,441

The present value of the expected profit in Period 2 discounted to Period 1 is given by

PVEP1D = 120, E = 9.90, 12 = EP1D = 120, E = 9.90, 12 > 11 + k2 = 3,301, 441 > 1.1 = ; 3,001,310

D = 120, E = 9.90 from its opera- tions in Period 1. The offshore plant produces 120,000 panels at a variable cost of 340 yuan and obtains revenue of €70 per panel. Revenues and costs are evaluated as follows:

Revenue from manufacture and sale of 120,000 panels

= 120,000 * 70 = ; 8,400,000 Fixed + variable cost of onshore plant = 8,000,000 + 1120,000 * 3402

= 48,800,000 yuan

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The expected profit for D-Solar at the node D = 120, E = 9.90 in Period 1 is given by

P1D = 120, E = 9.90, 12 = 8,400,000 – 148,800,00 > 9.902 + PVEP1D = 120, E = 9.90, 12 = 3,470,707 + 3,001,310 = ; 6,472,017

For the offshore option, the expected profits for all nodes in Period 1 are shown in Table 6-17. Observe that for the node D = 80, E = 8.10, D-Solar has a lower expected profit from the

offshore option (Table 6-17) relative to the onshore option (see Table 6-15) because the offshore plant incurs high variable cost, given its lack of flexibility (cost is incurred for 100,000 units even though only 80,000 are sold), and all offshore costs become expensive, given the strong yuan.

PERIOD 0 EVALUATION In Period 0, the demand and exchange rate are given by D = 100, E = 9. In addition to the revenue and cost at this node, we also need to consider the present value of expected profit from the four nodes in Period 1. The expected profit for the offshore option is given by

TABLE 6-17 Period 1 Profits for Offshore Option D

E

 

Sales

Production Cost

Quantity

Revenue

(euro)

 

Cost (yuan)

Expected

Profit (euro)

120 9.90 120,000 120,000 8,400,000 48,800,000 6,472,017

120 8.10 120,000 120,000 8,400,000 48,800,000 4,301,354

80 9.90 80,000 100,000 5,600,000 42,000,000 3,007,859

80 8.10 80,000 100,000 5,600,000 42,000,000 1,164,757

EP1D = 100, E = 9.00, 02 = 0.24 * P1D = 120, E = 9.90, 12 + 0.56 * P1D = 120, E = 8.10, 12 + 0.06 * P1D = 80, E = 9.90, 12 + 0.14 * P1D = 80, E = 8.10, 12

= (0.24 * 6,472,017) + (0.56 * 4,301,354) + (0.06 * 3,007,859) + (0.14 * 1,164,757) = ; 4,305,580

The present value of the expected profit in Period 1 discounted to Period 0 is given by

PVEP1D = 100, E = 9.00, 02 = EP1D = 100, E = 9.00, 02 > 11 + k2 = 4,305,580 > 1.1 = ; 3,914,164

– ture and sale of 100,000 panels.

Revenue from manufacture and sale of 100,000 panels

= 100,000 * 70 = ; 7,000,000 Fixed + variable cost of offshore plant = 8,000,000 + 1100,000 * 3402

= 42,000,000 yuan

The expected profit for D-Solar at the node D = 100, E = 9.00 in Period 0 is given by

P1D = 100, E = 9.00, 02 = 7,000,000 – 142,000,00 > 9.002 + PVEP1D = 100, E = 9.00, 02 = 2,333,333 + 3,914,164 = ; 6,247,497

Thus, building the offshore plant has an expected payoff of €6,247,497 over the evaluation period.

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Observe that the use of a decision tree that accounts for both demand and exchange rate fluctuation shows that the onshore option, with its flexibility, is in fact more valuable (worth €6,429,091) than the offshore facility (worth €6,247,497), which is less flexible. This is in direct contrast to the decision that would have resulted if we had simply used the expected demand and exchange rate for each year. When using the expected demand and exchange rate, the onshore option provided expected profits of €6,429,091, whereas the offshore option provided expected profits of €6,822,302. The offshore option is overvalued in this case because the potential fluc- tuations in demand and exchange rates are wider than the expected fluctuations. Using the expected fluctuation thus does not fully account for the lack of flexibility in the offshore facility and the big increase in costs that may result if the yuan strengthens more than the expected value.

De Treville and Trigeorgis (2010) discuss the importance of evaluating all global supply chain design decisions using the decision tree or real options methodology. They give the exam- ple of Flexcell, a Swiss company that offered lightweight solar panels. In 2006, the company was looking to expand its operations by building a new plant. The three locations under discussion were China, eastern Germany, and near the company headquarters in Switzerland. Even though the Chinese and eastern German plants were cheaper than the Swiss plant, Flexcell management justified building the high-cost Swiss plant because of its higher flexibility and ability to react to changing market conditions. If only the expected values of future scenarios had been used, the more expensive Swiss plant could not be justified. This decision paid off for the company because the Swiss plant was flexible enough to handle the considerable variability in demand that resulted during the downturn in 2008.

When underlying decision trees are complex and explicit solutions for the underlying decision tree are difficult to obtain, firms should use simulation for evaluating decisions (see Chapter 13). In a complex decision tree, thousands or even millions of possible paths may arise from the first period to the last. Transition probabilities are used to generate probability- weighted random paths within the decision tree. For each path, the stage-by-stage decision and the present value of the payoff are evaluated. The paths are generated in such a way that the probability of a path being generated during the simulation is the same as the probability of the path in the decision tree. After generating many paths and evaluating the payoffs in each case, the payoffs obtained during the simulation are used as a representation of the payoffs that would result from the decision tree. The expected payoff is then found by averaging the payoffs obtained in the simulation.

6.7 MAKING GLOBAL SUPPLY CHAIN DESIGN DECISIONS UNDER UNCERTAINTY IN PRACTICE

Managers should consider the following ideas to help them make better network design deci- sions under uncertainty.

1. Combine strategic planning and financial planning during global network design. In most organizations, financial planning and strategic planning are performed independently. Strategic planning tries to prepare for future uncertainties but often without rigorous quan- titative analysis, whereas financial planning performs quantitative analysis but assumes a predictable or well-defined future. This chapter presents methodologies that allow integra- tion of financial and strategic planning. Decision makers should design global supply chain networks considering a portfolio of strategic options—the option to wait, build excess capacity, build flexible capacity, sign long-term contracts, purchase from the spot market, and so forth. The various options should be evaluated in the context of future uncertainty.

2. Use multiple metrics to evaluate global supply chain networks. Because one metric can give only part of the picture, it is beneficial to examine network design decisions using multiple metrics such as firm profits, supply chain profits, customer service levels, and response times. Good decisions perform well along most relevant metrics.

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3. Use financial analysis as an input to decision making, not as the decision-making process. Financial analysis is a great tool in the decision-making process, as it often pro- duces an answer and an abundance of quantitative data to back up that answer. However, financial methodologies alone do not provide a complete picture of the alternatives, and other nonquantifiable inputs should also be considered.

4. Use estimates along with sensitivity analysis. Many of the inputs into financial analysis are difficult, if not impossible, to obtain accurately. This can cause financial analysis to be a long, drawn-out process. One of the best ways to speed the process along and arrive at a good decision is to use estimates of inputs when it appears that finding an accurate input would take an inordinate amount of time. As we discuss in some of the other practice- oriented sections, using estimates is fine when the estimates are backed up by sensitivity analysis. By performing sensitivity analysis on the input’s range, managers can often show that no matter where the true input lies within the range, the decision remains the same. When this is not the case, they have highlighted a key variable to making the decision and it likely deserves more attention to arrive at a more accurate answer.

6.8 SUMMARY OF LEARNING OBJECTIVES

1. Identify factors that need to be included in total cost when making global sourcing decisions. Besides unit cost, total cost should include the impact of global sourcing on freight, inventories, lead time, quality, on-time delivery, minimum order quantity, working capital, and stockouts. Other factors to be considered include the impact on supply chain visibility, order communication, invoicing errors, and the need for currency hedging.

2. Define uncertainties that are particularly relevant when designing global supply chains. The performance of a global supply chain is affected by uncertainty in a number of input factors such as demand, price, exchange rates, and other economic factors. These uncertainties and any flexibility in the supply chain network must be taken into account when evaluating alternative designs of a supply chain.

3. Explain different strategies that may be used to mitigate risk in global supply chains. Operational strategies that help mitigate risk in global supply chains include car- rying excess capacity and inventory, flexible capacity, redundant suppliers, improved responsiveness, and aggregation of demand. Hedging fuel costs and currencies are finan- cial strategies that can help mitigate risk. It is important to keep in mind that no risk mitiga- tion strategy will always pay off. These mitigation strategies are designed to guard against certain extreme states of the world that may arise in an uncertain global environment.

4. Understand decision tree methodologies used to evaluate supply chain design decisions under uncertainty. When valuing the streams of cash flows resulting from the perfor- mance of a supply chain, decision trees are a basic approach to valuing alternatives under uncertainty. Uncertainty along different dimensions over the evaluation period is repre- sented as a tree with each node corresponding to a possible scenario. Starting at the last period of the evaluation interval, the decision tree analysis works back to Period 0, identi- fying the optimal decision and the expected cash flows at each step.

Discussion Questions 1. Why is it important to consider uncertainty when evaluating

supply chain design decisions? 2. What are the major sources of uncertainty that can affect the

value of supply chain decisions? 3. Describe the basic principle of DCFs and how they can be

used to compare different streams of cash flows.

4. Summarize the basic steps in the decision tree analysis meth- odology.

5. Discuss why using expected trends for the future can lead to different supply chain decisions relative to decision tree anal- ysis that accounts for uncertainty.

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6. What are the major financial uncertainties faced by an elec- tronic components manufacturer deciding whether to build a plant in Thailand or the United States?

7. What are some major nonfinancial uncertainties that a com- pany should consider when making decisions on where to source product?

Exercises 1. Moon Micro is a small manufacturer of servers that currently

builds its entire product in Santa Clara, California. As the market for servers has grown dramatically, the Santa Clara plant has reached capacity of 10,000 servers per year. Moon is considering two options to increase its capacity. The first option is to add 10,000 units of capacity to the Santa Clara plant at an annualized fixed cost of $10 million plus $500 labor per server. The second option is to have Molec- tron, an independent assembler, manufacture servers for Moon at a cost of $2,000 for each server (excluding raw materials cost). Raw materials cost $8,000 per server, and Moon sells each server for $15,000.

Moon must make this decision for a two-year time horizon. During each year, demand for Moon servers has an 80 percent chance of increasing 50 percent from the year before and a 20 percent chance of remaining the same as the year before. Molectron’s prices may change as well. They are fixed for the first year but have a 50 percent chance of increasing 20 percent in the second year and a 50 percent chance of remaining where they are.

Use a decision tree to determine whether Moon should add capacity to its Santa Clara plant or if it should outsource to Molectron. What are some other factors that we have not discussed that would affect this decision?

2. Unipart, a manufacturer of auto parts, is considering two B2B marketplaces to purchase its MRO supplies. Both mar- ketplaces offer a full line of supplies at very similar prices for products and shipping. Both provide similar service levels and lead times.

However, their fee structures are quite different. The first marketplace, Parts4u.com, sells all of its products with a 5 percent commission tacked on top of the price of the prod- uct (not including shipping). AllMRO.com’s pricing is based on a subscription fee of $10 million that must be paid up front for a two-year period and a commission of 1 percent on each transaction’s product price.

Unipart spends about $150 million on MRO supplies

year will likely be a strong year, in which high utilization will keep MRO spending at $150 million. However, there is a 25 percent chance that spending will drop by 10 percent. The second year, there is a 50 percent chance that the spending level will stay where it was in the first year and a 50 percent chance that it will drop by another 10 percent. Unipart uses a discount rate of 20 percent. Assume all costs are incurred at the beginning of each year (so Year 1 costs are incurred now and Year 2 costs are incurred in a year).

From which B2B marketplace should Unipart buy its parts?

3. Alphacap, a manufacturer of electronic components, is trying to select a single supplier for the raw materials that go into its main product, the doublecap. This is a new capacitor that is used by cellular phone manufacturers to protect microproces- sors from power spikes. Two companies can provide the nec- essary materials—MultiChem and Mixemat.

MultiChem has a solid reputation for its products and charges a higher price on account of its reliability of supply and delivery. MultiChem dedicates plant capacity to each cus- tomer, and therefore supply is ensured. This allows MultiChem to charge $1.20 for the raw materials used in each doublecap.

Mixemat is a small raw materials supplier that has lim- ited capacity but charges only $0.90 for a unit’s worth of raw materials. Its reliability of supply, however, is in question. Mixemat does not have enough capacity to supply all its cus- tomers all the time. This means that orders to Mixemat are not guaranteed. In a year of high demand for raw materials, Mixemat will have 90,000 units available for Alphacap. In low-demand years, all product will be delivered.

If Alphacap does not get raw materials from suppliers, it needs to buy them on the spot market to supply its custom- ers. Alphacap relies on one major cell phone manufacturer for the majority of its business. Failing to deliver could lead to losing this contract, essentially putting the firm at risk. Therefore, Alphacap will buy raw material on the spot mar- ket to make up for any shortfall. Spot prices for single-lot purchases (such as Alphacap would need) are $2.00 when raw materials demand is low and $4.00 when demand is high.

Demand in the raw materials market has a 75 percent chance of being high each of the next two years. Alphacap sold 100,000 doublecaps last year and expects to sell 110,000 this year. However, there is a 25 percent chance it will sell only

– ing 20 percent over this year and a 25 percent chance of falling 10 percent. Alphacap uses a discount rate of 20 percent. Assume all costs are incurred at the beginning of each year (Year 1 costs are incurred now and Year 2 costs are incurred in a year) and that Alphacap must make a decision with a two-year horizon. Only one supplier can be chosen, as these two suppliers refuse to supply someone who works with their competitor.

Which supplier should Alphacap choose? What other information would you like to have to make this decision?

4. Bell Computer is reaching a crossroads. This PC manufac- turer has been growing at a rapid rate, causing problems for its operations as it tries to keep up with the surging demand. Bell executives can plainly see that within the next half year, the systems used to coordinate its supply chain are going to fall apart because they will not be able to handle the volume of Bell projects they will have.

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To solve this problem, Bell has brought in two supply chain software companies that have made proposals on sys- tems that could cover the volume and the complexity of tasks Bell needs to have handled. These two software companies are offering different types of products, however.

The first company, SCSoftware, proposes a system for which Bell will purchase a license. This will allow Bell to use the software as long as it wants. However, Bell will be responsible for maintaining this software, which will require significant resources.

The second company, SC–ASP, proposes that Bell pay a subscription fee on a monthly basis for SC–ASP to host Bell’s supply chain applications on SC–ASP’s machines. Bell employees will access information and analysis via a web browser. Information will be fed automatically from the ASP servers to the Bell servers whenever necessary. Bell will continue to pay the monthly fee for the software, but all maintenance will be performed by SC–ASP.

How should Bell go about making a decision regard- ing which software company to choose? What specific pieces of information does Bell need to know (both about the soft- ware and about the future conditions Bell will experience) to make a decision? What are some of the qualitative issues Bell must think about when making this decision?

5. Reliable is a cell phone manufacturer serving the Asian and –

is 4 million. Over the next two years, demand in Asia is expected to go up either by 50 percent, with a probability of 0.7, or by 20 percent, with a probability of 0.3. Over the same

10 percent, with a probability of 0.5, or go down 10 percent, with a probability of 0.5. Reliable currently has a production facility in Asia with a capacity of 2.4 million units per year

per year. The variable production cost per phone in Asia is

It costs $3 to ship a phone between the two markets. Each phone sells for $40 in both markets.

Reliable is debating whether to add 2 million units or 1.5 million units of capacity to the Asia plant. The larger plant increase will cost $18 million, whereas the smaller addition will cost $15 million. Assume that Reli- able uses a discount factor of 10 percent. What do you recommend?

6. A European apparel manufacturer has production facilities in Italy and China to serve its European market, where annual demand is for 1.9 million units. Demand is expected to stay at the same level over the foreseeable future. Each facility has a capacity of 1 million units per year. With the current exchange rates, the production and distribution cost from Italy is 10 euro per unit, whereas the production and distribu- tion cost from China is 7 euro. Over each of the next three years, the Chinese currency could rise relative to the euro by 15 percent with a probability of 0.5 or drop by 5 percent with a probability of 0.5. An option being considered is to shut down 0.5 million units of capacity in Italy and move it to China at a one-time cost of 2 million euro. Assume a discount factor of 10 percent over the three years. Do you recommend this option?

7. A chemical manufacturer is setting up capacity in Europe

in each market is 2 million kilograms (kg) and is likely to stay at that level. The two choices under consideration are

building 2 million units of capacity in each of the two loca- tions. Building two plants will incur an additional one-time

America (for either a large or a small plant) is currently $10/kg, whereas the cost in Europe is 9 euro/kg. The cur- rent exchange rate is 1 euro for U.S. $1.33. Over each of the next three years, the dollar is expected to strengthen by 10 percent, with a probability of 0.5, or weaken by 5 per- cent, with a probability of 0.5. Assume a discount factor of 10 percent. What should the chemical manufacturer do? At what initial cost differential from building the two plants will the chemical manufacturer be indifferent between the two options?

Bibliography Real Options. Cambridge,

MA: Harvard Business School Press, 1999. Bovet, David. “The Supply Chain Manager as Global Econo-

mist.” Supply Chain Management Review (September 2008): 17–24.

Brealey, Richard A., and Stewart C. Myers. Principles of Corpo- rate Finance

Manufacturing Flexibility with Environmental Uncertainty: Evidence from High-Technology Component Manufacturers in Taiwan.” International Journal of Production Research (2002): 40(18), 4765–4780.

Chopra, Sunil, and ManMohan S. Sodhi. “Managing Risk to Avoid Supply Chain Breakdown.” Sloan Management Review (2004): 46(1), 53–61.

De Treville, Suzanne, and Lenos Trigeorgis. “It May Be Cheaper to Manufacture at Home.” Harvard Business Review (October 2010): 84–87.

Farrell, Diana. “Beyond Offshoring: Assess Your Company’s Global Potential.” Harvard Business Review (December 2004): 82–90.

Favre, Donavon, and John McCreery. “Coming to Grips with Rising Supplier Risk.” Supply Chain Management Review (September 2008): 26–32.

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Ferreira, John, and Len Prokopets. “Does Offshoring Still Make Sense?” Supply Chain Management Review (January/February 2009): 20–27.

Garber, Randy, and Suman Sarkar. “Want a More Flexible Supply Chain?” Supply Chain Management Review (January/February 2007): 28–34.

Rethink Offshoring?” McKinsey on Business Technology 14 (Winter 2008): 32–35.

Harding, Mary Lu. “Gauging Total Cost, Supplier by Supplier.” CSCMP’s Supply Chain Quarterly (Q4 2007): 64–68.

Hoberg, Kai, and Knut Alicke. “Lessons for Supply Chains from the Financial Crisis.” Supply Chain Management Review (September/October 2013): 48–55.

Horngren, Charles T., George Foster, and Srikant M. Datar. Cost Accounting

Jordan, W. C., and S. C. Graves. “Principles on the Benefits of Manufacturing Process Flexibility.” Management Science 41 (April 1995): 577–594.

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University, Evanston, IL, 2008.

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pher W. Zobel, and John R. Macdonald. “Understanding Sup- ply Chain Resilience.” Supply Chain Management Review (January/February 2014): 34–41.

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Smith, Adam. An Inquiry into the Nature and Causes of the Wealth of Nations, 5th ed. London: Methuen & Co., Ltd., 1904.

Swaminathan, Jayashankar M., and Brian Tomlin. “How to Avoid the Six Risk Management Pitfalls.” Supply Chain Manage- ment Review (July/August 2007): 34–42.

Tanowitz, Marc, and David Rutchik. “Squeezing Opportunity Out of Higher Fuel Costs.” Supply Chain Management Review (October 2008): 34–40.

Trigeorgis, Lenos. Real Options. Cambridge, MA: MIT Press, 1996.

CASE STUDY BioPharma, Inc.1

In 2013, Phillip (Phil) Landgraf faced several glaring problems in the financial performance of his company, BioPharma, Inc. The firm had experienced a steep decline in profits and high costs at its plants in Germany and Japan. Landgraf, the company’s president for world- wide operations, knew that demand for the company’s products was stable across the globe. As a result, the sur- plus capacity in his global production network looked like a luxury he could no longer afford.

Any improvement in financial performance was dependent on having the most efficient network in place, because revenues were unlikely to grow. Cutting costs was thus a top priority for the coming year. To help design a more cost-effective network, Landgraf assigned a task force to recommend a course of action.

Background

BioPharma, Inc. is a global manufacturer of bulk chemi- cals used in the pharmaceutical industry. The company holds patents on two chemicals that are called Highcal and Relax internally. These bulk chemicals are used by

the company’s pharmaceutical division and are also sold to other drug manufacturers. There are distinctions in the precise chemical specifications to be met in different parts of the world. All plants, however, are currently set up to be able to produce both chemicals for any part of the world.

For 2013, sales of each product by region and the production and capacity at each plant are shown in Table 6-18. The plant capacity, measured in millions of kilograms of production, can be assigned to either chem- ical, as long as the plant is capable of producing both. BioPharma has forecast that its sales for the two chemi- cals are likely to be stable for all parts of the world, except for Asia without Japan, where sales are expected to grow by 10 percent annually for each of the next five years before stabilizing.

The Japanese plant is a technology leader within the BioPharma network in terms of its ability to handle regulatory and environmental issues. Some develop- ments in the Japanese plant had been transferred to other plants in the network. The German plant is a leader in terms of its production ability. The plant has routinely

1This case was inspired by Applichem (A), Harvard Business School Case 9-685-051, 1985.

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had the highest yields within the global network. The Brazilian, Indian, and Mexican plants have somewhat outdated technology and are in need of an update.

Current Plant Costs at BioPharma

After considerable debate, the task force identified the cost structure at each plant in 2013 as shown in Table 6-19. Each plant incurs an annual fixed cost that is independent of the level of production in the plant. The fixed cost includes depreciation, utilities, and the salaries and fringe benefits of employees involved in general management, scheduling, expediting, account- ing, maintenance, and so forth. Each plant that is capa- ble of producing either Highcal or Relax also incurs a product-related fixed cost that is independent of the quantity of each chemical produced. The product- related fixed cost includes depreciation of equipment specific and other fixed costs that are specific to a chemical. If a plant maintains the capability to produce a particular chemical, it incurs the corresponding

product-related fixed cost even if the chemical is not produced at the plant.

The variable production cost of each chemical consists of two components: raw materials and produc- tion costs. The variable production cost is incurred in proportion to the quantity of chemical produced and includes direct labor and scrap. The plants themselves can handle varying levels of production. In fact, they can also be idled for the year, in which case they incur only the fixed cost and none of the variable cost.

BioPharma transports the chemicals in specialized containers by sea and in specialized trucks on land. The transportation costs between plants and markets are as shown in Table 6-20. Historical exchange rates are shown in Table 6-21 and the regional import duties in Table 6-22. Given regional trade alliances, import duties in reality vary based on the origin of the chemical. For simplicity’s sake, however, the task force has assumed that the duties are driven only by the destination. Local production within each region is assumed to result in no

TABLE 6-18 Sales by Region and Production/Capacity by Plant of Highcal and Relax (in Millions of Kilograms) Highcal Relax

Region

Plant

Capacity

2013 Sales

2013 Production

2013 Sales

2013 Production

Latin America Brazil 18.0 7.0 11.0 7.0 7.0

Europe Germany 45.0 15.0 15.0 12.0 0.0

Asia w/o Japan India 18.0 5.0 10.0 3.0 8.0

Japan Japan 10.0 7.0 2.0 8.0 0.0

Mexico Mexico 30.0 3.0 12.0 3.0 18.0

U.S. U.S. 22.0 18.0 5.0 17.0 17.0

TABLE 6-19 Fixed and Variable Production Costs at Each BioPharma Plant in 2013 (U.S.$) Highcal Relax

Plant

Plant Fixed Cost (million $)

Highcal Fixed Cost (million $)

Relax Fixed Cost (million $)

Raw Material

($/kg)

Production cost

($/kg)

Raw Material

($/kg)

Production cost

($/kg)

Brazil 20.0 5.0 5.0 3.6 5.1 4.6 6.6

Germany 45.0 13.0 13.0 3.9 6.0 5.0 7.0

India 14.0 3.0 3.0 3.6 4.5 4.5 6.0

Japan 13.0 4.0 4.0 3.9 6.0 5.1 7.0

Mexico 30.0 6.0 6.0 3.6 5.0 4.6 6.5

U.S. 23.0 5.0 5.0 3.6 5.0 4.5 6.5

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import duty. Thus, production from Brazil, Germany, and India can be sent to Latin America, Europe, and the rest of Asia excluding Japan, respectively, without incur- ring any import duties. Duties apply only to the raw material, production, and transportation cost component and not to the fixed cost component. Thus, a product entering Latin America with a raw material, production, and transportation cost of $10 incurs import duties of $3.

Network Options Under Consideration

The task force is considering a variety of options for its analysis. One option is to keep the global network with its current structure and capabilities. Other options include shutting down some plants or limiting the capa- bility of some plants to producing only one chemical. Closing down a plant eliminates all variable costs and saves 80 percent of the annual fixed costs (the remaining

20 percent accounts for costs that are incurred related to the plant shutdown). Similarly, if a plant is limited to producing only one chemical, the plant saves 80 percent of the fixed cost associated with the chemical that is no longer produced. The two options being seriously con- sidered are shutting the Japanese plant and limiting the German plant to a single chemical.

Study Questions

1. How should BioPharma have used its production network in 2013? Should any of the plants have been idled? What is the annual cost of your proposal, including import duties?

2. How should Landgraf structure his global production net- work? Assume that the past is a reasonable indicator of the future in terms of exchange rates.

3. Is there any plant for which it may be worth adding a mil- lion kilograms of additional capacity at a fixed cost of $3 million per year?

TABLE 6-20 Transportation Costs from Plants to Markets (U.S.$/kg) From/To Latin America Europe Asia w/o Japan Japan Mexico U.S.

Brazil 0.20 0.45 0.50 0.50 0.40 0.45

Germany 0.45 0.20 0.35 0.40 0.30 0.30

India 0.50 0.35 0.20 0.30 0.50 0.45

Japan 0.50 0.40 0.30 0.10 0.45 0.45

Mexico 0.40 0.30 0.50 0.45 0.20 0.25

U.S. 0.45 0.30 0.45 0.45 0.25 0.20

TABLE 6-21 History of Exchange Rates in Currency/U.S.$1 (at the Beginning of Each Year) Brazilian Real Euro Indian Rupee Japanese Yen Mexican Peso U.S. Dollar

2013 2.15 0.75 58.44 97.58 12.75 1.00

2012 1.95 0.78 53.46 79.79 13.15 1.00

2011 1.67 0.72 46.85 79.70 12.42 1.00

2010 1.75 0.75 45.72 87.78 12.63 1.00

2009 1.99 0.72 48.42 93.58 13.48 1.00

2008 1.83 0.68 43.62 103.42 11.13 1.00

2007 1.94 0.73 41.34 117.77 10.92 1.00

2006 2.17 0.80 45.18 116.29 10.89 1.00

TABLE 6-22 Import Tariffs (Percentage of Value of Product Imported, Including Transportation) Latin America Europe Asia w/o Japan Japan Mexico U.S.

30% 3% 27% 6% 35% 4%

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4. How are your recommendations affected by the reduction of duties?

5. The analysis has assumed that each plant has a 100 percent yield (percentage output of acceptable quality). How would

you modify your analysis to account for yield differences across plants?

6. What other factors should be accounted for when making your recommendations?

CASE STUDY The Sourcing Decision at Forever Young

Forever Young is a retailer of trendy and low-cost apparel in the United States. The company divides the year into four sales seasons of about three months each and brings in new merchandise for each season. The company has historically outsourced production to China, given the lower costs there. Sourcing from the Chinese supplier costs 55 yuan/unit (inclusive of all delivery costs), which at the current exchange rate of 6.5 yuan/$ gives a vari- able cost of under $8.50/unit. The Chinese supplier, however, has a long lead time, forcing Forever Young to pick an order size well before the start of the season. This does not leave the company any flexibility if actual demand differs from the order size.

A local supplier has come to management with a proposal to supply product at a cost of $10/unit but to do so quickly enough that Forever Young will be able to make supply in the season exactly match demand. Man- agement is concerned about the higher variable cost but finds the flexibility of the onshore supplier very attrac- tive. The challenge is to value the responsiveness pro- vided by the local supplier.

Uncertainties Faced by Forever Young

To better compare the two suppliers, management iden- tifies demand and exchange rates as the two major uncertainties faced by the company. Over each of the next two periods (assume them to be a year each), demand may go up by 10 percent, with a probability of 0.5, or down by 10 percent, with a probability of 0.5. Demand in the current period was 1,000 units. Similarly, over each of the next two periods, the yuan may strengthen by 5 percent, with a probability of 0.5, or weaken by 5 percent, with a probability of 0.5. The exchange rate in the current period was 6.5 yuan/$.

Ordering Policies with the Two Suppliers

Given the long lead time of the offshore supplier, For- ever Young commits to an order before observing any

demand signal. Given the demand uncertainty over the next two periods and the fact that the margin from each unit (about $11.50) is higher than the loss if the unit remains unsold at the end of the season (loss of about $8.50), management decides to commit to an order that is somewhat higher than expected demand. Given that expected demand is 1,000 units over each of the next two periods, management decides to order 1,040 units from the Chinese supplier for each of the next two peri- ods. If demand in a period turns out to be higher than 1,040 units, Forever Young will sell 1,040 units. How- ever, if demand turns out to be lower than 1,040, the company will have leftover product for which it will not be able to recover any revenue.

The short lead time of the local supplier allows Forever Young to keep bringing product in a little bit at a time, based on actual sales. Thus, if the local supplier is used, the company is able to meet all demand in each period without having any unsold inventory or lost sales. In other words, the final order from the local supplier will exactly equal the demand observed by Forever Young.

A Potential Hybrid Strategy

The local supplier has also offered another proposal that would allow Forever Young to use both suppliers, each playing a different role. The Chinese supplier would produce a base quantity for the season and the local sup- plier would cover any shortfalls that result. The short lead time of the local supplier would ensure that no sales are lost. In other words, if Forever Young committed to a base load of 900 units with the Chinese supplier in a given period and demand was 900 units or less, nothing would be ordered from the local supplier. If demand, however, was larger than 900 units (say, 1,100), the shortfall of 200 units would be supplied by the local sup- plier. Under a hybrid strategy, the local supplier would end up supplying only a small fraction of the season’s

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demand. For this extra flexibility and reduced volumes, however, the local supplier proposes to charge $11/unit if it is used as part of a hybrid strategy.

Study Questions

1. Draw a decision tree reflecting the uncertainty over the next two periods. Identify each node in terms of demand and exchange rate and the transition probabilities.

2. If management at Forever Young is to pick only one of the two suppliers, which one would you recommend? What is

each of the two choices? Assume a discount factor of k = 0.1 per period.

3. What do you think about the hybrid approach? Is it worth paying the local supplier extra to use it as part of a hybrid strategy? For the hybrid approach, assume that manage- ment will order a base load of 900 units from the Chinese supplier for each of the two periods, making up any short-

of expected profits for the hybrid option assuming a dis- count factor of k = 0.1 per period.

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All supply chain decisions made before demand has materialized are made to a forecast. In this chapter, we explain how historical demand information can be used to forecast future demand and how these forecasts affect the supply chain. We describe several methods to forecast demand and estimate a forecast’s accuracy. We then discuss how these meth- ods can be implemented using Microsoft Excel.

7.1 THE ROLE OF FORECASTING IN A SUPPLY CHAIN

Demand forecasts form the basis of all supply chain planning. Consider the push/pull view of the supply chain discussed in Chapter 1. All push processes in the supply chain are performed in anticipation of customer demand, whereas all pull processes are performed in response to cus- tomer demand. For push processes, a manager must plan the level of activity, be it production, transportation, or any other planned activity. For pull processes, a manager must plan the level of available capacity and inventory, but not the actual amount to be executed. In both instances, the first step a manager must take is to forecast what customer demand will be.

A Home Depot store selling paint orders the base paint and dyes in anticipation of customer orders, whereas it performs final mixing of the paint in response to customer orders. Home Depot uses a forecast of future demand to determine the quantity of paint and dye to have on hand (a push process). Farther up the supply chain, the paint factory that produces the base also needs

Demand Forecasting in a Supply Chain

 

C H A P T E R

7 7

LEARNING OBJECTIVES After reading this chapter, you will be able to

177

1. Understand the role of forecasting for both an enterprise and a supply chain.

2. Identify the components of a demand forecast.

3. Forecast demand in a supply chain given historical demand data using time-series methodologies.

4. Analyze demand forecasts to estimate forecast error.

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forecasts to determine its own production and inventory levels. The paint factory’s suppliers also need forecasts for the same reason. When each stage in the supply chain makes its own separate forecast, these forecasts are often very different. The result is a mismatch between supply and demand. When all stages of a supply chain work together to produce a collaborative forecast, however, the forecast tends to be much more accurate. The resulting forecast accuracy enables supply chains to be both more responsive and more efficient in serving their customers. Leaders in many supply chains, from electronics manufacturers to packaged-goods retailers, have improved their ability to match supply and demand by moving toward collaborative forecasting.

Consider the value of collaborative forecasting for Coca-Cola and its bottlers. Coca-Cola decides on the timing of promotions based on the demand forecast over the coming quarter. Pro- motion decisions are then incorporated into an updated demand forecast. The updated forecast is essential for the bottlers to plan their capacity and production decisions. A bottler operating without an updated forecast based on the promotion is unlikely to have sufficient supply avail- able for Coca-Cola, thus hurting supply chain profits.

Mature products with stable demand, such as milk or paper towels, are usually easiest to forecast. Forecasting and the accompanying managerial decisions are extremely difficult when either the supply of raw materials or the demand for the finished product is highly unpredictable. Fashion goods and many high-tech products are examples of items that are difficult to forecast. In both instances, an estimate of forecast error is essential when designing the supply chain and planning its response.

Before we begin an in-depth discussion of the components of forecasts and forecasting methods in the supply chain, we briefly list characteristics of forecasts that a manager must understand to design and manage his or her supply chain effectively.

7.2 CHARACTERISTICS OF FORECASTS

Companies and supply chain managers should be aware of the following characteristics of forecasts.

1. Forecasts are always inaccurate and should thus include both the expected value of the forecast and a measure of forecast error. To understand the importance of forecast error, consider two car dealers. One of them expects sales to range between 100 and 1,900 units, whereas the other expects sales to range between 900 and 1,100 units. Even though both dealers anticipate average sales of 1,000, the sourcing policies for each dealer should be very different, given the difference in forecast accuracy. Thus, the forecast error (or demand uncertainty) is a key input into most supply chain decisions. Unfortunately, most firms do not maintain any estimates of forecast error.

2. Long-term forecasts are usually less accurate than short-term forecasts; that is, long- term forecasts have a larger standard deviation of error relative to the mean than short-term fore-

company has instituted a replenishment process that enables it to respond to an order within hours. For example, if a store manager places an order by 10 a.m., the order is delivered by 7 p.m. the same day. Therefore, the manager has to forecast what will sell that night only less than 12 hours before the actual sale. The short lead time allows a manager to take into account current information that could affect product sales, such as the weather. This forecast is likely to be more accurate than if the store manager had to forecast demand a week in advance.

3. Aggregate forecasts are usually more accurate than disaggregate forecasts, as they tend to have a smaller standard deviation of error relative to the mean. For example, it is easy to forecast

error. However, it is much more difficult to forecast yearly revenue for a company with less than a 2 percent error, and it is even harder to forecast revenue for a given product with the same degree of accuracy. The key difference among the three forecasts is the degree of aggregation. The GDP is an aggregation across many companies, and the earnings of a company are an aggregation across sev- eral product lines. The greater the aggregation, the more accurate the forecast.

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4. In general, the farther up the supply chain a company is (or the farther it is from the consumer), the greater the distortion of information it receives. One classic example of this phe- nomenon is the bullwhip effect (see Chapter 10), in which order variation is amplified as orders move farther from the end customer. Collaborative forecasting based on sales to the end cus- tomer helps upstream enterprises reduce forecast error.

In the next section, we discuss the basic components of a forecast, explain the four classi- fications into which forecasting methods fall, and introduce the notion of forecast error.

7.3 COMPONENTS OF A FORECAST AND FORECASTING METHODS

Yogi Berra, the former New York Yankees catcher who is famous for his malapropisms, once said, “It’s tough to make predictions, especially about the future.” One may be tempted to treat demand forecasting as magic or art and leave everything to chance. What a firm knows about its customers’ past behavior, however, sheds light on their future behavior. Demand does not arise in a vacuum. Rather, customer demand is influenced by a variety of factors and can be predicted, at least with some probability, if a company can determine the relationship between these factors and future demand. To forecast demand, companies must first identify the factors that influence future demand and then ascertain the relationship between these factors and future demand.

Companies must balance objective and subjective factors when forecasting demand. Although we focus on quantitative forecasting methods in this chapter, companies must include

system that makes a demand forecast and provides a recommended order. The store manager, however, is responsible for making the final decision and placing the order, because he or she may have access to information about market conditions that are not available in historical demand data. This knowledge of market conditions is likely to improve the forecast. For exam- ple, if the store manager knows that the weather is likely to be rainy and cold the next day, he or she can reduce the size of an ice cream order to be placed with an upstream supplier, even if demand was high during the previous few days when the weather was hot. In this instance, a change in market conditions (the weather) would not have been predicted using historical demand data. A supply chain can experience substantial payoffs from improving its demand forecasting through qualitative human inputs.

A company must be knowledgeable about numerous factors that are related to the demand forecast, including the following:

A company must understand such factors before it can select an appropriate forecasting methodology. For example, historically a firm may have experienced low demand for chicken

Forecasting methods are classified according to the following four types:

1. Qualitative: Qualitative forecasting methods are primarily subjective and rely on human judgment. They are most appropriate when little historical data are available or when

– sary to forecast demand several years into the future in a new industry.

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2. Time series: Time-series forecasting methods use historical demand to make a fore- cast. They are based on the assumption that past demand history is a good indicator of future demand. These methods are most appropriate when the basic demand pattern does not vary sig- nificantly from one year to the next. These are the simplest methods to implement and can serve as a good starting point for a demand forecast.

3. Causal: Causal forecasting methods assume that the demand forecast is highly cor- related with certain factors in the environment (the state of the economy, interest rates, etc.). Causal forecasting methods find this correlation between demand and environmental factors and use estimates of what environmental factors will be to forecast future demand. For example, product pricing is strongly correlated with demand. Companies can thus use causal methods to determine the impact of price promotions on demand.

4. Simulation: rise to demand to arrive at a forecast. Using simulation, a firm can combine time-series and causal methods to answer such questions as: What will be the impact of a price promotion? What will be the impact of a competitor opening a store nearby? Airlines simulate customer buying behavior to forecast demand for higher-fare seats when no seats are available at lower fares.

A company may find it difficult to decide which method is most appropriate for forecast- ing. In fact, several studies have indicated that using multiple forecasting methods to create a combined forecast is more effective than using any one method alone.

In this chapter, we deal primarily with time-series methods, which are most appropriate when future demand is related to historical demand, growth patterns, and any seasonal patterns. With any forecasting method, there is always a random element that cannot be explained by his- torical demand patterns. Therefore, any observed demand can be broken down into a systematic and a random component:

Observed demand 1O2 = systematic component 1S2 + random component 1R2 The systematic component measures the expected value of demand and consists of

what we will call level, the current deseasonalized demand; trend, the rate of growth or decline in demand for the next period; and seasonality, the predictable seasonal fluctuations in demand.

The random component is the part of the forecast that deviates from the systematic part. A company cannot (and should not) forecast the direction of the random component. All a com- pany can predict is the random component’s size and variability, which provides a measure of forecast error. The objective of forecasting is to filter out the random component (noise) and estimate the systematic component. The forecast error measures the difference between the fore- cast and actual demand. On average, a good forecasting method has an error whose size is com- parable to the random component of demand. A manager should be skeptical of a forecasting method that claims to have no forecasting error on historical demand. In this case, the method has merged the historical random component with the systematic component. As a result, the forecasting method will likely perform poorly.

7.4 BASIC APPROACH TO DEMAND FORECASTING

The following five points are important for an organization to forecast effectively:

1. Understand the objective of forecasting. 2. Integrate demand planning and forecasting throughout the supply chain. 3. Identify the major factors that influence the demand forecast. 4. Forecast at the appropriate level of aggregation. 5. Establish performance and error measures for the forecast.

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Understand the Objective of Forecasting

Every forecast supports decisions that are based on it, so an important first step is to identify these decisions clearly. Examples of such decisions include how much of a particular product to make, how much to inventory, and how much to order. All parties affected by a supply chain decision should be aware of the link between the decision and the forecast. For example,

– facturer, the transporter, and others involved in filling demand, as they all must make decisions that are affected by the forecast of demand. All parties should come up with a common forecast for the promotion and a shared plan of action based on the forecast. Failure to make these deci- sions jointly may result in either too much or too little product in various stages of the supply chain.

Integrate Demand Planning and Forecasting Throughout the Supply Chain

A company should link its forecast to all planning activities throughout the supply chain. These include capacity planning, production planning, promotion planning, and purchasing, among others. In one unfortunately common scenario, a retailer develops forecasts based on promo- tional activities, whereas a manufacturer, unaware of these promotions, develops a different fore- cast for its production planning based on historical orders. This leads to a mismatch between supply and demand, resulting in poor customer service. To accomplish integration, it is a good idea for a firm to have a cross-functional team, with members from each affected function responsible for forecasting demand—and an even better idea is to have members of different companies in the supply chain working together to create a forecast.

Identify Major Factors That Influence the Demand Forecast

Next, a firm must identify demand, supply, and product-related phenomena that influence the demand forecast. On the demand side, a company must ascertain whether demand is growing or declining or has a seasonal pattern. These estimates must be based on demand, not on sales data.

demand for this cereal was high, whereas the demand for other, comparable cereal brands was

demand forecast, the supermarket must understand what the demand would have been in the absence of promotion activity and how demand is affected by promotions and competitor actions. A combination of these pieces of information will allow the supermarket to forecast demand for

On the supply side, a company must consider the available supply sources to decide on the accuracy of the forecast desired. If alternate supply sources with short lead times are available, a highly accurate forecast may not be especially important. However, if only a single supplier with a long lead time is available, an accurate forecast will have great value.

On the product side, a firm must know the number of variants of a product being sold and whether these variants substitute for or complement one another. If demand for a product influences or is influenced by demand for another product, the two forecasts are best made jointly. For example, when a firm introduces an improved version of an existing product, it is likely that the demand for the existing product will decline because customers will buy the improved version. Although the decline in demand for the original product is not indicated by historical data, the historical demand is still useful in that it allows the firm to estimate the combined total demand for the two versions. Clearly, demand for the two products should be forecast jointly.

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Forecast at the Appropriate Level of Aggregation

Given that aggregate forecasts are more accurate than disaggregate forecasts, it is important to forecast at a level of aggregation that is appropriate, given the supply chain decision that is driven by the forecast. Consider a buyer at a retail chain who is forecasting to select an order size for shirts. One approach is to ask each store manager the precise number of shirts needed and add up all the requests to get an order size with the supplier. The advantage of this approach is that it uses local market intelligence that each store manager has. The problem with this approach is that it makes store managers forecast well before demand arises at a time when their forecasts are unlikely to be accurate. A better approach may be to forecast demand at the aggregate level when ordering with the supplier and ask each store manager to forecast only when the shirts are to be allocated across the stores. In this case, the long lead time forecast (supplier order) is aggregate, thus lowering error. The disaggregate store-level forecast is made close to the sales season, when local market intelligence is likely to be most effective.

Establish Performance and Error Measures for the Forecast

Companies should establish clear performance measures to evaluate the accuracy and timeliness of the forecast. These measures should be linked to the objectives of the business decisions based on these forecasts. Consider a mail-order company that uses a forecast to place orders with its suppli- ers, which take two months to send in the orders. The mail-order company must ensure that the forecast is created at least two months before the start of the sales season because of the two-month lead time for replenishment. At the end of the sales season, the company must compare actual demand to forecasted demand to estimate the accuracy of the forecast. Then plans for decreasing future forecast errors or responding to the observed forecast errors can be put into place.

In the next section, we discuss techniques for static and adaptive time-series forecasting.

7.5 TIME-SERIES FORECASTING METHODS

The goal of any forecasting method is to predict the systematic component of demand and esti- mate the random component. In its most general form, the systematic component of demand data contains a level, a trend, and a seasonal factor. The equation for calculating the systematic com- ponent may take a variety of forms:

Multiplicative: ystematic component = level * trend * seasonal factor Additive: ystematic component = level + trend + seasonal factor Mixed: ystematic component = 1level + trend2 * seasonal factor The specific form of the systematic component applicable to a given forecast depends on

the nature of demand. Companies may develop both static and adaptive forecasting methods for each form. We now describe these static and adaptive forecasting methods.

Static Methods

A static method assumes that the estimates of level, trend, and seasonality within the systematic component do not vary as new demand is observed. In this case, we estimate each of these parameters based on historical data and then use the same values for all future forecasts. In this section, we discuss a static forecasting method for use when demand has a trend as well as a seasonal component. We assume that the systematic component of demand is mixed; that is,

ystematic component = 1level + trend2 * seasonal factor A similar approach can be applied for other forms as well. We begin with a few basic

definitions:

L = estimate of level at t = 0 (the deseasonalized demand estimate during Period t = 0) T = estimate of trend (increase or decrease in demand per period)

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St = estimate of seasonal factor for Period t Dt = actual demand observed in Period t Ft = forecast of demand for Period t

In a static forecasting method, the forecast in Period t for demand in Period t + l is a prod- uct of the level in Period t + l and the seasonal factor for Period t + l. The level in Period t + l is the sum of the level in Period 0 (L) and (t + l) times the trend T. The forecast in Period t for demand in Period t + l is thus given as

Ft + l = 3L + 1t + l2T4St + l (7.1) We now describe one method for estimating the three parameters L, T, and S. As an example,

consider the demand for rock salt used primarily to melt snow. This salt is produced by a firm called

sample of its retailers, but the company has noticed that these retailers always overestimate their purchases, leaving Tahoe (and even some retailers) stuck with excess inventory. After meeting with

retailers to create a more accurate forecast based on the actual retail sales of their salt. Quarterly retail demand data for the past three years are shown in Table 7-1 and charted in Figure 7-1.

TABLE 7-1 Quarterly Demand for Tahoe Salt Year Quarter Period, t Demand, Dt

1 2 1 8,000

1 3 2 13,000

1 4 3 23,000

2 1 4 34,000

2 2 5 10,000

2 3 6 18,000

2 4 7 23,000

3 1 8 38,000

3 2 9 12,000

3 3 10 13,000

3 4 11 32,000

4 1 12 41,000

40,000

30,000

20,000

10,000

0

50,000

1, 2 1, 3 1, 4 2, 1 2, 2 Period

D em

an d

2, 3 2, 4 3, 1 3, 2 3, 3 3, 4 4, 1

FIGURE 7-1 Quarterly Demand at Tahoe Salt

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In Figure 7-1, observe that demand for salt is seasonal, increasing from the second quarter of a given year to the first quarter of the following year. The second quarter of each year has the lowest demand. Each cycle lasts four quarters, and the demand pattern repeats every year. There is also a growth trend in the demand, with sales growing over the past three years. The company estimates that growth will continue in the coming year at historical rates. We now describe the following two steps required to estimate each of the three parameters—level, trend, and seasonal factors.

1. Deseasonalize demand and run linear regression to estimate level and trend. 2. Estimate seasonal factors.

ESTIMATING LEVEL AND TREND The objective of this step is to estimate the level at Period 0 and the trend. We start by deseasonalizing the demand data. Deseasonalized demand represents the demand that would have been observed in the absence of seasonal fluctuations. The periodic- ity (p the pattern repeats every year. Given that we are measuring demand on a quarterly basis, the periodicity for the demand in Table 7-1 is p =

To ensure that each season is given equal weight when deseasonalizing demand, we take the average of p consecutive periods of demand. The average of demand from Period l + 1 to Period l + p provides deseasonalized demand for Period l + (p + 1)/2. If p is odd, this method provides deseasonalized demand for an existing period. If p is even, this method provides deseasonalized demand at a point between Period l + (p/2) and Period l + 1 + (p/2). By taking the average of deseasonalized demand provided by Periods l + 1 to l + p and l + 2 to l + p + 1, we obtain the deseasonalized demand for Period l + 1 + (p/2) if p is even. Thus, the deseasonalized demand, Dt, for Period t, can be obtained as follows:

 

Dt = e cDt – (p>2) + Dt + (p>2) + at – 1 + (p>2)i = t + 1 – (p>2)2Di d n(2p) for p even a

t + 3(p – 1)>24 i = t – 3(p – 1)>24Di>p for p odd

(7.2)

In our example, p = t = 3, we obtain the deseasonalized demand using Equa- tion 7.2 as follows:

D3 = cDt – (p>2) + Dt + (p>2) + at-1 + (p>2) i = t + 1-(p>2)2Di d n(2p) = D1 + D + ai = 22Din8

With this procedure, we can obtain deseasonalized demand between Periods 3 and 10 as shown in Figures 7-2 and 7-3 (all details are available in the accompanying spreadsheet Chapter 7-Tahoe-salt).

The following linear relationship exists between the deseasonalized demand, Dt, and time t, based on the change in demand over time:

Dt = L + Tt (7.3)

In Equation 7.3, Dt represents deseasonalized demand and not the actual demand in Period t, L represents the level or deseasonalized demand at Period 0, and T represents the rate of growth of deseasonalized demand or trend. We can estimate the values of L and T for the deseasonalized demand using linear regression with deseasonalized demand (see Figure 7-2) as the dependent

Excel (Data ! Data Analysis ! Regression). This sequence of commands opens the Regression

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enter

Input Y Range:C :C11

Input X Range:A :A11

and click the OK button. A new sheet containing the results of the regression opens up (see work- sheet Regression-1). This new sheet contains estimates for both the initial level L and the trend T. The initial level, L, is obtained as the intercept coefficient, and the trend, T, is obtained as the X variable coefficient (or the slope) from the sheet containing the regression results. For the Tahoe

L = T = Regression-1 and numbers are rounded to integer values). For this example, deseasonalized demand Dt for any Period t is thus given by

Dt = 18, 39 + 2 t (7.4)

It is not appropriate to run a linear regression between the original demand data and time to estimate level and trend because the original demand data are not linear and the resulting linear regres- sion will not be accurate. The demand must be deseasonalized before we run the linear regression.

C5:C117.2=(B2+B6+2*SUM(B3:B5))/8C4

Copied toEquationCell FormulaCell

FIGURE 7-2 Excel Workbook with Deseasonalized Demand for Tahoe Salt

40,000

30,000

20,000

10,000

0

50,000

1 2 3 4 5 6 7 8 9 10 11 12

Actual Demand Deseasonalized Demand

Period

D em

an d

FIGURE 7-3 Deseasonalized Demand for Tahoe Salt

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ESTIMATING SEASONAL FACTORS We can now obtain deseasonalized demand for each St for Period t is the ratio of

actual demand Dt to deseasonalized demand Dt and is given as

St = Dt Dt

(7.5)

Figure 7-4). Given the periodicity p, we obtain the seasonal factor for a given period by averaging sea-

sonal factors that correspond to similar periods. For example, if we have a periodicity of p =

as the average of the three seasonal factors. Given r seasonal cycles in the data, for all periods of the form pt + i, 1 … i … p, we obtain the seasonal factor as

Si = a r – 1

j = 0 Sjp + i

r

(7.6)

p = are r = 3 seasonal cycles in the data. We obtain seasonal factors using Equation 7.6 as

S1 = 1S1 + S + S92 >3 = 10. 2 + 0. 7 + 0. 22 >3 = 0. 7 S2 = 1S2 + S6 + S102 >3 = 10.67 + 0.83 + 0. 2 >3 = 0.68 S3 = 1S3 + S7 + S112 >3 = 11.1 + 1.0 + 1.322 >3 = 1.17 S = 1S + S8 + S122 >3 = 11.66 + 1.68 + 1.662 >3 = 1.67

At this stage, we have estimated the level, trend, and all seasonal factors. We can now obtain the forecast for the next four quarters using Equation 7.1. In the example, the forecast for the next four periods using the static forecasting method is given by

F13 = 1L + 13T2S13 = 118, 39 + 13 * 2 20. 7 = 11,868 F1 = 1L + 1 T2S1 = 118, 39 + 1 * 2 20.68 = 17, 27

D3:D137.5=B2/C2D2

C3:C137.4=18439+A2*524C2

Copied toEquationCell FormulaCell

FIGURE 7-4 Deseasonalized Demand and Seasonal Factors for Tahoe Salt

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F1 = 1L + 1 T2S1 = 118, 39 + 1 * 2 21.17 = 30,770 F16 = 1L + 16T2S16 = 118, 39 + 16 * 2 21.67 = ,79

– ing of sell-through information between the retailers and the manufacturer, this supply chain would have a less accurate forecast, and a variety of production and inventory inefficiencies would result.

Adaptive Forecasting

In adaptive forecasting, the estimates of level, trend, and seasonality are updated after each demand observation. The main advantage of adaptive forecasting is that estimates incorporate all new data that are observed. We now discuss a basic framework and several methods that can be used for this type of forecast. The framework is provided in the most general setting, when the systematic component of demand data has the mixed form and contains a level, a trend, and a seasonal factor. It can easily be modified for the other two cases, however. The framework can also be specialized for the case in which the systematic component contains no seasonality or trend. We assume that we have a set of historical data for n periods and that demand is seasonal, with periodicity p. Given quarterly data, wherein the pattern repeats itself every year, we have a periodicity of p =

We begin by defining a few terms:

Lt = estimate of level at the end of Period t Tt = estimate of trend at the end of Period t St = estimate of seasonal factor for Period t Ft = forecast of demand for Period t (made in Period t − 1 or earlier) Dt = actual demand observed in Period t Et = Ft − Dt = forecast error in Period t

In adaptive methods, the forecast for Period t + l in Period t uses the estimate of level and trend in Period t (Lt and Tt respectively) and is given as

Ft + l = 1Lt + lTt2St + l (7.7) The four steps in the adaptive forecasting framework are as follows:

1. Initialize: Compute initial estimates of the level (L0), trend (T0), and seasonal factors (S1, . . . , Sp) from the given data. This is done exactly as in the static forecasting method discussed earlier in the chapter with L0 = L and T0 = T.

2. Forecast: Given the estimates in Period t, forecast demand for Period t + 1 using Equa- tion 7.7. Our first forecast is for Period 1 and is made with the estimates of level, trend, and seasonal factor at Period 0.

3. Estimate error: Record the actual demand Dt+1 for Period t + 1 and compute the error Et+1 in the forecast for Period t + 1 as the difference between the forecast and the actual demand. The error for Period t + 1 is stated as

Et + 1 = Ft + 1 – Dt + 1 (7.8)

4. Modify estimates: Modify the estimates of level (Lt+1), trend (Tt+1), and seasonal factor (St+p+1), given the error Et+1 in the forecast. It is desirable that the modification be such that if the demand is lower than forecast, the estimates are revised downward, whereas if the demand is higher than forecast, the estimates are revised upward.

The revised estimates in Period t + 1 are then used to make a forecast for Period t + 2, and n have been covered. The esti-

mates at Period n are then used to forecast future demand.

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We now discuss various adaptive forecasting methods. The method that is most appropriate depends on the characteristic of demand and the composition of the systematic component of demand. In each case, we assume the period under consideration to be t.

MOVING AVERAGE The moving average method is used when demand has no observable trend or seasonality. In this case,

ystematic component of demand = level

In this method, the level in Period t is estimated as the average demand over the most recent N periods. This represents an N-period moving average and is evaluated as follows:

Lt = 1Dt + Dt – 1 + g + Dt – N + 12 >N (7.9) The current forecast for all future periods is the same and is based on the current estimate of level. The forecast is stated as

Ft + 1 = Lt and Ft + n = Lt (7.10)

After observing the demand for Period t + 1, we revise the estimates as follows:

Lt + 1 = 1Dt + 1 + Dt + # # # + Dt – N + 22 >N, Ft + 2 = Lt + 1 To compute the new moving average, we simply add the latest observation and drop the old-

est one. The revised moving average serves as the next forecast. The moving average corresponds to giving the last N periods of data equal weight when forecasting and ignoring all data older than this new moving average. As we increase N, the moving average becomes less responsive to the most recently observed demand. We illustrate the use of the moving average in Example 7-1.

EXAMPLE 7-1 Moving Average

A supermarket has experienced weekly demand of milk of D1 = 120, D2 = 127, D3 = D =

Analysis:

t = N = obtain

L = 1D + D3 + D2 + D12 > = 1122 + 11 + 127 + 1202 > = 120.7 F = L = 120.7 gallons

D

E = F – D = 12 – 120.7 = .2

L = 1D + D + D3 + D22 > = 112 + 122 + 11 + 1272 > = 122 SIMPLE EXPONENTIAL SMOOTHING The simple exponential smoothing method is appropri- ate when demand has no observable trend or seasonality. In this case,

ystematic component of demand = level

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The initial estimate of level, L0, is taken to be the average of all historical data because demand has been assumed to have no observable trend or seasonality. Given demand data for Periods 1 through n, we have the following:

L0 = 1 n a

n

i = 1 Di (7.11)

The current forecast for all future periods is equal to the current estimate of level and is given as

Ft + 1 = Lt and Ft + n = Lt (7.12)

After observing the demand, Dt+1, for Period t + 1, we revise the estimate of the level as follows:

Lt + 1 = aDt + 1 + 11 – a2Lt (7.13) where a (0 6 a 6 1) is a smoothing constant for the level. The revised value of the level is a weighted average of the observed value of the level (Dt+1) in Period t + 1 and the old estimate of the level (Lt) in Period t. Using Equation 7.13, we can express the level in a given period as a function of the current demand and the level in the previous period. We can thus rewrite Equation 7.13 as

Lt + 1 = a t – 1

n = 0 a11 – a2nDt + 1 – n + 11 – a2tD1

The current estimate of the level is a weighted average of all the past observations of demand, with recent observations weighted higher than older observations. A higher value of a corresponds to a forecast that is more responsive to recent observations, whereas a lower value of a represents a more stable forecast that is less responsive to recent observations. We illustrate the use of exponential smoothing in Example 7-2.

EXAMPLE 7-2 Simple Exponential Smoothing

Consider the supermarket in Example 7-1, in which weekly demand for milk has been D1 = 120, D2 = 127, D3 = D = 122 gallons over the past four weeks. Forecast demand for Period

a = 0.1.

Analysis In this case, we have demand data for n = level (rounded to 2 decimals) is expressed by

L0 = a i = 1

Di> = 120.7 The forecast for Period 1 (using Equation 7.12) is thus given by

F1 = L0 = 120.7

The observed demand for Period 1 is D1 = 120. The forecast error for Period 1 is given by

E1 = F1 – D1 = 120.7 – 120 = 0.7

With a = 0.1, the revised estimate of level for Period 1 using Equation 7.13 is given by

L1 = aD1 + 11 – a2L0 = (0.1 * 120) + (0.9 * 120.7 ) = 120.68 Observe that the estimate of level for Period 1 is lower than for Period 0 because the demand

in Period 1 is lower than the forecast for Period 1. We thus obtain F2 = L1 = 120.68. Given that D2 = 127, we obtain L2 = (0.1 × 127) + (0.9 × 120.68) = 121.31. This gives F3 = L2 = 121.31.

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Given that D3 = L3 = + (0.9 × 121.31) = F = L3 = D = 122, we obtain L = (0.1 × 122) + = 120.72. This gives

F = L = 120.72.

TREND-CORRECTED EXPONENTIAL SMOOTHING (HOLT’S MODEL) The trend-corrected exponential smoothing (Holt’s model) method is appropriate when demand is assumed to have a level and a trend in the systematic component, but no seasonality. In this case, we have

ystematic component of demand = level + trend

We obtain an initial estimate of level and trend by running a linear regression between demand, Dt, and time, Period t, of the form

Dt = at + b

In this case, running a linear regression between demand and time periods is appropriate because we have assumed that demand has a trend but no seasonality. The underlying relation- ship between demand and time is thus linear. The constant b measures the estimate of demand at Period t = 0 and is our estimate of the initial level L0. The slope a measures the rate of change in demand per period and is our initial estimate of the trend T0.

In Period t, given estimates of level Lt and trend Tt, the forecast for future periods is expressed as

Ft + 1 = Lt + Tt and Ft + n = Lt + nTt (7.14)

After observing demand for Period t, we revise the estimates for level and trend as follows:

Lt + 1 = aDt + 1 + 11 – a21Lt + Tt2 (7.15) Tt + 1 = b1Lt + 1 – Lt2 + 11 – b2Tt (7.16) where a(0 6 a 6 1) is a smoothing constant for the level and b (0 6 b 6 1) is a smoothing constant for the trend. Observe that in each of the two updates, the revised estimate (of level or trend) is a weighted average of the observed value and the old estimate. We illustrate the use of Holt’s model in Example 7-3 (see associated spreadsheet Examples 1–4 Chapter 7).

EXAMPLE 7-3 Holt’s Model

An electronics manufacturer has seen demand for its latest MP3 player increase over the past six months. Observed demand (in thousands) has been D1 = D2 = 8,732, D3 = D = 9,808, D = D6 = 11,961. Forecast demand for Period 7 using trend-corrected expo- nential smoothing with a = 0.1, b = 0.2.

Analysis The first step is to obtain initial estimates of level and trend using linear regression. We first run a linear regression (using the Excel tool Data ! Data Analysis ! Regression) between demand and time periods. The estimate of initial level L0 is obtained as the intercept coefficient, and the trend T0 is obtained as the X variable coefficient (or the slope) in the spreadsheet Examples 1-4 Chapter 7 (there is some variation between the spreadsheet and the results shown here because of rounding). For the MP3 player data, we obtain

L0 = 7,367 and T0 = 673

F1 = L0 + T0 = 7,367 + 673 = 8,0 0

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The observed demand for Period 1 is D1 =

E1 = F1 – D1 = 8,0 0 – 8, 1 = -37

With a = 0.1, b = 0.2, the revised estimate of level and trend for Period 1 using Equa-

L1 = aD1 + 11 – a21L0 + T02 = 10.1 * 8, 1 2 + 10.9 * 8,0 02 = 8,078 T1 = b1L1 – L02 + 11 – b2T0 = 30.2 * 18,078 – 7,36724 + 10.8 * 6732 = 681

Observe that the initial estimate for demand in Period 1 is too low. As a result, our updates have increased the estimate of level L1 –

Period 2:

F2 = L1 + T1 = 8,078 + 681 = 8,7 9

Continuing in this manner, we obtain L2 = 8,7 , T2 = 680, L3 = 9,393, T3 = 672, L = 10,039, T = 666, L = 10,676, T = 661, L6 = 11,399, T6 = 673. This gives us a fore- cast for Period 7 of

F7 = L6 + T6 = 11,399 + 673 = 12,072

TREND- AND SEASONALITY-CORRECTED EXPONENTIAL SMOOTHING (WINTER’S MODEL) This method is appropriate when the systematic component of demand has a level, a trend, and a sea- sonal factor. In this case we have

ystematic component of demand = 1level + trend2 * seasonal factor Assume periodicity of demand to be p. To begin, we need initial estimates of level (L0),

trend (T0), and seasonal factors (S1, . . . , Sp). We obtain these estimates using the procedure for static forecasting described earlier in the chapter.

In Period t, given estimates of level, Lt, trend, Tt, and seasonal factors, St, . . . , St+p-1, the forecast for future periods is given by

Ft + 1 = 1Lt + Tt2St + 1 and Ft + l = 1Lt + lTt2St + l (7.17) On observing demand for Period t + 1, we revise the estimates for level, trend, and sea-

sonal factors as follows:

Lt + 1 = a1Dt + 1>St + 12 + 11 – a21Lt + Tt2 (7.18) Tt + 1 = b1Lt + 1 – Lt2 + 11 – b2Tt (7.19) St + p + 1 = g1Dt + 1>Lt + 12 + 11 – g2St + 1 (7.20) where a (0 6 a 6 1) is a smoothing constant for the level; b (0 6 b 6 1) is a smoothing con- stant for the trend; and g (0 6 g 6 1) is a smoothing constant for the seasonal factor. Observe that in each of the updates (level, trend, or seasonal factor), the revised estimate is a weighted average of the observed value and the old estimate. We illustrate the use of Winter’s model in

Example 7-4).

EXAMPLE 7-4 Winter’s Model

seasonality-corrected exponential smoothing with a = 0.1, b = 0.2, g = 0.1.

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Analysis We obtain the initial estimates of level, trend, and seasonal factors exactly as in the static case. They are expressed as follows:

L0 = 18, 39 T0 = 2 S1 = 0. 7 S2 = 0.68 S3 = 1.17 S = 1.67

The forecast for Period 1 (using Equation 7.17) is thus given by

F1 = 1L0 + T02S1 = 118, 39 + 2 20. 7 = 8,913 The observed demand for Period 1 is D1 = 8,000. The forecast error for Period 1 is thus

given by

E1 = F1 – D1 = 8,913 – 8,000 = 913

With a = 0.1, b = 0.2, g = 0.1, the revised estimate of level and trend for Period 1 and

L1 = a1D1>S12 + 11 – a21L0 + T02 = 30.1 * 18,000>0. 724 + 30.9 * 118, 39 + 2 24 = 18,769

T1 = b1L1 – L02 + 11 – b2T0 = 30.2 * 118,769 – 18, 3924 + 10.8 * 2 2 = 8 S = g1D1>L12 + 11 – g2S1 = 30.1 * 18,000>18,76924 + 10.9 * 0. 72 = 0. 7

The forecast of demand for Period 2 (using Equation 7.17) is thus given by

F2 = 1L1 + T12S2 = 118,769 + 8 2 * 0.68 = 13,093 The forecasting methods we have discussed and the situations in which they are generally

applicable are as follows:

Forecasting Method Applicability

Moving average No trend or seasonality

Simple exponential smoothing No trend or seasonality

Holt’s model Trend but no seasonality

Winter’s model Trend and seasonality

its retailers, Winter’s model is the best choice, because its demand experiences both a trend and seasonality.

out? Forecast error helps identify instances in which the forecasting method being used is inap- propriate. In the next section, we describe how a manager can estimate and use forecast error.

7.6 MEASURES OF FORECAST ERROR

As mentioned earlier, every instance of demand has a random component. A good forecasting method should capture the systematic component of demand but not the random component. The random component manifests itself in the form of a forecast error. Forecast errors contain valu- able information and must be analyzed carefully for two reasons:

1. Managers use error analysis to determine whether the current forecasting method is pre- dicting the systematic component of demand accurately. For example, if a forecasting method consistently produces a positive error, the forecasting method is overestimating the systematic component and should be corrected.

2. All contingency plans must account for forecast error. Consider a mail-order company with two suppliers. The first is in the Far East and has a lead time of two months. The second is

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local and can fill orders with one week’s notice. The local supplier is more expensive than the Far East supplier. The mail-order company wants to contract a certain amount of contin- gency capacity with the local supplier to be used if the demand exceeds the quantity the Far East supplier provides. The decision regarding the quantity of local capacity to contract is closely linked to the size of the forecast error with a two-month lead time.

As long as observed errors are within historical error estimates, firms can continue to use their current forecasting method. Finding an error that is well beyond historical estimates may indicate that the forecasting method in use is no longer appropriate or demand has fundamentally changed. If all of a firm’s forecasts tend to consistently over- or underestimate demand, this may be another signal that the firm should change its forecasting method.

As defined earlier, forecast error for Period t is given by Et, where the following holds:

Et = Ft – Dt That is, the error in Period t is the difference between the forecast for Period t and the actual demand in Period t. It is important that a manager estimate the error of a forecast made at least as far in advance as the lead time required for the manager to take whatever action the forecast is to be used for. For example, if a forecast will be used to determine an order size and the supplier’s lead time is six months, a manager should estimate the error for a forecast made six months before demand arises. In a situation with a six-month lead time, there is no point in estimating errors for a forecast made one month in advance.

One measure of forecast error is the mean squared error (the denominator in Equation 7.21 can also have n − 1 instead of n):

MSEn = 1 n a

n

t = 1 E2t (7.21)

errors much more significantly than small errors because all errors are squared. Thus, if we select

9 will be preferred to a method with an error sequence of 1, 3, 2, and 20. Thus, it is a good idea

forecast error has a distribution that is symmetric about zero. Define the absolute deviation in Period t, At, to be the absolute value of the error in Period t;

that is,

At = !Et ! Define the mean absolute deviation (MAD) to be the average of the absolute deviation

over all periods, as expressed by

MADn = 1 n a

n

t = 1 At (7.22)

The MAD can be used to estimate the standard deviation of the random component assum- ing that the random component is normally distributed. In this case the standard deviation of the random component is

s = 1.2 MAD (7.23)

We then estimate that the mean of the random component is 0, and the standard deviation of the random component of demand is s forecast error does not have a symmetric distribution. Even when the error distribution is sym- metric, MAD is an appropriate choice when selecting forecasting methods if the cost of a fore- cast error is proportional to the size of the error.

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The mean absolute percentage error (MAPE) is the average absolute error as a percentage of demand and is given by

MAPEn = a

n

t = 1 ` Et Dt

` 100 n

(7.24)

The MAPE is a good measure of forecast error when the underlying forecast has signifi- cant seasonality and demand varies considerably from one period to the next. Consider a sce- nario in which two methods are used to make quarterly forecasts for a product with seasonal

periods, Method 2 results in a MAPE of 9.9 percent, whereas Method 1 results in a much higher

Method 1. When a forecast method stops reflecting the underlying demand pattern (for instance, if

demand drops considerably as it did for the automotive industry in 2008–2009), the forecast errors are unlikely to be randomly distributed around 0. In general, one needs a method to track and control the forecasting method. One approach is to use the sum of forecast errors to evaluate the bias, where the following holds:

biasn = a n

t = 1 Et (7.25)

The bias will fluctuate around 0 if the error is truly random and not biased one way or the other. Ideally, if we plot all the errors, the slope of the best straight line passing through should be 0.

The tracking signal

TSt = biast

MADt (7.26)

{6, this is a signal that the forecast is biased and is either underforecasting (TS 6 -6) or overforecasting (TS 7 +6). This may happen because the forecasting method is flawed or the underlying demand pattern has shifted. One

manager is using a forecasting method such as moving average. Because trend is not included,

that the forecasting method consistently underestimates demand and alerts the manager. The tracking signal may also get large when demand has suddenly dropped (as it did for

many industries in 2009) or increased by a significant amount, making historical data less rele- vant. If demand has suddenly dropped, it makes sense to increase the weight on current data relative to older data when making forecasts. McClain (1981) recommends the “declining alpha” method when using exponential smoothing when the smoothing constant starts large (to give greater weight to recent data) but then decreases over time. If we are aiming for a long-term smoothing constant of a = 1 – r, a declining alpha approach would be to start with a0 = 1 and reset the smoothing constant as follows:

at = at – 1

r + at – 1 =

1 – r 1 – rt

In the long term, the smoothing constant will converge to a = 1 – r with the forecasts becoming more stable over time.

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7.7 SELECTING THE BEST SMOOTHING CONSTANT

When using exponential smoothing, the value of the smoothing constant chosen has a direct impact on the sensitivity of the forecast to recent data. If a manager has a good sense of the underlying demand pattern, it is best to use a smoothing constant that is no larger than 0.2. In general, it is best to pick smoothing constants that minimize the error term that a manager is most

We illustrate the impact of picking smoothing constants that minimize different error mea-

spreadsheet Chapter 7-Tahoe-salt and worksheet Figures 7-5, 6). The initial level is estimated using Equation 7.11 and is shown in cell C2. The smoothing constant a

FIGURE 7-5 Selecting Smoothing Constant by Minimizing MSE

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– a = = =

MAPE = 2.1 percent.

MAPE at the end of 10 periods. In Figure 7-6, we show the results from minimizing MAD (cell G13). The forecasts and errors with the resulting a = 0.32 are shown in Figure 7-6. In this case,

that reduces large errors, whereas minimizing MAD picks a smoothing constant that gives equal weight to reducing all errors even if large errors get somewhat larger.

FIGURE 7-6 Selecting Smoothing Constant by Minimizing MAD

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7.8 FORECASTING DEMAND AT TAHOE SALT

Chapter 7-Tahoe-salt

K7:K137.26=Sum($E$6:E6)/H6K6

J7:J137.24=Average($I$6:I6)J6

I7:I13=100*(F6/B6)I6

H7:H137.22=Sum($F$6:F6)/(A6-4)H6

G7:G137.21=Sumsq($E$6:E6)/(A6-4)G6

F7:F13=Abs(E6)F6

E7:E137.8=D6-B6E6

D7:D137.10=C5D6

C6:C137.9=Average(B2:B5)C5

Copied toEquationCell FormulaCell

FIGURE 7-7 Tahoe Salt Forecasts Using Four-Period Moving Average

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Demand in this case clearly has both a trend and seasonality in the systematic component. Thus, the team initially expects Winter’s model to produce the best forecast.

Moving Average

The forecasting team initially decides to test a four-period moving average for the forecast- ing. All calculations are shown in Figure 7-7 (see worksheet Figure 7-7 in spreadsheet Chap- ter 7-Tahoe-salt) and are as discussed in the section on the moving-average method earlier in this chapter. The team uses Equation 7.9 to estimate level and Equation 7.10 to forecast demand.

{6 range, which indi- cates that the forecast using the four-period moving average does not contain any significant bias. It does, however, have a fairly large MAD12 of 9,719, with a MAPE12 Figure 7-7, observe that

L12 = 2 , 00

Thus, using a four-period moving average, the forecast for Periods 13 through 16 (using Equation 7.10) is given by

F13 = F1 = F1 = F16 = L12 = 2 , 00

Given that MAD12 is 9,719, the estimate of standard deviation of forecast error, using a four-period moving average, is 1.2 * 9,719 = 12,1 9. In this case, the standard deviation of forecast error is fairly large relative to the size of the forecast.

Simple Exponential Smoothing

The forecasting team next uses a simple exponential smoothing approach, with a = 0.1, to fore- cast demand. This method is also tested on the 12 quarters of historical data. Using Equation 7.11, the team estimates the initial level for Period 0 to be the average demand for Periods 1 through 12 (see worksheet Figure 7-8). The initial level is the average of the demand entries in cells B3 to

L0 = 22,083

The team then uses Equation 7.12 to forecast demand for the succeeding period. The esti- mate of level is updated each period using Equation 7.13. The results are shown in Figure 7-8.

– – nential smoothing with a = 0.1 does not indicate any significant bias. However, it has a fairly large MAD12 of 10,208, with a MAPE12

L12 = 23, 90

Thus, the forecast for the next four quarters (using Equation 7.12) is given by

F13 = F1 = F1 = F16 = L12 = 23, 90

In this case, MAD12 is 10,208 and MAPE12 deviation of forecast error using simple exponential smoothing is 1.2 * 10,208 = 12,760. In this case, the standard deviation of forecast error is fairly large relative to the size of the forecast.

Trend-Corrected Exponential Smoothing (Holt’s Model)

The team next investigates the use of Holt’s model. In this case, the systematic component of demand is given by

ystematic component of demand = level + trend

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Dt t. holts-regression

L = 12 15 a T = 1 549

a = 1 b = 2 Figure 7-9

– a = 1 b = 2

12 12

L12 = 44 a T12 = 1 541

K4:K147.26=Sum($E$3:E3)/H3K3

J4:J147.24=Average($I$3:I3)J3

I4:I14=100*(F3/B3)I3

H4:H147.22=Sum($F$3:F3)/A3H3

G4:G147.21=Sumsq($E$3:E3)/A3G3

F4:F14=Abs(E3)F3

E4:E147.8=D3-B3E3

D4:D147.12=C2D3

C4:C147.13=0.1*B3+(1-0.1)*C2C3

Copied toEquationCell FormulaCell

FIGURE 7-8 Tahoe Salt Forecasts Using Simple Exponential Smoothing

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:

F 3 = L 2 + T 2 = 30 3 + 5 = 3 98 F = L 2 + 2T 2 = 30 3 + 2 * 5 = 33 525 F 5 = L 2 + 3T 2 = 30 3 + 3 * 5 = 35 066 F 6 = L 2 + T 2 = 30 3 + * 5 = 36 607

= a = 0 b = 0 2 25 * 8 836 = 0 5 –

L4:L147.26=Sum($F$3:F3)/I3L3

K4:K147.24=Average($J$3:J3)K3

J4:J14=100*(G3/B3)J3

I4:I147.22=Sum($G$3:G3)/A3I3

H4:H147.21=Sumsq($F$3:F3)/A3H3

G4:G14=Abs(F3)G3

F4:F147.8=E3-B3F3

E4:E147.14=C2+D2E3

D4:D147.16=0.2*(C3-C2)+(1-0.2)*D2D3

C4:C147.15=0.1*B3+(1-0.1)*(C2+D2)C3

Copied toEquationCell FormulaCell

FIGURE 7-9 Trend-Corrected Exponential Smoothing

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Trend- and Seasonality-Corrected Exponential Smoothing (Winter’s Model)

– p =

deseasonalized

winters-regression deseasonalized

L = 8 439 T = 5 4 S = 47 S = 68 S3 = 7 S4 = 67

a = 5 b = g = Figure 7-10 –

M4:M147.26=Sum($G$3:G3)/J3M3

L4:L147.24=Average($K$3:K3)L3

K4:K14=100*(H3/B3)K3

J4:J147.22=Sum($H$3:H3)/A3J3

I4:I147.21=Sumsq($G$3:G3)/A3I3

H4:H14=Abs(G3)H3

G4:G147.8=F3-B3G3

F4:F187.17=(C2+D2)*E3F3

E8:E187.20=0.1*(B3/C3)+(1-0.1)*E3E7

D4:D147.19=0.1*(C3-C2)+(1-0.1)*D2D3

C4:C147.18=0.05*(B3/E3)+(1-0.05)*(C2+D2)C3

Copied toEquationCell FormulaCell

FIGURE 7-10 Trend- and Seasonality-Corrected Exponential Smoothing

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obtained with any of the other methods. From Figure 7-10, observe that

L12 = 2 ,791 T12 = 32 S13 = 0. 7 S1 = 0.68 S1 = 1.17 S16 = 1.67

Using Winter’s model (Equation 7.17), the forecast for the next four periods is

F13 = 1L12 + T122S13 = 12 ,791 + 322 * 0. 7 = 11,902 F1 = 1L12 + 2T122S1 = 12 ,791 + 2 * 322 * 0.68 = 17, 81 F1 = 1L12 + 3T122S1 = 12 ,791 + 3 * 322 * 1.17 = 30,873 F16 = 1L12 + T122S16 = 12 ,791 + * 322 * 1.67 = ,9

In this case, MAD12 = using Winter’s model with a = 0.0 , b = 0.1, and g = 0.1 is 1.2 * 1, 69 = 1,836. In this case, the standard deviation of forecast error relative to the demand forecast is much smaller than with the other methods.

The team compiles the error estimates for the four forecasting methods as shown in Table 7-2. Based on the error information in Table 7-2, the forecasting team decides to use Winter’s

model. It is not surprising that Winter’s model results in the most accurate forecast, because the demand data have both a growth trend as well as seasonality. Using Winter’s model, the team forecasts the following demand for the coming four quarters:

The standard deviation of forecast error is 1,836.

7.9 THE ROLE OF IT IN FORECASTING

There is a natural role for IT in forecasting, given the large amount of data involved, the fre- quency with which forecasting is performed, and the importance of getting the highest quality results possible. A good forecasting package provides forecasts across a wide range of products that are updated in real time by incorporating any new demand information. This helps firms respond quickly to changes in the marketplace and avoid the costs of a delayed reaction. Good demand planning modules link not only to customer orders but often directly to customer sales information as well, thus incorporating the most current data into the demand forecast. A positive outcome of the investment in ERP systems has been a significant improvement in supply chain transparency and data integration, thus allowing potentially better forecasts. Although this tech- nical improvement can help produce better forecasts, firms must develop the organizational capabilities required to take advantage of this improvement.

TABLE 7-2 Error Estimates for Tahoe Salt Forecasting Forecasting Method MAD MAPE (%) TS Range

Four-period moving average 9,719 49 –1.52 to 2.21

Simple exponential smoothing 10,208 59 –1.38 to 2.15

Holt’s model 8,836 52 –2.15 to 2.00

Winter’s model 1,469 8 –2.74 to 4.00

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Besides providing a rich library of forecasting methodologies, a good demand planning module should provide support in helping select the right forecasting model for the given demand pattern. This has become particularly important as the available library of forecasting method- ologies has grown.

As the name demand planning suggests, these modules facilitate the shaping of demand. Good demand planning modules contain tools to perform what-if analysis regarding the impact of potential changes in prices on demand. These tools help analyze the impact of promotions on demand and can be used to determine the extent and timing of promotions. This link is discussed in greater detail in Chapter 9 under sales and operations planning.

An important development is the use of demand correlated data (e.g. price, weather, other purchases, social data) to improve forecast accuracy or, in some cases, spur demand. In a well- publicized case, Target predicted that women were pregnant based on other products they were purchasing. A purchase of “cocoa-butter lotion, a purse large enough to double as a diaper bag, zinc and magnesium supplements and a bright blue rug” was a strong predictor of the woman’s pregnacy.2 Target then used this information to send suitable coupons to entice these women or

this can be used to not only improve forecast accuracy but also identify suitable marketing oppor- tunities to spur future demand.

Keep in mind that none of these tools is foolproof. Forecasts are virtually always inaccu- rate. A good IT system should help track historical forecast errors so they can be incorporated into future decisions. A well-structured forecast, along with a measure of error, can significantly improve decision making. Even with all these sophisticated tools, sometimes it is better to rely on human intuition in forecasting. One of the pitfalls of these IT tools is relying on them too much, which eliminates the human element in forecasting. Use the forecasts and the value they deliver, but remember that they cannot assess some of the more qualitative aspects about future demand that you may be able to do on your own.

software survey, and a discussion of each vendor is available at http://www.lionhrtpub.com/

7.10 FORECASTING IN PRACTICE

Collaborate in building forecasts. Collaboration with one’s supply chain partners can often create a much more accurate forecast. It takes an investment of time and effort to build the relationships with one’s partners to begin sharing information and creating collaborative forecasts. However, the supply chain benefits of collaboration are often an order of magnitude greater than the cost (collaborative planning, forecasting, and replenishment are discussed in greater detail in Chapter 10). The reality today, however, is that most forecasts do not even account for all the information available across the different functions of a firm. As a result, firms should aim to put a sales and operations planning process in place (discussed in Chapter 9) that brings together the sales and operations functions when planning.

Share only the data that truly provide value. The value of data depends on where one sits in the supply chain. A retailer finds point-of-sale data to be quite valuable in measuring the performance of its stores. However, a manufacturer selling to a distributor that, in turn, sells to retailers does not need all the point-of-sale detail. The manufacturer finds aggregate demand data to be quite valuable, with marginally more value coming from detailed point-of-sale data. Keeping the data shared to what is truly required decreases investment in IT and improves the chances of successful collaboration.

2 New York Times, February 16, 2012.

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Be sure to distinguish between demand and sales. Often, companies make the mistake of looking at historical sales and assuming that this is what the historical demand was. To get true demand, however, adjustments need to be made for unmet demand due to stockouts, competitor actions, pricing, and promotions. Failure to do so results in forecasts that do not rep- resent the current reality.

7.11 SUMMARY OF LEARNING OBJECTIVES

1. Understand the role of forecasting for both an enterprise and a supply chain. Fore- casting is a key driver of virtually every design and planning decision made in both an enterprise and a supply chain. Enterprises have always forecast demand and used it to make decisions. A relatively recent phenomenon, however, is to create collaborative forecasts for an entire supply chain and use these as the basis for decisions. Collaborative forecasting greatly increases the accuracy of forecasts and allows the supply chain to maximize its performance. Without collabo- ration, supply chain stages farther from demand will likely have poor forecasts that will lead to supply chain inefficiencies and a lack of responsiveness.

2. Identify the components of a demand forecast. Demand consists of a systematic and a random component. The systematic component measures the expected value of demand. The random component measures fluctuations in demand from the expected value. The systematic component consists of level, trend, and seasonality. Level measures the current deseasonalized

predictable seasonal fluctuations in demand.

3. Forecast demand in a supply chain given historical demand data using time-series methodologies. Time-series methods for forecasting are categorized as static or adaptive. In static methods, the estimates of parameters and demand patterns are not updated as new demand

each time a new demand is observed. Adaptive methods include moving averages, simple expo- nential smoothing, Holt’s model, and Winter’s model. Moving averages and simple exponential smoothing are best used when demand displays neither trend nor seasonality. Holt’s model is best when demand displays a trend but no seasonality. Winter’s model is appropriate when demand displays both trend and seasonality.

4. Analyze demand forecasts to estimate forecast error. Forecast error measures the random component of demand. This measure is important because it reveals how inaccurate a

the forecast consistently over- or underforecasts or if demand has deviated significantly from historical norms.

Discussion Questions 1. What role does forecasting play in the supply chain of a

build-to-order server manufacturer such as Dell? 2. How could Apple use collaborative forecasting with its sup-

pliers to improve its supply chain? 3. What role does forecasting play in the supply chain of a mail-

order firm such as L.L. Bean? 4. What systematic and random components would you expect

in demand for chocolates? 5. Why should a manager be suspicious if a forecaster claims to

forecast historical demand without any forecast error?

6. Give examples of products that display seasonality of demand.

7. What is the problem if a manager uses last year’s sales data instead of last year’s demand to forecast demand for the com- ing year?

8. How do static and adaptive forecasting methods differ? 9.

a manager? How can the manager use this information? 10.

How can the manager use this information?

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2. Weekly demand figures at Hot Pizza are as follows:

Week Demand ($)

1 108

2 116

3 118

4 124

5 96

6 119

7 96

8 102

9 112

10 102

11 92

12 91

– ing average as well as simple exponential smoothing with a = 0.1 in each case. Which of the two methods do you prefer? Why?

3. Quarterly demand for flowers at a wholesaler are as shown.

– tial smoothing with a = 0.1 as well as Holt’s model with a = 0.1 and b = 0.1. Which of the two methods do you prefer? Why?

Year Quarter Demand ($000)

1 I 98

II 106

III 109

IV 133

2 I 130

II 116

III 133

IV 116

3 I 138

II 130

III 147

IV 141

4 I 144

II 142

III 165

IV 173

Exercises 1. Consider monthly demand for the ABC Corporation, as

shown in Table 7-3. Forecast the monthly demand for Year 6

TABLE 7-3 Monthly Demand for ABC Corporation Sales Year 1 Year 2 Year 3 Year 4 Year 5

January 2,000 3,000 2,000 5,000 5,000

February 3,000 4,000 5,000 4,000 2,000

March 3,000 3,000 5,000 4,000 3,000

April 3,000 5,000 3,000 2,000 2,000

May 4,000 5,000 4,000 5,000 7,000

June 6,000 8,000 6,000 7,000 6,000

July 7,000 3,000 7,000 10,000 8,000

August 6,000 8,000 10,000 14,000 10,000

September 10,000 12,000 15,000 16,000 20,000

October 12,000 12,000 15,000 16,000 20,000

November 14,000 16,000 18,000 20,000 22,000

December 8,000 10,000 8,000 12,000 8,000

Total 78,000 89,000 98,000 115,000 113,000

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4. Consider monthly demand for the ABC Corporation as shown in Table 7-3. Forecast the monthly demand for Year 6 using moving average, simple exponential smoothing, Holt’s model, and Winter’s model. In each case, evaluate the bias,

you prefer? Why? 5. For the Hot Pizza data in Exercise 2, compare the perfor-

mance of simple exponential smoothing with a = 0.1 and a = 0.9. What difference in forecasts do you observe? Which of the two smoothing constants do you prefer?

6. Monthly demand at A&D Electronics for flat-screen TVs are as follows:

Month Demand (units)

1 1,000

2 1,113

3 1,271

4 1,445

5 1,558

6 1,648

7 1,724

8 1,850

9 1,864

10 2,076

11 2,167

12 2,191

Estimate demand for the next two weeks using simple expo- nential smoothing with a = 0.3 and Holt’s model with

a = 0.0 and b = 0.1. For the simple exponential smooth- ing model, use the level at Period 0 to be L0 = average demand over the 12 months). For Holt’s model, use level at Period 0 to be L0 = T0 = 109 (both are obtained through regression). Evaluate the

two methods do you prefer? Why? 7. Using the A&D Electronics data in Exercise 6, repeat Holt’s

model with a = 0. and b = 0. . Compare the performance of Holt’s model with a = 0.0 and b = 0.1. Which combi- nation of smoothing constants do you prefer? Why?

8. Weekly demand for dry pasta at a supermarket chain is as follows:

Week Demand (units)

1 517

2 510

3 557

4 498

5 498

6 444

7 526

8 441

9 541

10 445

Estimate demand for the next four weeks using a five-week moving average, as well as simple exponential smoothing with a = 0.2 in each case. Which of the two methods do you prefer? Why?

Bibliography

Forecasters Worth Listening To?” Harvard Business Review

Bowerman, Bruce L., and Richard T. O’Connell. Forecasting and Time Series: An Applied Approach, 3d ed. Belmont, CA: Duxbury, 1993.

Time Series Analysis: Forecasting and Control. Oakland, CA: Holden-Day, 1976.

Brown, Robert G. Statistical Forecasting for Inventory Control.

“How to Choose the Right Forecasting Technique.” Harvard Business Review

Forecasting with Regression Analysis. Cambridge, MA: Harvard

Georgoff, David M., and Robert G. Murdick. “Manager’s Guide to Forecasting.” Harvard Business Review 1986): 2–9.

Gilliland, Michael. “Is Forecasting a Waste of Time?” Supply Chain Management Review

Journal of Operations Manage- ment

a Forecasting Competition.” Journal of Forecasting (April–

Forecasting Methods for Management. New York: Wiley, 1989.

Harvard Busi- ness Review

ORMS Today

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CASE STUDY Specialty Packaging Corporation

marketing vice president and staff members from their key customers. The goal of this team was to improve

meet demand effectively over the previous several

– ager that the team’s first task was to establish a collab-

customers. This forecast would serve as the basis for improving the firm’s performance, as managers could use this more accurate forecast for their production

improve delivery performance.

SPC

containers for the food industry. Polystyrene is pur- chased as a commodity in the form of resin pellets. The resin is unloaded from bulk rail containers or overland trailers into storage silos. Making the food containers is a two-step process. First, resin is conveyed to an extruder, which converts it into a polystyrene sheet that is wound into rolls. The plastic comes in two forms— clear and black. The rolls are either used immediately

rolls are loaded onto thermoforming presses, which

form the sheet into containers and trim the containers from the sheet. The two manufacturing steps are shown in Figure 7-11.

Over the past five years, the plastic packaging business has grown steadily. Demand for containers made from clear plastic comes from grocery stores, bak- eries, and restaurants. Caterers and grocery stores use the black plastic trays as packaging and serving trays. Demand for clear plastic containers peaks in the summer months, whereas demand for black plastic containers peaks in the fall. Capacity on the extruders is not suffi- cient to cover demand for sheets during the peak sea- sons. As a result, the plant is forced to build inventory of each type of sheet in anticipation of future demand.

demand for each of the two types of containers (clear

accounting for lost sales to obtain true demand data.

never have known this information, as the company did not keep track of lost orders.

Forecasting

As a first step in the team’s decision making, it wants to forecast quarterly demand for each of the two types of containers for years 6 to 8. Based on historical trends, demand is expected to continue to grow until year 8,

appropriate forecasting method and estimate the likely forecast error. Which method should she choose? Why? Using the method selected, forecast demand for years 6 to 8.

Resin Storage

Extruder Roll Storage

Thermo- forming

Press

Step 1 Step 2

FIGURE 7-11 Manufacturing Process at SPC

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TABLE 7-4 Quarterly Historical Demand for Clear and Black Plastic Containers Year

Quarter

Black Plastic Demand (000 lb)

Clear Plastic Demand (000 lb)

1 I 2,250 3,200

II 1,737 7,658

III 2,412 4,420

IV 7,269 2,384

2 I 3,514 3,654

II 2,143 8,680

III 3,459 5,695

IV 7,056 1,953

3 I 4,120 4,742

II 2,766 13,673

III 2,556 6,640

IV 8,253 2,737

4 I 5,491 3,486

II 4,382 13,186

III 4,315 5,448

IV 12,035 3,485

5 I 5,648 7,728

II 3,696 16,591

III 4,843 8,236

IV 13,097 3,316

16000

18000

14000

12000

10000

8000

6000

4000

2000

0

D em

an d

Year 1 Year 2 Year 3 Year 4 Year 5 Year 6

Black Plastic Demand Clear Plastic Demand

FIGURE 7-12 Plot of Quarterly Demand for Clear and Black Plastic Containers

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In this chapter, we discuss how aggregate planning is used to make decisions about produc-tion, outsourcing, inventory, and backlogs in a supply chain. We identify the information required to produce an aggregate plan and outline the basic trade-offs that must be made to create an optimal aggregate plan. We also describe how to formulate and solve an aggregate planning problem using Microsoft Excel.

8.1 THE ROLE OF AGGREGATE PLANNING IN A SUPPLY CHAIN

Imagine a world in which manufacturing, transportation, warehousing, and even information capacity are all limitless and free. Imagine lead times of zero, allowing goods to be produced and delivered instantaneously. In this world, there would be no need to plan in anticipation of demand, because whenever a customer demands a product, the demand would be instantly satisfied. In this world, aggregate planning plays no role.

In the real world, however, capacity has a cost, and lead times are often long. Therefore, companies must make decisions regarding capacity levels, production levels, outsourcing, and promotions well before demand is known. A company must anticipate demand and determine, in advance of that demand, how to meet it. Should a company invest in a plant with large capacity that is able to produce enough to satisfy demand even in the busiest months? Or should a com- pany build a smaller plant but incur the costs of holding inventory built during slow periods in

Aggregate Planning in a Supply Chain

C H A P T E R

8

LEARNING OBJECTIVES After reading this chapter, you will be able to

209

1. Understand the importance of aggregate planning as a supply chain activity.

2. Describe the information needed to produce an aggregate plan and the outputs obtained.

3. Explain the basic trade-offs to consider when creating an aggregate plan.

4. Formulate and solve basic aggregate planning problems using Microsoft Excel.

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anticipation of demand in later months? These are the types of questions that aggregate planning helps companies answer.

Aggregate planning is a process by which a company determines planned levels of capacity, production, subcontracting, inventory, stockouts, and even pricing over a specified time horizon. The goal of aggregate planning is to build a plan that satisfies demand while maximizing profit. Aggregate planning, as the name suggests, solves problems involving aggregate decisions rather than stock-keeping unit (SKU)-level decisions. For example, aggre- gate planning determines the total production level in a plant for a given month, but it does so without determining the quantity of each individual SKU that will be produced. This level of detail makes aggregate planning a useful tool for thinking about decisions with an intermedi- ate time frame of between roughly 3 and 18 months. In this time frame, it is too early to deter- mine production levels by SKU, but it is also generally too late to arrange for additional capacity. Therefore, aggregate planning answers the question: How should a firm best utilize the facilities that it currently has?

To be effective, aggregate planning requires inputs from all stages of the supply chain, and its results have a tremendous impact on supply chain performance. As we saw in Chapter 7, collaborative forecasts are created by multiple supply chain enterprises and are an important input for aggregate planning. In addition, many constraints that are key inputs to aggregate planning come from supply chain partners outside the enterprise. Without these inputs from both up and down the supply chain, aggregate planning cannot realize its full potential to create value. The output from aggregate planning is also of value to both upstream and downstream

– straints for customers. This chapter is meant to create a foundation for using aggregate planning both solely within an enterprise as well as across the entire supply chain. The supply chain implications of aggregate planning will become even clearer in Chapter 9, in which we discuss sales and operations planning.

As an example, consider how a premium paper supply chain uses aggregate planning to maximize profit. Many types of paper mills face seasonal demand that ripples up from customers to printers to distributors and, finally, to the manufacturers. Many types of premium paper have demand peaks in the spring, when annual reports are printed, and in the fall, when new-car bro- chures are released. Building a mill with capacity to meet demand in the spring and fall on an as-needed basis is too costly, because of the high cost of mill capacity. On the other side of the supply chain, premium papers often require special additives and coatings that may be in short supply. The paper manufacturer must deal with these constraints and maximize profit around them. The mills use aggregate planning to determine production levels and inventory levels that they should build up in the slower months for sale in the spring and fall, when demand is greater than the mill’s capacity. By taking into account the inputs from throughout the supply chain, aggregate planning allows the mill and the supply chain to maximize profit.

The aggregate planner’s main objective is to identify the following operational parameters over the specified time horizon:

Production rate: the number of units to be completed per unit time (such as per week or per month)

Workforce: the number of workers or units of labor capacity required Overtime: the amount of overtime production planned Machine capacity level: the number of units of machine capacity needed for production Subcontracting: the subcontracted capacity required over the planning horizon Backlog: demand not satisfied in the period in which it arises, but is carried over to future periods

Inventory on hand: the planned inventory carried over the various periods in the plan- ning horizon

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The aggregate plan serves as a broad blueprint for operations and establishes the parameters within which short-term production and distribution decisions are made. The aggregate plan allows the supply chain to alter capacity allocations and change supply contracts. As mentioned in earlier chapters, the entire supply chain should be involved with the planning process. If a manufacturer has planned an increase in production over a given time period, the supplier, trans- porter, and warehouse must be aware of this plan and incorporate the increase into their own plans. Ideally, all stages of the supply chain should work together on an aggregate plan that opti- mizes supply chain performance. If each stage develops its own aggregate plan independently, it is extremely unlikely that all the plans will mesh in a coordinated manner. This lack of coordina- tion results in shortages or oversupply in the supply chain. Therefore, it is important to form aggregate plans over a wide scope of the supply chain.

In the next section, we formally define the aggregate planning problem. We specify the information required for aggregate planning and discuss the decision outcomes that aggregate planning can provide.

8.2 THE AGGREGATE PLANNING PROBLEM

The objective of the aggregate plan is to satisfy demand in a way that maximizes profit for the firm. We can state the aggregate planning problem formally as follows:

Given the demand forecast for each period in the planning horizon, determine the produc- tion level, inventory level, capacity level (internal and outsourced), and any backlogs (unmet demand) for each period that maximize the firm’s profit over the planning horizon.

To create an aggregate plan, a company must specify the planning horizon. A planning horizon is the time period over which the aggregate plan is to produce a solution—usually between 3 and 18 months. A company must also specify the duration of each period within the planning horizon (e.g., weeks, months, or quarters). In general, aggregate planning takes place over months or quarters. Next, a company specifies key information required to produce an aggregate plan and to make the decisions for which the aggregate plan will develop recommen- dations. In this section, this information and the recommendations are specified for a generic aggregate planning problem. The model we propose in the next section is flexible enough to accommodate situation-specific requirements.

An aggregate planner requires the following information:

Ft t in a planning horizon that extends over T periods

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Using this information, a company makes the following determinations through aggregate planning:

Production quantity from regular time, overtime, and subcontracted time: used to determine number of workers and supplier purchase levels

Inventory held: used to determine the warehouse space and working capital required Backlog/stockout quantity: used to determine customer service levels Workforce hired/laid off: used to determine any labor issues likely to be encountered Machine capacity increase/decrease: used to determine whether new production equip- ment should be purchased or available equipment idled

The quality of an aggregate plan has a significant impact on the profitability of a firm. A poor aggregate plan can result in lost sales and lost profits if the available inventory and capacity are unable to meet demand. A poor aggregate plan may also result in a large amount of excess inven- tory and capacity, thereby raising costs. Therefore, aggregate planning is an important tool to optimally match supply and demand.

Identifying Aggregate Units of Production

An important first step in aggregate planning is the identification of a suitable aggregate unit of production. When planning is done at the aggregate level, it is important that the aggregate unit be identified in a way that when the final production schedule is built (this has to be at the disag- gregate product level), the results of the aggregate plan reflect approximately what can be accom- plished in practice. Given that the bottleneck is likely to be the most constraining area in any manufacturing facility, it is important to focus on the bottleneck when selecting the aggregate unit and identifying capacity as well as production times. When evaluating production times, it is also important to account for activities—such as setups and maintenance—that use up capacity but do not result in any production. Otherwise, the aggregate plan will overestimate the produc- tion capacity available, resulting in a plan that cannot be implemented in practice. We now dis- cuss a simple approach that can be used to identify aggregate units and also to evaluate costs, revenues, and times for this aggregate unit.

Consider, for example, Red Tomato Tools, a small manufacturer of gardening equipment with manufacturing facilities in Mexico. The company makes six product families at its manu- facturing plant. The costs, revenues, production times, setup times, and historical batch sizes of production for each family are as shown in Table 8-1.

In Table 8-1 (see spreadsheet Chapter8-Table 8-1), the net production time per unit is obtained by adding the changeover time allocated to each unit and the production time (setup

TABLE 8-1 Costs, Revenues, and Times at Red Tomato Tools

Family

Material Cost/

Unit ($) Revenue/ Unit ($)

Setup Time/Batch

(hour)

Average Batch Size

Production Time/Unit

(hour)

Net Production Time/Unit

(hour)

Percentage Share of

Units Sold

A 15 54 8 50 5.60 5.76 10

B 7 30 6 150 3.00 3.04 25

C 9 39 8 100 3.80 3.88 20

D 12 49 10 50 4.80 5.00 10

E 9 36 6 100 3.60 3.66 20

F 13 48 5 75 4.30 4.37 15

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+ + =

A simple approach to defining the aggregate unit is based on the weighted average of the percentage of sales represented by each family. Such an approach is meaningful if management is relatively confident of the mix of sales and all the product families use roughly the same set of resources at a plant. Taking this approach, the material cost per aggregate unit is obtained as

* + (7 * + (9 * + * + (9 * + (13 * = similar evaluation, we obtain that the revenue per aggregate unit = time per aggregate unit =

Other potential aggregate units could be tons of output (likely to be suitable for continuous flows, such as gasoline or paper) or dollars of sales. For example, a paper mill might produce papers of different thickness and quality. If tons of output is used as the aggregate unit, all capac- ity, cost, and revenue calculations should account for the product mix.

8.3 AGGREGATE PLANNING STRATEGIES

The aggregate planner must make trade-offs among capacity, inventory, and backlog costs. An aggregate plan that increases one of these costs typically results in reduction of the other two. In this sense, the costs represent a trade-off: To lower inventory cost, a planner must increase capac- ity cost or delay delivery to the customer. Thus, the planner trades inventory cost for capacity or backlog cost. Arriving at the most profitable combination of trade-offs is the goal of aggregate planning. Given that demand varies over time, the relative level of the three costs leads to one of them being the key lever the planner uses to maximize profits. If the cost of varying capacity is low, a company may not need to build inventory or carry backlogs. If the cost of varying capacity is high, a company may compensate by building some inventory and carrying some backlogs from peak demand periods to off-peak demand periods.

In general, a company attempts to use a combination of the three costs to best meet demand. Therefore, the fundamental trade-offs available to a planner are among the following:

There are essentially three distinct aggregate planning strategies for achieving balance among these costs. These strategies involve trade-offs among capital investment, workforce size,

combination of these three and are referred to as tailored or hybrid strategies. The three strate- gies are as follows:

1. Chase strategy—using capacity as the lever: With this strategy, the production rate is synchronized with the demand rate by varying machine capacity or hiring and laying off employ- ees as the demand rate varies. In practice, achieving this synchronization can be problematic because of the difficulty of varying capacity and workforce on short notice. This strategy can be expensive to implement if the cost of varying machine or labor capacity over time is high. It can also have a significant negative impact on the morale of the workforce. The chase strategy results in low levels of inventory in the supply chain and high levels of change in capacity and work- force. It should be used when the cost of carrying inventory is high and costs to change levels of machine and labor capacity are low.

2. Flexibility strategy—using utilization as the lever: This strategy may be used if there is

workforce shows scheduling flexibility. In this case, the workforce (capacity) is kept stable, but the number of hours worked is varied over time in an effort to synchronize production with demand. A planner can use variable amounts of overtime or a flexible schedule to achieve this synchronization.

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Although this strategy does require that the workforce be flexible, it avoids some of the problems associated with the chase strategy—most notably, changing the size of the workforce. This strategy results in low levels of inventory but with lower average machine utilization. It should be used when inventory carrying costs are relatively high and machine capacity is relatively inexpensive.

3. Level strategy—using inventory as the lever: With this strategy, a stable machine capacity and workforce are maintained with a constant output rate. Shortages and surpluses result in inventory levels fluctuating over time. In this case, production is not synchronized with demand. Either inventories are built up in anticipation of future demand or backlogs are carried over from high- to low-demand periods. Employees benefit from stable working conditions. A drawback associated with this strategy is that large inventories may accumulate and customer orders may be delayed. This strategy keeps capacity and costs of changing capacity relatively low. It should be used when inventory carrying and backlog costs are relatively low.

In practice, a planner is most likely to come up with a tailored or hybrid strategy that com- bines aspects of all three approaches.

8.4 AGGREGATE PLANNING AT RED TOMATO TOOLS

We illustrate aggregate planning methodologies using Red Tomato Tools. Red Tomato’s products are sold through retailers in the United States. Red Tomato’s operations consist of the assembly of purchased parts into a multipurpose gardening tool. Because of the limited equipment and space required for its assembly operations, Red Tomato’s capacity is determined mainly by the size of its workforce.

For this example, we use a six-month time period because this is a long enough time hori- zon to illustrate many of the main points of aggregate planning.

Red Tomato Tools

The demand for Red Tomato’s gardening tools from consumers is highly seasonal, peaking in the spring as people plant their gardens. This seasonal demand ripples up the supply chain from the retailer to Red Tomato, the manufacturer. The options Red Tomato has for handling the seasonal- ity are adding workers during the peak season, subcontracting out some of the work, building up inventory during the slow months, or building up a backlog of orders that will be delivered late to customers. To determine how to best use these options through an aggregate plan, Red Tomato’s vice president of supply chain starts with the first task—building a demand forecast. Although Red Tomato could attempt to forecast this demand itself, a much more accurate forecast comes from a collaborative process used by both Red Tomato and its retailers to produce the forecast

to sell and be in terms of aggregate units defined earlier.

TABLE 8-2 Demand Forecast at Red Tomato Tools Month Demand Forecast

January 1,600

February 3,000

March 3,200

April 3,800

May 2,200

June 2,200

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215

straight time and the rest on overtime. As discussed previously, the capacity of the produc- tion operation is determined primarily by the total labor hours worked. Therefore, machine capacity does not limit the capacity of the production operation. Because of labor rules, no

Table 8-3. It is important that the costs and labor hours are in aggregate units, as discussed

backlog. All stockouts are backlogged and supplied from the following months’ production. Inventory costs are incurred on the ending inventory in the month. The supply chain manager’s goal is to obtain the optimal aggregate plan that allows Red Tomato to end June with at least

planning horizon. For now, given Red Tomato’s desire for a high level of customer service, assume all demand is to be met, although it can be met late. Therefore, the revenues earned over the planning horizon are fixed. As a result, minimizing cost over the planning horizon is the same as maximizing profit. In many instances, a company has the option of not meeting certain demand, or price itself may be a variable that a company must determine based on the aggregate plan. In such a scenario, minimizing cost is not equivalent to maximizing profits.

In the next two sections, we discuss methodologies commonly used for aggregate plan-

8.5 AGGREGATE PLANNING USING LINEAR PROGRAMMING

As we discussed earlier, the goal of aggregate planning is to maximize profit while meeting demand. Every company, in its effort to meet customer demand, faces certain constraints, such as the capacity of its facilities or a supplier’s ability to deliver a component. A highly effective tool for a company to use when it tries to maximize profits while being subjected to a series of constraints is linear programming. Linear programming finds the solution that creates the highest profit while satisfying the constraints that the company faces. We now illustrate a lin- ear programming approach to aggregate planning using Red Tomato Tools.

TABLE 8-3 Costs for Red Tomato

Item Cost

Material cost $10/unit

Inventory holding cost $2/unit/month

Marginal cost of stockout/backlog $5/unit/month

Hiring and training costs $300/worker

Layoff cost $500/worker

Labor hours required 4/unit

Regular time cost $4/hour

Overtime cost $6/hour

Cost of subcontracting $30/unit

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Decision Variables

The first step in constructing an aggregate planning model is to identify the set of decision vari- ables whose values are to be determined as part of the aggregate plan. For Red Tomato, the fol- lowing decision variables are defined for the aggregate planning model:

Wt = workforce size for Month t, t = Ht = number of employees hired at the beginning of Month t, t = Lt = number of employees laid off at the beginning of Month t, t = Pt = number of units produced in Month t, t = It = inventory at the end of Month t, t = St = t, t = Ct = number of units subcontracted for Month t, t = Ot = number of overtime hours worked in Month t, t =

The next step in constructing an aggregate planning model is to define the objective function.

Objective Function

t by Dt. The values of Dt are as specified by the demand forecast in

profit as all demand is to be satisfied) incurred during the planning horizon. The cost incurred has the following components:

These costs are evaluated as follows:

1. Regular-time labor cost. Recall that workers * * Wt is the number of workers in

t, the regular-time labor cost over the planning horizon is given by

Regular@time labor cost = a t = 1

Wt

2. Overtime labor cost. Ot t, the overtime cost over the planning

horizon is

Overtime labor cost = a t = 1

Ot

3. Cost of hiring and layoffs. Ht and Lt represent the number hired and the number laid

t. Thus, the cost of hiring and layoff is given by

Cost of hiring and layoff = a t = 1

3 Ht + a t = 1

Lt

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4. Cost of inventory and stockout. It and St represent

t. Thus, the cost of holding inventory and stocking out is

Cost of holding inventory and stocking out = a t = 1

It + a t = 1

St

5. Cost of materials and subcontracting. – Pt represents the quantity produced and Ct represents

t. Thus, the material and subcontracting cost is

Cost of materials and subcontracting = a t = 1

1 Pt + a t = 1

3 Ct

The total cost incurred during the planning horizon is the sum of all the aforementioned costs and is given by

a t = 1

Wt + a t = 1

Ot + a t = 1

3 Ht + a t = 1

Lt + a t = 1

It (8.1)

+ a t = 1

St + a t = 1

1 Pt + a t = 1

3 Ct

Red Tomato’s objective is to find an aggregate plan that minimizes the total cost (Equation 8.1) incurred during the planning horizon.

The values of the decision variables in the objective function cannot be set arbitrarily. They are subject to a variety of constraints defined by available capacity and operating policies. The next step in setting up the aggregate planning model is to define clearly the constraints linking the decision variables.

Constraints

Red Tomato’s vice president must now specify the constraints that the decision variables must not violate. They are as follows:

1. Workforce, hiring, and layoff constraints. The workforce size Wt t is obtained by adding the number hired Ht t to the workforce size Wt – 1 t – 1, and subtracting the number laid off Lt t as follows:

Wt = Wt – 1 + Ht – Lt for t = 1, . . . , (8.2)

The starting workforce size is given by W0 = 2. Capacity constraints. In each period, the amount produced cannot exceed the avail-

able capacity. This set of constraints limits the total production by the total internally available capacity (which is determined based on the available labor hours, regular or overtime). Subcon- tracted production is not included in this constraint because the constraint is limited to produc-

per unit as specified in Table 8-3) and one unit for every four hours of overtime, we have the following:

Pt … Wt + Ot for t = 1, c , (8.3)

3. Inventory balance constraints. The third set of constraints balances inventory at the t is obtained as the sum of the current demand Dt and

the previous backlog St – 1. This demand is either filled from current production (in-house

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production Pt or subcontracted production Ct) and previous inventory It – 1 (in which case some inventory It may be left over) or part of it is backlogged St. This relationship is captured by the following equation:

It – 1 + Pt + Ct = Dt + St – 1 + It – St for t = 1, . . . , (8.4)

The starting inventory is given by I0 = units (i.e., I Ú ), and initially there are no backlogs (i.e., S0 =

4. Overtime limit constraints. The fourth set of constraints requires that no employee

overtime hours available as follows:

Ot … 1 Wt for t = 1, . . . , (8.5)

In addition, each variable must be nonnegative and there must be no backlog at the end of S6 =

When implementing the model in Microsoft Excel, which we discuss later, it is easiest if

Ot – 1 Wt … for t = 1, . . . ,

Observe that one can easily add constraints that limit the amount purchased from subcontrac- tors each month or the maximum number of employees to be hired or laid off. Any other con- straints limiting backlogs or inventories can also be accommodated. Ideally, the number of employees hired or laid off should be integer variables. Fractional variables may be justified if some employees work for only part of a month. Such a linear program can be solved using the tool Solver in Excel.

By optimizing the objective function (minimizing cost in Equation 8.1) subject to the listed

– sheet Chapter8,9-examples.)

For this aggregate plan, we have the following:

Total cost over planning horizon = ,

company maintains the workforce and production level. It uses the subcontractor during the

TABLE 8-4 Aggregate Plan for Red Tomato

Period, t

No. Hired,

Ht

No. Laid

Off, Lt

Workforce Size, Wt

Overtime, Ot

Inventory, It

Stockout, St

Subcontract, Ct

Total Production,

Pt Demand,

Dt 0 0 0 80 0 1,000 0 0

1 0 16 64 0 1,960 0 0 2,560 1,600

2 0 0 64 0 1,520 0 0 2,560 3,000

3 0 0 64 0 880 0 0 2,560 3,200

4 0 0 64 0 0 220 140 2,560 3,800

5 0 0 64 0 140 0 0 2,560 2,200

6 0 0 64 0 500 0 0 2,560 2,200

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plans no stockouts. In fact, it carries inventory in all other periods. We describe this inventory as seasonal inventory because it is carried in anticipation of a future increase in demand.

If the seasonal fluctuation of demand grows, synchronization of supply and demand becomes more difficult, resulting in an increase in either inventory or backlogs as well as an increase in the total cost to the supply chain. This is illustrated in Example 8-1, in which the demand forecast is more variable.

TABLE 8-5 Demand Forecast with Higher Seasonal Fluctuation Month Demand Forecast

January 1,000

February 3,000

March 3,800

April 4,800

May 2,000

June 1,400

TABLE 8-6 Optimal Aggregate Plan for Demand in Table 8-5 Period,

t No.

Hired, Ht No. Laid Off, Lt

Workforce Size, Wt

Overtime, Ot

Inventory, It

Stockout, St

Subcontract, Ct

Total Production, Pt

Demand, Dt

0 0 0 80 0 1,000 0 0

1 0 16 64 0 2,560 0 0 2,560 1,000

2 0 0 64 0 2,120 0 0 2,560 3,000

3 0 0 64 0 880 0 0 2,560 3,800

4 0 0 64 0 0 1,220 140 2,560 4,800

5 0 0 64 0 0 660 0 2,560 2,000

6 0 0 64 0 500 0 0 2,560 1,400

EXAMPLE 8-1 Impact of Higher Demand Variability

All the data are exactly the same as in our previous discussion of Red Tomato, except for the

Obtain the optimal aggregate plan in this case.

Analysis In this case, the optimal aggregate plan (using the same costs as those used before) is shown in

Observe that monthly production remains the same, but both inventories and stockouts

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From Example 8-1, we can see that the increase in demand variability at the retailer increases seasonal inventory as well as planned costs.

Using the Red Tomato example, we also see that the optimal trade-off changes as the costs

decrease, it is better to vary capacity with demand while having less inventory and fewer backlogs.

EXAMPLE 8-2 Impact of Lower Costs of Hiring and Layoff

Analysis

a new optimal aggregate plan yields the plan shown in Table 8-7. Observe that the workforce size

As expected, the workforce size is varied (because the cost of varying capacity has decreased), whereas inventory and stockouts have decreased compared with the aggregate plan

hiring and layoff) not only decreases the total cost but also shifts the optimal balance toward using the volume flexibility while carrying lower inventories and allowing less stockout.

In the next section, we explain how to implement the linear programming methodology for aggregate planning using Microsoft Excel.

8.6 AGGREGATE PLANNING IN EXCEL

first do so by building a simple spreadsheet that allows what-if analysis and then build a more sophisticated model that allows optimization using linear programming.

Building a Basic Aggregate Planning Spreadsheet

The aggregate planner must decide on the number of people hired (Ht) or laid off (Lt) each month, along with any overtime (Ot) or subcontracting (Ct). Once these decisions have been

TABLE 8-7 Optimal Aggregate Plan for Hiring and Layoff Cost of $50/Worker Period,

t No.

Hired, Ht No. Laid Off, Ot

Workforce Size, Wt

Overtime, Ot

Inventory, It

Stockout, St

Subcontract, Ct

Total Production, Pt

Demand, Dt

0 0 0 80 0 1,000 0 0

1 0 35 45 0 1,200 0 0 1,800 1,600

2 0 0 45 0 0 0 0 1,800 3,000

3 42 0 87 0 280 0 0 3,480 3,200

4 1 0 88 0 0 0 0 3,520 3,800

5 0 27 61 0 240 0 0 2,440 2,200

6 0 0 61 0 500 0 20 2,440 2,200

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made, one can determine the workforce (Wt), production (Pt), inventory (It) and stockout (St) for each month (see Table 8-8), thus completing the aggregate plan. Figure 8-1 shows the final spreadsheet (available as Chapter8-trial-aggplan try different inputs for each decision variable in the appropriate cells in worksheet Planning. It is

* Wt and is shown in cells

each period. For each set of inputs, the outputs are calculated as shown in Table 8-8.

= *sum(D :D1 ) + *sum(E :E1 ) + 3 *sum(B :B1 ) + *sum(C :C1 ) + *sum(F :F1 ) + *sum(G :G1 ) + 1 *sum(I :I1 ) + 3 *sum(H :H1 )

The goal is to build an aggregate plan by changing inputs in a way that minimizes the total cost

Building an Aggregate Planning Spreadsheet Using Solver

To access Excel’s linear programming capabilities, use Solver (Data !Analysis ! Solver). To

Chapter8,9-examples), containing the following decision variables:

Wt = workforce size for Month t, t = Ht = number of employees hired at the beginning of Month t, t = Lt = number of employees laid off at the beginning of Month t, t = Pt = number of units produced in Month t, t = It = inventory at the end of Month t, t = St = number of units stocked out at the end of Month t, t = Ct = number of units subcontracted for Month t, t = Ot = number of overtime hours worked in Month t, t =

FIGURE 8-1 Basic Aggregate Planning Spreadsheet

TABLE 8-8 Building the Basic Aggregate Planning Spreadsheet Output Cells Relationship to Inputs Formula in Row 5 Copied to Cells

Workforce D5:D10 Wt = Wt-1 + Ht – Lt = D4 + B5 – C5 D6:D10 Production I5:I10 Pt = 40 * Wt + Ot/4 = 40*D5 + (E5/4) I6:I10 Inventory F5:F10 It = max(It-1 + Pt + Ct – Dt – St-1, 0) = max(F4 + I5 + H5 – G4 – J5,0) F6:F10 Stockout G5:G10 St = max(0, St-1 + Dt – It−1 – Pt – Ct) = max(0,J5 + G4 – I5 – H5 – F4) G6:G10

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Planning) illustrates what this table should look like. The deci-

Also note that column J contains the actual demand. The demand information is included because it is required to calculate the aggregate plan.

– straint table may be constructed as shown in Figure 8-3.

the six periods. Each constraint will eventually be written in Solver as

Cell value5… , = , or Ú6 In our case, we have constraints

M :M1 = , N :N1 Ú , O :O1 = , : 1 Ú

FIGURE 8-2 Spreadsheet Area for Decision Variables

Cell

M5

N5

O5

P5

Equation

8.2

8.3

8.4

8.5

Copied to

M6:M10

N6:N10

O6:O10

P6:P10

Cell Formula

=D5-D4-B5+C5

=40*D5+E5/4-I5

=F4-G4+I5+H5-J5-F5+G5

=-E5+10*D5

FIGURE 8-3 Spreadsheet Area for Constraints

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The third step is to create a cell containing the objective function, which is the way each solution is judged. This cell need not contain the entire formula but can be written as a formula using cells with intermediate cost calculations. For the Red Tomato example, the spreadsheet

hiring cost per worker, which is obtained from Table 8-3. Other cells are filled similarly. Cell

The fourth step is to use Data !Analysis ! – eters dialog box, enter the following information to represent the linear programming model:

Equal to: Select Min

Subject to the constraints:

= integer {Number of workers hired or laid off is integer} B :I1 Ú {All decision variables are nonnegative} F1 Ú G1 = M :M1 = 5Wt – Wt – 1 – Ht + Lt = for t = 1, . . . , 6 N :N1 Ú 5 Wt + Ot> – Pt Ú for t = 1, . . . , 6 O :O1 = {It – 1 – St – 1 + Pt + Ct – Dt – It + St = for t = 1, . . . , 6

: 1 Ú 51 Wt – Ot Ú for t = 1, . . . , 6 should be returned. If Solver does not return the optimal solution, solve the problem again after saving the solution that Solver has returned. (In some cases, multiple repetitions of this step may be required because of some flaws in the version of Solver that comes with Excel. Add-ins that do not have any of these issues are available at relatively low cost.) The optimal solution turns

Forecast Error in Aggregate Plans

The aggregate planning methodology we have discussed in this chapter does not take into account any forecast error. However, we know that all forecasts have errors. To improve the quality of these aggregate plans, forecasting errors must be considered. Forecasting errors are dealt with using either safety inventory, defined as inventory held to satisfy demand that is higher than fore- cast , or safety capacity, defined as capacity used to satisfy

FIGURE 8-4 Spreadsheet Area for Cost Calculations

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demand that is higher than forecast. A company can create a buffer for forecast error using safety inventory and safety capacity in a variety of ways, some of which are listed here:

8.7 BUILDING A ROUGH MASTER PRODUCTION SCHEDULE

From an aggregate plan, a planner must disaggregate the available information and build a rough

of each product family. We return to the Red Tomato example to illustrate a simple approach to disaggregate an aggregate plan. Although this approach is not necessarily optimal, it is simple to implement and allows for a feasibility check. More sophisticated methods (e.g., see Bitran and Hax, 1981) are available if a planner wants to search for better solutions. These methods, how- ever, are difficult to implement and may not be able to reflect all the complex realities. For this reason, we propose this simple approach.

at the aggregate level, but we will need to check feasibility at the disaggregate level. The first

expected sales (using percentage share from Table 8-1), as shown in Table 8-9 (see worksheet Chapter8-Table8-9

family. To get the feasibility of the plan, we divide the planned production quantity by the aver- age batch size and then round the answer down. For Family A, the planned number of setups

=

8-9. To check the feasibility of the planned schedule, we calculate the setup time and the produc- tion time for the planned number of batches and units of each product family. From Table 8-9,

+ – * =

FIGURE 8-5 Solver Parameters Dialog Box

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8.8 THE ROLE OF IT IN AGGREGATE PLANNING

Aggregate planning is arguably the supply chain area in which information technology has been used the most. The earliest IT supply chain products were aggregate planning modules, often called factory, production, or manufacturing planning. Some of the early modules focused only on obtaining a feasible production plan subject to constraints arising from demand and available

production plans, based on objectives such as increased output or lowered cost. These classic solutions generally formulated the aggregate planning problem as a linear

program to get a production schedule of products to be made in each period of time. Today, some planning modules incorporate nonlinear optimization to account for the fact that not all con- straints or reasonable objective functions are linear functions. However, given the large amount of data considered in producing aggregate plans—which can render nonlinear problems compu- tationally prohibitive—and the ability to create linear approximations of nonlinear functions, linear programming is often the best way to solve these problems.

results can be fairly unstable relative to inputs. A small change in an input such as demand can produce a new optimal plan that is quite different from the original plan. If plans become too volatile, the entire supply chain quickly begins to distrust them, effectively rendering them use- less. It is thus important to ensure that as new data arrive, plans are modified while trying to ensure some degree of stability.

times or capacities that are different from reality, the resulting aggregate plan is likely to lead to unhappy customers and high costs. It is thus important to track the accuracy of these parameters and ensure that people are held accountable for these inputs.

8.9 IMPLEMENTING AGGREGATE PLANNING IN PRACTICE

1. Think beyond the enterprise to the entire supply chain. Most aggregate planning today takes only the enterprise as its breadth of scope. However, many factors outside the enter- prise and throughout the supply chain can affect the optimal aggregate plan dramatically. There- fore, avoid the trap of thinking only about your own enterprise when planning. Work with downstream partners to produce forecasts, with upstream partners to determine constraints, and with any other supply chain entities that can improve the quality of the inputs into the aggregate plan. The plan is only as good as the quality of the inputs, so using the supply chain to increase the quality of the inputs will greatly improve the quality of the aggregate plan. Also make sure to communicate the aggregate plan to all supply chain partners who will be affected by it.

TABLE 8-9 Disaggregating the Aggregate Plan at Red Tomato Tools for Period 1

Family

Setup Time/Batch

(hour) Average

Batch Size

Production Time/Unit

(hour) Production Quantity

Number of Setups

Setup Time (hours)

Production Time (hours)

A 8 50 5.60 256 5 40 1,433.6

B 6 150 3.00 640 4 24 1,920.0

C 8 100 3.80 512 5 40 1,945.6

D 10 50 4.80 256 5 50 1,228.8

E 6 100 3.60 512 5 30 1,843.2

F 5 75 4.30 384 5 25 1,651.2

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2. Make plans flexible, because forecasts are always inaccurate. Aggregate plans are based on forecasts of future demand. Given that these forecasts are always inaccurate to some degree, the aggregate plan needs to have some flexibility built into it if it is to be useful. By building flexibility into the plan, when future demand changes or other changes occur, such as increases in costs, the plan can adjust appropriately to handle the new situation.

How do we create this flexibility? In addition to the suggestions earlier in the chapter, we recommend that a manager perform sensitivity analysis on the inputs into an aggregate plan. For example, if the plan recommends expanding expensive capacity while facing uncertain demand, examine the outcome of a new aggregate plan when demand is higher and lower than expected. If this examination reveals a small savings from expanding capacity when demand is high but a large increase in cost when demand is lower than expected, deciding to postpone the capacity investment decision is a potentially attractive option. Using sensitivity analysis on the inputs into the aggregate plan enables a planner to choose the best solution for the range of possibilities that could occur.

3. Rerun the aggregate plan as new data emerge. As we have mentioned, aggregate plans provide a map for the next 3 to 18 months. This does not mean that a firm should run aggre- gate plans only once every 3 to 18 months. As inputs such as demand forecasts change, managers should use the latest values of these inputs and rerun the aggregate plan. Be careful, however, to modify plans in a way that limits volatility.

4. Use aggregate planning as capacity utilization increases. Surprisingly, many com- panies do not create aggregate plans and instead rely solely on orders from their distributors or warehouses to determine their production schedules. These orders are driven either by actual demand or through inventory management algorithms. If a company has no trouble meeting demand efficiently this way, then the lack of aggregate planning may not harm the company sig- nificantly. However, when utilization becomes high and capacity is an issue, relying on orders to set the production schedule can lead to shortages and delays. When utilization is high, aggregate planning helps the firm effectively meet the forecasted demand.

8.10 SUMMARY OF LEARNING OBJECTIVES

1. Understand the importance of aggregate planning as a supply chain activity. Aggre- gate planning has a significant impact on supply chain performance and must be viewed as an activity that involves all supply chain partners. An aggregate plan prepared by an enterprise in isolation is not very useful because it does not take into account all requirements of the customer

of matching supply and demand. Good aggregate planning is done in collaboration with both customers and suppliers because accurate input is required from both stages. The quality of these inputs, in terms of both the demand forecast to be met and the constraints to be dealt with, deter- mines the quality of the aggregate plan. The results of the aggregate plan must also be shared across the supply chain because they influence activities for both customers and suppliers. For

determines planned supply.

2. Describe the information needed to produce an aggregate plan and the outputs obtained. To create an aggregate plan, a planner needs a demand forecast, cost and production information, and any supply constraints. The demand forecast consists of an estimate of demand for each period of time in the planning horizon. The production and cost data consist of capacity levels and costs to raise and lower them, production costs, costs to store the product, costs of stocking out the product, and any restrictions that limit these factors. Supply constraints deter- mine limits on outsourcing, overtime, or materials. The aggregate plan then determines capacity, production, and inventory decisions over the next 3 to 18 months.

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3. Explain the basic trade-offs to consider when creating an aggregate plan. The basic trade-offs involve balancing the cost of capacity, the cost of inventory, and the cost of stockouts to maximize profitability. Increasing any one of the three allows the planner to lower the other two.

4. Formulate and solve aggregate planning problems using Microsoft Excel. Aggre- gate planning problems can be solved in Excel by setting up cells for the objective function and the constraints and using the Solver to produce the solution.

Discussion Questions 1. What are some industries in which aggregate planning would

be particularly important? 2. What are the characteristics of the industries from Question 1

that make them good candidates for aggregate planning? 3. What are the main differences among the aggregate planning

strategies? 4. What types of industries or situations are best suited to the

chase strategy? The flexibility strategy? The level strategy? 5. What are the major cost categories needed as inputs for

aggregate planning?

6. How does the availability of subcontracting affect the aggre- gate planning problem?

7. If a company currently employs the chase strategy and the cost of training increases dramatically, how might this change the company’s aggregate planning strategy?

8. What are some key issues to consider when picking an aggre- gate unit of analysis?

9. How can aggregate planning be used in an environment of high demand uncertainty?

Exercises

TABLE 8-10 Monthly Demand for Cell Phones, in Thousands Month Demand

January 1,000

February 1,100

March 1,000

April 1,200

May 1,500

June 1,600

July 1,600

August 900

September 1,100

October 800

November 1,400

December 1,700

1. Skycell, a major European cell phone manufacturer, is mak- ing production plans for the coming year. Skycell has worked with its customers (the service providers) to come up with forecasts of monthly requirements (in thousands of phones)

Manufacturing is primarily an assembly operation, and capacity is governed by the number of people on the

hours each day. One person can assemble a phone every –

cent premium for overtime. The plant currently employs

product prices, carrying inventory from one month to the next incurs a cost of 3 euros per phone per month. Skycell currently has a no-layoff policy in place. Overtime is limited

wants to end the year with the same level of inventory. a. Assuming no backlogs, no subcontracting, and no new

hires, what is the optimum production schedule? What is the annual cost of this schedule?

b. Is there any value for management to negotiate an increase

c. Reconsider parts (a) and (b) if Skycell starts with only

additional overtime as the workforce size decreases? d. Consider part (a) for the case in which Skycell aims for a

level production schedule such that the quantity pro- duced each month does not exceed the average demand

Thus, monthly production, including overtime, should be

level production schedule? What is the value of overtime flexibility?

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2. Reconsider the Skycell data in Exercise 1. Assume that the

a. What is the average per unit of in-house production (including inventory holding and overtime cost) if the third party is not used?

b. How should Skycell use the third party? How does your

unit? c. Should Skycell use the third party if the per unit cost is

d. Why would Skycell use the third party even when the per- unit cost of the third party is higher than the average per- unit cost (including inventory holding and overtime) for in-house production?

3. Reconsider the Skycell data in Exercise 1. Assume that the

assume no subcontracting option. Skycell has a team of

a. What is the optimal production, hiring, and layoff schedule? b. How does the optimal schedule change if the seasonal

employees, will Skycell gain significantly if it carries

employees?

more by eliminating its no-layoff policy for its permanent employees or by increasing the seasonal employee pool

be hired or laid off at the same cost as the seasonal employees.

4. FlexMan, an electronics contract manufacturer, uses its Topeka, Kansas, facility to produce two product categories: routers and switches. Consultation with customers has indicated a demand

– sands of units) to be as shown in Table 8-11.

Manufacturing is primarily an assembly operation, and capacity is governed by the number of people on the pro-

switches in inventory. The cost of holding a router in inven-

products are paid for by the customer at existing market rates when purchased. Thus, if FlexMan produces early and holds

in inventory, the company recovers less given the rapidly dropping component prices. a. Assuming no backlogs, no subcontracting, no layoffs, and

no new hires, what is the optimum production schedule for FlexMan? What is the annual cost of this schedule? What inventories does the optimal production schedule build? Does this seem reasonable?

b. Is there any value for management to negotiate an increase

c. Reconsider parts (a) and (b) if FlexMan starts with only

additional overtime as the workforce size decreases? 5.

considering the option of changing workforce size with

reach full production capacity. During those two months, a –

pating a similar demand pattern next year, FlexMan aims to

a. What is the optimal production, hiring, and layoff sched- ule? What is the cost of such a schedule?

b. If FlexMan could improve its training so new employees achieve full productivity right away, how much improve- ment in annual cost would the company see? How is the hiring and layoff policy during the year affected by this change?

6. FlexMan has identified a third party that is willing to produce routers and switches as needed. The third party will charge

a. How should FlexMan use the third party if new employ-

months?

TABLE 8-11 Demand Forecast for FlexMan Month Router Demand Switch Demand

January 1,800 1,600

February 1,600 1,400

March 2,600 1,500

April 2,500 2,000

May 800 1,500

June 1,800 900

July 1,200 700

August 1,400 800

September 2,500 1,400

October 2,800 1,700

November 1,000 800

December 1,000 900

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b. How should FlexMan use the third party if new employ- ees are able to achieve full productivity right away?

c. Why does the use of the third party change with the pro- ductivity of new employees?

7. has signed a service-level agreement with its customers and committed to carry safety inventory from one month to

month’s demand. Thus, FlexMan is committed to carrying over at least .1 * 1,8 , = 7 , routers and

.1 * 1, , = , switches in inventory from December to January.

a. Assuming no backlogs, no subcontracting, no layoffs, and no new hires, what is the optimum production schedule for FlexMan? What is the annual cost of this schedule?

b. How much does the service contract mandating minimum inventories increase costs for FlexMan?

c. What would be the increase in cost if FlexMan agreed to a

minimum for routers? What would be the increase in cost

the two is better for FlexMan?

Bibliography Bitran, G. R., and A. Hax. “Disaggregation and Resource Alloca-

– ables.” Management Science

Jacobs, F. Robert, Richard B. Chase, and Nicholas J. Aquilano. Operations and Supply Management,

Nahmias, Steven. Production & Operations Analysis

CASE STUDY Kloss Planters and Harvesters

– –

ning team wondering whether it made sense to replace the separate plants for planters and harvesters with a single plant that could assemble both.

A Brief History of Planters and Harvesters

Since humans started farming, they have sought to ease the task of planting and harvesting their crops. In his Naturalis historica a reaping machine that broke off the ears of corn and accumulated them in a box. In 1799, the first verifiable patent for a reaping machine was granted to the English inventor Joseph Boyce. In 1831, Cyrus Hall McCormick introduced the world’s first corn reaper under the name

– ered by a horse. Over time, other companies introduced combine harvesters that were self-propelled and pow- ered by a combustion engine. Harvesters today allow a farmer to harvest large cornfields in the comfort of air- conditioned cabs!

After years of planting corn the way Squanto had taught the settlers, help arrived for American farmers when hand planters were introduced during the late

farmers to plant two acres per day instead of the single acre possible with hand-planting. Today, planters can

The evolution of both planters and harvesters has cer- tainly made the farmer much more effective.

KPH Production Planning

– vesters in Ames, Iowa. Demand for each product was

– erally planted between March and May and harvested between September and November. As a result, demand for planters peaked in March, whereas demand for har- vesters peaked in September. Each plant aimed for a pro- duction plan that allowed them to meet annual demand at the lowest possible cost.

The capacity of each plant was determined by the number of assembly workers available. Each machine

during regular time. They could be asked to work up to

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230

– –

ing the low season and rehired them for the peak. Each

Each plant generally built inventory in anticipation of

delay a customer order by stocking out in a given month and filling the stockout from next month’s production.

in discounts offered to customers to keep them happy. The company had a policy of ensuring that there were no

stockouts in December so the new year started out with- out any unfilled orders. The material cost for each

inventory. The production plan at each plant attempted to meet demand in Table 8-8 at the lowest possible cost while ensuring that the plant ended December of the coming year with the same labor and inventory as the previous December.

Options for New Plants

Given the age of the current plants, Kevin planned to replace them with new plants. One option was to replace each plant with a similar new plant. The other option was to expand the Davenport plant to allow for both planter and harvester assembly. The investment for the two options was going to be very similar. Thus, Kevin requested his strategic planning team to identify any advantages of a single plant.

The team pointed out that countercyclical nature of demand for planters and harvesters canceled their individual seasonality of demand to some extent. Com- mon assembly steps for the two products would allow

had the potential to significantly reduce the number of employees laid off and rehired each year. Besides improving employee morale, such a change had the pos- sibility of reducing costs. Kevin asked the team to quan- tify any cost related and employee related advantages offered by the single plant. He would base his decision on these numbers.

TABLE 8-12 Demand Forecast for Planters and Harvesters Month Planters Harvesters

January 600 100

February 850 100

March 1,300 100

April 800 100

May 550 100

June 100 200

July 100 500

August 100 1,000

September 100 1,500

October 100 700

November 100 450

December 300 100

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In Chapter 8, we discussed how companies use aggregate planning to make supply plans in a way that maximizes profits. In this chapter, we build on the knowledge we gained from Chapter 8 and expand our scope beyond the enterprise to the supply chain as we deal with predictable variability of demand. We also discuss how demand may be managed to counter pre- dictable variability through the use of price and promotion. By managing supply and demand together, managers can maximize overall profitability of a supply chain.

9.1 RESPONDING TO PREDICTABLE VARIABILITY IN THE SUPPLY CHAIN

In Chapter 8, we discussed how companies use aggregate planning to optimally plan supply. Demand for many products changes frequently from period to period, often because of a predict- able influence. These influences include seasonal factors that affect products (e.g., lawn mowers and ski jackets), as well as nonseasonal factors (e.g., promotions or product adoption rates) that may cause large, predictable increases or declines in sales.

Predictable variability is change in demand that can be forecast. Products that undergo this type of change in demand create numerous problems in the supply chain, ranging from high lev- els of stockouts during peak demand periods to high levels of excess inventory during periods of low demand. These problems increase the costs and decrease the responsiveness of the supply

Sales and Operations Planning Planning Supply and Demand in a Supply Chain

C H A P T E R

9

LEARNING OBJECTIVES After reading this chapter, you will be able to

231

1. Manage supply to improve synchronization in a supply chain in the face of predictable variability.

2. Manage demand to improve synchronization in a supply chain in the face of predictable variability.

3. Use sales and operations planning to maximize profitability when faced with predictable variability in a supply chain.

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– nificantly improve performance when applied to predictably variable products.

Faced with predictable variability, a company’s goal is to respond in a manner that bal- ances supply with demand to maximize profitability. The goal of sales and operations planning is

1. Manage supply using capacity, inventory, subcontracting, and backlogs. 2. Manage demand using short-term price discounts and promotions.

The use of these tools enables the supply chain to increase profitability, because supply and demand are matched in a more coordinated fashion.

To illustrate some of the issues involved, let us consider John Deere, a manufacturer of agricultural equipment such as planters and combine harvesters. Demand for planters is sea-

way requires John Deere to carry enough manufacturing capacity to meet demand for planters during the peak demand period. The advantage of this approach is that John Deere incurs low inventory costs because no inventory is carried from period to period. The disadvantage, how- ever, is that much of the expensive capacity is unused during most months, when demand is lower.

Another approach to meeting seasonal demand is for Deere to build up inventory dur- ing the off-season to meet demand during the peak months. The advantage of this approach lies in the fact that Deere can get by with a lower-capacity, less expensive factory. High inventory carrying costs, however, add to the cost of this alternative. A third approach is for Deere to work with its retail partners in the supply chain to offer a price promotion to farm- ers before the peak months, during periods of low demand. This promotion shifts some of the peak demand for planters forward into a slow period, thereby reducing the seasonal

– mizes its profitability.

– tions, with sales typically managing demand while operations manages supply. At a higher level, supply chains suffer from this phenomenon as well, with retailers managing demand indepen- dently and manufacturers managing supply independently. Lack of coordination hurts supply chain profits when supply and demand management decisions are made independently. There- fore, supply chain partners must work together across functions and enterprises to coordinate

– –

is one of the biggest differentiators between top performers and other organizations. First, we focus on actions that a supply chain can take to deal with predictable variability

by managing supply.

9.2 MANAGING SUPPLY

1. Production capacity 2. Inventory

In general, companies use a combination of varying capacity and inventory to manage sup- ply. In the following sections, we list some specific approaches that allow firms to reduce the amount of capacity and inventory required to deal with predictable variability.

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Managing Capacity

Firms use a combination of the following approaches to reduce the cost of capacity required to

Time flexibility from workforce: In this approach, a firm uses flexible work hours by the workforce to vary capacity with demand. In many instances, plants do not operate continu- ously and are left idle during portions of the day or week. Therefore, spare plant capacity exists in the form of hours when the plant is not operational. For example, many plants do not run three shifts, so the existing workforce could work overtime during peak periods to produce more to meet demand. The overtime is varied to match the fluctuation in demand. In such settings, use of a part-time workforce can further increase capacity flexibility by enabling the firm to put more people to work during peak periods. This system allows production from the plant to match demand from customers more closely.

Use of seasonal workforce: In this approach, a firm uses a temporary workforce dur- ing the peak season to increase capacity to match demand. The tourism industry often uses sea- sonal workers. A base of full-time employees exists, and more are hired only for the peak season. Toyota regularly uses a seasonal workforce in Japan to match supply and demand better. This approach may be hard to sustain, however, if the labor market is tight.

Use of dual facilities—specialized and flexible: In this approach, a firm uses

output of products over time in an efficient manner. Flexible facilities produce a widely varying volume and variety of products, but at a higher unit cost. For instance, an electron- ics component manufacturer might have specialized facilities for each type of circuit board as well as a flexible facility that can manufacture all types of circuit boards. Each special- ized facility can produce at a relatively steady rate, with fluctuations being absorbed by the flexible facility.

Use of subcontracting: In this approach, a firm subcontracts peak production so inter- nal production remains level and can be done cheaply. For such an approach to work, the subcon- tractor must have flexible capacity and the ability to lower cost by pooling the fluctuations in demand across different manufacturers. Thus, the flexible subcontractor capacity must have both volume (fluctuating demand from a manufacturer) as well as variety (demand from several man- ufacturers) flexibility to be sustainable. For example, most power companies do not have the capacity to supply their customers with all the electricity demanded on peak days. They rely instead on being able to purchase power from suppliers and subcontractors that have excess elec- tricity. This allows the power companies to maintain a level supply and, consequently, a lower cost.

Designing product flexibility into the production processes: In this approach, a firm has flexible production lines whose production rate can easily be varied. Production is then changed to match demand. Hino Trucks in Japan has several production lines for different prod- uct families in the same plant. The production lines are designed so that changing the number of workers on a line can vary the production rate. As long as variation of demand across different product lines is complementary (i.e., when one goes up, the other tends to go down), the capacity

requires that the workforce be multiskilled and able to adapt easily to being moved from line to line. Production flexibility can also be achieved if the production machinery is flexible and can be changed easily from producing one product to producing another. This approach is effective

products with seasonal demand try to exploit this approach by carrying a portfolio of products that have peak demand seasons distributed over the year. A classic example is that of a lawn mower manufacturer that also manufactures snowblowers to maintain a steady demand on its factory throughout the year.

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Managing Inventory

Firms use a combination of the following approaches to reduce the level of inventory required to

Using common components across multiple products: In this approach, a firm designs common components to be used in multiple products. The total demand of these components is relatively stable, even though each product displays predictable variability. The use of a common engine for both lawn mowers and snowblowers allows for engine demand to be relatively stable even though lawn mower and snowblower demand fluctuates over the year. Therefore, the part of the supply chain that produces components can easily synchronize supply with demand, and a relatively low inventory of parts has to be built up.

Build inventory of high-demand or predictable-demand products: When most of the products a firm produces have the same peak demand season, the previous approach is not fea- sible. In such an environment, it is best for the firm to build products that have more predictable demand during the off-season, because there is less to be learned about their demand by waiting. Production of more uncertain items should take place closer to the selling season, when demand is more predictable. Consider a manufacturer of winter jackets that produces jackets both for retail sale and for the Boston police and fire departments. Demand for the Boston police and fire jackets is more predictable; these jackets can be made in the off-season and stocked up until winter. The retail jackets’ demand, however, will likely be better known closer to the time when they are sold, because fashion trends can change quickly. Therefore, the manufacturer should produce the retail jackets close to the peak season, when demand is easier to predict. This strat- egy helps the supply chain synchronize supply and demand better.

Next, we consider actions a supply chain can take to improve profitability by managing demand.

9.3 MANAGING DEMAND

– ple, John Deere offers a discount to farmers who are willing to take ownership of a planter during the off-season. The further from the peak that a farmer places an order, the larger the discount offered by Deere. The goal here is to move demand from the peak period to the off-peak period, thus reducing predictable variability. It is thus important to understand how promotions influ- ence demand.

When a promotion is offered during a period, that period’s demand tends to go up. This

1. Market growth: An increase in consumption of the product occurs from either new or existing customers. For example, when Toyota offers a price promotion on the Camry, it may attract buyers who were considering the purchase of a lower-end model. Thus, the promotion increases the size of the overall family sedan market as well as increasing Toyota’s sales.

2. Stealing share: Customers substitute the firm’s product for a competitor’s product. When Toyota offers a Camry promotion, buyers who might have purchased a Honda Accord may now purchase a Camry. Thus, the promotion increases Toyota’s sales while keeping the overall size of the family sedan market the same.

3. Forward buying: Customers move up future purchases (as discussed in Chapter 11) to the present. A promotion may attract buyers who would have purchased a Camry a few months down the road. Forward buying does not increase Toyota’s sales in the long run and also leaves the family sedan market the same size.

The first two factors increase the overall demand for Toyota, whereas forward buying simply shifts future demand to the present. It is important to understand the relative impact from the three factors as a result of a promotion before making a decision regarding the optimal timing of

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the promotion. In general, as the fraction of increased demand coming from forward buying

promotion during a peak period that has significant forward buying creates even more variable demand than before the promotion. Product that was once demanded in the slow period is now demanded in the peak period, making this demand pattern even more costly to serve.

Factors Influencing the Timing of a Promotion

If a promotion primarily results in forward buying (as may be the case for a product like detergent), it is best to use promotions to reduce the seasonal peak by offering a price discount

if the manufacturer has a high cost of holding inventory or finds it expensive to change produc- tion levels. It is for this reason that John Deere offers its promotion during low-demand periods before the peak. In contrast, if a promotion results in a significant increase in sales by attracting new buyers, it may be better to offer a price discount during the peak period, when many buyers are in the market for the product. The increased cost of production because of the higher peak demand resulting from a promotion is likely to be offset by the margin obtained from new buy- ers. Table 9-1 summarizes the impact of various factors on the optimal timing of promotions.

9.4 SALES AND OPERATIONS PLANNING AT RED TOMATO

Promotion decisions are often made by retailers without taking into account the impact on the rest of the supply chain. In this section, our goal is to show how supply chain members can col- laborate on pricing and aggregate planning (both demand and supply management) decisions to maximize supply chain profitability. Let us return to Red Tomato Tools, the gardening tools manufacturer discussed in Chapter 8. Green Thumb Gardens is a large retail chain that has signed an exclusive contract to sell all products made by Red Tomato Tools. Demand for garden tools peaks in the spring months of March and April, as gardeners prepare to begin planting. In plan- ning, the goal of both firms should be to maximize supply chain profits, because this outcome leaves them more money to share. For profit maximization to take place, Red Tomato and Green Thumb need to devise a way to collaborate and, just as important, determine a way to split the

TABLE 9-1 Summary of Impact on Promotion Timing Factor Impact on Timing of Promotion/Forward Buy

High forward buying Favors promotion during low-demand periods

High ability to steal market share Favors promotion during peak-demand periods

High ability to increase overall market Favors promotion during peak-demand periods

High margin Favors promotion during peak-demand periods

Low margin Favors promotion during low-demand periods

High manufacturer holding costs Favors promotion during low-demand periods

High costs of changing capacity Favors promotion during low-demand periods

High retailer holding costs Decreases forward buying by retailer

High promotion elasticity of consumer Decreases forward buying by retailer

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supply chain profits. Determining how these profits will be allocated to different members of the supply chain is key to successful collaboration.

Red Tomato and Green Thumb are exploring how the timing of retail promotions affects profitability. Are they in a better position if they offer the price promotion during the peak period of demand or during a low-demand period? Green Thumb’s vice president of sales favors a pro- motion during the peak period because this increases revenue by the largest amount. In contrast, Red Tomato’s vice president of manufacturing is against such a move because it increases manu-

The Base Case

We start by considering the base case discussed in Chapter 8. Each tool has a retail price of $40. Red Tomato ships assembled tools to Green Thumb, where all inventory is held. Green Thumb has a starting inventory in January of 1,000 tools. At the beginning of January, Red Tomato has a workforce of 80 employees at its manufacturing facility in Mexico. There are 20 working days in each month, and Red Tomato workers earn the equivalent of $4 per hour. Each employee works eight hours on normal time and the rest on overtime. Because the Red Tomato operation consists mostly of hand assembly, the capacity of the production operation is determined primarily by the total labor hours worked (i.e., it is not limited by machine capacity). No employee works more than 10 hours of overtime per month. The various costs are shown in Table 9-2.

There are no limits on subcontracting, inventories, and stockouts. All stockouts are back- logged and supplied from the following month’s production. Inventory costs are incurred on the ending inventory in each month. The companies’ goal is to obtain the optimal aggregate plan that leaves at least 500 units of inventory at the end of June (i.e., no stockouts at the end of June and

TABLE 9-2 Costs for Red Tomato and Green Thumb Item Cost

Material cost $10/unit

Inventory holding cost $2/unit/month

Marginal cost of a stockout $5/unit/month

Hiring and training costs $300/worker

Layoff cost $500/worker

Labor hours required 4/unit

Regular-time cost $4/hour

Overtime cost $6/hour

Cost of subcontracting $30/unit

FIGURE 9-1 Base Case Aggregate Plan for Red Tomato and Green Thumb

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All figures and analysis in this chapter come from the spreadsheet Chapter8,9-examples, which

Chapter8-trial-aggplan. The spreadsheet contains instructions for use and worksheets corre- sponding to Figures 9-1 to 9-5.

case aggregate plan for Red Tomato and Green Thumb is shown in Figure 9-1 (this is the same as discussed in Chapter 8 and shown in Table 8-4).

Total cost over planning horizon = $ 422,660 Revenue over planning horizon = $ 640,000 Profit over the planning horizon = $ 217,340

When to Promote: Peak or Off-Peak?

Green Thumb estimates that discounting a Red Tomato tool from $40 to $39 (a $1 discount) in any period results in the period demand increasing by 10 percent because of increased consump- tion or substitution. Further, 20 percent of each of the two following months’ demand is moved forward. Management would like to determine whether it is more effective to offer the discount in January or April. We analyze the two options by considering the impact of a promotion on demand and the resulting optimal aggregate plan.

IMPACT OF OFFERING A PROMOTION IN JANUARY The team first considers the impact of offering the discount in January. To simulate this option in the spreadsheet Chapter8,9-examples, enter 1 in cell E24 (this sets promotion to be on) and 1 in cell E25 (this sets the promotion in Period 1—i.e., January). The new forecast accounts for the fact that consumption will increase by 10 percent in January and 20 percent of the demand from February and March is moved for- ward to January. Thus, with a January promotion, the new demand forecast for January is obtained by adjusting the base case demand from Figure 9-1 and is given by (1,600 : 1.1) + [0.2 : (3,000 + 3,200)] = 3,000 (see Cell J5 in Figure 9-2). The new demand forecast for Febru- ary is 3,000 : 0.8 = 2,400, and the new demand forecast for March is 3,200 : 0.8 = 2,560. For

Total cost over planning horizon = $ 422,080 Revenue over planning horizon = $ 643,400

Profit over planning horizon = $ 221,320

FIGURE 9-2 Optimal Aggregate Plan When Discounting Price in January to $39

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Compared with the base case, offering a discount in January results in lower seasonal inventory, a somewhat lower total cost, and a higher total profit.

IMPACT OF OFFERING A PROMOTION IN APRIL Now, management considers the impact of offering the discount in April. To simulate this option in the spreadsheet Chapter8,9-examples, enter 1 in cell E24 (this sets promotion to be on) and 4 in cell E25 (this sets the promotion in Period 4—i.e., April). If Green Thumb offers the discount in April, the demand forecast is as

and is shown in Figure 9-3. Compared with discounting in January (Figure 9-2), discounting in April requires more capacity (in terms of workforce) and leads to a greater buildup of seasonal inventory and larger stockouts because of the big jump in demand in April. With a discount in

Total cost over planning horizon = $ 438,920 Revenue over planning horizon = $ 650,140

Profit over planning horizon = $ 211,220

a promotion in April results in a lower supply chain profit, compared with the base case of not

offer the discount in the off-peak month of January. Even though revenues are higher when the discount is offered in April, the increase in operating costs makes it a less profitable option. A promotion in January allows Red Tomato and Green Thumb to increase the profit they can share.

Note that this analysis is possible only because the retailer and manufacturer have an

our earlier statement that it is not appropriate for a supply chain to leave pricing decisions solely in the domain of retailers and aggregate planning solely in the domain of manufacturers, with each having individual forecasts. It is crucial that forecasts, pricing, and aggregate planning be coordinated in a supply chain.

optimal action is different if most of the demand increase comes from market growth or stealing market share rather than from forward buying. We now illustrate the scenario in which a discount leads to a large increase in consumption.

When to Offer a Promotion If Discount Leads to a Large Increase in Consumption

Reconsider the situation in which discounting a unit from $40 to $39 results in the period demand increasing by 100 percent (instead of the 10 percent considered in the previous analysis) because

FIGURE 9-3 Optimal Aggregate Plan When Discounting Price in April to $39

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of increased consumption or substitution. Further, 20 percent of each of the two following months’ demand is moved forward. The supply chain team wants to determine whether it is pref- erable to offer the discount in January or April under these conditions. To simulate this scenario, change the entry in cell H24 (increase in consumption) of spreadsheet Chapter8,9-examples

be on. The base case when no promotion is offered remains unchanged as shown in Figure 9-1. We now repeat the analysis for the cases in which the promotion is offered in January (off-peak) and April (peak).

IMPACT OF OFFERING A PROMOTION IN JANUARY For a January promotion, set the entry in cell E25 to 1 (Period 1, January). If the discount is offered in January, the January demand fore- cast is obtained as (1,600 : 2) + [0.2 : (3,000 + 3,200)] = 4,440. This is much higher than the same forecast in Figure 9-2 because we have assumed that consumption in the promotion month increases by 100 percent, rather than the 10 percent assumed earlier. The demand forecast for a

Total cost over planning horizon = $ 456,880 Revenue over planning horizon = $ 699,560

Profit over planning horizon = $ 242,680

higher profit than the base case (Figure 9-1).

IMPACT OF OFFERING A PROMOTION IN APRIL For an April promotion, set the entry in cell E25 to 4 (Period 4, April). If the discount is offered in April, the April demand forecast is obtained as (3,800 : 2) + [0.2 : (2,200 + 2,200)] = 8,480. With a promotion in April and a large increase in consumption, the April peak is much higher in Figure 9-5 compared with peak demand in Figure 9-4 (with a January promotion). For an April promotion with a large increase in consump-

Total cost over planning horizon = $ 536,200 Revenue over planning horizon = $ 783,520

Profit over planning horizon = $ 247,320

FIGURE 9-4 Optimal Aggregate Plan When Discounting Price in January to $39 with Large Increase in Demand

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When comparing Figures 9-5 and 9-4, observe that with an April promotion (Figure 9-5), there are no layoffs and the full workforce is maintained. The April promotion requires a much higher level of seasonal inventory and also uses stockouts and subcontracting to a greater extent than a January promotion. It is clear that costs will go up significantly with an April promotion. The interesting observation is that revenues go up even more (because of a larger consumption increase), making overall profits higher with an April promotion compared with a January pro- motion. As a result, when the increase in consumption from discounting is large and forward buying is a small part of the increase in demand from discounting, the supply chain is better off offering the discount in the peak-demand month of April, even though this action significantly increases supply chain costs.

Exactly as discussed earlier, the optimal aggregate plan and profitability can also be deter- mined for the case in which the unit price is $31 (enter 31 in cell H23) and the discounted price is $30. The results of the various instances are summarized in Table 9-3.

From the results in Table 9-3, we can draw the following conclusions regarding the impact

1. As seen in Table 9-3, average inventory increases if a promotion is run during the peak period and decreases if the promotion is run during the off-peak period.

2. Promoting during a peak-demand month may decrease overall profitability if there is a small increase in consumption and a significant fraction of the demand increase results from a forward buy. In Table 9-3, observe that running a promotion in April decreases profitability when forward buying is 20 percent and the demand increase from increased consumption and substitu- tion is 10 percent.

TABLE 9-3 Supply Chain Performance Under Different Scenarios

Regular Price

Promotion

Price

Promotion

Period

Percentage of Increase in Demand

Percentage of Forward

Buy

 

Profit

Average

Inventory

$40 $40 NA NA NA $217,340 875

$40 $39 January 10% 20% $221,320 515

$40 $39 April 10% 20% $211,220 932

$40 $39 January 100% 20% $242,680 232

$40 $39 April 100% 20% $247,320 1,492

$31 $31 NA NA NA $73,340 875

$31 $30 January 100% 20% $84,280 232

$31 $30 April 100% 20% $69,120 1,492

FIGURE 9-5 Optimal Aggregate Plan When Discounting Price in April to $39 with Large Increase in Demand

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3. As the consumption increase from discounting grows and forward buying becomes a smaller fraction of the demand increase from a promotion, it is more profitable to promote dur- ing the peak period. From Table 9-3, for a sale price of $40, it is optimal to promote in the off- peak month of January, when forward buying is 20 percent and increased consumption is 10 percent. When forward buying is 20 percent and increased consumption is 100 percent, however, it is optimal to promote in the peak month of April.

4. As the product margin declines, promoting during the peak-demand period becomes less profitable. In Table 9-3, observe that for a unit price of $40, it is optimal to promote in the peak month of April when forward buying is 20 percent and increased consumption is 100 per- cent. In contrast, if the unit price is $31, it is optimal to promote in the off-peak month of January for the same level of forward buying and increase in consumption.

A key point from the Red Tomato supply chain examples we have considered in this chap- ter is that when a firm is faced with seasonal demand, it should use a combination of pricing (to manage demand) and production and inventory (to manage supply) to improve profitability. The precise use of each lever varies with the situation. This makes it crucial that enterprises in a sup-

then are profits maximized.

9.5 IMPLEMENTING SALES AND OPERATIONS PLANNING IN PRACTICE

1. Coordinate planning across enterprises in the supply chain. For a supply chain to manage predictable variability successfully, the entire chain must work toward the one goal of maximizing profitability. Every member of a supply chain may agree with this in principle. In reality, however, it is difficult for an entire supply chain to agree on how to maximize profitabil- ity. Firms have even had trouble getting different functions within an enterprise to plan collab- oratively. Incentives play a large role in this. Within a company, sales often has incentives based on revenue, whereas operations has incentives based on cost. Within a supply chain, different enterprises are judged by their own profitability, not necessarily by the overall supply chain’s profitability. From the examples considered earlier, it is clear that without a focus on getting companies to work together, a supply chain will return suboptimal profits. Collaboration should occur through the formation of joint teams. Incentives of the members of a supply chain must be aligned. High-level support within an organization is needed because this coordination often requires groups to act against their traditional operating procedures. Although this collaboration is difficult, the payoffs are significant. The concept of collaborative forecasting, planning, and replenishment is discussed in greater detail in Chapter 10.

2. Take predictable variability into account when making strategic decisions. Predict- able variability has a tremendous impact on the operations of a company. A firm must always take this impact into account when making strategic decisions. However, predictable variability is not always taken into account when strategic plans are made, such as what type of products to offer, whether or not to build new facilities, and what sort of pricing structure a company should have. As indicated in this chapter, the level of profitability is greatly affected by predictable vari- ability and, therefore, the success or failure of strategic decisions can be determined by it.

3. Ensure that senior leadership owns the S&OP process. – cess owner is like that of the conductor of an orchestra—to bring different functions and organi- zations together in a supply chain. Given competing interests, this alignment is unlikely unless

4. Ensure that the S&OP process modifies plans as the reality or forecasts change. It

supply circumstances may leave the reality different from plan. In such a situation, it is important

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for the planners to alert the supply chain regarding the old plan and provide a new plan that –

cess should be modified as forecasts or marketing plans are adjusted.

9.6 SUMMARY OF LEARNING OBJECTIVES

1. Manage supply to improve synchronization in a supply chain in the face of predict- able variability. To manage supply with the goal of maximizing profit, companies can reduce the capacity required through the use of workforce flexibility, subcontracting, dual facilities, and product flexibility. Companies can reduce the inventory required by emphasizing common parts and building and holding products with predictable demand ahead of time. These methodologies, combined with aggregate planning, enable a company to manage supply effectively.

2. Manage demand to improve synchronization in a supply chain in the face of predict- able variability. To manage demand with the goal of maximizing profit, companies must use pricing and promotion decisions in concert with supply planning. The timing of promotions can have a tremendous impact on demand. Therefore, using pricing to shape demand can help syn- chronize the supply chain.

3. Use sales and operations planning to maximize profitability when faced with predict- able variability in a supply chain. To handle predictable variability in a profit-maximizing manner, supply chains must coordinate the management of both supply and demand. This requires coordinated planning across all stages of the supply chain to select pricing and promo- tion plans and aggregate plans that maximize supply chain profit.

Discussion Questions 1. What are some obstacles to creating a flexible workforce?

What are the benefits? 2. Discuss why the use of subcontractors to handle peak demand

can often allow a company to meet demand at lower cost. 3. In which industries would you tend to see dual facility types

(some facilities focusing on only one type of product and others able to produce a wide variety)? In which industries would this be relatively rare? Why?

4. Discuss how you would set up a collaboration mechanism for the enterprises in a supply chain.

5. What are some product lines that use common parts across many products? What are the advantages of doing this?

6. Discuss how a company can get sales and operations to work together with the common goal of coordinating supply and demand to maximize profitability.

7. How can a firm use pricing to change demand patterns? 8. Why would a firm want to offer pricing promotions in its

peak-demand periods? 9. Why would a firm want to offer pricing promotions during its

low-demand periods?

Exercises 1. Lavare, located in the Chicago suburbs, is a major manufac-

turer of stainless steel sinks. Lavare is in the middle of the demand and supply planning exercise for the coming year. Anticipated monthly demand from distributors over the 12 months is shown in Table 9-4.

Capacity at Lavare is governed by the number of machine operators it hires. The firm works 20 days a month, with a regular operating shift of eight hours per day. Any time beyond that is considered overtime. Regular-time pay is

– ited to 20 hours per month per employee. The plant currently

has 250 employees. Each sink requires two hours of labor input. It costs $3 to carry a sink in inventory for a month.

a price of $125 each. We assume that no stockouts are allowed and the starting inventory entering January is 5,000 units and the desired ending inventory in December is also 5,000 units.

Market research has indicated that a promotion drop- ping prices by 1 percent in a given month will increase sales in that month by 20 percent and bring forward 10 percent demand from each of the following two months. Thus, a 1 percent drop in price in March increases sales in March

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TABLE 9-4 Anticipated Monthly Demand at Lavare Month Demand Month Demand

January 10,000 July 30,000

February 11,000 August 29,000

March 15,000 September 21,000

April 18,000 October 18,000

May 25,000 November 14,000

June 26,000 December 11,000

TABLE 9-5 Anticipated Monthly Demand at Jumbo Month Demand Month Demand

January 12,000 July 24,000

February 11,000 August 20,000

March 14,000 September 15,000

April 20,000 October 10,000

May 25,000 November 11,000

June 27,000 December 10,000

by 3,000 (= 0.2 * 15,0002 and shifts 1,800 1= 0.1 * 18,0002 units in demand from April and 2,500 1= 0.1 * 25,0002 units from May forward to March. a. What is the optimal production plan for the year if we

assume no promotions? What is the annual profit from this plan? What is the cost of this plan?

b. Is it better to promote in April or July? How much increase in profit can be achieved as a result?

c. If sinks are sold for $250 instead of $125, does the deci- sion about the timing of the promotion change? Why?

2. Consider the data for Lavare in Exercise 1. We now assume that Lavare can change the size of the workforce by laying off or hiring employees. Hiring a new employee incurs a cost of $1,000; laying off an employee incurs a layoff cost of $2,000. a. What is the optimal production plan for the year if we

assume no promotions? What is the annual profit with this plan? What is the cost of this plan?

b. Is it better to promote in April or July? How much increase in profit can be achieved as a result?

c. If the holding cost for sinks increases from $3 per month to $5 per month, does the decision of the timing of pro- motion change? Why or why not?

3. Return to the data for Lavare in Exercise 1. Now, assume that a third party has offered to produce sinks at $74 per unit. How does this change affect the optimal production plan without a promotion? How does this change affect the opti- mal timing of a promotion? Explain the changes.

4. Jumbo manufactures bicycles for all ages. The demand fore- cast for the coming year is as shown in Table 9-5.

Capacity at Jumbo is limited by the number of employ- ees it hires. Employees are paid $10 per hour for regular time and $15 per hour for overtime. Each bicycle requires 2 hours of work from one employee. The plant works 20 days a

restricted to a maximum of 20 hours per employee per month. Jumbo currently has 250 employees and prefers not to change that number.

Each bicycle uses $35 of material. Carrying a bicycle in inventory from one month to the next costs $4. Jumbo starts with 4,000 bicycles in inventory and wants to end the year with 4,000 bicycles in inventory. Bicycles are currently sold to retailers for $80 each. The market is shared between

Jumbo is in the process of making its production plan- ning and promotion decisions. Jumbo wants to consider only

price by $3 (from $80 to $77) for one month in the year. The outcome of this action by Jumbo is influenced by the action

demand for Jumbo is as shown in Table 9-5. If Jumbo pro-

consumption (this does not include forward buying) in that month increase by 40 percent and forward buying of 10 per-

– motes in a given month but Jumbo does not, Jumbo sees consumption in the month drop by 40 percent, with no change in other months. If both promote in a given month, neither sees an increase in consumption but both see forward buying of 15 percent from each of the two following months. The debate within Jumbo is whether to promote, and if so, whether to do it in April or June. For the following questions,

a. What is the optimal production plan for Jumbo, assuming

annual profits for Jumbo?

April but Jumbo does not promote throughout the year (because it follows everyday low pricing)? What are the

not promote throughout the year? Comment on the bene- fit from promoting versus the loss from not promoting if the competitor does.

c. What are the optimal production plan and profits if both promote in April? Both promote in June? Jumbo pro-

d. What is the best decision for Jumbo if it can coordinate its

e. What is the best decision for Jumbo if it wants to maxi-

5. We now reconsider the issue of competitors and promotions in the context of a commodity product such as detergent, for

major detergent manufacturer with a demand forecast for the coming year as shown in Table 9-6 (in tons).

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the line runs. The line requires a team of 100 employees. Employees are paid $10 per hour for regular time and $15 per hour for overtime. Each ton of detergent requires 1 hour of operation of the line. The plant works 20 days a month,

– time is restricted to a maximum of 20 hours per employee per month.

Each ton of detergent uses $1,000 of material. Carry- ing a ton of detergent in inventory from one month to the

wants to end with the same level. During intermediate –

gent is currently sold to retailers for $2,600 per ton. The

Unilock. –

price by $260 per ton (from $2,600 to $2,340) for one month

TABLE 9-6 Anticipated Monthly Demand at Q&H Month Demand Month Demand

January 280 July 291

February 301 August 277

March 277 September 304

April 302 October 291

May 285 November 302

June 278 December 297

by the action taken by Unilock. If neither firm promotes, the

consumption (does not include forward buying) in that month increase by 50 percent and forward buying of 20 percent from each of the two following months. If Unilock promotes

in the month drop by 50 percent. If both promote in a given month, neither sees an increase in consumption but both see forward buying of 25 percent from each of the two following

if so, whether to do it in April or June. For the following questions, assume that demand for Unilock is like that of

(because it uses everyday low pricing)? What are the prof-

promote throughout the year? Comment on the benefit from promoting versus the loss from not promoting if the competitor does.

c. What are the optimal production plan and profits if both –

June but Unilock in April?

decision with Unilock?

its minimum profits no matter what Unilock does? 6.

party willing to manufacture detergent as needed for $2,300 per ton. Repeat the analysis for all questions (a) through (e).

Bibliography

Demand.” CSMP’s Supply Chain Quarterly –

ply Chain Planning.” Supply Chain Management Review

Supply Chain Management Review

Supply Chain Management Review

Marien, Edward J. “Why Focus on Demand Usage Management?” Supply Chain Management Review

Chain Constraints.” Supply Chain Management Review

– nity.” Supply Chain Management Review

– formers.” Supply Chain Management Review

Supply Chain Management Review (March

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CASE STUDY Mintendo Game Girl

are about to get together to discuss production and mar- keting plans for the next 6 months. Mintendo is the man- ufacturer of the popular Game Girl handheld electronic game that is sold exclusively through We “R” Toys retail stores. The second half of the year is critical to Game Girl’s success, because a majority of its sales occur dur- ing the holiday shopping period.

– ing holiday surge in demand will have on her production line. Costs to subcontract assembly of the Game Girls are expected to increase, and she has been trying to keep costs down, given that her bonus depends on the level of production costs.

Bill is worried about competing toy stores gaining share in the handheld electronic game market during the Christmas buying season. He has seen many companies lose their share by failing to keep prices in line with the performance of their products. He would like to maxi- mize the Game Girl market share in the handheld elec- tronic game market.

forecast of demand over the next six months, as shown in Table 9-7.

We “R” Toys sells Game Girls for $50 apiece. At the end of June, the company has an inventory of 50,000 Game Girls. Capacity of the production facility is set purely by the number of workers assembling the Game Girls. At the end of June, the company has a workforce of 300 employees, each of whom works 8 hours of regu- lar time at $15/hour for 20 days each month. Work rules require that no employee work more than 40 hours of

overtime per month. The various costs are shown in Table 9-8.

the periods of surging demand over the holidays, pro- poses to Bill that the price be lowered by $5 for the

– ber’s demand by 50 percent due to new customers being attracted to Game Girl. In addition, 30 percent of each of the following two months of demand would occur in

this leveling of demand will help the company. Bill counters with the idea of offering the same

promotion in November, during the heart of the buying season. In this case, the promotion increases Novem- ber’s demand by 50 percent, owing to new customers being attracted to Game Girl. Additionally, 30 percent of December’s demand would occur in November as for- ward buying. Bill wants to increase revenue and sees no better way to do this than to offer a promotion during the peak season.

Questions

1. Which option delivers the maximum profit for the supply

all? Assume starting and ending inventory of 0. 2. How does the answer change if a discount of $10 must be

given to reach the same level of impact that the $5 discount received?

3. come to fruition and the cost rises to $22/unit for subcon- tracting. Does this change the decision when the discount is $5?

TABLE 9-7 Demand for Game Girls Month Demand Forecast

July 100,000

August 110,000

September 130,000

October 180,000

November 250,000

December 300,000

TABLE 9-8 Costs for Mintendo/We “R” Toys Item Cost

Material cost $12/unit

Inventory holding cost $2/unit/month

Marginal cost of a stockout $10/unit/month

Hiring and training costs $3,000/worker

Layoff cost $5,000/worker

Labor hours required 0.25/unit

Regular-time cost $15/hour

Overtime cost $22.50/hour

Cost of subcontracting $18/unit

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CASE STUDY Promotion Challenges at Gulmarg Skis

– ous season when a competitor, Kitz, discounted their

was rare, this was an unusual move by Kitz. As a result,

and January. The company did not want to be caught unprepared for the upcoming season and was planning its response. Two alternatives being considered by

Gulmarg could not precisely predict what Kitz would do regarding promotions but felt that Kitz was likely to

previous year. Gulmarg and Kitz competed in high-performance

skis and sold direct to end consumers. The companies prided themselves on outstanding craftsmanship, using only the best materials. Both were known for the high quality of their skis and the fact that customers could design their own top sheet. Although each company had a loyal following, there was a significant fraction of cus- tomers who were happy to buy skis from either. It is this group that the two companies were competing for through price discounts.

The sale of skis was highly seasonal, with all sales

Table 9-9. Production capacity at the manufacturing plant was limited by the number of employees that Gul- marg hired. Employees were paid $15/hour for regular time and $23/hour for overtime. Each pair of skis required 4 hours of work from an employee. The plant worked 20 days a month, 8 hours a day on regular time.

employee per month. Gulmarg employed a total of 60 workers and felt that it could not let any of them go, even in months when demand was below the capacity provided by 60 workers. Given the high skill require- ments, the company had difficulty finding suitable peo- ple and as a result could hire only up to a maximum of 10 temporary employees. In other words, the number of employees could fluctuate between 60 and 70. Hiring each temporary employee cost $500, and letting each one go cost another $800.

Each pair of skis used material worth $300, mostly in the form of expensive carbon fiber, plastic, and alloys. Carrying a pair of skis in inventory from one month to the next cost $10. Given the seasonal nature of demand,

pairs of skis and preferred to end in March with no inventory to carry over. Any leftover inventory at the end of March cost Gulmarg the equivalent of $500 a pair because of the discounting required to sell it. Customers were not willing to wait for skis, so Gulmarg lost all sales that it could not meet in a month because of insuf- ficient inventory and production. Gulmarg’s skis were normally priced at $800 a pair.

Before making its production plans, Gulmarg had done market research to fully understand the impact of promotions on customer behavior. Dropping price from $800 to $750 attracted new customers, but also resulted in existing customers shifting the timing of their pur- chase to take advantage of the discount. Customer behavior was also affected by actions taken by the com- petitor, Kitz. If only one of the two companies promoted in a given month, it saw a 40 percent increase in sales for the month and a forward movement of 20 percent of demand from each of the three following months. In

did not, Gulmarg observed a 40 percent increase in

competitor that did not promote experienced a 20 per- cent drop in sales for the promotion month and a 10 per- cent drop in sales for each of the three following months.

other in December, changes in demand were cumulative, –

lowed by the December promotion. In other words, demand for each company shifted from that provided in

– ber promotion then affected the revised demand. For

TABLE 9-9 Demand Forecast for Gulmarg Skis Month Demand Forecast

October 1,600

November 2,400

December 4,200

January 3,800

February 2,200

March 2,200

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chose to promote in December, Gulmarg would observe

drop in demand in November, December, and January compared with the figures in Table 9-9. The December promotion would then increase demand in December by 40 percent of the reduced amount (because of the earlier

– ary would also be based on the reduced amount because

February and March would be based on demand not –

panies promoted in a given month, each experienced a growth of 10 percent for that month and forward buying equivalent to 20 percent of demand from each of the three following months.

should it promote? If not, why not?

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In this chapter, we extend the ideas from Chapter 9 and focus on improving coordination across the supply chain. We discuss how lack of coordination leads to a degradation of responsiveness and an increase in cost within a supply chain. We describe various obstacles that lead to this lack of coordination and exacerbate variability through the supply chain. We then identify appropriate managerial levers that can help overcome the obstacles and achieve coordi- nation. In particular, we discuss how collaboration can improve supply chain performance.

10.1 LACK OF SUPPLY CHAIN COORDINATION AND THE BULLWHIP EFFECT

Supply chain coordination improves if all stages of the chain take actions that are aligned and increase total supply chain surplus. Supply chain coordination requires each stage of the supply chain to share information and take into account the impact its actions have on other stages.

A lack of coordination occurs either because different stages of the supply chain have local objectives that conflict or because information moving between stages is delayed and distorted. Different stages of a supply chain may have conflicting objectives if each stage tries to maximize its own profits, resulting in actions that often diminish total supply chain profits (see Chapters 11, 13, and 15). Today, supply chains consist of stages with different owners. For example, Ford Motor Company has thousands of suppliers, from Goodyear to Motorola, and each of these suppliers has

Coordination in a Supply Chain

C H A P T E R

10

LEARNING OBJECTIVES After reading this chapter, you will be able to

248

1. Describe supply chain coordination and the bullwhip effect, and their impact on supply chain performance.

2. Identify obstacles to coordination in a supply chain.

3. Discuss managerial levers that help achieve coordination in a supply chain.

4. Understand the different forms of collaborative planning, forecasting, and replenishment that are possible in a supply chain.

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many suppliers in turn. Not only does each stage focus on its own objectives, but information is also often distorted as it moves across the supply chain because complete information is not shared between stages. This distortion is exaggerated by the fact that supply chains today produce a large variety of products. Ford produces different models, with several options for each model. The increased variety makes it difficult for Ford to coordinate information exchange with thousands of suppliers and dealers. The fundamental challenge today is for supply chains to achieve coordina- tion in spite of multiple ownership and increased product variety.

One outcome of the lack of supply chain coordination is the bullwhip effect, in which fluc- tuations in orders increase as they move up the supply chain from retailers to wholesalers to manufacturers to suppliers, as shown in Figure 10-1. The bullwhip effect distorts demand infor- mation within the supply chain, with each stage having a different estimate of what demand looks like.

Procter & Gamble (P&G) has observed the bullwhip effect in the supply chain for Pampers diapers (Lee, Padmanabhan, and Whang, 1997). The company found that raw material orders from P&G to its suppliers fluctuated significantly over time. Farther down the chain, when sales at retail stores were studied, the fluctuations, though present, were small. It is reasonable to assume that the consumers of diapers (babies) at the last stage of the supply chain used them at a steady rate. Although consumption of the end product was stable, orders for raw material were highly variable, increasing costs and making it difficult to match supply and demand.

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FIGURE 10-1 Demand Fluctuations at Different Stages of a Supply Chain

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Hewlett-Packard (HP) also found that the fluctuation in orders increased significantly as they moved from the resellers up the supply chain to the printer division to the integrated circuit division (ibid.). Once again, although product demand showed some variability, orders placed with the integrated circuit division were much more variable. This made it difficult for HP to fill orders on time and increased the cost of doing so.

Studies of the apparel and grocery industries have shown a similar phenomenon: The fluc- tuation in orders increases as one moves upstream in the supply chain from retail to manufactur- ing. Barilla, an Italian manufacturer of pasta, observed that weekly orders placed by a local distribution center fluctuated by up to a factor of 70 in the course of the year, whereas weekly sales at the distribution center (representing orders placed by supermarkets) fluctuated by a fac- tor of less than three (Hammond, 1994). Barilla was thus facing demand from the distribution center that was much more variable than customer demand. This led to increased inventories, poorer product availability, and a drop in profits.

A similar phenomenon, over a longer time frame, has been observed in several industries that are quite prone to “boom and bust” cycles. A good example is the production of memory chips for personal computers. Between 1985 and 1998, at least two cycles occurred during which prices of memory chips fluctuated by a factor of more than three. These large fluctuations in price were driven by either large shortages or surpluses in capacity. The shortages were exacer- bated by panic buying and overordering that was followed by a sudden drop in demand.

In the next section, we consider how lack of coordination affects supply chain performance.

10.2 THE EFFECT ON PERFORMANCE OF LACK OF COORDINATION

The lack of coordination in a supply chain increases variability and hurts the supply chain surplus. We discuss the impact of the bullwhip effect on various costs in the P&G diaper supply chain.

Manufacturing Cost

The lack of coordination increases manufacturing cost in the supply chain. As a result of the bullwhip effect, P&G and its suppliers must satisfy a stream of orders that is much more variable than customer demand. P&G can respond to the increased variability by either building excess capacity or holding excess inventory (see Chapter 8), both of which increase the manufacturing cost per unit produced.

Inventory Cost

The lack of coordination increases inventory cost in the supply chain. To handle the increased variability in demand, P&G must carry a higher level of inventory than would be required if the supply chain were coordinated. As a result, inventory costs in the supply chain increase. The high levels of inventory also increase the warehousing space required and thus the warehousing cost incurred.

Replenishment Lead Time

Lack of coordination increases replenishment lead times in the supply chain. The increased vari- ability as a result of the bullwhip effect makes scheduling at P&G and supplier plants much more difficult than when demand is level. There are times when the available capacity and inventory cannot supply the orders coming in. This results in higher replenishment lead times.

Transportation Cost

The lack of coordination increases transportation cost in the supply chain. The transportation requirements over time at P&G and its suppliers are correlated with the orders being filled. As a result of the bullwhip effect, transportation requirements fluctuate significantly over time. This

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raises transportation cost because surplus transportation capacity needs to be maintained to cover high-demand periods.

Labor Cost for Shipping and Receiving

The lack of coordination increases labor costs associated with shipping and receiving in the sup- ply chain. Labor requirements for shipping at P&G and its suppliers fluctuate with orders. A similar fluctuation occurs for the labor requirements for receiving at distributors and retailers. The various stages have the option of carrying excess labor capacity or varying labor capacity in response to the fluctuation in orders. Either option increases total labor cost.

Level of Product Availability

Lack of coordination hurts the level of product availability and results in more stockouts in the supply chain. The large fluctuations in orders make it harder for P&G to supply all distributor and retailer orders on time. This increases the likelihood that retailers will run out of stock, resulting in lost sales for the supply chain.

Relationships Across the Supply Chain

Lack of coordination has a negative effect on performance at every stage and thus hurts the rela- tionships among different stages of the supply chain. There is a tendency to assign blame to other stages of the supply chain because each stage thinks it is doing the best it can. The lack of coor- dination thus leads to a loss of trust among different stages of the supply chain and makes any potential coordination efforts more difficult.

From the earlier discussion, it follows that lack of coordination has a significant negative impact on the supply chain’s performance by increasing cost and decreasing responsiveness. The impact of the lack of coordination on different performance measures is summarized in Table 10-1.

In the next section, we discuss various obstacles to achieving coordination in the supply chain.

TABLE 10-1 Impact of the Lack of Coordination on Supply Chain Performance Performance Measure Impact of the Lack of Coordination

Manufacturing cost Increases

Inventory cost Increases

Replenishment lead time Increases

Transportation cost Increases

Shipping and receiving cost Increases

Level of product availability Decreases

Profitability Decreases

Key Point

The lack of coordination hurts both responsiveness and cost in a supply chain by making it more expen- sive to provide a given level of product availability.

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10.3 OBSTACLES TO COORDINATION IN A SUPPLY CHAIN

Any factor that leads to either local optimization by different stages of the supply chain or an increase in information delay, distortion, and variability within the supply chain is an obstacle to coordination. If managers in a supply chain are able to identify the key obstacles, they can then take suitable actions to help achieve coordination. We divide the major obstacles into five categories:

Incentive Obstacles

Incentive obstacles occur in situations when incentives offered to different stages or participants in a supply chain lead to actions that increase variability and reduce total supply chain profits.

LOCAL OPTIMIZATION WITHIN FUNCTIONS OR STAGES OF A SUPPLY CHAIN Incentives that focus only on the local impact of an action result in decisions that do not maximize total supply chain surplus. For example, if the compensation of a transportation manager at a firm is linked to the average transportation cost per unit, the manager is likely to take actions that lower transpor- tation costs even if they increase inventory costs or hurt customer service. It is natural for any participant in the supply chain to take actions that optimize performance measures along which they are evaluated. For example, managers at a retailer such as Kmart make all their purchasing and inventory decisions to maximize Kmart profits, not total supply chain profits. Buying deci- sions based on maximizing profits at a single stage of the supply chain lead to ordering policies that do not maximize supply chain profits (see Chapters 11, 13, and 15).

SALES FORCE INCENTIVES Improperly structured sales force incentives are a significant obsta- cle to coordination in a supply chain. In many firms, sales force incentives are based on exceed- ing sales thresholds during an evaluation period of a month or quarter. The sales typically measured by a manufacturer are the quantity sold to distributors or retailers (sell-in), not the quantity sold to final customers (sell-through). Measuring performance based on sell-in is often justified on the grounds that the manufacturer’s sales force does not control sell-through. For example, Barilla offered its sales force incentives based on the quantity sold to distributors dur- ing a four- to six-week promotion period. To maximize their bonuses, the Barilla sales force urged distributors to buy more pasta toward the end of the evaluation period, even if distributors were not selling as much to retailers. The sales force offered discounts they controlled to spur end-of-period sales. This increased variability in the order pattern, with a jump in orders toward the end of the evaluation period followed by few orders at the beginning of the next evaluation period. Order sizes from distributors to Barilla fluctuated by a factor of up to 70 from one week to the next. A sales force incentive based on sell-in thus results in order variability being larger than customer demand variability because the sales force tends to push product toward the end of the incentive period.

Information-Processing Obstacles

Information-processing obstacles occur when demand information is distorted as it moves between different stages of the supply chain, leading to increased variability in orders within the supply chain.

FORECASTING BASED ON ORDERS AND NOT CUSTOMER DEMAND When stages within a supply chain make forecasts that are based on orders they receive, any variability in customer

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demand is magnified as orders move up the supply chain to manufacturers and suppliers. In sup- ply chains where the fundamental means of communication among different stages are the orders that are placed, information is distorted as it moves up the supply chain (see Chen, Drezner, Ryan, and Simchi-Levi [2000] for a good quantitative analysis). Each stage views its primary role within the supply chain as one of filling orders placed by its downstream partner. Thus, each stage views its demand as the stream of orders received and produces a forecast based on this information.

In such a scenario, a small change in customer demand becomes magnified as it moves up the supply chain in the form of customer orders. Consider the impact of a random increase in customer demand at a retailer. The retailer may interpret part of this random increase as a growth trend. This interpretation will lead the retailer to order more than the observed increase in demand because the retailer expects growth to continue into the future and thus orders to cover for future anticipated growth. The increase in the order placed with the wholesaler is thus larger than the observed increase in demand at the retailer. Part of the increase is a one-time increase. The wholesaler, however, has no way to interpret the order increase correctly. The wholesaler simply observes a jump in the order size and infers a growth trend. The growth trend inferred by the wholesaler will be larger than that inferred by the retailer (recall that the retailer increased the order size to account for future growth). The wholesaler will thus place an even larger order with the manufacturer. As we go farther up the supply chain, the order size is magnified.

Now assume that periods of random increase are followed by periods of random decrease in demand. Using the same forecasting logic as earlier, the retailer will now anticipate a declin- ing trend and reduce order size. This reduction will also become magnified as we move up the supply chain.

LACK OF INFORMATION SHARING The lack of information sharing between stages of the sup- ply chain magnifies the information distortion. A retailer such as Walmart may increase the size of a particular order because of a planned promotion. If the manufacturer is not aware of the planned promotion, it may interpret the larger order as a permanent increase in demand and place orders with suppliers accordingly. The manufacturer and suppliers thus have much inventory right after Walmart finishes its promotion. Given the excess inventory, as future Walmart orders return to normal, manufacturer orders will be smaller than before. The lack of information shar- ing between the retailer and manufacturer thus leads to a large fluctuation in manufacturer orders.

Operational Obstacles

Operational obstacles occur when actions taken in the course of placing and filling orders lead to an increase in variability.

ORDERING IN LARGE LOTS When a firm places orders in lot sizes that are much larger than those in which demand arises, variability of orders is magnified up the supply chain. Firms may order in large lots because a significant fixed cost is associated with placing, receiving, or trans- porting an order (see Chapter 11). Large lots may also occur if the supplier offers quantity dis- counts based on lot size (see Chapter 11). Figure 10-2 shows both the demand and the order stream for a firm that places an order every five weeks. Observe that the order stream is far more erratic than the demand stream.

Because orders are batched and placed every five weeks, the order stream has four weeks without orders followed by a large order that equals five weeks of demand. A manufacturer sup- plying several retailers that batch their orders faces an order stream that is much more variable than the demand the retailers experience. If the manufacturer batches its orders to suppliers, the effect is further magnified. In many instances, there are certain focal-point periods, such as the first or the last week of a month, when a majority of the orders arrive. This synchronization of orders further exacerbates the impact of batching.

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LARGE REPLENISHMENT LEAD TIMES Information distortion is magnified if replenishment lead times between stages are long. Consider a situation in which a retailer has misinterpreted a random increase in demand as a growth trend. If the retailer faces a lead time of two weeks, it will incorporate the anticipated growth over two weeks when placing the order. In contrast, if the retailer faces a lead time of two months, it will incorporate into its order the anticipated growth over two months (which will be much larger). The same applies when a random decrease in demand is interpreted as a declining trend.

RATIONING AND SHORTAGE GAMING Rationing schemes that allocate limited production in proportion to the orders placed by retailers lead to a magnification of information distortion. This can occur when a high-demand product is in short supply. In such a situation, manufacturers come up with a variety of mechanisms to ration the scarce supply of product among various dis- tributors or retailers. One commonly used rationing scheme is to allocate the available supply of product based on orders placed. Under this rationing scheme, if the supply available is 75 percent of the total orders received, each retailer receives 75 percent of its order.

This rationing scheme results in a game in which retailers try to increase the size of their orders to increase the amount supplied to them. A retailer needing 75 units orders 100 units in the hope of getting 75. The net impact of this rationing scheme is to artificially inflate orders for the product. In addition, a retailer ordering based on what it expects to sell gets less and as a result loses sales, whereas a retailer that inflates its order is rewarded.

If the manufacturer is using orders to forecast future demand, it will interpret the increase in orders as an increase in demand, even though customer demand is unchanged. The manufac- turer may respond by building enough capacity to be able to fill all orders received. Once suffi- cient capacity becomes available, orders return to their normal level because they were inflated in response to the rationing scheme. The manufacturer is now left with a surplus of product and capacity. These boom-and-bust cycles thus tend to alternate. This phenomenon is fairly common in the electronics industry, in which alternating periods of component shortages followed by a component surplus are often observed.

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Pricing Obstacles

Pricing obstacles arise when the pricing policies for a product lead to an increase in variability of orders placed.

LOT-SIZE–BASED QUANTITY DISCOUNTS Lot-size–based quantity discounts increase the lot size of orders placed within the supply chain (see Chapter 11) because lower prices are offered for larger lots. As discussed earlier, the resulting large lots magnify the bullwhip effect within the supply chain.

PRICE FLUCTUATIONS Trade promotions and other short-term discounts offered by a manufac- turer result in forward buying, by which a wholesaler or retailer purchases large lots during the discounting period to cover demand during future periods. Forward buying results in large orders during the promotion period followed by very small orders after that (see Chapter 11), as shown in Figure 10-3 for chicken noodle soup.

Observe that the shipments during the peak period are higher than the sales during the peak period because of a promotion offered. The peak shipment period is followed by a period of low shipments from the manufacturer, indicating significant forward buying by distributors. The pro- motion thus results in a variability in manufacturer shipments that is significantly higher than the variability in retailer sales.

Behavioral Obstacles

Behavioral obstacles are problems in learning within organizations that contribute to information distortion. These problems are often related to the supply chain structure and the communica- tions among different stages. Some of the behavioral obstacles are as follows:

1. Each stage of the supply chain views its actions locally and is unable to see the impact of its actions on other stages.

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FIGURE 10-3 Retailer Sales and Manufacturer Shipments of Soup Source: Adapted from Marshall L. Fisher, “What Is the Right Supply Chain for Your Product?” by Harvard Business Review (March–April 1997): 83–93. Copyright © 1997 by the Harvard Business School Publishing Corporation; all rights reserved. Reprinted by permission of Harvard Business Review.

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2. Different stages of the supply chain react to the current local situation rather than trying to identify the root causes.

3. Based on local analysis, different stages of the supply chain blame one another for the fluc- tuations, with successive stages in the supply chain becoming enemies rather than partners.

4. No stage of the supply chain learns from its actions over time because the most significant consequences of its actions occur elsewhere. The result is a vicious cycle in which actions taken by one stage create the very problems that the stage blames on others.

5. A lack of trust among supply chain partners causes them to be opportunistic at the expense of overall supply chain performance. The lack of trust also results in significant duplication of effort. More important, information available at different stages either is not shared or is ignored because it is not trusted.

10.4 MANAGERIAL LEVERS TO ACHIEVE COORDINATION

Having identified obstacles to coordination, we now focus on actions a manager can take to help overcome the obstacles and achieve coordination in the supply chain. The following managerial actions increase total supply chain profits and moderate information distortion:

Aligning Goals and Incentives

Managers can improve coordination within the supply chain by aligning goals and incentives so every participant in supply chain activities works to maximize total supply chain profits.

ALIGNING GOALS ACROSS THE SUPPLY CHAIN Coordination requires every stage of the sup- ply chain to focus on the supply chain surplus or the total size of the pie rather than just its indi- vidual share. A key to coordination is coming up with mechanisms that allow the creation of a win–win scenario in which the supply chain surplus grows along with the profits for all supply chain stages. An example of such a mechanism occurs when Walmart pays Hewlett-Packard (HP) for each printer sold and gives HP the power to make replenishment decisions while limit- ing the amount of printer inventory that can be held at a store. This setup improves coordination because both parties gain if the supply of printers at a store matches demand.

ALIGNING INCENTIVES ACROSS FUNCTIONS One key to coordinated decisions within a firm is to ensure that the objective any function uses to evaluate a decision is aligned with the firm’s overall objective. All facility, transportation, and inventory decisions should be evaluated based on their effect on profitability or total costs, not functional costs. This helps prevent situations such as a transportation manager making decisions that lower transportation cost but increase overall supply chain costs (see Chapter 14).

PRICING FOR COORDINATION In many instances, suitable pricing schemes can help coordi- nate the supply chain. A manufacturer can use lot-size–based quantity discounts to achieve coor- dination for commodity products if the manufacturer has large fixed costs associated with each lot (see Chapter 11 for a detailed discussion). For products for which a firm has market power, a manufacturer can use two-part tariffs and volume discounts to help achieve coordination (see Chapter 11 for a detailed discussion). Given demand uncertainty, manufacturers can use buy- back, revenue-sharing, and quantity flexibility contracts to spur retailers to provide levels of

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product availability that maximize total supply chain profits (see Chapter 15 for a detailed dis- cussion). Buyback contracts have been used in the publishing industry to increase total supply chain profits. Quantity flexibility contracts have helped Benetton increase supply chain profits.

ALTERING SALES FORCE INCENTIVES FROM SELL-IN TO SELL-THROUGH Any change that reduces the incentive for a salesperson to push product to the retailer reduces the bullwhip effect. Manufacturers should link incentives for the sales staff to sell-through by the retailer rather than sell-in to the retailer. This action eliminates any motivation the sales staff may have to encourage forward buying. Elimination of forward buying helps reduce fluctuations in the order stream. If sales force incentives are based on sales over a rolling horizon, the incentive to push product is further reduced. This helps reduce forward buying and the resulting fluctuation in orders.

Improving Information Visibility and Accuracy

Managers can achieve coordination by improving the visibility and accuracy of information available to different stages in the supply chain.

SHARING CUSTOMER DEMAND DATA Sharing customer demand data across the supply chain can help reduce the bullwhip effect. A primary cause for information distortion is the fact that each stage of the supply chain uses orders to forecast future demand. Given that orders received by different stages vary, forecasts at different stages also vary. In reality, the only demand that the supply chain needs to satisfy is that from the final customer. If retailers share demand data with other supply chain stages, all stages can forecast future demand based on customer demand. Sharing of demand data helps reduce information distortion because all stages now respond to the same change in customer demand. Observe that sharing aggregate demand data is sufficient to dampen information distortion. It is not necessary to share detailed point-of-sale (POS) data. Use of appropriate information systems facilitates the sharing of such data.

Walmart has routinely shared its POS data with its suppliers. Dell shares demand data as well as current inventory positions of components with many of its suppliers via the Internet, thereby helping avoid unnecessary fluctuations in supply and orders placed. P&G has convinced many retailers to share demand data. P&G, in turn, shares the data with its suppliers, improving coordination in the supply chain.

IMPLEMENTING COLLABORATIVE FORECASTING AND PLANNING Once customer demand data are shared, different stages of the supply chain must forecast and plan jointly if complete coordination is to be achieved. Without collaborative planning, sharing of demand data does not guarantee coordination. A retailer may have observed large demand in the month of January because it ran a promotion. If no promotion is planned in the upcoming January, the retailer’s forecast will differ from the manufacturer’s forecast even if both have past POS data. The manu- facturer must be aware of the retailer’s promotion plans to achieve coordination. The key is to ensure that the entire supply chain is operating with a common forecast. To facilitate this type of coordination in the supply chain environment, the Voluntary Interindustry Commerce Standards (VICS) Association set up a Collaborative Planning, Forecasting, and Replenishment (CPFR) Committee that identified best practices and design guidelines for collaborative planning and forecasting. These practices are detailed later in the chapter.

DESIGNING SINGLE-STAGE CONTROL OF REPLENISHMENT Designing a supply chain in which a single stage controls replenishment decisions for the entire supply chain can help dimin- ish information distortion. As we mentioned earlier, a key cause of information distortion is that each stage of the supply chain uses orders from the previous stage as its historical demand. As a result, each stage views its role as one of replenishing orders placed by the next stage. In reality, the key replenishment is at the retailer, because that is where the final customer purchases. When

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a single stage controls replenishment decisions for the entire chain, the problem of multiple fore- casts is eliminated and coordination within the supply chain follows.

Several industry practices, such as continuous replenishment programs (CRPs) and vendor- managed inventories (VMIs) detailed later in the chapter, provide a single-point control over replenishment. Walmart typically assigns one of its suppliers as a leader for each major product category to manage store-level replenishment. This gives suppliers visibility into sales and a single decision maker for replenishment decisions.

Key Point

Demand planning at each stage in a supply chain based on the stream of orders received from its down- stream stage results in a magnification of fluctuation in orders as one moves up the supply chain from the retailer to the manufacturer. It is better for the entire supply chain to forecast based on end customer demand.

Improving Operational Performance

Managers can help dampen information distortion by improving operational performance and designing appropriate product rationing schemes in case of shortages.

REDUCING REPLENISHMENT LEAD TIME By reducing the replenishment lead time, managers can decrease the uncertainty of demand during the lead time (see Chapter 12). A reduction in lead time is especially beneficial for seasonal items because it allows for multiple orders to be placed with a significant increase in the accuracy of the forecast (see Chapter 13). If lead times are short enough, replenishment can be scheduled to actual consumption, thus eliminating the need for a forecast.

Managers can take a variety of actions at different stages of the supply chain to help reduce replenishment lead times. Ordering electronically, either online or through electronic data inter- change (EDI), can significantly cut the lead time associated with order placement and informa- tion transfer. If every stage shares its long-term plans with suppliers, potential orders can be scheduled into production well in advance, with the precise quantity being determined closer to actual production. This reduces the scheduling time, which is often the largest component of lead time. At manufacturing plants, increased flexibility and cellular manufacturing can be used to achieve a significant reduction in lead times. A dampening of information distortion further reduces lead times because of stabilized demand and, as a result, improved scheduling. This is particularly true when manufacturing produces a large variety of products. Advance shipping notices (ASNs) can be used to reduce the lead time as well as efforts associated with receiving. Cross-docking can be used to reduce the lead time associated with moving the product between stages in the supply chain. Walmart has used many of these approaches to significantly reduce lead time within its supply chain.

REDUCING LOT SIZES Managers can reduce information distortion by implementing opera- tional improvements that reduce lot sizes. A reduction of lot sizes decreases the amount of fluc- tuation that can accumulate between any pair of stages of a supply chain, thus decreasing distortion. To reduce lot sizes, managers must take actions that help reduce the fixed costs associ- ated with ordering, transporting, and receiving each lot (see Chapter 11). Walmart and Seven- Eleven Japan have been very successful at reducing replenishment lot sizes by aggregating deliveries across many products and suppliers.

Computer-assisted ordering (CAO) refers to the substitution through technology of the functions of a retail order clerk through the use of computers that integrate information about

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product sales, market factors affecting demand, inventory levels, product receipts, and desired service levels. CAO and EDI help reduce the fixed costs associated with placing each order.

In some cases, managers can simplify ordering by eliminating the use of purchase orders. In the auto industry, some suppliers are paid based on the number of cars produced, eliminating the need for individual purchase orders. This eliminates the order-processing cost associated with each replenishment order. Information systems also facilitate the settlement of financial transactions, eliminating the cost associated with individual purchase orders.

The large gap in the prices of truckload (TL) and less-than-truckload (LTL) shipping encourages shipment in TL quantities. In fact, with the efforts to reduce order-processing costs, transportation costs are now the major barrier to smaller lots in most supply chains. Managers can reduce lot sizes without increasing transportation costs by filling a truck using smaller lots from a variety of products (see Chapter 11). P&G, for example, requires all orders from retailers to be a full TL. The TL, however, may be built from any combination of products. A retailer can thus order small lots of each product as long as a sufficiently large variety of products are included on each truck. Seven-Eleven Japan has used this strategy with combined trucks, in which the separation is by the temperature at which the truck is maintained. All products to be shipped at a particular temperature are on the same truck. This has allowed Seven-Eleven Japan to reduce the number of trucks sent to retail outlets while keeping product variety high. Some firms in the grocery industry use trucks with different compartments, each at a different tempera- ture and carrying a variety of products, to help reduce lot sizes.

Managers can also reduce lot sizes by using milk runs that combine shipments for several retailers on a single truck, as discussed in Chapter 14. In many cases, third-party transporters combine shipments to competing retail outlets on a single truck. This reduces the fixed transpor- tation cost per retailer and allows each retailer to order in smaller lots. In Japan, Toyota uses a single truck from a supplier to supply multiple assembly plants, which enables managers to reduce the lot size received by any one plant. Managers can also reduce lot sizes by combining shipments from multiple suppliers on a single truck. In the United States, Toyota uses this approach to reduce the lot size it receives from any one supplier.

As smaller lots are ordered and delivered, both the pressure on and the cost of receiving can grow significantly. Thus, managers must implement technologies that simplify the receiving process and reduce the cost associated with receiving. For example, ASNs identify shipment content, count, and time of delivery electronically and help reduce unloading time and increase cross-dock efficiency. ASNs can be used to update inventory records electronically, thus reduc- ing the cost of receiving. Bar coding of pallets and the use of radio frequency identification (RFID) can further simplify receiving.

Another simple way to minimize the impact of batching is to break any synchronization of orders. Frequently, customers that order once a week tend to do so on either a Monday or Friday. Customers that order once a month tend to do so either at the beginning or the end of the month. In such situations, it is better to distribute customers ordering once a week evenly across all days of the week, and customers ordering once a month across all days of the month. In fact, regular ordering days may be scheduled in advance for each customer to level out the order stream arriv- ing at the manufacturer.

RATIONING BASED ON PAST SALES AND SHARING INFORMATION TO LIMIT GAMING To diminish information distortion, managers can design rationing schemes that discourage retailers from artificially inflating their orders in case of a shortage. One approach, referred to as turn- and-earn, is to allocate the available supply based on past retailer sales rather than current retailer orders. Tying allocation to past sales removes any incentive a retailer may have to inflate orders. In fact, during low-demand periods, the turn-and-earn approach pushes retailers to try to sell more to increase the allocation they receive during periods of shortage. Several firms, including General Motors, have historically used the turn-and-earn mechanism to ration available product in case of a shortage. Others, such as HP, have historically allocated based on retailer orders but are now switching to using past sales.

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Other firms have tried to share information across the supply chain to minimize shortage situations. Firms such as Sport Obermeyer offer incentives to their large customers to preorder at least a part of their annual order. This information allows Sport Obermeyer to improve the accu- racy of its own forecast and allocate production capacity accordingly. Once capacity has been allocated appropriately across different products, it is less likely that shortage situations will arise, thus dampening the inflation of orders. The availability of flexible capacity can also help in this regard, because flexible capacity can easily be shifted from a product whose demand is lower than expected to one whose demand is higher than expected.

Designing Pricing Strategies to Stabilize Orders

Managers can reduce information distortion by devising pricing strategies that encourage retail- ers to order in smaller lots and reduce forward buying.

MOVING FROM LOT-SIZE–BASED TO VOLUME-BASED QUANTITY DISCOUNTS As a result of lot-size–based quantity discounts, retailers increase their lot size to take full advantage of the discount. Offering volume-based quantity discounts eliminates the incentive to increase the size of a single lot because volume-based discounts consider the total purchases during a specified period (say, a year) rather than purchases in a single lot (see Chapter 11). Volume-based quantity discounts result in smaller lot sizes, thus reducing order variability in the supply chain. Volume- based discounts with a fixed end date at which discounts will be evaluated may lead to large lots close to the end date. Offering the discounts over a rolling time horizon helps dampen this effect.

STABILIZING PRICING Managers can dampen the bullwhip effect by eliminating promotions and using everyday low pricing (EDLP). The elimination of promotions removes forward buying by retailers and results in orders that match customer demand. P&G, Campbell Soup, and several other manufacturers have implemented EDLP to dampen the bullwhip effect.

Managers can place limits on the quantity that may be purchased during a promotion to decrease forward buying. This limit should be retailer specific and linked to historical sales by the retailer. Another approach is to tie the promotion dollars paid to the retailer to the amount of sell-through rather than the amount purchased by the retailer. As a result, retailers obtain no ben- efit from forward buying and purchase more only if they can sell more. Promotions based on sell-through significantly reduce information distortion.

Building Strategic Partnerships and Trust

Managers find it easier to use the levers discussed earlier to achieve coordination if trust and strategic partnerships are built within the supply chain. Sharing of accurate information that is trusted by every stage results in a better matching of supply and demand throughout the supply chain. A better relationship also tends to lower the transaction cost between supply chain stages. For example, a sup- plier can eliminate its forecasting effort if it trusts orders and forecast information received from the retailer. Similarly, the retailer can lessen the receiving effort by decreasing counting and inspections if it trusts the supplier’s quality and delivery. In general, stages in a supply chain can eliminate dupli- cated effort on the basis of improved trust and a better relationship. This lowering of transaction cost, along with accurate shared information, helps improve coordination. Walmart and P&G have tried to build a strategic partnership that will better coordinate their actions and be mutually beneficial.

Research by Kumar (1996) showed that the more retailers trusted their suppliers, the less likely they were to develop alternate sources while significantly increasing sales of their prod- ucts. In general, a high level of trust allows a supply chain to become more responsive at lower cost. Actions such as information sharing, changing of incentives, operational improvements, and stabilization of pricing typically help improve the level of trust. Growing the level of coop- eration and trust within a supply chain requires a clear identification of roles and decision rights for all parties, effective contracts, and good conflict resolution mechanisms.

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10.5 CONTINUOUS REPLENISHMENT AND VENDOR-MANAGED INVENTORIES

Information distortion can be dampened by practices that assign replenishment responsibility across the supply chain to a single entity. Replenishment decisions made by a single entity ensure visibility and a common forecast that drives orders across the supply chain. Two common indus- try practices that assign a single point of responsibility are continuous replenishment programs and vendor-managed inventories.

In continuous replenishment programs (CRPs), the wholesaler or manufacturer replen- ishes a retailer regularly based on POS data. CRP may be managed by the supplier, distributor, or a third party. In most instances, CRP systems are driven by actual withdrawals of inventory from retailer warehouses rather than POS data at the store level. Tying CRP systems to ware- house withdrawals is easier to implement, and retailers are often more comfortable sharing data at this level. IT systems that are linked across the supply chain provide a good information infrastructure on which a CRP may be based. In CRP, inventory at the retailer is owned by the retailer.

With vendor-managed inventory (VMI), the manufacturer or supplier is responsible for all decisions regarding product inventories at the retailer. As a result, the control of the replenish- ment decision moves to the manufacturer instead of the retailer. In many instances of VMI, the inventory is owned by the supplier until it is sold by the retailer. VMI requires the retailer to share demand information with the manufacturer to allow it to make inventory replenishment deci- sions. This helps improve manufacturer forecasts and better match manufacturer production with customer demand. VMI can allow a manufacturer to increase its profits—as well as profits for the entire supply chain—if both retailer and manufacturer margins are considered when making inventory decisions.

VMI has been implemented with significant success by, among others, Kmart (with about 50 suppliers) and Fred Meyer. Kmart saw inventory turns on seasonal items increase from 3 to between 9 and 11, and for nonseasonal items from 12 to 15 to 17 to 20. Fred Meyer saw invento- ries drop by 30 to 40 percent while fill rates increased to 98 percent. Other firms with successful implementations include Campbell Soup, Frito-Lay, and P&G.

One drawback of VMI arises because retailers often sell products from competing manu- facturers that are substitutes in the customer’s mind. For example, a customer may substitute detergent manufactured by P&G by detergent manufactured by Unilever. If the retailer has a VMI agreement with both manufacturers, each manufacturer will ignore the impact of substitu- tion when making inventory decisions. As a result, inventories at the retailer will be higher than optimal. In such a setting, the retailer may be better positioned to decide on the replenishment policy. Another possibility is for the retailer to define a category leader from among the suppliers and have the category leader manage replenishment decisions for all suppliers in the category. Walmart follows such a practice and assigns a category leader for most of its products. Walmart sets the targeted level of product availability across all products and the category leader designs replenishment policies that achieve these levels. This ensures that the category leader is not favoring any one supplier’s product over another. For example, HP was Walmart’s category leader for printers and managed all printer replenishment.

10.6 COLLABORATIVE PLANNING, FORECASTING, AND REPLENISHMENT

The VICS Association has defined CPFR as “a business practice that combines the intelligence of multiple partners in the planning and fulfillment of customer demand.” In this section, we describe CPFR and some successful implementations. It is important to understand that success- ful CPFR can be built only on a foundation in which the two parties have synchronized their data and established standards for exchanging information.

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Sellers and buyers in a supply chain may collaborate along any or all of the following four supply chain activities:

1. Strategy and planning. The partners determine the scope of the collaboration and assign roles, responsibilities, and clear checkpoints. In a joint business plan, they then identify significant events such as promotions, new product introductions, store openings/closings, and changes in inventory policy that affect demand and supply.

2. Demand and supply management. A collaborative sales forecast projects the partners’ best estimate of consumer demand at the point of sale. This is then converted to a collab- orative order plan that determines future orders and delivery requirements based on sales forecasts, inventory positions, and replenishment lead times.

3. Execution. As forecasts become firm, they are converted to actual orders. The fulfill- ment of these orders then involves production, shipping, receiving, and stocking of products.

4. Analysis. The key analysis tasks focus on identifying exceptions and evaluating metrics that are used to assess performance or identify trends.

A fundamental aspect of successful collaboration is the identification and resolution of exceptions. Exceptions refer to a gap between forecasts made by the two sides or some other performance metric that is falling or is likely to fall outside acceptable bounds. These metrics may include inventories that exceed targets or product availability that falls below targets. For successful CPFR, it is important to have a process in place that allows the two parties to resolve exceptions. Detailed processes for identifying and resolving exceptions are discussed in the VICS CPFR Voluntary Guidelines version 2.0 (2002).

One successful CPFR implementation has involved Henkel, a German detergent manu- facturer, and Eroski, a Spanish food retailer. Prior to CPFR, Eroski saw frequent stockouts of Henkel products, especially during promotions. At the inception of CPFR in December 1999, 70 percent of the sales forecasts had an average error of more than 50 percent, and only 5 per- cent of the forecasts had errors less than 20 percent. Within four months of the CPFR imple- mentation, however, 70 percent of the sales forecasts had errors less than 20 percent and only 5 percent had errors of more than 50 percent. CPFR resulted in a customer service level of 98 percent and an average inventory of only five days. This was accomplished despite 15 to 20 products being promoted every month.

Another successful implementation involved Johnson & Johnson and Superdrug, a chain in the United Kingdom. Over the three-month trial period beginning April 2000, Superdrug saw inventory levels at its DCs drop by 13 percent, while product availability at its DCs increased by 1.6 percent. As reported by Steerman (2003), Sears also saw significant benefits from its CPFR initiative with Michelin in 2001. In-stock levels at Sears improved by 4.3 percent, DCs-to-stores fill rate improved by 10.7 percent, and overall inventory levels fell by 25 percent.

VICS has identified the four scenarios in Table 10-2 as the most common areas in which large-scale CPFR deployments have taken place between a retailer and a manufacturer. Next, we describe each of the four scenarios.

TABLE 10-2 Four Common CPFR Scenarios CPFR Scenario Where Applied in Supply Chain Industries Where Applied

Retail event collaboration Highly promoted channels or categories All industries other than those that practice EDLP

DC replenishment collaboration Retail DC or distributor DC Drugstores, hardware, grocery

Store replenishment collaboration Direct store delivery or retail DC-to-store delivery

Mass merchants, club stores

Collaborative assortment planning Apparel and seasonal goods Department stores, specialty retail

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Retail Event Collaboration

In many retail environments, such as supermarkets, promotions and other retail events have a significant impact on demand. Stockouts, excess inventory, and unplanned logistics costs during these events affect financial performance for both the retailer and the manufacturer. In such a setting, collaboration between retailers and suppliers to plan, forecast, and replenish promotions is effective.

Retail event collaboration requires the two parties to identify brands and specific SKUs that are included in the collaboration. Details of the event—such as timing, duration, price point, advertising, and display tactics—are shared. It is important for the retailer to update this informa- tion as changes occur. Event-specific forecasts are then created and shared. These forecasts are then converted to planned orders and deliveries. As the event unfolds, sales are monitored to identify any changes or exceptions, which are resolved through an iterative process between the two parties.

P&G has implemented some form of retail event collaboration with a variety of partners, including Walmart.

DC Replenishment Collaboration

DC replenishment collaboration is perhaps the most common form of collaboration observed in practice and also the simplest to implement. In this scenario, the two trading partners collaborate on forecasting DC withdrawals or anticipated demand from the DC to the manufacturer. These forecasts are converted to a stream of orders from the DC to the manufacturer that are committed or locked over a specified time horizon. This information allows the manufacturer to include anticipated orders in future production plans and build the committed orders on demand. The result is a reduction in production cost at the manufacturer and a reduction of inventory and stockouts at the retailer.

DC replenishment collaboration is relatively easy to implement because it requires col- laboration on aggregate data and does not require sharing of detailed POS data. As a result, it is often the best scenario with which to start collaboration. Over time, this form of collaboration can be extended to include all storage points in the supply chain, from retail shelves to raw mate- rial warehouses. According to Hammond (1994), Barilla implemented this form of collaboration with its distributors.

Store Replenishment Collaboration

In store replenishment collaboration, trading partners collaborate on store-level POS forecasts. These forecasts are then converted to a series of store-level orders, with orders committed over a specified time horizon. This form of collaboration is much harder to implement than a DC-level collaboration, especially if stores are small; it is easier for large stores such as Costco and Home Depot. The benefits of store-level collaboration include greater visibility of sales for the manu- facturer, improved replenishment accuracy, improved product availability, and reduced invento- ries. Smith (2013) discusses how store level collaboration allowed suppliers to Canadian retailer West Marine, Inc. to improve “on-time shipments from a dismal 30 percent to over 90 percent.” This allowed West Marine to maintain product availability as high as 98 percent.

Collaborative Assortment Planning

Fashion apparel and other seasonal goods follow a seasonal pattern of demand. Thus, collabora- tive planning in these categories has a horizon of a single season and is performed at seasonal intervals. Given the seasonal nature, forecasts rely less on historical data and more on collabora- tive interpretation of industry trends, macroeconomic factors, and customer tastes. In this form of collaboration, the trading partners develop an assortment plan jointly. The output is a planned purchase order at the style/color/size level. The planned order is shared electronically in advance

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of a show, at which sample products are viewed and final merchandising decisions are made. The planned orders help the manufacturer purchase long-lead-time raw materials and plan capacity. This form of collaboration is most useful if capacity is flexible enough to accommodate a variety of product mix and raw materials have some commonality across end products.

Organizational and Technology Requirements for Successful CPFR

A successful CPFR implementation requires changes in the organizational structure and, to be scalable, requires the implementation of appropriate technology. Effective collaboration requires manufacturers to set up cross-functional, customer-specific teams that include sales, demand planning, and logistics, at least for large customers. Such a focus has become feasible with the consolidation in retailing. For smaller customers, such teams can be focused by geography or sales channel. Retailers should also attempt to organize merchandise planning, buying, and replenishment into teams around suppliers. This can be difficult, given the large number of sup- pliers that consolidated retailers have. They can then organize the teams by categories that include multiple suppliers. For retailers that have multiple levels of inventory, such as DCs and retail stores, it is important to combine the replenishment teams at the two levels. Without col- laborative inventory management at the two levels, duplication of inventories is common. The proposed organizational structure is illustrated in Figure 10-4.

The CPFR process is not dependent on technology but requires technology to be scalable. CPFR technologies have been developed to facilitate sharing of forecasts and historical informa- tion, evaluating exception conditions, and enabling revisions. These solutions must be integrated with enterprise systems that record all supply chain transactions.

Risks and Hurdles for a CPFR Implementation

It is important to realize that there are risks and hurdles for a successful CPFR implementation. Given the large-scale sharing of information, there is a risk of information misuse. Often one or both of the CPFR partners have relationships with the partner’s competitors. Another risk is that if one of the partners changes its scale or technology, the other partner will be forced to follow suit or lose the collaborative relationship. Finally, the implementation of CPFR and the resolu- tion of exceptions require close interactions between two entities whose cultures may be very

Customer 1 Team Demand Planning

Sales Customer Service/

Logistics

Customer 2 Team Demand Planning

Sales Customer Service/

Logistics

Category Team

Planning

Manufacturer Organization Retailer Organization

FIGURE 10-4 Collaborative Organizational Structure Source: Adapted from Voluntary Interindustry Commerce Standards, CPFR: An Overview, 2004.

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different. The inability to foster a collaborative culture across the partner organizations can be a major hurdle for the success of CPFR. One of the biggest hurdles to success is often that partners attempt store-level collaboration, which requires a higher organizational and technology invest- ment. It is often best to start with an event- or DC-level collaboration, which is more focused and easier to collaborate on. Another major hurdle for successful CPFR is that demand information shared with partners is often not used within the organization in an integrated manner. It is important to have integrated demand, supply, logistics, and corporate planning within the organi- zation to maximize the benefits of a CPFR effort with a partner.

10.7 ACHIEVING COORDINATION IN PRACTICE

1. Quantify the bullwhip effect. Companies often have no idea that the bullwhip effect plays a significant role in their supply chain. Managers should start by comparing the variability in the orders they receive from their customers with the variability in orders they place with their suppliers. This helps a firm quantify its own contribution to the bullwhip effect. Once its contri- bution is visible, it becomes easier for a firm to accept the fact that all stages in the supply chain contribute to the bullwhip effect, leading to a significant loss in profits. In the absence of this concrete information, companies try to react better to the variability rather than eliminate the variability itself. This leads companies to invest significant amounts in inventory management and scheduling systems, only to see little improvement in performance or profits. Evidence of the size of the bullwhip effect is effective in getting different stages of the supply chain to focus on efforts to achieve coordination and eliminate the variability created within the supply chain.

2. Get top management commitment for coordination. More than any other aspect of supply chain management, coordination can succeed only with top management’s commitment. Coordination requires managers at all stages of the supply chain to subordinate their local interests to the greater interest of the firm, and even of the supply chain. Coordination often requires the resolution of trade-offs in a way that requires many functions in the supply chain to change their traditional practices. These changes often run counter to approaches that were put in place when each function focused only on its local objective. Such changes within a supply chain cannot be implemented without strong top management commitment. Top management commitment was a key factor in helping Walmart and P&G set up collaborative forecasting and replenishment teams.

3. Devote resources to coordination. Coordination cannot be achieved without all parties involved devoting significant managerial resources to this effort. Companies often do not devote resources to coordination because they either assume that lack of coordination is something they have to live with or they hope that coordination will occur on its own. The problem with this approach is that it leaves all managers involved with only the separate areas that they control, while no one is responsible for highlighting the impact one manager’s actions have on other parts of the supply chain. One of the best ways to solve coordination problems is through teams made up of members from different companies throughout the supply chain. These teams should be made responsible for coordination and given the power to implement the changes required. Setting up a coordination team is fruitless unless the team has the power to act, because the team will run into conflict with functional managers who are currently maximizing local objectives. Coordination teams can be effective only once a sufficient level of trust builds between members from different firms. If they are used properly, coordination teams can provide significant benefit, as has happened with the collaborative forecasting and replenishment teams set up by Walmart and P&G.

4. Focus on communication with other stages. Good communication with other stages of a supply chain often creates situations that highlight the value of coordination for both sides. Companies often do not communicate with other stages of the supply chain and are unwilling to share information. However, often all companies in the supply chain are frustrated by the lack of coordination and would be happy to share information if it helped the supply chain operate in a more effective manner. Regular communication among the parties involved facilitates change in

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such a setting. For instance, a major PC company had been ordering its microprocessors in batches of several weeks of production. It was trying to move to a build-to-order environment in which it would place microprocessor orders on a daily basis. The PC company assumed that the microprocessor supplier would be reluctant to go along with this approach. However, once com- munication was opened up with the supplier, the opposite turned out to be true. The supplier also wanted to reduce lot sizes and increase the frequency of orders. It had just assumed that the PC manufacturer wanted large lots and thus never requested a change. Regular communication helps different stages of the supply chain share their goals and identify common goals and mutually beneficial actions that improve coordination.

5. Try to achieve coordination in the entire supply chain network. The full benefit of coordination is achieved only when the entire supply chain network is coordinated. It is not enough for two stages in a supply chain to coordinate. The most powerful party in a supply chain should make an effort to achieve coordination in the entire network. Toyota has been very effec- tive in achieving knowledge sharing and coordination in its entire network.

6. Use technology to improve connectivity in the supply chain. The Internet and a vari- ety of software systems can be used to increase the visibility of information throughout the sup- ply chain. Until now, most IT implementations have achieved visibility of information only within a firm. Visibility across the supply chain still requires additional effort in most cases. From the discussion in this chapter, it should be clear that the major benefits of IT systems can be realized only if the systems help increase visibility across the supply chain and facilitate coor- dination. If firms are to realize the full benefit of the huge investments they make in current IT systems, particularly ERP systems, it is crucial that they make the extra effort required to use these systems to facilitate collaborative forecasting and planning across the supply chain. The Internet should be used to share information and increase connectivity in the supply chain.

7. Share the benefits of coordination equitably. The greatest hurdle to coordination in the supply chain is the belief on the part of any stage that the benefits of coordination are not being shared equitably. Managers from the stronger party in the supply chain relationship must be sensitive to this fact and ensure that all parties perceive that the way benefits are shared is fair.

10.8 SUMMARY OF LEARNING OBJECTIVES

1. Describe supply chain coordination and the bullwhip effect, and their impact on sup- ply chain performance. Supply chain coordination requires all stages to take actions that maxi- mize total supply chain profits. A lack of coordination results if different stages focus on optimizing their local objectives or if information is distorted as it moves across the supply chain. The phenomenon that fluctuation in orders increases as one moves up the supply chain from retailers to wholesalers to manufacturers to suppliers is referred to as the bullwhip effect. This effect results in an increase in all costs in the supply chain and a decrease in customer service levels. The bullwhip effect moves all parties in the supply chain away from the efficient frontier and results in a decrease of both customer satisfaction and profitability within the supply chain.

2. Identify obstacles to coordination in a supply chain. A key obstacle to coordination in the supply chain is misaligned incentives that result in different stages optimizing local objectives instead of total supply chain profits. Other obstacles include lack of information sharing, operational inefficiencies leading to large replenishment lead times and large lots, sales force incentives that encourage forward buying, rationing schemes that encourage inflation of orders, promotions that encourage forward buying, and a lack of trust that makes any effort toward coordination difficult.

3. Discuss managerial levers that help achieve coordination in a supply chain. Man- agers can help achieve coordination in the supply chain by aligning goals and incentives across different functions and stages of the supply chain. Other actions that managers can take to achieve coordination include sharing of sales information and collaborative forecasting and plan- ning, implementation of single-point control of replenishment, improving operations to reduce

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lead times and lot sizes, EDLP and other strategies that limit forward buying, and the building of trust and strategic partnerships within the supply chain.

4. Understand the different forms of collaborative planning, forecasting, and replenish- ment that are possible in a supply chain. Partners may set CPFR relationships to collaborate on store events, DC replenishment, store replenishment, or assortment planning. DC replenish- ment collaboration is often the easiest to implement because it requires aggregate-level data. Store replenishment collaboration requires a higher level of investment in technology and data sharing to be successful.

Discussion Questions 1. What is the bullwhip effect, and how does it relate to lack of

coordination in a supply chain? 2. What is the impact of lack of coordination on the perfor-

mance of a supply chain? 3. In what way can improper incentives lead to a lack of coordi-

nation in a supply chain? What countermeasures can be used to offset this effect?

4. What problems result if each stage of a supply chain views its demand as the orders placed by the downstream stage? How should firms within a supply chain communicate to facilitate coordination?

5. What factors lead to a batching of orders within a supply chain? How does this affect coordination? What actions can minimize large batches and improve coordination?

6. How do trade promotions and price fluctuations affect coordi- nation in a supply chain? What pricing and promotion policies can facilitate coordination?

7. How is the building of strategic partnerships and trust valuable within a supply chain?

8. What are the different CPFR scenarios and how do they benefit supply chain partners?

Bibliography Bowersox, Donald J., David J. Closs, and Theodore P. Stank. “21st

Century Logistics: Making Supply Chain Integration a Reality.” Supply Chain Management Review (Fall 1999): 44–49.

Brunell, Tom. “Managing a Multicompany Supply Chain.” Sup- ply Chain Management Review (Spring 1999): 45–52.

Cederlund, Jerold P., Rajiv Kohli, Susan A. Sherer, and Yuiling Yao. “How Motorola put CPFR into Action.” Supply Chain Management Review (October 2007): 28–35.

Chen, Frank, Zvi Drezner, Jennifer K. Ryan, and David Simchi- Levi. “Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Informa- tion.” Management Science (2000): 46, 436–443.

Continuous Replenishment: An ECR Best Practices Report. Washington, DC: Grocery Manufacturers Association, 1994.

Crum, Colleen, and George E. Palmatier. “Demand Collabora- tion: What’s Holding Us Back?” Supply Chain Management Review (January–February 2004): 54–61.

Disney, S. M., and D. R. Towill. “The Effect of Vendor Managed Inventory (VMI) Dynamics on the Bullwhip Effect in Supply Chains.” International Journal of Production Economics (2003): 85, 199–215.

Hammond, Janice H. 1994. Barilla Spa (A–D). Harvard Business School Case 9–694–046.

Kumar, Nirmalya. “The Power of Trust in Manufacturer–Retailer Relationships.” Harvard Business Review (November– December 1996): 92–106.

Lee, Hau L., V. Padmanabhan, and Seungjin Whang. “The Bull- whip Effect in Supply Chains.” Sloan Management Review (Spring 1997): 93–102.

Mariotti, John L. “The Trust Factor in Supply Chain Management.” Supply Chain Management Review (Spring 1999): 70–77.

Sabath, Robert E., and John Fontanella. “The Unfulfilled Promise of Supply Chain Collaboration.” Supply Chain Management Review (July–August 2002): 24–29.

Seifert, Dirk. Collaborative Planning, Forecasting, and Replen- ishment: How to Create a Supply Chain Advantage. New York: AMACOM, 2003.

Senge, Peter M. The Fifth Discipline. New York: Currency and Doubleday, 1990.

Smeltzer, Larry R. “Integration Means Everybody—Big and Small.” Supply Chain Management Review (September– October 2001): 36–44.

Smith, L. “West Marine: A CPFR Success Story.” Supply Chain Management Review (March 2006): 29–36.

Smith, Larry. “Connecting the Consumer to the Factory.” Supply Chain Management Review (May–June 2013): 10–17.

Steerman, Hank. “A Practical Look at CPFR: The Sears-Michelin Experience.” Supply Chain Management Review (July– August 2003): 46–53.

Voluntary Interindustry Commerce Standards. Collaborative Planning, Forecasting, and Replenishment, Version 2.0, 2002.

Voluntary Interindustry Commerce Standards. CPFR: An Overview, 2004.

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Cycle inventory exists because producing or purchasing in large lots allows a stage of the supply chain to exploit economies of scale and thus lower cost. The presence of fixed costs associated with ordering and transportation, quantity discounts in product pricing, and short-term discounts or promotions encourages different stages of a supply chain to exploit economies of scale and order in large lots. In this chapter, we study how each of these factors affects the lot size and cycle inventories within a supply chain. Our goal is to identify managerial levers that reduce cycle inventory in a supply chain without raising cost.

11.1 THE ROLE OF CYCLE INVENTORY IN A SUPPLY CHAIN

A lot or batch size is the quantity that a stage of a supply chain either produces or purchases at a time. Consider, for example, a computer store that sells an average of four printers a day. The store manager, however, orders 80 printers from the manufacturer each time he places an order. The lot or batch size in this case is 80 printers. Given daily sales of four printers, it takes an aver- age of 20 days before the store sells the entire lot and purchases a replenishment lot. The com- puter store holds an inventory of printers because the manager purchases a lot size larger than the store’s daily sales. Cycle inventory is the average inventory in a supply chain due to either pro- duction or purchases in lot sizes that are larger than those demanded by the customer.

Managing Economies of Scale in a Supply Chain

Cycle Inventory

C H A P T E R

11

268

LEARNING OBJECTIVES After reading this chapter, you will be able to 1. Balance the appropriate costs to choose the

optimal lot size and cycle inventory in a supply chain.

2. Understand the impact of quantity discounts on lot size and cycle inventory.

3. Devise appropriate discounting schemes for a supply chain.

4. Understand the impact of trade promotions on lot size and cycle inventory.

5. Identify managerial levers that reduce lot size and cycle inventory in a supply chain without increasing cost.

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Q

D

Here, we ignore the impact of demand variability and assume that demand is stable. In Chapter 12, we introduce demand variability and its impact on safety inventory.

for jeans is relatively stable at D = currently purchases in lots of Q = 1,000 pairs. The inventory profile plot depicting the level of inventory over time, as shown in Figure 11-1.

Because purchases are in lots of Q = 1,000 units, whereas demand is only D = 100 units per day, it takes 10 days for an entire lot to be sold. Over these 10 days, the inventory of jeans at

repeats itself every 10 days, as shown in the inventory profile in Figure 11-1.

Cycle inventory = lot size

2 =

Q 2

(11.1)

Q/2 = 500 pairs of

chain in which stages produce or purchase in larger lots has more cycle inventory than a supply chain in which stages produce and purchase in smaller lots. For example, if a competing depart- ment store with the same demand purchases in lot sizes of 200 pairs of jeans, it will carry a cycle inventory of only 100 pairs of jeans.

Lot sizes and cycle inventory also influence the flow time of material within the supply chain. Recall from Little’s Law that

Average flow time = average inventory average flow rate

For any supply chain, average flow rate equals demand. We thus have

Average flow time resulting from cycle inventory = cycle inventory

demand =

Q 2D

For lot sizes of 1,000 pairs of jeans and daily demand of 100 pairs of jeans, we obtain

Average flow time resulting from cycle inventory = Q 2D

= 1,000

2 * 100 = 5 days

Time t

Q

Inventory

FIGURE 11-1 Inventory Profile of Jeans at Jean-Mart

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that jeans spend in the supply chain. The larger the cycle inventory, the longer the lag time between when a product is produced and when it is sold. A lower level of cycle inventory is always desir- able, because long time lags leave a firm vulnerable to demand changes in the marketplace. A lower cycle inventory also decreases a firm’s working capital requirement. Toyota, for example, keeps a cycle inventory of only a few hours of production between the factory and most suppliers. As a result, Toyota is never left with unneeded parts, and its working capital requirements are less than those of its competitors. Toyota also allocates very little space in the factory to inventory.

– ishment batch thus contains only about two days of demand. This ensures that Zara’s inventory on hand closely tracks customer demand. In both instances the firms have used small batch replenishment to ensure that their supply closely tracks customer demand trends.

Before we suggest actions that a manager can take to reduce cycle inventory, it is important to understand why stages of a supply chain produce or purchase in large lots and how lot size reduction affects supply chain performance.

Cycle inventory is held to take advantage of economies of scale and reduce cost within a supply chain. For example, apparel is shipped from Asia to North America in full container loads to reduce

per lot to spread that high cost of setup over a large batch. To understand how the supply chain achieves these economies of scale, we first identify supply chain costs that are influenced by lot size.

The average price paid per unit purchased is a key cost in the lot-sizing decision. A buyer may increase the lot size if this action results in a reduction in the price paid per unit purchased. For example, if the jeans manufacturer charges $20 per pair for orders under 500 pairs of jeans

– ing in lots of at least 500 pairs of jeans. The price paid per unit is referred to as the material cost and is denoted by C. It is measured in dollars per unit. In many practical situations, material cost displays economies of scale—increasing lot size decreases material cost.

The fixed ordering cost includes all costs that do not vary with the size of the order but are incurred each time an order is placed. There may be a fixed administrative cost to place an order,

incurs a cost of $400 for the truck regardless of the number of pairs of jeans shipped. If the truck can hold up to 2,000 pairs of jeans, a lot size of 100 pairs results in a transportation cost of $4/pair, whereas a lot size of 1,000 pairs results in a transportation cost of $0.40/pair. Given the fixed transportation cost per batch, the store manager can reduce transportation cost per unit by increasing the lot size. The fixed ordering cost per lot or batch is denoted by S

economies of scale—increasing the lot size decreases the fixed ordering cost per unit purchased. Holding cost is the cost of carrying one unit in inventory for a specified period of time,

usually one year. It is a combination of the cost of capital, the cost of physically storing the inventory, and the cost that results from the product becoming obsolete. The holding cost is denoted by H and is measured in dollars per unit per year. It may also be obtained as a fraction, h, of the unit cost of the product. Given a unit cost of C, the holding cost H is given by

H = hC (11.2) The total holding cost increases with an increase in lot size and cycle inventory.

C/unit S/lot

H/unit/year = hC

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Later in the chapter, we discuss how the various costs may be estimated in practice. How- ever, for the purposes of this discussion, we assume they are already known.

The primary role of cycle inventory is to allow different stages in a supply chain to pur- chase product in lot sizes that minimize the sum of the material, ordering, and holding costs. If a manager considers the holding cost alone, he or she will reduce the lot size and cycle inventory.

size and cycle inventory. A manager must make the trade-off that minimizes total cost when making lot-sizing decisions.

Ideally, cycle inventory decisions should be made considering the total cost across the entire supply chain. In practice, however, it is generally the case that each stage makes its cycle inventory decisions independently. As we discuss later in the chapter, this practice increases the level of cycle inventory as well as the total cost in the supply chain.

Key Point

Cycle inventory exists in a supply chain because different stages exploit economies of scale to lower total cost. The costs considered include material cost, fixed ordering cost, and holding cost.

Any stage of the supply chain exploits economies of scale in its replenishment decisions in

1. A fixed cost is incurred each time an order is placed or produced. 2. The supplier offers price discounts based on the quantity purchased per lot. 3. The supplier offers short-term price discounts or holds trade promotions.

In the following sections, we review how purchasing managers can best respond to these situations.

11.2 ESTIMATING CYCLE INVENTORY-RELATED COSTS IN PRACTICE

When setting cycle inventory levels in practice, a common hurdle is estimating the ordering and holding costs. Given the robustness of cycle inventory models, it is better to get a good approxi- mation quickly rather than spend a lot of time trying to estimate costs exactly.

Our goal is to identify incremental costs that change with the lot-sizing decision. We can ignore costs that are unchanged with a change in lot size. For example, if a factory is running at 50 percent of capacity and all labor is full time and not earning overtime, it can be argued that the incremental setup cost for labor is zero. Reducing the lot size in this case will not have any

Inventory Holding Cost

Holding cost is estimated as a percentage of the cost of a product and is the sum of the following

Cost of capital: This is the dominant component of holding cost for products that do not become obsolete quickly. The appropriate approach is to evaluate the weighted-average cost of capital

of equity and debt financing that the firm has. The formula for the WACC is

WACC = E

D + E 1Rf + b * MRP2+ DD + E Rb 1 – t

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where E = amount of equity D = amount of debt Rf = b = the firm’s beta, a measure of volatility of stock price MRP = Rb = t = tax rate

research report on the company. The borrowing rate comes from tables listing the rates charged

Treasury bonds, and the market risk premium is the return of the market above the risk-free rate. If access to a company’s financial structure is not available, a good approximation can be made by using numbers from public companies in the same industry and of similar size. Obsolescence (or spoilage) cost: The obsolescence cost estimates the rate at which the value of the stored product drops because its market value or quality falls. This cost can range dramatically, from rates of many-thousand percent to virtually zero, depending on

– ables can have high obsolescence rates if they have short life cycles. A product with a life cycle of six months has an effective obsolescence cost of 200 percent. At the other end of the spectrum are products such as crude oil that take a long time to spoil or become obso- lete. For such products, a low obsolescence rate may be applied. Handling cost: Handling cost should include only incremental receiving and storage

that vary with the number of orders should be included in the order cost. The quantity- dependent handling cost often does not change if quantity varies within a range. If the

quantity handled requires more people, an incremental handling cost is added to the hold- ing cost. Occupancy cost: The occupancy cost reflects the incremental change in space cost due to changing cycle inventory. If the firm is being charged based on the actual number of units held in storage, we have the direct occupancy cost. Firms often lease or purchase a fixed amount of space. As long as a marginal change in cycle inventory does not change the space requirements, the incremental occupancy cost is zero. Occupancy costs often take the form of a step function, with a sudden increase in cost when capacity is fully uti- lized and new space must be acquired. Miscellaneous costs: The final component of holding cost deals with a number of other relatively small costs. These costs include theft, security, damage, tax, and additional insurance charges that are incurred. Once again, it is important to estimate the incremental change in these costs on changing cycle inventory.

Ordering Cost

The ordering cost includes all incremental costs associated with placing or receiving an extra order that are incurred regardless of the size of the order. Components of ordering cost include

Buyer time: Buyer time is the incremental time of the buyer placing the extra order. This cost should be included only if the buyer is utilized fully. The incremental cost of getting

ordering can significantly reduce the buyer time to place an order.

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Transportation costs: A fixed transportation cost is often incurred regardless of the size of the order. For instance, if a truck is sent to deliver every order, it costs the same amount to send a half-empty truck as it does a full truck. Less-than-truckload pricing also includes a fixed component that is independent of the quantity shipped and a variable component that increases with the quantity shipped. The fixed component should be included in the ordering cost. Receiving costs: These include any administration work such as purchase order matching and any effort associated with updating inventory records. Receiving costs that are quantity dependent should not be included here. Other costs: are incurred for each order regardless of the quantity of that order.

The ordering cost is estimated as the sum of all its component costs.

11.3 ECONOMIES OF SCALE TO EXPLOIT FIXED COSTS

To better understand the trade-offs discussed in this section, consider a situation that often arises in daily life—the purchase of groceries and other household products. These may be purchased

which is generally located much farther away. The fixed cost of going shopping is the time it takes to go to either location. This fixed cost is much lower for the nearby convenience store. Prices, however, are higher at the local convenience store. Taking the fixed cost into account, we tend to tailor our lot size decision accordingly. When we need only a small quantity, we go to the nearby convenience store because the benefit of a low fixed cost outweighs the cost of higher prices at the convenience store. When we are buying a large quantity, however, we go to Costco, where the lower prices over the larger quantity purchased more than make up for the higher fixed cost.

In this section, we focus on the situation in which a fixed cost associated with placing, receiving, and transporting an order is incurred for each order. A purchasing manager wants to minimize the total cost of satisfying demand and must therefore make the appropriate cost trade- offs when making the lot-sizing decision. We start by considering the lot-sizing decision for a single product.

Lot Sizing for a Single Product (Economic Order Quantity)

As Best Buy sells its current inventory of HP computers, the purchasing manager places a replen- ishment order for a new lot of Q computers. Including the cost of transportation, Best Buy incurs a fixed cost of $S per order. The purchasing manager must decide on the number of computers to

D = Annual demand of the product S = Fixed cost incurred per order C = Cost per unit of product h = Holding cost per year as a fraction of product cost

Assume that HP does not offer any discounts, and each unit costs $C no matter how large an order is. The holding cost is thus given by H = hC –

1. Demand is steady at D units per unit time. 2. No shortages are allowed—that is, all demand must be supplied from stock. 3.

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The purchasing manager makes the lot-sizing decision to minimize the total cost for the

Because purchase price is independent of lot size, we have

Annual material cost = CD

The number of orders must suffice to meet the annual demand D. Given a lot size of Q, we thus have

Number of orders per year = D Q

(11.3)

Because an order cost of S is incurred for each order placed, we infer that

Annual ordering cost = aD Q bS (11.4)

Given a lot size of Q, we have an average inventory of Q/2. The annual holding cost is thus the cost of holding Q/2 units in inventory for one year and is given as

Annual holding cost = aQ 2 bH = aQ

2 bhC

The total annual cost, TC, is the sum of all three costs and is given as

Total annual cost, TC = CD + aD Q bS + aQ

2 bhC

Figure 11-2 shows the variation in different costs as the lot size is changed. Observe that the annual holding cost increases with an increase in lot size. In contrast, the annual ordering cost declines with an increase in lot size. The material cost is independent of lot size because we have assumed the price to be fixed. The total annual cost thus first declines and then increases with an increase in lot size.

From the perspective of the manager at Best Buy, the optimal lot size is one that minimizes the total cost to Best Buy. It is obtained by taking the first derivative of the total cost with respect to Q

Cost

Total Cost

Holding Cost

Ordering Cost

Material Cost

Lot Size

FIGURE 11-2 Effect of Lot Size on Costs at Best Buy

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is referred to as the economic order quantity Q* and is given by the fol-

Optimal lot size, Q* = A2DShC (11.5) For this formula, it is important to use the same time units for the holding cost rate h and the

demand D. With each lot or batch of size Q*, the cycle inventory in the system is given by Q*/2. The flow time spent by each unit in the system is given by Q* D does the cycle inventory and the flow time. The optimal ordering frequency is given by n*, where

n* = D

Q* = ADhC2S (11.6)

Chapter11-examples1-6, worksheet Example 11-1

EXAMPLE 11-1 Economic Order Quantity

Demand for the Deskpro computer at Best Buy is 1,000 units per month. Best Buy incurs a fixed

number of computers that the store manager should order in each replenishment lot.

Analysis:

Annual demand, D = 1,000 * 12 = 12,000 units Order cost per lot, S = $4,000 Unit cost per computer, C = $500 Holding cost per year as a fraction of unit cost, h = 0.2

Optimal order size = Q* = A2 * 12,000 * 4,0000.2 * 500 = 980 To minimize the total cost at Best Buy, the store manager orders a lot size of 980 computers for

Cycle inventory = Q*

2 =

980 2

= 490

For a lot size of Q* = 980, the store manager evaluates

Number of orders per year = D

Q* =

12,000 980

= 12.24

Annual ordering and holding cost = D

Q* S + aQ*

2 bhC = $97,980

Average flow time = Q*

2D =

490 12,000

= 0.041 year = 0.49 month

purchased in a batch of 980.

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Example11-1

though the order size is more than 10 percent larger than the optimal order size Q*, total cost

half a case and may want to charge extra for this service. Our discussion illustrates that Best Buy is perhaps better off with lot sizes of six or seven cases, because this change has a small impact on its inventory-related costs but can save on any fee that the manufacturer charges for shipping half a case.

Key Point

Total ordering and holding costs are relatively stable around the economic order quantity. A firm is often

placed per year also doubles. In contrast, average flow time decreases by a factor of 2. In other

should reduce if the lot-sizing decision is made optimally. This observation can be stated as the

Key Point

If demand increases by a factor of k, the optimal lot size increases by a factor of 1k. The number of orders placed per year should also increase by a factor of 1k. Flow time attributed to cycle inventory should decrease by a factor of 1k.

Let us return to the situation in which monthly demand for the Deskpro model is 1,000 computers. Now assume that the manager would like to reduce the lot size to Q = 200 units to reduce flow time. If this lot size is decreased without any other change, we have

Annual inventory@related costs = aD Q bS + aQ

2 bhC = $250,000

This is significantly higher than the total cost of $97,980 that Best Buy incurred when

store manager would be unwilling to reduce the lot size to 200. To make it feasible to reduce the lot size, the manager should work to reduce the fixed order cost S. If the fixed cost associated

Example 11-2

EXAMPLE 11-2 Relationship Between Desired Lot Size and Ordering Cost

The store manager at Best Buy would like to reduce the optimal lot size from 980 to 200. For this lot size reduction to be optimal, the store manager wants to evaluate how much the ordering cost per lot should be reduced.

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Analysis: In this case, we have

Desired lot size, Q* = 200 Annual demand, D = 1,000 : 12 = 12,000 units Unit cost per computer, C = $500 Holding cost per year as a fraction of inventory value, h = 0.2

S = hC1Q*22

2D =

0.2 * 500 * 2002

2 * 12,000 = $1 .7

Thus, the store manager at Best Buy would have to reduce the order cost per lot from

Key Point

To reduce the optimal lot size by a factor of k, the fixed order cost S must be reduced by a factor of k2.

Production Lot Sizing

While this may be a reasonable assumption for a retailer receiving a replenishment lot, it is not reasonable in a production environment in which production occurs at a specified rate, say, P. In a production environment, inventory thus builds up at a rate of P – D when production is on, and inventory is depleted at a rate of D when production is off.

With D, h, C, and S –

QP = A 2DS1 – D > P hC The annual setup cost in this case is given bya D

QP bS

The annual holding cost is given by 11 – D > P2 aQP 2 bhC

approaches 1 as the production rate becomes much faster than the demand. For the remainder of this chapter, we restrict our attention to the case in which the entire

replenishment lot arrives at the same time, a scenario that applies in most supply chain settings.

Lot Sizing with Capacity Constraint

In our discussion so far we have assumed that the economic order quantity for a retailer will fit on the truck. In reality the truck has a limited capacity, say K. If the economic order quantity Q is more than the K, the retailer will have to pay for more than one truck. In this case, the optimal

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order quantity is obtained by comparing the cost of ordering K Q units <Q>K= S arises primarily from the cost of a truck, it is never optimal to K

11.4 AGGREGATING MULTIPLE PRODUCTS IN A SINGLE ORDER

As we have discussed earlier, a key to reducing lot size is the reduction of the fixed cost incurred per lot. One major source of fixed costs is transportation. In several companies, the array of prod- ucts sold is divided into families or groups, with each group managed independently by a sepa- rate product manager. This results in separate orders and deliveries for each product family, thus increasing the overall cycle inventory. Aggregating orders and deliveries across product families is an effective mechanism to lower cycle inventories. We illustrate the idea of aggregating ship- ments using the following example.

– els, and the demand for each of the four models is 1,000 units per month. In this case, if each product

the four models, the total cycle inventory would thus be 4 : 980/2 = Now consider the case in which a store manager at Best Buy realizes that all four model

purchasing to ensure that all four products arrive on the same truck. In this case, the optimal S = $4,000, D = 4 :

12,000 = 48,000, hC = $500 : 0.2 = units for each model. As a result of aggregating orders and spreading the fixed transportation cost across multiple products originating from the same supplier, it becomes financially optimal for the store manager at Best Buy to reduce the lot size for each individual product. This action sig- nificantly reduces the cycle inventory, as well as the cost to Best Buy.

Another way to achieve this result is to have a single delivery coming from multiple sup-

and more frequent deliveries from each supplier. The benefits of aggregation can be stated as in

Key Point

Aggregating replenishment across products, retailers, or suppliers in a single order allows for a reduc- tion in lot size for individual products because fixed ordering and transportation costs are now spread across multiple products, retailers, or suppliers.

across multiple supply and delivery points without storing intermediate inventories through the

delivery destined for multiple retail stores. At the DC, each inbound truck is unloaded, product is –

gated from several suppliers destined for one retail store. When considering fixed costs, one cannot ignore the receiving or loading costs. As more

products are included in a single order, the product variety on a truck increases. The receiving warehouse now has to update inventory records for more items per truck. In addition, the task of

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putting inventory into storage now becomes more expensive because each distinct item must be stocked in a separate location. Thus, when attempting to reduce lot sizes, it is important to focus

– tain precise records of the contents of the truck that are sent electronically by the supplier to the customer. These electronic notices facilitate updating of inventory records as well as the decision regarding storage locations, helping reduce the fixed cost of receiving. RFID technology is also likely to help reduce the fixed costs associated with receiving that are related to product variety. The reduced fixed cost of receiving makes it optimal to reduce the lot size ordered for each prod- uct, thus reducing cycle inventory.

We next analyze how optimal lot sizes may be determined when there are fixed costs asso- ciated with each lot as well as the variety in the lot.

Lot Sizing with Multiple Products or Customers

In general, the ordering, transportation, and receiving costs of an order grow with the variety of products or pickup points. For example, it is cheaper for Walmart to receive a truck containing a single product than it is to receive a truck containing many different products, because the inven- tory update and restocking effort is less for a single product. A portion of the fixed cost of an

such a setting. Our objective is to arrive at lot sizes and an ordering policy that minimize the total cost. We

Di i

S included in the order

si i is included in the order

Let us consider the case in which Best Buy purchases multiple models of a product. The

1. 2. The product managers jointly order every product in each lot. 3. Product managers order jointly but not every order contains every product; that is, each

order contains a selected subset of the products.

The first approach does not use any aggregation and results in high cost. The second approach aggregates all products in each order. The weakness of the second approach is that low- demand products are aggregated with high-demand products in every order. Complete aggrega- tion results in high costs if the product-specific order cost for the low-demand products is large. In such a situation, it may be better to order the low-demand products less frequently than the high-demand products. This practice results in a reduction of the product-specific order cost associated with the low-demand product. As a result, the third approach is likely to yield the low- est cost. However, it is more complex to coordinate.

We consider the example of Best Buy purchasing computers and illustrate the effect of each of the three approaches on supply chain costs.

LOTS ARE ORDERED AND DELIVERED INDEPENDENTLY FOR EACH PRODUCT In this approach, each product is ordered independently of the others. This scenario is equivalent to applying the

worksheet Example 11-3 in spreadsheet Chapter11-examples1-6

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EXAMPLE 11-3 Multiple Products with Lots Ordered and Delivered Independently

demands for the three products are DL = 12,000 for the Litepro, DM = – pro, and DH = – tion cost of $4,000 is incurred each time an order is delivered. For each model ordered and delivered on the same truck, an additional fixed cost of $1,000 per model is incurred for receiv-

Buy manager should order if lots for each product are ordered and delivered independently. Also evaluate the annual cost of such a policy.

Analysis:

Demand, DL = 12,000/year, DM = 1,200/year, DH = 120/year Common order cost, S = $4,000 Product-specific order cost, sL = $1,000, sM = $1,000, sH = $1,000 Holding cost, h = 0.2 Unit cost, CL = $500, CM = $500, CH = $500

Because each model is ordered and delivered independently, a separate truck delivers each +

are shown in Table 11-1.

year, and the Heavypro model is ordered 1.1 times each year. The annual ordering and holding cost Best Buy incurs if the three models are ordered independently turns out to be $155,140.

TABLE 11-1 Lot Sizes and Costs for Independent Ordering Litepro Medpro Heavypro

Demand per year 12,000 1,200 120

Fixed cost/order $5,000 $5,000 $5,000

Optimal order size 1,095 346 110

Cycle inventory 548 173 55

Annual holding cost $54,772 $17,321 $5,477

Order frequency 11.0/year 3.5/year 1.1/year

Annual ordering cost $54,772 $17,321 $5,477

Average flow time 2.4 weeks 7.5 weeks 23.7 weeks

Annual cost $109,544 $34,642 $10,954

Note: Although these figures are correct, some may differ from calculations due to rounding.

Independent ordering is simple to execute but ignores the opportunity to aggregate orders. Thus, the product managers at Best Buy could potentially lower costs by combining orders on a single truck. We next consider the scenario in which all three products are ordered and delivered on the same truck each time an order is placed.

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LOTS ARE ORDERED AND DELIVERED JOINTLY FOR ALL THREE MODELS Given that all three models are included each time an order is placed, the combined fixed order cost per order is given by

S* = S + sL + sM + sH The next step is to identify the optimal ordering frequency. Let n be the number of orders

placed per year. We then have

Annual order cost = S*n

Annual holding cost = DLhCL

2n +

DMhCM 2n

+ DHhCH

2n

The total annual cost is thus given by

Total annual cost = DLhCL

2n +

DMhCM 2n

+ DHhCH

2n + S*n

The optimal order frequency minimizes the total annual cost and is obtained by taking the first derivative of the total cost with respect to n and setting it equal to 0. This results in the opti- mal order frequency n*, where

n* = ADLhCL + DMhCM + DHhCH2S* (11.7) k items consolidated on a

n* = Eg ki = 1DihCi2S* (11.8) Truck capacity can also be included in this setting by comparing the total load for the opti-

mal n* with truck capacity. If the optimal load exceeds truck capacity, n* is increased until the k, we can also find

the optimal number of items or suppliers to be aggregated in a single delivery.

Example 11-4

EXAMPLE 11-4 Products Ordered and Delivered Jointly

each model.

Analysis: Because all three models are included in each order, the combined order cost is

S* = S + sL + sM + sH = $ 7,000 per order

n* = A 12,000 * 100 + 1,200 * 100 + 120 * 1002 * 7,000 = 9.75 Thus, if each model is to be included in every order and delivery, the product managers at Best Buy should place 9.75 orders each year. In this case, the ordering policies and costs are as shown in Table 11-2.

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Because 9.75 orders are placed each year and each order costs a total of $7,000, we have

Annual order cost = 9.75 * 7,000 = $ 8,250

The annual ordering and holding cost, across the three sizes, of the aforementioned policy is given by

Annual ordering and holding cost = $ 1,512 + $ ,151 + $ 15 + $ 8,250 = $1 ,528

by ordering all products jointly. This represents a decrease of about 12 percent.

Example 11-5

EXAMPLE 11-5 Aggregation with Capacity Constraint

W.W. Grainger sources from hundreds of suppliers and is considering the aggregation of inbound shipments to lower costs. Truckload shipping costs $500 per truck along with $100 per pickup.

incurs a holding cost of 20 percent. What is the optimal order frequency and order size if Grainger decides to aggregate four suppliers per truck? What is the optimal order size and frequency if each truck has a capacity of 2,500 units?

Analysis:

Demand per product, Di = 10,000 Holding cost, h = 0.2 Unit cost per product, Ci = $50 Common order cost, S = $500

si = $100

The combined order cost from four suppliers is given by

S* = S + s1 + s2 + s + s4 = $ 900 per order

n* = Ea 4i = 1DihCi2S* = A4 * 10,000 * 0.2 * 502 * 900 = 14.91

TABLE 11-2 Lot Sizes and Costs for Joint Ordering at Best Buy Litepro Medpro Heavypro

Demand per year (D) 12,000 1,200 120

Order frequency (n*) 9.75/year 9.75/year 9.75/year

Optimal order size (D/n*) 1,230 123 12.3

Cycle inventory 615 61.5 6.15

Annual holding cost $61,512 $6,151 $615

Average flow time 2.67 weeks 2.67 weeks 2.67 weeks

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It is thus optimal for Grainger to order 14.91 times per year. The annual ordering cost per supplier is

Annual order cost = 14.91 * 900 4

= $ , 55

The quantity ordered from each supplier is Q = 10,000/14.91 = holding cost per supplier is

Annual holding cost per supplier = hCiQ

2 = 0.2 * 50 * 71

2 = $ , 55

This policy, however, requires a total capacity per truck of 4 * 71 = 2, 84 units. Given a truck capacity of 2,500 units, the order frequency must be increased to ensure that the order quantity from each supplier is 2,500/4 =

= –

The main advantage of ordering all products jointly is that the system is easy to administer and implement. The disadvantage is that it is not selective enough in combining the particular models that should be ordered together. If product-specific order costs are high and products vary significantly in terms of their sales, it is possible to lower costs by being selective about the prod- ucts being aggregated in a joint order.

Next, we consider a policy under which the product managers do not necessarily order all models each time an order is placed, but still coordinate their orders.

LOTS ARE ORDERED AND DELIVERED JOINTLY FOR A SELECTED SUBSET OF THE PROD- UCTS We first illustrate how being selective in aggregating orders into a single order can lower

disadvantage of this policy is that the Heavypro, with annual demand of only 120 units, is also ordered 9.75 times. Given that a model-specific cost of $1,000 is incurred with each order, we are

= $81.25 in order cost to each Heavypro. If we were to include :

= : 0.2 : : =

:

This example points to the value of being more selective when aggregating orders. We now discuss a procedure that is more selective in combining products to be ordered

jointly. The procedure we discuss here does not necessarily provide the optimal solution. It does, however, yield an ordering policy whose cost is close to optimal. The approach of the procedure is to first identify the “most frequently” ordered product that is included in every order. The base fixed cost S is then entirely allocated to this product. For each of the “less frequently” ordered products i, the ordering frequency is determined using only the product-specific ordering cost si. The frequencies are then adjusted so that each product i is included every mi orders, where mi is an integer. We now detail the procedure used.

We first describe the procedure in general and then apply it to the specific example. Assume that the products are indexed by i, where i varies from 1 to l l product i has an annual demand Di, a unit cost Ci, and a product-specific order cost si. The com- mon order cost is S.

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Step 1: As a first step, identify the most frequently ordered product, assuming each product is ordered independently. In this case, a fixed cost of S + si is allocated to each product. For each product i

ni = B hCiDi21S + si2 This is the frequency at which product i would be ordered if it were the only product

S + si n be the frequency of the most frequently ordered product, i*; that is, ni* is the maximum among all ni 1n = ni* = max 5ni, i = 1, c , l62. The most frequently ordered product is i*, which is included each time an order is placed.

Step 2: For all products i ≠ i*

ni = B hCiDi2si ni represents the desired order frequency if product i incurs the product-specific fixed cost si only each time it is ordered.

Step 3: Our goal is to include each product i ≠ i* with the most frequently ordered product i* after an integer number of orders. For all i ≠ i*, evaluate the frequency of product i relative to the most frequently ordered product i* to be mi, where

mi = <n/ni= In this case, < = is the operation that rounds a fraction up to the closest integer. Product i is included with the most frequently ordered product i* every mi orders. Given that the most frequently ordered product i* is included in every order, mi* = 1.

Step 4: Having decided the ordering frequency of each product i, recalculate the ordering frequency of the most frequently ordered product i* to be n, where

n = E a li = 1hCimiDi21S + a li = 1si > mi2 (11.9) Note that n is a better ordering frequency for the most frequently ordered product i* than n because it takes into account the fact that each of the other products i is included with i* every mi orders.

Step 5: For each product, evaluate an order frequency of ni = n/mi and the total cost of such an ordering policy. The total annual cost is given by

TC = nS + a l

i = 1 nisi + a

l

i = 1 a Di

2ni bhCi

This procedure results in tailored aggregation, with higher-demand products ordered more

Example 11-6

EXAMPLE 11-6 Lot Sizes Ordered and Delivered Jointly for a Selected Subset That Varies by Order

costs using the procedure discussed previously.

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Analysis: Recall that S = $4,000, sL = $1,000, sM = $1,000, sH =

nL = B hCLDL21S + sL2 = 11.0, nM = B hCMDM21S + sM2 = .5, nH = B hCHDH21S + sH2 = 1.1 Clearly, Litepro is the most frequently ordered model. Thus, we set n = 11.0.

included with Litepro in the order. We first obtain

nM = B hCMDM2sM = 7.7 and nH = B hCHDH2sH = 2.4 mM = l nnM m = l 11.07.7 m = 2 and mH = l nnH m = l 11.02.4 m = 5

ordering frequency of the most frequently ordered model as

n = AhCLmLDL + hCMmMDM + hCHmMDH21S + sL > mL + sM > mM + sH > mH2 = 11.47 –

nL = 11.47/year, nM = 11.47 > 2 = 5.74 > year, and nH = 11.47 > 5 = 2.29/year nS + nLsL + nMsM + nHsH = $ 5, 8 .50

results because each model-specific fixed cost of $1,000 is not incurred with every order.

TABLE 11-3 Lot Sizes and Costs for Ordering Policy Using Heuristic Litepro Medpro Heavypro

Demand per year (D) 12,000 1,200 120

Order frequency (n) 11.47/year 5.74/year 2.29/year

Order size (D/n) 1,046 209 52

Cycle inventory 523 104.5 26

Annual holding cost $52,307 $10,461 $2,615

Average flow time 2.27 weeks 4.53 weeks 11.35 weeks

From the Best Buy examples, it follows that aggregation can provide significant cost sav- si

are small relative to the fixed cost S, complete aggregation, whereby every product is included in every order, is very effective. Tailored aggregation provides little additional value in this setting

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with si = Example 11-3 aggregation decreases costs by only about 1 percent relative to complete aggregation, whereas complete aggregation decreases costs by more than 25 percent relative to no aggregation. As product-specific order costs increase, however, tailored aggregation becomes more effective. If

si = Example 11-3 aggregation. Tailored aggregation, however, decreases costs by about 10 percent relative to no aggregation. In general, complete aggregation should be used when product-specific order costs are small, and tailored aggregation should be used when product-specific order costs are large.

Next, we consider lot sizes when material cost displays economies of scale.

Key Point

A key to reducing cycle inventory is the reduction of lot size. A key to reducing lot size without increas- ing costs is reducing the fixed cost associated with each lot. This may be achieved by reducing the fixed cost itself or by aggregating lots across multiple products, customers, or suppliers. When aggregating across multiple products, customers, or suppliers, simple aggregation is effective when product-specific order costs are small, and tailored aggregation is best if product-specific order costs are large.

11.5 ECONOMIES OF SCALE TO EXPLOIT QUANTITY DISCOUNTS

We now consider pricing schedules that encourage buyers to purchase in large lots. There are many instances in business-to-business transactions in which the pricing schedule displays econ- omies of scale, with prices decreasing as lot size increases. A discount is lot-size based if the pricing schedule offers discounts based on the quantity ordered in a single lot. A discount is volume based if the discount is based on the total quantity purchased over a given period, regard- less of the number of lots purchased over that period. In this section, we will see that lot-size– based quantity discounts tend to increase the lot size and cycle inventory in a supply chain. Two

To investigate the impact of such quantity discounts on the supply chain, we must answer

1. Given a pricing schedule with quantity discounts, what is the optimal purchasing decision for a buyer seeking to maximize profits? How does this decision affect the supply chain in terms of lot sizes, cycle inventories, and flow times?

2. Under what conditions should a supplier offer quantity discounts? What are appropriate pricing schedules that a supplier seeking to maximize profits should offer?

– er’s objective is to select lot sizes to minimize the total annual material, order, and holding costs. Next, we evaluate the optimal lot size in the case of all unit quantity discounts.

All Unit Quantity Discounts

In all unit quantity discounts, the pricing schedule contains specified break points q0, q1, … , qr, where q0 = 0. If an order placed is at least as large as qi but smaller than qi+1, each unit is obtained at a cost of Ci. In general, the unit cost decreases as the quantity ordered increases; that

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is, C0 Ú C1 Ú g Ú Cr. For all unit discounts, the average unit cost varies with the quantity

profits or, equivalently, to minimize the sum of material, order, and holding costs. The solution procedure evaluates the optimal lot size for each price and picks the lot size that minimizes the overall cost.

Step 1: Ci,0 … i … r

Qi = A2DShCi (11.10) Step 2: We next select the order quantity Q*i for each price Ci. There are three possible cases for Qi

1. qi … Qi 6 qi + 1 2. Qi 6 qi 3. Qi Ú qi + 1

Qi because it is considered for Qi+1. Thus, we need to con- sider only the first two cases. If qi … Qi 6 qi + 1, then set Q*i = Qi. If Qi 6 qi, then a lot size of Qi does not result in a discount. In this case, set Q

* i = qi to qualify for the

discounted price of Ci per unit.

Step 3: For each i, calculate the total annual cost of ordering Q*i

Total annual cost, TCi = a DQ*i bS + Q*i2 hCi + DCi (11.11) Step 4: Over all i select order quantity Q*i with the lowest total cost TCi.

cutoff price C* above which the optimal solution cannot occur. Recall that Cr is the lowest unit cost above the final threshold quantity qr

C* = 1 D aDCr + DSqr + h2qrCr – 12hDSCrb

worksheets Example 11-7 and Example 11-7 check in spreadsheet Chapter11-examples7-8

Quantity Purchased

C0

0 q1 q2 q3

C1

C2 A

ve ra

ge C

os t p

er U

ni t P

ur ch

as ed

FIGURE 11-3 Average Unit Cost with All Unit Quantity Discounts

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EXAMPLE 11-7 All Unit Quantity Discounts

represent a significant percentage of its sales. Demand for vitamins is 10,000 bottles per month. DO incurs a fixed order placement, transportation, and receiving cost of $100 each time an order for vitamins is placed with the manufacturer. DO incurs a holding cost of 20 percent. The manu-

the DO manager should order in each lot.

Order Quantity Unit Price

0–4,999 $3.00

5,000–9,999 $2.96

10,000 or more $2.92

Analysis:

q0 = 0, q1 = 5,000, q2 = 10,000 C0 = C1 = C2 = $2.92 D = 120,000/year, S = $100/lot, h = 0.2

Q0 = A2DShC0 = , 25; Q1 = A2DShC1 = , 7; Q2 = A2DShC2 = ,411 i = 0 because Q0 = , 25 7 q1 = 5,000. For i = 1, 2, we obtain

Q*1 = Q1 = , 7; Q*2 = q2 = 10,000

TC1 = a DQ*1 bS + aQ*12 bhC1 + DC1 = $ 58,9 9; TC2 = $ 54,520 Observe that the lowest total cost is for i = 2. Thus, it is optimal for DO to order Q*2 = 10,000 bottles per lot and obtain the discount price of $2.92 per bottle.

10,000 bottles, raising both the cycle inventory and the flow time. The impact of the discount is further magnified if DO works hard to reduce its fixed ordering cost to S =

the all unit quantity discount, the optimal lot size is still 10,000 bottles. In this case, the presence of quantity discounts leads to an eightfold increase in average inventory and flow time at DO.

Given that all unit quantity discounts increase the average inventory and flow time in a supply chain, it is important to identify how these discounts add value in a supply chain. Before we consider this question, we discuss marginal unit quantity discounts.

Marginal Unit Quantity Discounts

multi-block tariffs. In this case, the pricing schedule contains specified break points q0, q1, … , qr. It is not the average

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cost of a unit but the marginal cost q is placed, the first q1 – q0 units are priced at C0, the

next q2 – q1 are priced at C1, and, in general, qi+1 – qi units are priced at Ci. The marginal cost per unit varies with the quantity purchased, as shown in Figure 11-4.

Faced with such a pricing schedule, the retailer’s objective is to decide on a lot size that maximizes profits or, equivalently, minimizes material, order, and holding costs.

The solution procedure discussed here evaluates the optimal lot size for each marginal price Ci qi and qi+1 minimizes the overall cost. A more streamlined procedure has been provided by Hu and

For each value of i, 0 … i … r, let Vi be the cost of ordering qi units. Define V0 = 0 and Vi for 0 … i … r

Vi = C01q1 – q02 + C11q2 – q12 + g + Ci – 11qi – qi – 12 (11.12) For each value of i, 0 … i … r – 1, consider an order of size Q in the range qi to qi+1

units; that is, qi + 1 Ú Q Ú qi. The material cost of each order of size Q is given by Vi + 1Q – qi2Ci

Annual order cost = aD Q bS

Annual holding cost = 3Vi + 1Q – qi2Ci4h > 2 Annual material cost =

D Q 3Vi + 1Q – qi2Ci4

The total annual cost is the sum of the three costs and is given by

Total annual cost = aD Q bS + 3Vi + 1Q – qi2Ci4h > 2 + DQ3Vi + Q – qi Ci4

The optimal lot size for price Ci is obtained by taking the first derivative of the total cost with

Optimal 1ot size for price Ci is Qi = B 2D S + Vi – qiCihCi (11.13)

Quantity Purchased

C0

0 q1 q2

C1

C2 M

ar gi

na l C

os t p

er U

ni t P

ur ch

as ed

FIGURE 11-4 Marginal Unit Cost with Marginal Unit Quantity Discount

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fixed cost per order by Vi – qiCi S to S + Vi – qiCi

Step 1: Ci. Step 2: We next select the order quantity Q*i for each price Ci. There are three possible cases for

Qi 1. If qi … Qi … qi + 1 then set Q*i = Qi 2. If Qi 6 qi then set Qi* = qi 3. If Qi 7 qi + 1 then set Qi* = qi + 1

Step 3: Calculate the total annual cost of ordering Q*i

TCi = a DQ*i bS + 3Vi + 1Q*i – qi2Ci4h > 2 + DQ*i 3Vi + 1Q*i – qi2Ci4 (11.14) Step 4: Over all i, select order size Q*i with the lowest cost TCi.

Example 11-8 and Example 11-8 check in spreadsheet Chapter11-examples7-8

EXAMPLE 11-8 Marginal Unit Quantity Discounts

Order Quantity Marginal Unit Price

0–5,000 $3.00

5,000–10,000 $2.96

Over 10,000 $2.92

order in each lot.

Analysis: In this case, we have

q0 = O, q1 = 5,000, q2 = 10,000 C0 = $ .00, C1 = $2.9 , C2 = $2.92 D = 120,000 > year, S = $100 > lot, h = 0.2

V0 = 0; V1 = * 15,000 – 02 = $15,000 V2 = * 15,000 – 02 + 2.9 * 110,000 – 5,0002 = $29,800

Q0 = B 2D S + V0 – q0C0hC0 = , 25 Q1 = B 2D S + V1 – q1C1hC1 = 11,028 Q2 = B 2D S + V2 – q2C2hC2 = 1 ,9 1

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Q*0 = q1 = 5,000 because Q0 = . q1 = Q*1 = q2 = 10,000 Q1 = 11,028 > q2 = Q*2 = Q2 = 1 ,9 1.

i =

TC0 = a DQ*0 bS + 3V0 + 1Q*0 – q02C04 h > 2 + DQ*03V0 + 1Q*0 – q02C04 = $ ,900 TC1 = a DQ*1 bS + 3V1 + 1Q1* – q12C14 h > 2 + DQ*13V1 + 1Q*1 – q12C14 = $ 1,780 TC2 = a DQ*2 bS + 3V2 + 1Q2* – q22C24 h > 2 + DQ*23V2 + 1Q*2 – q22C24 = $ 0, 5

Observe that the lowest cost is for i = 2. Thus, it is optimal for DO to order in lots of Q*2 = 1 ,9 1

offer any discount.

If the fixed cost of ordering is $4, the optimal lot size for DO is 15,755 with the discount

be significant order sizes—and, thus, significant cycle inventory—in the absence of any formal fixed ordering costs as long as quantity discounts are offered. Thus, quantity discounts lead to a significant buildup of cycle inventory in a supply chain. In many supply chains, quantity dis- counts contribute more to cycle inventory than fixed ordering costs. This forces us once again to question the value of quantity discounts in a supply chain.

Why Do Suppliers Offer Quantity Discounts?

We have seen that the presence of lot-size–based quantity discounts tends to increase the level of cycle inventory in the supply chain. We now develop reasons why suppliers may offer lot-size– based quantity discounts in a supply chain. In each case, we look for circumstances under which

1. Improved coordination to increase total supply chain profits 2.

to offer quantity discounts. We now discuss each of the two situations in greater detail.

COORDINATION TO INCREASE TOTAL SUPPLY CHAIN PROFITS A supply chain is coordi- nated if the decisions the retailer and supplier make maximize total supply chain profits. In real- ity, each stage in a supply chain may have a separate owner and thus attempt to maximize its own profits. For example, each stage of a supply chain is likely to make lot-sizing decisions with an objective of minimizing its own overall costs. The result of this independent decision making can be a lack of coordination in a supply chain because actions that maximize retailer profits may not maximize supply chain profits. In this section, we discuss how a manufacturer may use appropri- ate quantity discounts to ensure that total supply chain profits are maximized even if the retailer is acting to maximize its own profits.

Quantity discounts for commodity products. commodity products such as milk, a competitive market exists and prices are driven down to the products’ marginal cost. In this case, the market sets the price and the firm’s objective is to lower costs in order to increase profits. Consider, for example, the online retailer DO, discussed earlier. It can be argued that it sells a commodity product. In this supply chain, both the manufacturer and DO incur costs related to each order placed by DO. Assume that the manufacturer has a fixed

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cost SM, a unit cost CM, and a holding cost hM. The manufacturer incurs fixed costs related to SM hMCM

the order. Assume that the retailer has a fixed cost SR, a unit cost CR, and a holding cost hR. Thus, SR hRCR

sizing decision made by DO, the retailer makes its lot-sizing decisions based solely on minimiz- ing its local costs. This results in lot-sizing decisions that are locally optimal but do not maximize

Chapter11- quantity discounts worksheet Example 11-9

EXAMPLE 11-9 The Impact of Locally Optimal Lot Sizes on a Supply Chain

Demand for vitamins is 10,000 bottles per month. DO incurs a fixed order placement, transporta- tion, and receiving cost of $100 each time it places an order for vitamins with the manufacturer.

The manufacturer has a line packing bottles at a steady rate that matches demand. The manufac- turer incurs a fixed-order filling cost of $250, production cost of $2 per bottle, and a holding cost of 20 percent. What is the annual fulfillment and holding cost incurred by the manufacturer as a result of DO’s ordering policy?

Analysis: In this case, we have

D = 120,000 > year, SR = $100 > lot, hR = 0.2, CR = $ SM = $250 > lot, hM = 0.2, CM = $2

QR = B 2DSRhRCR = B 2 * 120,000 * 1000.2 * = , 25 Annual cost for DO = a D

QR bSR + aQR2 bhRCR = $ ,795

If DO orders in lots sizes of QR =

Annual cost for manufacturer = a D QR

bSM + aQR2 bhMCM = $ ,008 + + =

own costs. From a supply chain perspective, the optimal lot size should account for the fact that both DO and the manufacturer incur costs associated with each replenishment lot. If we assume

total supply chain cost of using a lot size Q

Annual cost for DO and manufacturer = aD Q bSR + aQ2 bhRCR + aDQ bSM + aQ2 bhMCM

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Q*

cost with respect to Q Example 11-9

Q* = B 2D SR + SMhRCR + hMCM = 9,1 5 If DO orders in lots of Q* =

Annual cost for DO = a D Q*

bSR + aQ*2 bhRCR = $4,059 Annual cost for manufacturer = a D

Q* bSM + aQ*2 bhMCM = $5,10

per year. Thus, the manufacturer must offer DO a suitable incentive for DO to raise its lot size. A

worksheet Example 11-10 –

EXAMPLE 11-10 Designing a Suitable Lot-Size–Based Quantity Discount

Analysis:

=

more.

Observe that offering a lot-size–based discount in this case decreases total supply chain cost. It does, however, increase the lot size the retailer purchases and thus increases cycle inven- tory in the supply chain.

Key Point

For commodity products for which price is set by the market, manufacturers with large fixed costs per lot can use lot-size–based quantity discounts to maximize total supply chain profits. Lot-size–based discounts, however, increase cycle inventory in the supply chain.

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Our discussion on coordination for commodity products highlights the important link between the lot-size–based quantity discount offered and the order costs incurred by the manu- facturer. As the manufacturer works on lowering order or setup cost, the discount it offers to retailers should change. For a low enough setup or order cost, the manufacturer gains little from

lowers its fixed cost per order from $250 to $100, the total supply chain costs are close to the minimum without quantity discounts even if DO is trying to minimize its cost. Thus, if its fixed order costs are lowered to $100, it makes sense for the manufacturer to eliminate all quantity discounts. In most companies, however, marketing and sales design quantity discounts, whereas operations works on reducing setup or order cost. As a result, changes in pricing do not always occur in response to setup cost reduction in manufacturing. It is important that the two functions coordinate these activities.

Quantity discounts for products for which the firm has market power. Now, ,

which is derived from herbal ingredients and has other properties highly valued in the market. Few competitors have a similar product, so it can be argued that the price at which the retailer

p, where p manufacturer incurs a production cost of CM = must decide on the price CR to charge DO, and DO in turn must decide on the price p to charge

ProfR ProfM given by

ProfR = 1p – CR21 0,000 – 0,000 p2; ProfM = 1CR – CM21 0,000 – 0,000 p2 DO picks the price p to maximize ProfR. Taking the first derivative with respect to p and setting it to 0, we obtain the following relationship between p and CR

p = + CR 2

(11.15)

Given that the manufacturer is aware that DO is aiming to optimize its own profits, the manufacturer is able to use the relationship between p and CR to obtains its own profits to be

ProfM = 1CR – CM2 a 0,000 – 0,000a + CR2 b b = 1CR – 221180,000 – 0,000 CR2 The manufacturer picks its price CR to maximize ProfM. Taking the first derivative of ProfM

with respect CR to and setting it to 0 we obtain CR = we obtain p = $5. Thus, when DO and the manufacturer make their pricing decisions indepen- dently, it is optimal for the manufacturer to charge a wholesale price of CR = $4 and for DO to charge a retail price of p = p =

ProfR = : = manufacturer makes a profit of ProfM = : = – sheet 2-stage

Now, consider the case in which the two stages coordinate their pricing decisions with a goal of maximizing the supply chain profit ProfSC, which is given by

ProfSC = 1p – CM21 0,000 – 0,000p2 The optimal retail price is obtained by setting the first derivative of ProfSC with respect to p to 0. We thus obtain the coordinated retail price to be

p = + CM 2

= + 2 2

= $4

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If the two stages coordinate pricing and DO prices at p = p = 120,000 bottles. The total supply chain profit if the two stages coordinate is

ProfSC = 1$4 – $22 * 120,000 = $240,000. As a result of each stage settings its price inde- dou-

ble marginalization. Double marginalization leads to a loss in profit because the supply chain margin is divided between two stages, but each stage makes its pricing decision considering only its own local profits.

Key Point

The supply chain profit is lower if each stage of the supply chain makes its pricing decisions indepen- dently, with the objective of maximizing its own profit. A coordinated solution results in higher profit.

Given that independent pricing decisions lower supply chain profits, it is important to con- sider pricing schemes that may help recover some of these profits even when each stage of the supply chain continues to act independently. We propose two pricing schemes that the manufac- turer may use to achieve the coordinated solution and maximize supply chain profits even though DO acts in a way that maximizes its own profit.

1. Two-part tariff: In this case, the manufacturer charges its entire profit as an up-front franchise fee ff ProfM and the difference between the coordinated supply chain profit and the noncoordinated retailer profit, ProfSC – ProfR wholesale price CR = CM. This pricing scheme is referred to as a two-part tariff because the manufacturer sets both the franchise fee and the wholesale price. The retail pricing decision is

p – CM p ff. Under the two-part tariff, the franchise fee ff is paid up front and is thus a fixed cost that does not change with the retail price p. The retailer DO is thus effectively maximizing the coordinated supply chain profits ProfSC = p – CM p p and setting it equal to 0, the optimal coordinated retail price p is evaluated to be

p = + CM 2

In the case of DO, recall that total supply chain profit when the two stages coordinate is ProfSC =

the two stages do not coordinate is ProfR = to construct a two-part tariff by which DO is charged an upfront fee of ff = ProfSC – ProfR =

2-part-tariff CR = CM = $2 per bottle. DO maxi- mizes its profit if it prices the vitamins at p = + CM / 2 = + 2 / 2 = $4 per bottle. It has annual

p = $180,000, which it charges up front. Observe that the use of a two-part tariff has increased sup- ply chain profits from $180,000 to $240,000 even though the retailer DO has made a locally optimal pricing decision given the two-part tariff. A similar result can be obtained as long as the manufacturer sets the up-front fee ff to be any value between $120,000 and $180,000 with a wholesale price of CR = CM = 2.

2. Volume-based quantity discount: Observe that the two-part tariff is really a volume- based quantity discount whereby the retailer DO pays a lower average unit cost as it purchases

ff can be made explicit by designing a volume-based discount scheme that gets the retailer DO to purchase and sell the quantity sold when the two stages coordinate their actions.

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Recall that the coordinated solution results in a retail price of p = + CM/2 = + 2/2 = 4. This retail price results in total demand of dcoord = : = 120,000. The objec- tive of the manufacturer is to design a volume-based discounting scheme that gets the retailer DO

dcoord = 120,000 units each year. The pricing scheme must be such that retailer

charge a wholesale price of CR = dcoord =120,000 units, and to

charge CR = Volume Discount

price them at p = earned by DO 1 0,000 – 0,000p2 * 1p – CR2 = $ 0,000. The total profit earned by the manufacturer is 120,000 * 1CR – $22 = 180,000 when CR = profit is $240,000, which is higher than the $180,000 that the supply chain earned when actions were not coordinated.

is still optimal for DO to order 120,000 units in the year and price them at p = $4 per bottle. The only difference is that the total profit earned by DO now increases to $120,000, whereas that for the manufacturer now drops to $120,000. The total supply chain profits remain at $240,000. The

more will depend on the relative bargaining power of the two parties. At this stage, we have seen that even in the absence of inventory-related costs, quantity dis-

counts play a role in supply chain coordination and improved supply chain profits. Unless the manufacturer has large fixed costs associated with each lot, the discount schemes that are optimal are volume based and not lot-size based. It can be shown that even in the presence of large fixed costs for the manufacturer, a two-part tariff or volume-based discount, with the manufacturer pass- ing on some of the fixed cost to the retailer, optimally coordinates the supply chain and maximizes profits given the assumption that customer demand decreases when the retailer increases price.

A key distinction between lot-size–based and volume discounts is that lot-size discounts –

trast, are based on the rate of purchase or volume purchased on average per specified time period

contrast, are compatible with small lots that reduce cycle inventory. Lot-size–based discounts make sense only when the manufacturer incurs high fixed cost per order. In all other instances, it is better to have volume-based discounts.

Key Point

For products for which the firm has market power, two-part tariffs or volume-based quantity discounts can be used to achieve coordination in the supply chain and maximize supply chain profits.

Key Point

For products for which a firm has market power, lot-size–based discounts are not optimal for the supply chain even in the presence of inventory costs. In such a setting, either a two-part tariff or a volume-based discount, with the supplier passing on some of its fixed cost to the retailer, is needed for the supply chain to be coordinated and maximize profits.

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One can make the point that even with volume-based discounts, retailers will tend to increase the size of the lot toward the end of the evaluation period. For example, assume that the

quarter exceeds 40,000. This policy will not affect the lot sizes DO orders early during the quarter, and DO will order in small lots to match the quantity ordered with demand. Consider a situation,

To get the quantity discount, DO may order 10,000 bottles over the last week even though it

fact that there is no lot-size–based quantity discount. The situation in which orders peak toward the end of a financial horizon is referred to as the hockey stick phenomenon because demand increases dramatically toward the end of a period, similar to the way a hockey stick bends upward toward its end. This phenomenon has been observed in many industries. One possible solution is to base the volume discounts on a rolling horizon. For example, each week the manufacturer may

– ens the hockey stick phenomenon by making each week the last week in some 12-week horizon.

Thus far, we have discussed only the scenario in which the supply chain has a single retailer. One may ask whether our insights are robust and also apply if the supply chain has multiple retail- ers, each with different demand curves, all supplied by a single manufacturer. As one would expect, the form of the discount scheme to be offered becomes more complicated in these settings

change. The optimal discount continues to be volume based, with the average price charged to the

PRICE DISCRIMINATION TO MAXIMIZE SUPPLIER PROFITS Price discrimination is the prac- tice in which a firm charges different prices to maximize profits. An example of price discrimina-

a fixed price for all units does not maximize profits for the manufacturer. In principle, the manufacturer can obtain the entire area under the demand curve above its marginal cost by pricing each unit differently based on customers’ marginal will-

because customers pay different prices based on the quantity purchased. Next we discuss trade promotions and their impact on lot sizes and cycle inventory in the

supply chain.

Key Point

Price discrimination to maximize profits at the manufacturer may also be a reason to offer quantity dis- counts within a supply chain.

11.6 SHORT-TERM DISCOUNTING: TRADE PROMOTIONS

trade promotions to offer a discounted price to retailers and set a time period over which the discount is effective. For example, a manufacturer of canned soup may offer a price discount of 10 percent for the shipping period December 15 to January 25. For all pur- chases within the specified time horizon, retailers get a 10 percent discount. In some cases, the manufacturer may require specific actions from the retailer, such as displays, advertising, promo- tion, and so on, to qualify for the trade promotion. Trade promotions are quite common in the consumer packaged-goods industry, with manufacturers promoting different products at differ- ent times of the year.

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The goal of trade promotions is to influence retailers to act in a way that helps the manu-

1. Induce retailers to use price discounts, displays, or advertising to spur sales. 2. 3. Defend a brand against competition.

Although these may be the manufacturer’s objectives, it is not clear that they are always achieved as the result of a trade promotion. Our goal in this section is to investigate the impact of a trade promotion on the behavior of the retailer and the performance of the entire supply chain. The key to understanding this impact is to focus on how a retailer reacts to a trade promotion that

1. Pass through some or all of the promotion to customers to spur sales. 2. Pass through very little of the promotion to customers but purchase in greater quantity dur-

ing the promotion period to exploit the temporary reduction in price.

The first action lowers the price of the product for the end customer, leading to increased pur- chases and, thus, increased sales for the entire supply chain. The second action does not increase purchases by the customer, but increases the amount purchased and held at the retailer. As a result, the cycle inventory and flow time within the supply chain increase.

A forward buy occurs when a retailer purchases in the promotional period for sales in future periods. A forward buy helps reduce the retailer’s future cost of goods for product sold after the promotion ends. Although a forward buy is often the retailer’s appropriate response to a price promotion, it can decrease supply chain profits because it results in higher demand vari- ability, with a resulting increase in inventory and flow times within the supply chain.

Our objective in this section is to understand a retailer’s optimal response when faced with a trade promotion. We identify the factors affecting the forward buy and quantify the size of a forward buy by the retailer. We also identify factors that influence the amount of the promotion that a retailer passes on to the customer.

We first illustrate the impact of a trade promotion on forward buying behavior of the retailer. Consider a Cub Foods supermarket selling chicken noodle soup manufactured by the Campbell

D cans per year. Campbell charges $C per can. Cub Foods incurs a holding cost of h

Q* = B 2DShC Campbell announces that it is offering a discount of $d per can for the coming four-week

period. Cub Foods must decide how much to order at the discounted price compared with the lot size of Q* that it normally orders. Let Qd be the lot size ordered at the discounted price.

The costs the retailer must consider when making this decision are material cost, holding cost, and order cost. Increasing the lot size Qd lowers the material cost for Cub Foods because it

size Qd increases the holding cost because inventories increase. Increasing the lot size Qd lowers the order cost for Cub Foods because some orders that would otherwise have been placed are now not necessary. Cub Foods’ goal is to make the trade-off that minimizes the total cost.

The inventory pattern when a lot size of Qd is followed by lot sizes of Q* is shown in Figure 11-5. The objective is to identify Qd + ordering cost + holding

Qd consumed.

The precise analysis in this case is complex, so we present a result that holds under some

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assumption is that the discount is offered once, with no future discounts. The second key assump- –

ence customer demand. The customer demand thus remains unchanged. The third key assumption is that we analyze a period over which the demand is an integer multiple of Q*. With these assumptions, the optimal order quantity at the discounted price is given by

Qd = dD1C – d2h + CQ*C – d (11.16)

In practice, retailers are often aware of the timing of the next promotion. If the demand until the next anticipated trade promotion is Q1, it is optimal for the retailer to order min{Q

d, Q1} Observe that the quantity Qd ordered as a result of the promotion is larger than the regular order quantity Q*. The forward buy in this case is given by

Forward buy = Qd – Q*

Chapter11-examples11-12

EXAMPLE 11-11 Impact of Trade Promotions on Lot Sizes

holding cost of 20 percent. DO currently orders in lots of Q* = , 25 bottles. The manufacturer has offered a discount of $0.15 for all bottles purchased by retailers over the coming month. How

Analysis: In the absence of any promotion, DO orders in lot sizes of Q* = , 25 bottles. Given a monthly demand of D =

Cycle inventory at DO = Q*/2 = , 25/2 = ,1 2.50 bottles Average flow time = Q*/2D = , 25/ 2D = 0. 1 2 months

I(t)

t

Q* Q* Q* Q* Q*

Qd

FIGURE 11-5 Inventory Profile for Forward Buying

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Qd = dD1C – d2h + CQ*C – d = 0.15 * 120,0001 .00 – 0.152 * 0.20 + * , 25.00 – 0.15 = 8,2

have

Cycle inventory at DO = Qd / 2 = 8,2 > 2 = 19,118 bottles Average flow time = Qd / 2D = 8,2 / 20,000 = 1.9118 months

In this case, the forward buy is given by

Forward buy = Qd – Q* = 8,2 – , 25 = 1,911 bottles

=

As the example illustrates, forward buying as a result of trade promotions leads to a sig- nificant increase in the quantity ordered by the retailer. The large order is then followed by a period of small orders to compensate for the inventory built up at the retailer. The fluctuation in orders as a result of trade promotions is one of the major contributors to the bullwhip effect dis- cussed in Chapter 10. The retailer can justify the forward buying during a trade promotion because it decreases its total cost. In contrast, the manufacturer can justify this action only as a

up a lot of excess inventory or the forward buy allows the manufacturer to smooth demand by shifting it from peak- to low-demand periods. In practice, manufacturers often build up inventory in anticipation of planned promotions. During the trade promotion, this inventory shifts to the retailer, primarily as a forward buy. If the forward buy during trade promotions is a significant fraction of total sales, manufacturers end up reducing the revenues they earn from sales because most of the product is sold at a discount. The increase in inventory and the decrease in revenues often lead to a reduction in manufacturer as well as total supply chain profits as a result of trade

Key Point

Trade promotions lead to a significant increase in lot size and cycle inventory because of forward buying by the retailer. This generally results in reduced supply chain profits unless the trade promotion reduces demand fluctuations.

Now, let us consider the extent to which the retailer may find it optimal to pass through

for the retailer to pass through the entire discount to the customer. In other words, it is optimal for the retailer to capture part of the promotion and pass through only part of it to the customer.

EXAMPLE 11-12 How Much of a Discount Should the Retailer Pass Through?

– p. The normal price charged by the manufacturer to the retailer is CR = costs, evaluate the optimal response of DO to a discount of $0.15 per unit.

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Analysis:

ProfR = 1 00,000 – 0,000 p2 p – 1 00,000 – 0,000 p2CR The retailer prices to maximize profits, and the optimal retail price is obtained by setting the first derivative of retailer profits with respect to p to 0. This implies that

00,000 – 120,000p + 0,000 CR = 0

or

p = 1 00,000 + 0,000 CR2 / 120,000 (11.17) CR = p = $4. As a result, the customer

demand at the retailer in the absence of the promotion is

DR = 0,000 – 0,000 p = 0,000

During the promotion, the manufacturer offers a discount of $0.15, resulting in a price to the retailer of CR =

p = 1 00,000 + 0,000 * 2.852 / 120,000 = $ .925 Observe that the retailer’s optimal response is to pass through only $0.075 of the $0.15 discount to the customer. The retailer does not pass through the entire discount. At the discounted price, DO experiences a demand of

DR = 00,000 – 0,000 p = 4,500

This represents an increase of 7.5 percent in demand relative to the base case. It is optimal here for DO to pass on half the trade promotion discount to the customers. This action results in a 7.5 percent increase in customer demand.

impact of the increase in customer demand may be further dampened by customer behavior. For many products, such as detergent and toothpaste, most of the increase in customer purchases is a forward buy by the customer; customers are unlikely to start brushing their teeth more frequently simply because they have purchased a lot of toothpaste. For such products, a trade promotion does not truly increase demand.

Key Point

Faced with a short-term discount, it is optimal for retailers to pass through only a fraction of the discount

purchase lot size and forward buy for future periods. Thus, trade promotions often lead to an increase of cycle inventory in a supply chain without a significant increase in customer demand.

attributed to forward buying. Our previous discussion supports the claim that trade promotions generally increase cycle

inventory in a supply chain and hurt performance. This realization has led many firms, including the world’s largest retailer, Walmart, and several manufacturers, such as P&G, to adopt “everyday

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This eliminates any incentive for forward buying. As a result, all stages of the supply chain pur- chase in quantities that match demand.

In general, the discount passed through by the retailer to the consumer is influenced by the retailer deal elasticity, which is the increase in retail sales per unit discount in price. The higher the deal elasticity, the more of the discount the retailer is likely to pass through to the consumer. Thus, trade promotions by the manufacturer may make sense for products with a high deal elas- ticity that ensures high pass-through by the retailer, and high holding costs that ensure low for-

elasticity and holding cost. They also identify trade promotions as being more effective with strong brands relative to weak brands.

Trade promotions may also make sense as a competitive response. In a category such as cola, some customers are loyal to their brand, whereas others switch depending on the brand being offered at the lowest price. Consider a situation in which one of the competitors—say, Pepsi—offers retailers a trade promotion. Retailers increase their purchases of Pepsi and pass through some of the discount to the customer. Price-sensitive customers increase their purchase of Pepsi. If a competitor such as Coca-Cola does not respond, it loses some market share in the form of price-sensitive customers. A case can be made that a trade promotion by Coca-Cola is justified in such a setting as a competitive response. Observe that with both competitors offering trade promotions, there is no real increase in demand for either unless customer consumption grows. Inventory in the supply chain, however, does increase for both brands. This is, then, a situation in which trade promotions are a competitive necessity, but they increase supply chain inventory, leading to reduced profits for all competitors.

Trade promotions should be designed so retailers limit their forward buying and pass along more of the discount to end customers. The manufacturer’s objective is to increase market share and sales without allowing the retailer to forward buy significant amounts. This outcome can be achieved by offering discounts to the retailer that are based on actual sales to customers rather than the amount purchased by the retailer. The discount price thus applies to items sold to cus-

This eliminates all incentive for forward buying. Given the information technology in place, many manufacturers today offer scanner-based

promotions by which the retailer receives credit for the promotion discount for every unit sold. Another option is to limit the allocation to a retailer based on past sales. This is also an effort to limit the amount that the retailer can forward buy. It is unlikely, however, that retailers will accept such schemes for weak brands.

11.7 MANAGING MULTIECHELON CYCLE INVENTORY

A multiechelon supply chain has multiple stages and possibly many players at each stage. The lack of coordination in lot sizing decisions across the supply chain results in high costs and more cycle inventory than required. The goal in a multiechelon system is to decrease total costs by coordinating orders across the supply chain.

Consider a simple multiechelon system with one manufacturer supplying one retailer. Assume that production is instantaneous, so the manufacturer can produce a lot when needed. If the two stages are not synchronized, the manufacturer may produce a new lot of size Q right after shipping a lot of size Q to the retailer. Inventory at the two stages in this case is as shown in Fig-

Q/2 and the manufacturer car- ries an average inventory of about Q.

Overall supply chain inventory can be lowered if the manufacturer synchronizes its pro- duction to be ready just in time to be shipped to the retailer. In this case, the manufacturer carries no inventory and the retailer carries an average inventory of Q

Q/2 to Q/2.

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For a simple multiechelon supply chain with only one player at each stage, ordering policies in which the lot size at each stage is an integer multiple of the lot size at its immediate customer have been shown to be quite close to optimal. When lot sizes are integer multiples, coordination of ordering across stages allows for a portion of the delivery to a stage to be cross-docked on to the next stage. The extent of cross-docking depends on the ratio of the fixed cost of ordering S and the holding cost H at each stage. The closer this ratio is between two stages, the higher is the optimal

quantities in a multiechelon setting with a single manufacturer supplying a single retailer.

stage of the supply chain, it is important to distinguish retailers with high demand from those

retailers are grouped such that all retailers in one group order together and, for any retailer, either the ordering frequency is an integer multiple of the ordering frequency at the distributor or the ordering frequency at the distributor is an integer multiple of the frequency at the retailer. An integer replenishment policy has every player ordering periodically, with the length of the reor- der interval for each player an integer multiple of some base period. An example of such a policy is shown in Figure 11-7. Under this policy, the distributor places a replenishment order every two

orders every two or four weeks. Observe that for retailers ordering more frequently than the dis- tributor, the retailers’ ordering frequency is an integer multiple of the distributor’s frequency. For retailers ordering less frequently than the distributor, the distributor’s ordering frequency is an integer multiple of the retailers’ frequency.

If an integer replenishment policy is synchronized across the two stages, the distributor can cross-dock part of its supply on to the next stage. All shipments to retailers ordering no more

cross-docked, with the other half shipped from inventory, as shown in Figure 11-7.

Manufacturer Inventory

Retailer lot is shipped Manufacturer lot arrives

Retailer Inventory

Time

Q

Time

Q

FIGURE 11-6 Inventory Profile at Retailer and Manufacturer with No Synchronization

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Integer replenishment policies for the supply chain shown in Figure 11-8 can be summa-

the same supplier and have the same reorder interval.

stage is synchronized with the shipment of a replenishment order to at least one customer. The synchronized portion can be cross-docked.

interval an integer multiple of the supplier’s interval and synchronize replenishment at the two stages to facilitate cross-docking. In other words, a supplier should cross-dock all orders from customers that reorder less frequently than the supplier.

Distributor replenishment order arrives

Distributor replenishes every two weeks

Retailer replenishes every week

Retailer shipment is cross-docked

Retailer shipment is from inventory

Retailer shipment is cross-docked

Retailer replenishes every two weeks

Retailer shipment is cross-docked

Retailer replenishes every four weeks

FIGURE 11-7 Illustration of an Integer Replenishment Policy

Key Point

Integer replenishment policies can be synchronized in multiechelon supply chains to keep cycle inventory and order costs low. Under such policies, the reorder interval at any stage is an integer multiple of a base

across the supply chain.

interval an integer multiple of the customer’s interval and synchronize replenishment at the

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two stages to facilitate cross-docking. In other words, a supplier should cross-dock one out of every k shipments to a customer that orders more frequently than the supplier, where k is an integer.

at different parties.

Although the integer policies discussed above synchronize replenishment within the supply chain and decrease cycle inventories, they increase safety inventories, because of the lack of flex- ibility with the timing of a reorder, as discussed in Chapter 12. Thus, these polices make the most sense for supply chains in which cycle inventories are large and demand is relatively predictable.

11.8 SUMMARY OF LEARNING OBJECTIVES

1. Balance the appropriate costs to choose the optimal lot size and cycle inventory in a supply chain. Cycle inventory generally equals half the lot size. Therefore, as the lot size grows, so does the cycle inventory. In deciding on the optimal amount of cycle inventory, the supply chain goal is to minimize the total cost—the order cost, holding cost, and material cost. As cycle inventory increases, so does the holding cost. However, the order cost and, in some

balances the three costs to obtain the optimal lot size. The higher the order and transportation cost, the higher the lot size and cycle inventory.

2. Understand the impact of quantity discounts on lot size and cycle inventory. Lot-size– based quantity discounts increase the lot size and cycle inventory within the supply chain because they encourage buyers to purchase in larger quantities to take advantage of the decrease in price.

3. Devise appropriate discounting schemes for a supply chain. justified to increase total supply chain profits when independent lot-sizing decisions in a supply chain lead to suboptimal solutions from an overall supply chain perspective. If suppliers have large fixed costs, suitable lot-size–based quantity discounts can be justified because they help

– counts in increasing supply chain profits without increasing lot size and cycle inventory.

Group of Customers

Stage 1

Stage 2

Stage 3

Stage 4

Stage 5

FIGURE 11-8 A Multiechelon Distribution Supply Chain

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4. Understand the impact of trade promotions on lot size and cycle inventory. Trade promotions increase inventory and total supply chain costs through forward buying, which shifts future demand to the present and creates a spike in demand followed by a dip. The increased variability raises inventories and costs.

5. Identify managerial levers that reduce lot size and cycle inventory in a supply chain without increasing cost. The key managerial levers for reducing lot size, and thus cycle inven-

– counting schemes.

sell-through rather than sell-in to the retailer.

Discussion Questions 1. Consider a supermarket deciding on the size of its replenish-

ment order from Procter & Gamble. What costs should it take into account when making this decision?

2. change as it decreases the lot size ordered from Procter & Gamble.

3. how would you expect the cycle inventory measured in days

4. decrease the lot size without increasing the costs he incurs. What actions can he take to achieve this objective?

5. Discuss why supply chain profits may be hurt by a retailer making lot-sizing decisions with the sole objective of

minimizing its own costs. What advantage would result if the entire supply chain could coordinate this decision?

6. When are quantity discounts justified in a supply chain? 7. What is the difference between lot-size–based and volume-

based quantity discounts? 8.

promotions? What impact do trade promotions have on the supply chain? How should trade promotions be structured to maximize their impact while minimizing the additional cost they impose on the supply chain?

9. Why is it appropriate to include only the incremental cost when estimating the holding and order cost for a firm?

Exercises 1.

are transported between the two plants using trucks, with each trip costing $1,000. The motorcycle plant assembles and

– ley incurs a holding cost of 20 percent per year. How many engines should Harley load onto each truck? What is the cycle inventory of engines at Harley?

2. –

Harley has reduced the number of engines loaded on each truck to 100. If each truck trip still costs $1,000, how does this decision affect annual inventory costs at Harley? What should the cost of each truck be if a load of 100 engines is to be optimal for Harley?

3. A North Face retail store in Chicago sells 500 jackets each

an annual holding cost of 25 percent. The fixed cost of a

store currently places a replenishment order every month for

500 jackets. What is the annual holding and ordering cost? On average, how long does a jacket spend in inventory? If the retail store wants to minimize ordering and holding cost, what order size do you recommend? How much would the optimal order reduce holding and ordering cost relative to the current policy?

4. Target purchases home goods made by a supplier in China.

– pany has an annual holding cost of 20 percent. Placing a replenishment order incurs clerical costs of $500/order. The shipping company charges $5,000 as a fixed cost per ship- ment along with a variable cost of $0.10 per unit shipped. What is the optimal order size for Target? What is the annual holding cost of the optimal policy? How many orders per year does Target place? What is the annual fixed transporta- tion cost? What is the annual variable transportation cost? What is the annual clerical cost?

5. Amazon sells 20,000 units of consumer electronics from

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a holding cost of 20 percent. The fixed clerical and transpor-

$4,000. What is the optimal size of the order that Amazon –

tories, Amazon would like to reduce the size of each order it

reduce the fixed cost per order for an order of 2,500 units to be optimal?

6. costs $500 and Amazon has a holding cost of 20 percent. For what fixed cost per order would an order size of 10,000 units be optimal? For what fixed cost per order would an order size of 2,500 units be optimal?

7. A steel rolling mill can produce I-beams at the rate of 20 tons per week. Customer demand for the beams is 5 tons per week. To produce I-beams, the mill must go through a setup that requires changing to the appropriate rolling patterns.

– tion. I-beams cost the mill $2,000 per ton and the mill has a holding cost of 25 percent. What is the optimal production batch size for I-beams? What is the annual setup cost of the optimal policy? What is the annual holding cost?

8. A steel rolling mill can produce I-beams at the rate of 20 tons per week. Customer demand for the beams is 5 tons per week. I-beams cost the mill $2,000 per ton and the mill has a holding cost of 25 percent. To produce I-beams, the mill must go through a setup that requires changing to the appropriate roll- ing patterns. The mill would like to produce I-beams in batches

For what changeover cost would this batch size be optimal? 9. An electronics company has two contract manufacturers in

– lets and smartphones is 10,000 units, whereas that for laptops is 4,000. Tablets cost the company $100, laptops cost $400, and the company has a holding cost of 25 percent. Currently the company has to place separate orders with Foxconn and Flextronics and receives separate shipments. The fixed cost of each shipment is $10,000. What is the optimal order size and order frequency with each of Foxconn and Flextronics?

The company is thinking of combining all assembly with the same contract manufacturer. This will allow for a single shipment of all products from Asia. If the fixed cost of each shipment remains $10,000, what is the optimal order frequency and order size from the combined orders? How much reduction in cycle inventory can the company expect as a result of combining orders and shipments?

10. Harley purchases components from three suppliers. Compo-

used at the rate of 20,000 units per month. Components pur-

the rate of 2,500 units per month. Components purchased

900 units per month. Currently, Harley purchases a separate truckload from each supplier. As part of its JIT drive, Harley

has decided to aggregate purchases from the three suppliers. The trucking company charges a fixed cost of $400 for the truck with an additional charge of $100 for each stop. Thus, if Harley asks for a pickup from only one supplier, the truck- ing company charges $500; from two suppliers, it charges

replenishment strategy for Harley that minimizes annual cost. Assume a holding cost of 20 percent per year. Compare the cost of your strategy with Harley’s current strategy of ordering separately from each supplier. What is the cycle inventory of each component at Harley?

11.

Ford spare parts is 100 units per month, whereas demand for

both companies have a holding cost of 20 percent. Currently,

truck has a fixed cost of $500. What is the optimal order size –

ing and holding cost for each company? A third-party logistics provider has offered to combine

shipments for each of the two companies on a single truck.

companies agree to the joint shipment, what is the optimal order frequency and size? What is the annual ordering and

divide the fixed cost per truck among themselves? 12. Prefab, a furniture manufacturer, uses 20,000 square feet of

plywood per month. Its trucking company charges Prefab $400 per shipment, independent of the quantity purchased. The manufacturer offers an all unit quantity discount with a price of $1 per square foot for orders under 20,000 square feet, $0.98 per square foot for orders between 20,000 square

orders larger than 40,000 square feet. Prefab incurs a holding cost of 20 percent. What is the optimal lot size for Prefab? What is the annual cost of such a policy? What is the cycle inventory of plywood at Prefab? How does it compare with the cycle inventory if the manufacturer does not offer a quan-

Now consider the case in which the manufacturer offers a marginal unit quantity discount for the plywood. The first 20,000 square feet of any order are sold at $1 per square foot, the next 20,000 square feet are sold at $0.98 per square foot, and any quantity larger than 40,000 square feet is sold

Prefab given this pricing structure? How much cycle inven- tory of plywood will Prefab carry given the ordering policy?

13. Demand for fasteners at W.W. Grainger is 20,000 boxes per month. The holding cost at Grainger is 20 percent per year.

all unit discount pricing scheme with a price of $5 per box for

replenishment?

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14. – count. Demand for fasteners at W.W. Grainger is 20,000 boxes per month. The holding cost at Grainger is 20 percent

offers a marginal unit discount pricing scheme with a price of

should Grainger order per replenishment? 15. Demand for phones at Amazon is 5,000 per month. The hold-

ing cost at Amazon is 25 percent and the company incurs a fixed cost of $500 for each order placed. The supplier offers an all unit quantity discount with a price of $200 per phone for all orders under 10,000, a price of $195 for all orders of 10,000 or more but under 20,000 and a price of $190 for all orders of 20,000 or more. How many phones should Amazon order per replenishment?

16. Demand for phones at Amazon is 5,000 per month. The hold- ing cost at Amazon is 25 percent and the company incurs a fixed cost of $500 for each order placed. The supplier offers a marginal unit quantity discount with a price of $200 per phone for the first 10,000 phones in an order, a price of $195 for the next 10,000 phones in the order, and a price of $190 for the quantity above 20,000 in the order. How many phones should Amazon order per replenishment?

17. Dominick’s supermarket chain sells Nut Flakes, a popular cereal manufactured by the Tastee cereal company. Demand for Nut Flakes is 1,000 boxes per week. Dominick’s has a holding cost of 25 percent and incurs a fixed trucking cost of $200 for each replenishment order it places with Tastee. Given that Tastee normally charges $2 per box of Nut Flakes, how much should Dominick’s order in each replenishment lot?

Tastee runs a trade promotion for a month, lowering the price of Nut Flakes to $1.80. How much should Domi- nick’s order, given the short-term price reduction?

18. Flanger is an industrial distributor that sources from hundreds of suppliers. The two modes of transportation available for

Flanger uses a holding cost of 20 percent. Flanger incurs a fixed cost of $100 for each order placed with a supplier. a. Determine a threshold for annual demand above which

TL is preferred and below which LTL is preferred.

Which mode becomes preferable as unit cost grows?

19. the Chicago region and is deciding on a policy for the use of TL or LTL transportation for inbound shipping. LTL ship- ping costs $1 per unit. TL shipping costs $800 per truck plus $100 per pickup. Thus, a truck used to pick up from three suppliers costs 800 + * 100 = $1,100. A truck can

for each order placed with a supplier. Thus, an order with

– cent. Assume that product from each supplier has an annual

and the company must decide on the number of suppliers to group per truck if using TL. a. What is the optimal order size and annual cost if LTL

shipping is used? What is the time between orders? b. What is the optimal order size and annual cost if TL ship-

ping is used with a separate truck for each supplier? What is the time between orders?

c. What is the optimal order size and annual cost per product if TL shipping is used but two suppliers are grouped together per truck?

d. What is the optimal number of suppliers that should be grouped together? What is the optimal order size and annual cost per product in this case? What is the time between orders?

e. Which shipping policy would you recommend if each

policy would you recommend for products with an annual demand of 1,500? Which shipping policy would you recommend for products with an annual demand of 18,000?

20. PlasFib is a manufacturer of synthetic fibers used for making furniture upholstery. PlasFib manufactures fiber in 50 colors on one line. When changing over from one color to the next, part of the line must be cleaned, leading to a loss of material.

labor. Assume that each changeover requires the line to shut down for 0.5 hour. When it is running, the line produces fiber at the rate of 100 pounds per hour.

The fibers sold by PlasFib are divided into three cate- gories. There are 5 fast-moving colors that average sales of

moving colors that average sales of 12,000 pounds per color per year. The remaining are slow-moving products and aver-

costs $5 and PlasFib has a holding cost of 20 percent. a. What is the batch size that PlasFib should produce for

each fast-, medium-, and slow-moving color? How many days of demand does this translate into?

b. What is the annual setup and holding cost of the policies

c. How many hours of plant operation will the above sched-

21. TopOil, a refiner in Indiana, serves three customers near Nashville, Tennessee, and maintains consignment inventory

TL transportation to deliver separately to each customer.

each customer separately costs $1,050 per truck. TopOil is considering aggregating deliveries to Nashville on a single

at the medium customer is 24 tons per year, and demand at

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the small customer is 8 tons per year. Product cost for TopOil is $10,000 per ton, and it uses a holding cost of 25 percent. Truck capacity is 12 tons. a. What is the annual transportation and holding cost if

TopOil ships a full truckload each time a customer is run- ning out of stock? How many days of inventory is carried at each customer under this policy?

b. What is the optimal delivery policy to each customer if TopOil ships separately to each of them? What is the annual transportation and holding cost? How many days of inventory is carried at each customer under this policy?

c. What is the optimal delivery policy to each customer if TopOil aggregates shipments to each of the three custom- ers on every truck that goes to Nashville? What is the annual transportation and holding cost? How many days of inventory are carried at each customer under this policy?

d. Can you come up with a tailored policy that has lower

inventories for your suggested policy? 22. Crunchy, a cereal manufacturer, has dedicated a plant for one

20,000 boxes a month and production at the plant keeps pace

Crunchy and the retailer use a holding cost of 20 percent. For each order placed, the retailer incurs an ordering cost of $200. Crunchy incurs the cost of transportation and loading that totals $1,000 per order shipped. a. Given that it is trying to minimize its ordering and hold-

ing costs, what lot size will the retailer ask for in each order? What are the annual ordering and holding costs for the retailer as a result of this policy? What are the annual ordering and holding costs for Crunchy as a result of this policy? What is the total inventory cost across both parties as a result of this policy?

from this policy? c. Design an all unit quantity discount that results in the

d. How much of the $1,000 delivery cost should Crunchy pass along to the retailer for each lot to get the retailer to

23. A steel service center sources products from an integrated steel mill at a cost of $2,000 per ton. Demand for steel at the service center is 50 tons per month. The service center has a holding cost of 25 percent and incurs a fixed cost of $2,000

for each order. How many tons of steel should the service center order per replenishment? What is the annual ordering and holding cost incurred by the service center?

The integrated steel mill incurs a fixed cost of $4,000

mill $1,000 per ton and the mill has a holding cost of 20 per-

fixed cost and holding cost incurred by the mill as a result of the service center’s ordering policy? What is the annual cost incurred by both the service center and the steel mill?

If the steel mill and the service center could work in a coordinated manner, what is the optimal order size that mini- mizes their joint fixed and holding costs? What annual savings could the supply chain expect as a result of coordination? Design an all unit quantity discount that the integrated steel mill could use to get the service center to order the coordinated amount without increasing annual costs at the service center.

24. The Orange company has introduced a new music device called the J-Pod. The J-Pod is sold through Good Buy, a major electronics retailer. Good Buy has estimated that demand for the J-Pod will depend on the final retail price p according to the demand curve

Demand D = 2,000,000 – 2,000p

The production cost for Orange is $100 per J-Pod. a. What wholesale price should Orange charge for the

J-Pod? At this wholesale price, what retail price should Good Buy set? What are the profits for Orange and Good Buy at equilibrium?

b. If Orange decides to discount the wholesale price by $40, how much of a discount should Good Buy offer to cus- tomers if it wants to maximize its own profits? What frac- tion of the discount offered by Orange does Good Buy pass along to the customer?

25. The Orange company prices J-Pods at $550 per unit. Good Buy sells the J-Pods at $775. Annual demand at this retail price turns out to be 450,000 units. Good Buy incurs order- ing, receiving, and transportation costs of $10,000 for each lot of J-Pods ordered. The holding cost used by the retailer is 20 percent. a. What is the optimal lot size that Good Buy should order? b. The Orange company has discounted J-Pods by $40 for

decided not to change the retail price but may change the lot size ordered from Orange. How should Good Buy adjust its lot size given this discount? How much does the lot size increase because of the discount?

Bibliography Sales Promotion: Con-

cepts, Methods, and Strategies – tice Hall, 1990.

Principles of Corpo- rate Finance

Bargain of Trade Promotions.” Harvard Business Review

Harvard Business Review

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Research Opportunities.” Marketing Science 1–24.

Distribution Networks.” Management Science 710–727.

Production Planning & Control

Journal of the Operational Research Society

Efficient Consumer Response.

Sloan Management Review

Consistent and Realistic Reorder Intervals in Production-

Operations Research

Interfaces

– Production and Operations

Management

Management Science

Mathe- matics of Operations Research

Inventory Management and Production Planning and Scheduling. New

Zipkin, Paul H. Foundations of Inventory Management. Boston,

CASE STUDY Delivery Strategy at MoonChem

John Kresge, vice president of supply chain, was very con- –

turer of specialty chemicals. The year-end meeting evaluated financial performance and discussed the fact that the firm was achieving only two inventory turns a year. A more careful look revealed that more than half the inven-

– tomers. This was very surprising, given that only 20 percent of its customers carried consignment inventory. John was responsible for inventory as well as transportation costs. He decided to take a careful look at the management of con- signment inventory and come up with an appropriate plan.

MoonChem Operations

eight manufacturing plants and 40 distribution centers. The plants manufactured the base chemicals, and the distribution centers mixed them to produce hundreds of end products that fit customer specifications. In the spe-

consignment inventory to its customers. The company wanted to take this strategy national if it proved effec-

customers’ sites. Customers used the chemicals as

ensure availability. In most instances, consumption of

the consignment inventories and was paid for the chemi- cals as they were used.

Distribution at MoonChem

40,000 pounds; Golden charged a fixed rate given the origin and destination, regardless of the quantity shipped

customer to replenish its consignment inventory.

The Illinois Pilot Study

John decided to take a careful look at his distribution operations. He focused on Illinois, which was supplied from the Chicago distribution center. He broke up Illinois into a collection of zip codes that were contiguous, as shown in Figure 11-9. He restricted attention to the Peoria

study of the Peoria region revealed two large customers, six medium-sized customers, and twelve small customers. The annual consumption at each type of customer was as shown in Table 11-4. Golden charged $400 for each ship-

was to send a full truckload to each customer as needed.

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ROCKFORD

(LA OFFICES)

610

614

528 527

601 603

600 602

611

618 619

CHAMPAIGN

624

627

617 616

613

605

615

628

EFFINGHAM

631 ST. LOUIS, MO

629 CARBONDALE

622 (SOUTH)

620 (NORTH)

CENTRALIA

SPRINGFIELD QUINCY

623

607 606

604 609

CHICAGO

625 626

PALATINE

FOX VALLEY

KANKAKEE SOUTH

SUBURBAN

BLOOMINGTON PEORIA

612

ROCK ISLAND LA SALLE

GALESBURG

(MO OFFICES)

635 634

FIGURE 11-9 Illinois Zip Code Map

TABLE 11-4 Customer Profile for MoonChem in Peoria Region Customer Type

Number of Customers

Consumption (Pounds per Month)

Small 12 1,000

Medium 6 5,000

Large 2 12,000

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John checked with Golden to find out what it would take to include shipments for multiple customers on a single load. Golden informed him that it would

which Golden was responsible. Thus, if Golden carried a truck that had to make one delivery, the total charge would be $400. However, if a truck had to make four deliveries, the total charge would be $550.

25 percent. John wanted to analyze a few different options for distribution available in the Peoria region to decide on the optimal distribution policy. One was to aggregate all 20 customers into each truck going to Peoria. The other was to separate the 20 customers into two groups with

one large, three medium, and six small customers in each

truck going to Peoria. The detailed study of the Peoria region would provide the blueprint for the distribution

Questions

1. – ing full truckloads to each customer in the Peoria region to replenish consignment inventory?

2. Consider different delivery options and evaluate the cost of each. What delivery option do you recommend for

3. How does your recommendation impact consignment

CASE STUDY Pricing and Delivery at KAR Foods

Carlos Ramos, head of supply chain at KAR Foods, wondered why his inventories had not declined despite the significant improvement his team had made in its ability to handle mixed-load and small lot orders from customers. He felt that the problem was the discounting scheme offered by the sales team that encouraged cus- tomers to place large orders. Carlos arranged for a meet-

discuss future plans.

Historical Pricing and Costs at KAR

KAR was a large Brazilian food processing company, –

had become a major global player after several acquisi- tions across the world. The company sold its products to several supermarket chains within Brazil. A typical supermarket chain purchased 10,000 kg of meat each month at a price of 4 real/kg from KAR. KAR incurred a cost of 2.50 real/kg to produce the meat. KAR opera- tions were set up to produce at a steady rate that matched demand. Historically, KAR had encouraged its custom- ers to order in large lots by offering quantity discounts of

customers ordered lots of 27,500 kg or more. The quan- tity discounts were justified by the high fixed cost of

4,000 real that was incurred by KAR to process, load and deliver each order.

Supply Chain Improvements at KAR

As the company grew, it became clear that supply chain operations required significant improvement to compete with other multinationals that were entering the Brazil- ian market. Carlos Ramos was hired to lead this effort, given his extensive experience in the consumer pack- aged goods industry. A quick review of the status quo by Carlos identified several opportunities for improve- ment. He decided to focus on the large amount of inven- tory that was built up to fill customer orders. A reduction in inventory would free up capital and expensive cold storage space, and would also streamline operations. At the current holding cost of 20 percent, reduction in inventories could save a significant amount in overall holding costs. He quickly realized that the inflexibility of the current distribution system resulted in the high cost of 4,000 real to process, load, and deliver each order. Carlos changed processes and invested in tech- nology to increase flexibility and make it cheaper to handle mixed loads. He also brought in routing software that made it easier to plan deliveries to multiple custom- ers on a single truck. This helped reduce the fixed cost per customer order down to 400 real. Carlos hoped that

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APPENDIX 11A Economic Order Quantity

Objective

Analysis:

Given an annual demand D, order cost S, unit cost C, and holding cost h, our goal is to estimate the lot size Q that minimizes the total annual cost. For a lot size of Q, the total annual cost is given by

Total annual cost, TC = 1D/Q2S + 1Q/22 hC + CD To minimize the total cost, we take the first derivative with respect to the lot size Q and set

it to zero. Taking the first derivative with respect to Q, we have

d1TC2 dQ

= – DS Q2

+ hC 2

Q2 = 2DS hC or Q = A2DShC

these improvements would significantly reduce lot sizes and thus inventory.

Costs Faced by Customers

Given that there was very little decrease in lot sizes and inventories, Carlos wanted to understand why things

sought to learn about the costs faced by supermarket chains ordering from KAR Foods. He learned that each supermarket chain itself incurred a fixed cost of 100 real associated with each order. This fixed cost was incurred for order placement and receiving. He also

learned that each supermarket chain incurred a holding cost of 20 percent.

Questions

1. What do you think of the discounting scheme that KAR had used historically? Do you think it was justified given the circumstances?

2. Once KAR has reduced its fixed cost per order to 400 real, what are the downsides to leaving the discounting scheme unchanged?

3. meeting? What are the potential gains for KAR from this suggestion?

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In this chapter, we discuss how safety inventory can help a supply chain improve product availability in the presence of supply and demand variability. We discuss various measures of product availability and how managers can set safety inventory levels to provide the desired product availability. We also explore what managers can do to reduce the amount of safety inventory required while maintaining or even improving product availability.

12.1 THE ROLE OF SAFETY INVENTORY IN A SUPPLY CHAIN

Safety inventory is inventory carried to satisfy demand that exceeds the amount forecast. Safety inventory is required because demand is uncertain, and a product shortage may result if actual demand exceeds the forecast demand. Consider, for example, Bloomingdale’s, a high-end department store. Bloomingdale’s sells purses purchased from Gucci, an Italian manufacturer. Given the high transportation cost from Italy, the store manager at Bloomingdale’s orders in lots of 600 purses. Demand for purses at Bloomingdale’s averages 100 a week. Gucci takes three weeks to deliver the purses to Bloomingdale’s in response to an order. If there is no demand uncertainty and exactly 100 purses are sold each week, the store manager at Blooming- dale’s can place an order when the store has exactly 300 purses remaining. In the absence of demand uncertainty, such a policy ensures that the new lot arrives just as the last purse is being sold at the store.

Managing Uncertainty in a Supply Chain

Safety Inventory

C H A P T E R

12

LEARNING OBJECTIVES After reading this chapter, you will be able to

314

1. Describe different measures of product availability.

2. Understand the role of safety inventory in a supply chain.

3. Identify factors that influence the required level of safety inventory.

4. Use available managerial levers to lower safety inventory without hurting product availability.

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However, given demand fluctuations and forecast errors, actual demand over the three weeks may be higher or lower than the 300 purses that were forecast. If the actual demand at Bloomingdale’s is higher than 300, some customers will be unable to purchase purses, resulting in a potential loss of margin for Bloomingdale’s. The store manager thus decides to place an order with Gucci when the store still has 400 purses. This policy improves product availability for the customer because the store now runs out of purses only if the demand over the three weeks exceeds 400. Given an average weekly demand of 100 purses, the store will have an aver- age of 100 purses remaining when the replenishment lot arrives. Safety inventory is the average inventory remaining when the replenishment lot arrives. Thus, Bloomingdale’s carries a safety inventory of 100 purses.

Given a lot size of Q = 600 purses, the cycle inventory, the focus of the previous chapter, is Q>2 = 300 purses. The inventory profile at Bloomingdale’s in the presence of safety inven- tory is shown in Figure 12-1, which illustrates that the average inventory at Bloomingdale’s is the sum of the cycle and safety inventories.

This example illustrates a trade-off that a supply chain manager must consider when plan- ning safety inventory. On one hand, raising the level of safety inventory increases product avail- ability, and thus the margin captured from customer purchases. On the other hand, raising the level of safety inventory increases inventory holding costs. This issue is particularly significant in industries in which product life cycles are short and demand is volatile. Carrying excessive inven- tory can help counter demand volatility but can really hurt if new products come onto the market and demand for the product in inventory dries up. The inventory on hand then becomes worthless.

In today’s business environment, it has become easier for customers to search across stores for product availability. If Amazon is out of a book, for example, a customer can easily check to see whether barnesandnoble.com has the title available. The increased ease of searching puts pressure on firms to improve product availability. Simultaneously, product variety has grown with increased customization. As a result, markets have become increasingly heterogeneous and demand for individual products is unstable and difficult to forecast. Both the increased variety and the greater pressure for availability push firms to raise the level of safety inventory they hold. Given the product variety and high demand uncertainty in most high-tech supply chains, a sig- nificant fraction of the inventory carried is safety inventory.

As product variety has grown, however, product life cycles have shrunk. Thus, it is more likely that a product that is “hot” today will be obsolete tomorrow, which increases the cost to firms of carrying too much inventory. Thus, a key to the success of any supply chain is to figure out ways to decrease the level of safety inventory carried without hurting the level of product availability.

The importance of reduced safety inventories is emphasized by the experience of Nord-

Average Inventory

Cycle Inventory

Safety Inventory

Inventory

Time

Q

FIGURE 12-1 Inventory Profile with Safety Inventory

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316

ability to provide a high level of product availability to customers while carrying low levels of safety inventory in its supply chain. This fact has also played an important role in the success of Zara, Walmart, and Seven-Eleven Japan.

For any supply chain, three key questions need to be considered when planning safety

1. What is the appropriate level of product availability? 2. How much safety inventory is needed for the desired level of product availability? 3. What actions can be taken to reduce safety inventory without hurting product availability?

The first question is discussed in detail in Chapter 13. The remainder of this chapter focuses on answering the second and third questions, assuming a desired level of product avail- ability. Next, we consider factors that influence the appropriate level of safety inventory.

12.2 FACTORS AFFECTING THE LEVEL OF SAFETY INVENTORY

As the uncertainty of supply or demand grows, the required level of safety inventories increases. Demand for milk at a supermarket is quite predictable. As a result, supermarkets can operate with low levels of safety inventory relative to demand. In contrast, demand for spices at the same supermarket is much harder to predict. Thus the supermarket needs to carry high levels of safety inventory for spices relative to demand. Whereas most of the milk inventory at a super-

safety inventory carried to deal with uncertainty of demand. As the desired level of product availability increases, the required level of safety inventory

also increases. If the supermarket targets a higher level of product availability for a certain spice, it must carry a higher level of safety inventory for that spice.

Next, we discuss some measures of demand uncertainty.

Measuring Demand Uncertainty

has a systematic as well as a random component. The random component is a measure of demand uncertainty. The goal of forecasting is to predict the system- atic component and estimate the random component. The random component is usually esti- mated as the standard deviation of forecast error. We illustrate our ideas using uncertain demand

D

sD

Even though standard deviation of demand is not necessarily the same as forecast error, we treat the two to be interchangeable in our discussion. Safety inventory calculations should really be based on forecast error.

Lead time is the gap between the time an order is placed and when it is received. In our discussion, we denote the lead time by L L

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lead time, not just in a single period. We now evaluate the distribution of demand over L periods, given the distribution of demand during each period.

EVALUATING DEMAND DISTRIBUTION OVER L PERIODS Assume that demand for each period i, i = 1, . . . , L, is normally distributed with a mean Di and standard deviation si. Let rij be the correlation coefficient of demand between periods i and j. In this case, the total demand during L periods is normally distributed with a mean of DL and a standard deviation of sL, where

DL = a L

i = 1 Di, sL = AaLi = 1s2i + 2ai 7 jrijsisj (12.1)

Demand in two periods is perfectly positively correlated if rij = 1. Demand in two periods is perfectly negatively correlated if rij = -1. Demand in two periods is independent if rij = 0. If demand during each of L periods is independent and normally distributed with a mean of D and a standard deviation of sD, Equation 12.1 can be used to show that total demand during the L periods is normally distributed with a mean DL and a standard deviation of sL, where the fol-

DL = D * L, sL = 1LsD (12.2) Another important measure of uncertainty is the coefficient of variation cv

ratio of the standard deviation to the mean. Given demand with a mean of m and a standard deviation of s, we have

cv = s > m The coefficient of variation measures the size of the uncertainty relative to demand. It cap-

tures the fact that a product with a mean demand of 100 and a standard deviation of 100 has greater demand uncertainty than a product with a mean demand of 1,000 and a standard devia- tion of 100. Considering the standard deviation alone cannot capture this difference.

Next, we discuss some measures of product availability.

Measuring Product Availability

Product availability reflects a firm’s ability to fill a customer order out of available inventory. A stockout results if a customer order arrives when product is not available. There are several ways to measure product availability. Some of the important measures are listed next.

1. Product fill rate (fr) is the fraction of product demand that is satisfied from product in inventory. Fill rate is equivalent to the probability that product demand is supplied from available inventory. Fill rate should be measured over specified amounts of demand rather than over time. Thus, it is more appropriate to measure fill rate over every million units of demand rather than

– tory, with the remaining 10 percent lost to a neighboring competitor because of a lack of avail-

2. Order fill rate is the fraction of orders that are filled from available inventory. Order fill rate should also be measured over a specified number of orders rather than over time. In a multiproduct scenario, an order is filled from inventory only if all products in the order can be

with a laptop. The order is filled from inventory only if both the phone and the laptop are avail- able through the store. Order fill rates tend to be lower than product fill rates because all products must be in stock for an order to be filled.

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3. Cycle service level (CSL) is the fraction of replenishment cycles that end with all the customer demand being met. A replenishment cycle is the interval between two successive replenishment deliveries. The CSL is equal to the probability of not having a stockout in a replen- ishment cycle. CSL should be measured over a specified number of replenishment cycles. If

the store does not run out of inventory in 6 out of 10 replenishment cycles, the store achieves a CSL of 0.6 or 60 percent. Observe that a CSL of 0.6 typically results in a much higher fill rate.

satisfied from available inventory. In the 40 percent of cycles in which a stockout does occur, most of the customer demand is satisfied from inventory. Only the small fraction toward the end

higher than 0.6.

The distinction between product fill rate and order fill rate is usually not significant in a single-product situation. When a firm is selling multiple products, however, this difference may be significant. For example, if most orders include 10 or more products that are to be shipped, an out-of-stock situation of one product results in the order not being filled from stock. The firm in this case may have a poor order fill rate even though it has good product fill rates. Tracking order fill rates is important when customers place a high value on the entire order being filled at one time.

Next, we describe two replenishment policies that are often used in practice.

Replenishment Policies

A replenishment policy consists of decisions regarding when to reorder and how much to reor- der. These decisions determine the cycle and safety inventories along with the fill rate fr and the cycle service level CSL. Replenishment policies may take any of several forms. We restrict atten-

1. Continuous review: Inventory is continuously tracked, and an order for a lot size Q is

the inventory drops below ROP = 400. In this case, the size of the order does not change from one order to the next. The time between orders may fluctuate, given variable demand.

2. Periodic review: Inventory status is checked at regular periodic intervals, and an order is placed to raise the inventory level to a specified threshold. As an example, consider the pur-

– ously. Every Thursday, employees check flash drive inventory, and the manager orders enough so that the total of the available inventory and the size of the order equals 1,000 flash drives. In this case, the time between orders is fixed. The size of each order, however, can fluctuate given vari- able demand.

These inventory policies are not comprehensive, but they suffice to illustrate the key mana- gerial issues concerning safety inventories.

12.3 DETERMINING THE APPROPRIATE LEVEL OF SAFETY INVENTORY

We now discuss the relationship between safety inventory and the CSL and fr. In this section, we restrict our attention to the continuous review policy. The periodic review policy is discussed in detail in Section 12.6. The continuous review policy consists of a lot size Q ordered when the inventory on hand declines to the ROP. Assume that weekly demand is normally distributed, with mean D and standard deviation sD. Assume replenishment lead time of L weeks.

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Linking Safety Inventory and Cycle Service Level

– tory given a desired cycle service level.

EVALUATING SAFETY INVENTORY GIVEN A REPLENISHMENT POLICY safety inventory corresponds to the average number of phones on hand when a replenishment order arrives. Given the lead time of L weeks and a mean weekly demand of D, using Equation 12.2, we have

Expected demand during lead time = D * L

Given that the store manager places a replenishment order when ROP phones are on hand, we have

Safety inventory, ss = ROP – D * L (12.3)

This is because, on average, D * L phones will sell over the L weeks between when the order is placed and when the lot arrives. The average safety inventory when the replenishment lot arrives is thus ROP – D * L. The evaluation of safety inventory for a given inventory policy is

Chapter 12-examples worksheet Example 12-1

EXAMPLE 12-1 Evaluating Safety Inventory Given an Inventory Policy

– tory on hand drops to 6,000. Evaluate the safety inventory and the average inventory carried by

Analysis: Under this replenishment policy, we have

Average demand per week, D = 2, 00 Standard deviation of weekly demand, sD = 00 Average lead time for replenishment, L = 2 weeks Reorder point, ROP = 6,000 Average lot size, Q = 10,000

Using Equation 12.3, we thus have

Safety inventory, ss = ROP – D * L = 6,000 – ,000 = 1,000

From Chapter 11, recall that

Cycle inventory = Q > 2 = 10,000 > 2 = ,000 We thus have

Average inventory = cycle inventory + safety inventory = ,000 + 1,000 = 6,000

, we have

Average flow time = average inventory/throughput = 6,000 > 2, 00 = 2.4 weeks

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Next, we discuss how to evaluate the CSL given a replenishment policy.

EVALUATING CYCLE SERVICE LEVEL GIVEN A REPLENISHMENT POLICY Given a replenish- ment policy, our goal is to evaluate the CSL, the probability of not stocking out in a replenish-

Q units when the inventory on hand drops to the ROP. The lead time is L weeks and weekly demand is normally distributed, with a mean of D and a standard deviation of sD. Observe that a stockout occurs in a cycle if demand during the lead time is larger than the ROP. Thus, we have

CSL = Prob1demand during lead time of L weeks … ROP2 To evaluate this probability, we need to obtain the distribution of demand during the lead

time. From Equation 12.2, we know that demand during lead time is normally distributed, with a mean of DL and a standard deviation of sL. Using the notation for the normal distribution from Appendix 12A and the equivalent Excel function from Equation 12.22 in Appendix 12B, the CSL is

CSL = F1ROP, DL, sL2 = NORMDIST1ROP, DL, sL, 12 (12.4) Example 12-2

EXAMPLE 12-2 Evaluating Cycle Service Level Given a Replenishment Policy

– dent from one week to the next. Evaluate the CSL resulting from a policy of ordering 10,000 phones when there are 6,000 phones in inventory.

Analysis: In this case, we have

Q = 10,000, ROP = 6,000, L = 2 weeks D = 2, 00 > week, sD = 00

an order is placed and when the replenishment arrives. Thus, whether or not a stockout occurs depends on the demand during the lead time of two weeks.

Because demand across time is independent, we use Equation 12.2 to obtain demand dur- ing the lead time to be normally distributed with a mean of DL and a standard deviation of sL, where

DL = D * L = 2 * 2, 00 = ,000, sL = 1LsD = 12 * 00 = 0 Using Equation 12.4, the CSL is evaluated as

CSL = F1ROP, DL, sL2 = NORMDIST1ROP, DL, sL, 12 = NORMDIST16000, 000, 0 , 12 = 0. 2

demand is not satisfied because of the lack of inventory.

We now discuss how the appropriate level of safety inventory may be obtained given a desired CSL.

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Evaluating Safety Inventory Given Desired Cycle Service Level

In many practical settings, firms have a desired level of product availability and want to design replenishment polices that achieve this level. For example, Walmart has a desired level of prod- uct availability for each product sold in a store. The store manager must design a replenishment policy with the appropriate level of safety inventory to meet this goal. The desired level of prod- uct availability may be determined by trading off the cost of holding inventory with the cost of a stockout. This trade-off is discussed in detail in Chapter 13. In other instances, the desired level

– ment must design replenishment policies that achieve the desired target.

EVALUATING REQUIRED SAFETY INVENTORY GIVEN DESIRED CYCLE SERVICE LEVEL Our goal is to obtain the appropriate level of safety inventory given the desired CSL. We assume that a continuous review replenishment policy is followed. Consider the store manager at Walmart responsible for designing replenishment policies for all products in the store. He has targeted a CSL for the basic box of Lego building blocks. Given a lead time of L, the store manager wants to identify a suitable reorder point ROP and safety inventory that achieves the desired service level. Assume that demand for Legos at Walmart is normally distributed and independent from

Desired cycle service level = CSL = DL

Standard deviation of demand during lead time = sL From Equation 12.3, recall that ROP = DL + ss. The store manager needs to identify safety inventory ss

Probability1demand during lead time … DL + ss2 = CSL Given that demand is normally distributed, the store manager must identify safety inven-

tory ss

F1DL + ss, DL, sL2 = CSL Given the definition of the inverse normal in Appendix 12A and the equivalent Excel func-

tion from Appendix 12B, we obtain

DL + ss = F-11CSL, DL, sL2 = NORMINV1CSL, DL, sL2 or ss = F-11CSL, DL, sL2 – DL = NORMINV1CSL, DL, sL2 – DL

Using the definition of the standard normal distribution and its inverse from Appendix 12A, and the equivalent Excel function from Appendix 12B, it can also be shown that the following is

ss = F – 1S 1CSL2 * sL = FS- 11CSL2 * 1LsD = NORMSINV1CSL2 * 1LsD (12.5) Example 12-3

inventory given a desired CSL.

EXAMPLE 12-3 Evaluating Safety Inventory Given a Desired Cycle Service Level

– ous-review replenishment policy, evaluate the safety inventory that the store should carry to

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Analysis: In this case we have

D = 2, 00 > week, sD = 00, CSL = 0. , L = 2 weeks Because demand across time is independent, we use Equation 12.2 to find demand during the lead time to be normally distributed with a mean of DL and a standard deviation of sL, where

DL = D * L = 2 * 2, 00 = ,000; sL = 1LsD = 12 * 00 = 0 ss = Fs- 11CSL2 * sL = NORMSINV1CSL2 * sL = NORMSINV10. 02 * 0 = 06

Linking Safety Inventory and Fill Rate

a desired fill rate.

EVALUATING FILL RATE GIVEN A REPLENISHMENT POLICY Recall that fill rate measures the proportion of customer demand that is satisfied from available inventory. Fill rate is generally a more relevant measure than cycle service level because it allows the retailer to estimate the frac- tion of demand that is turned into sales. The two measures are closely related, as raising the cycle service level also raises the fill rate for a firm. Our discussion focuses on evaluating fill rate for a continuous review policy under which Q units are ordered when the quantity on hand drops to the ROP.

To evaluate the fill rate, it is important to understand the process by which a stockout occurs during a replenishment cycle. A stockout occurs if the demand during the lead time exceeds the ROP. We thus need to evaluate the average amount of demand in excess of the ROP in each replenishment cycle.

The expected shortage per replenishment cycle are not satisfied from inventory in stock per replenishment cycle. Given a lot size of Q

ESC > Q. The product fill rate fr is thus given by

fr = 1 – ESC > Q = 1Q – ESC2Q (12.6) A shortage occurs in a replenishment cycle only if the demand during the lead time exceeds

the ROP. Let f x ESC is given by

ESC = L ∞

x = ROP 1x – ROP2f1x2dx (12.7)

When demand during the lead time is normally distributed with mean DL and standard deviation sL, given a safety inventory ss

ESC = -ss c 1 – Fsa sssL b d + sL fsa sssL b (12.8) where Fs is the standard normal cumulative distribution function and fs is the standard nor- mal density function. The standard normal distribution has a mean of 0 and a standard devia- tion of 1. A detailed description of the normal distribution is given in Appendix 12A. Details

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ESC = -ss31 – NORMDIST1ss>sL, 0, 1, 124 + sL NORMDIST1ss > sL, 0, 1, 02 (12.9)

Given the ESC, we can use Equation 12.6 to evaluate the fill rate fr. Next, we illustrate this Example 12-4

EXAMPLE 12-4 Evaluating Fill Rate Given a Replenishment Policy

Assume that the demand is independent from one week to the next. Evaluate the fill rate resulting from the policy of ordering 10,000 phones when there are 6,000 phones in inventory.

Analysis: From the analysis of Example 12-2, we have

Lot size, Q = 10,000 Average demand during lead time, DL = ,000 Standard deviation of demand during lead time, sL = 0

Using Equation 12.3, we obtain

Safety inventory, ss = ROP – DL = 6,000 – ,000 = 1,000

ESC = -1,00031 – NORMDIST11,000 > 0 , 0, 1, 124 + 0 NORMDIST11,000 > 0 , 0, 1, 02 = 2

12.5=(A3-B9)/A3C9

12.8=-E3*(1-NORMDIST(E3/B6, 0, 1, 1)) + B6*NORMDIST(E3/B6, 0, 1, 0)

B9

12.4=NORMDIST(A6+E3, A6, B6, 1)A9

12.2=SQRT(D3)*C3B6

12.2=B3*D3A6

EquationCell FormulaCell

FIGURE 12-2 Excel Solution of Example 12-4

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fr = 1Q – ESC2 >Q = 110,000 – 2 2 >10,000 = 0.

– Exam-

ple 12-4

results in replenishment occurring once every eight weeks instead of once every four weeks.

per year. With a lot size of 20,000, we have, on average, one stockout every two years. Thus, the fill rate is higher.

Key Point

Both fill rate and cycle service level increase as the safety inventory is increased. For the same safety inventory, an increase in lot size increases the fill rate but not the cycle service level.

EVALUATING REQUIRED SAFETY INVENTORY GIVEN DESIRED FILL RATE For a continuous review replenishment policy, we now evaluate the required safety inventory given a desired fill rate fr. Consider the store manager at Walmart targeting a fill rate fr for Lego building blocks. The current replenishment lot size is Q. The first step is to obtain the ESC using Equation 12.6.

The next step is to obtain a safety inventory ss

obtained easily using Excel and trying different values of ss. In Excel, the safety inventory may also be obtained directly using the tool GOALSEEK, – sheet Example 12-5

EXAMPLE 12-5 Evaluating Safety Inventory Given Desired Fill Rate

currently orders replenishment lots of 10,000 boxes from Lego. Assuming a continuous-review replenishment policy, evaluate the safety inventory the store should carry to achieve a fill rate of

Analysis: In this case, we have

Desired fill rate, fr = 0. Lot size, Q = 10,000 boxes Standard deviation of demand during lead time, sL = 12 * 00 = 0

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From Equation 12.6, we thus obtain an ESC as

ESC = 11 – fr2 Q = 11 – 0. 210,000 = 2 0 ss, where

ESC = 2 0 = -ss c 1 – Fsa sssL b d + sL fsa sssL b = -ss c 1 – Fsa ss0 b d + 0 fsa ss0 b 2 0 = -ss31 – NORMDIST1ss> 0 ,0,1,124 + 0 NORMDIST1ss> 0 ,0,1,02 (12.10) Equation 12.10 may be solved in Excel by trying different values of ss until the equation is satis- fied. A more elegant approach for solving Equation 12.10 is to use the Excel tool GOALSEEK, as follows.

In the worksheet Example 12-5, invoke GOALSEEK using Data | What-If Analysis ” Goal Seek. In the GOALSEEK dialog box, enter the data as shown in Figure 12-3 and click the OK

Using GOALSEEK, we obtain a safety inventory of ss = 6 boxes, as shown in Figure 12-3.

Impact of Desired Product Availability and Uncertainty on Safety Inventory

The two key factors that affect the required level of safety inventory are the desired level of prod- uct availability and uncertainty. We now discuss the impact that each factor has on the safety inventory.

As the desired product availability goes up, the required safety inventory also increases because the supply chain must now be able to accommodate uncommonly high demand or

safety inventory for varying levels of fill rate as shown in Table 12-1.

– tory grows as product availability rises. This phenomenon highlights the importance of selecting

12.10-E3*(1-NORMSDIST(E3/B3, 0, 1,1)) + B3*NORMDIST(E3/B3, 0, 1, 0)A6

EquationCell FormulaCell

FIGURE 12-3 Spreadsheet to Solve for ss Using GOALSEEK

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suitable product availability levels. It is important for a supply chain manager to be aware of the products that require a high level of availability and hold high safety inventories only for those products. It is not appropriate to select a high level of product availability and require it arbi- trarily for all products.

TABLE 12-1 Required Safety Inventory for Different Values of Fill Rate Fill Rate Safety Inventory

97.5% 67

98.0% 183

98.5% 321

99.0% 499

99.5% 767

Key Point

The required safety inventory grows rapidly with an increase in the desired product availability.

ss is also influenced by the standard deviation of demand during the lead time, sL. The standard deviation of demand during the lead time is influenced by the duration of the lead time L and the standard deviation of peri- odic demand sD, as shown in Equation 12.2. The relationship between safety inventory and sD is linear, in that a 10 percent increase in sD results in a 10 percent increase in safety inventory. Safety inventory also increases with an increase in lead time L. The safety inventory, however, is

grows more slowly than the lead time itself.

Key Point

The required safety inventory increases with an increase in the lead time and the uncertainty of periodic demand.

A goal of any supply chain manager is to reduce the level of safety inventory required in a way that does not adversely affect product availability. The previous discussion highlights two

1. Reduce the supplier lead time L: If lead time decreases by a factor of k, the required safety inventory decreases by a factor of 1k. The only caveat here is that reducing the supplier lead time requires significant effort from the supplier, whereas reduction in safety inventory occurs at the retailer. Thus, it is important for the retailer to share some of the resulting benefits, as discussed in Chapter 10. Walmart, Seven-Eleven Japan, and many other retailers apply tre- mendous pressure on their suppliers to reduce the replenishment lead time. Apparel retailer Zara has built its entire strategy around using local flexible production to reduce replenishment lead times. In each case, the benefit has manifested itself in the form of reduced safety inventory while maintaining the desired level of product availability.

2. Reduce the underlying uncertainty of demand (represented by sD): If uncertainty represented by sD is reduced by a factor of k, the required safety inventory also decreases by a factor of k. A reduction in uncertainty may be achieved by better market intelligence, increased

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supply chain visibility, and the use of more sophisticated forecasting methods. Seven-Eleven Japan provides its store managers with detailed data about prior demand along with weather and other factors that may influence demand. This market intelligence allows the store managers to make better forecasts, reducing uncertainty. In most supply chains, however, the key to reducing the underlying forecast uncertainty is to link all forecasts throughout the supply chain to cus- tomer demand data. A lot of the demand uncertainty exists only because each stage of the supply chain plans and forecasts independently. This distorts demand throughout the supply chain, increasing uncertainty. Improved visibility and coordination, as discussed in Chapter 10, can often reduce the demand uncertainty significantly. Zara plans its production and replenishment based on actual sales at its retail stores to ensure that no unnecessary uncertainties are intro- duced. Both Walmart and Seven-Eleven Japan share demand information with their suppliers, reducing uncertainty and thus safety inventory within the supply chain.

We illustrate the benefits of reducing lead time and demand uncertainty in Example 12-6 Example 12-6

EXAMPLE 12-6 Benefits of Reducing Lead Time and Demand Uncertainty

inventory can the store expect if the supplier reduces lead time to one week? What savings in safety inventory can the store expect if reduced demand uncertainty results in a standard devia- tion of demand of 400?

Analysis: For the base case, we have

D = 2, 00>week, sD = 00, CSL = 0. ss = NORMSINV1CSL2 * 1LsD = NORMSINV1. 2 * 1 * 00 = 3, 4

If the suppler reduces the lead time L to one week, the required safety inventory is given by

ss = NORMSINV1CSL2 * 1LsD = NORMSINV1. 2 * 11 * 00 = 1,316 Thus, reducing the lead time from nine weeks to one week reduces the required safety inventory by 2,632 shirts.

We now consider the benefits of reducing forecast error. If Target reduces the standard

ss = NORMSINV1CSL2 * 1LsD = NORMSINV1. 2 * 1 * 400 = 1, 4 12.4 IMPACT OF SUPPLY UNCERTAINTY ON SAFETY INVENTORY

In our discussion to this point, we have focused on situations with demand uncertainty in the form of a forecast error. In many practical situations, supply uncertainty also plays a significant

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Napoli 1,000 tons of nickel, a key ingredient of stainless steel. Given that 1,000 tons was almost 20 per-

to market resulted in significant shortages and raised the price of nickel by about 20 percent in

production delays, transportation delays, and quality problems. Supply chains must account for supply uncertainty when planning safety inventories.

In this section, we incorporate supply uncertainty by assuming that lead time is uncertain and identify the impact of lead time uncertainty on safety inventories. Assume that the customer demand per period for tablets at Amazon and the replenishment lead time from the supplier are

D

sD

L

sL

We consider the safety inventory requirements given that Amazon follows a continuous review policy to manage tablet inventory. Amazon experiences a stockout of product if demand during the lead time exceeds the ROP—that is, the quantity on hand when Amazon places a replenish- ment order. Thus, we need to identify the distribution of customer demand during the lead time. Given that both lead time and periodic demand are uncertain, demand during the lead time is normally distributed with a mean of DL and a standard deviation sL, where

DL = D * L; sL = 2Ls2D + D2s2L (12.11) Given the distribution of demand during the lead time in Equation 12.11 and a desired

– ity is specified as a fill rate, Amazon can obtain the required safety inventory using the procedure

Example 12-7

EXAMPLE 12-7 Impact of Lead Time Uncertainty on Safety Inventory

L = days to replenish inventory at

its tablet inventory. Evaluate the safety inventory of tablets that Amazon must carry if the stan- dard deviation of the lead time is seven days. Amazon is working with the supplier to reduce the standard deviation to zero. Evaluate the reduction in safety inventory that Amazon can expect as a result of this initiative.

Analysis: In this case, we have

Average demand per period, D = 2, 00 Standard deviation of demand per period, sD = 00 Average lead time for replenishment, L = days Standard deviation of lead time, sL = days

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We first evaluate the distribution of demand during the lead time. Using Equation 12.11, we have

ean demand during lead time, DL = D * L = 2, 00 * = 1 , 00

Standard deviation of demand during lead time, sL = 2Ls2D + D2s2L = 2 * 002 + 2, 002 * 2 = 1 , 0

ss = NORMSINV1CSL2 * sL = NORMSINV10. 02 * 1 , 0 = 22,4 1 tablets If the standard deviation of lead time is seven days, Amazon must carry a sa