Fundamentals of Supply Chain Theory

Comprehensively teaches the fundamentals of supply chain theory

This book presents the methodology and foundations of supply chain management and also demonstrates how recent developments build upon classic models. The authors focus on strategic, tactical, and operational aspects of supply chain management and cover a broad range of topics from forecasting, inventory management, and facility location to transportation, process flexibility, and auctions. Key mathematical models for optimizing the design, operation, and evaluation of supply chains are presented as well as models currently emerging from the research frontier.

Fundamentals of Supply Chain Theory, Second Edition contains new chapters on transportation (traveling salesman and vehicle routing problems), integrated supply chain models, and applications of supply chain theory. New sections have also been added throughout, on topics including machine learning models for forecasting, conic optimization for facility location, a multi-supplier model for supply uncertainty, and a game-theoretic analysis of auctions. The second edition also contains case studies for each chapter that illustrate the real-world implementation of the models presented. This edition also contains nearly 200 new homework problems, over 60 new worked examples, and over 140 new illustrative figures.

Plentiful teaching supplements are available, including an Instructor’s Manual and PowerPoint slides, as well as MATLAB programming assignments that require students to code algorithms in an effort to provide a deeper understanding of the material.

Ideal as a textbook for upper-undergraduate and graduate-level courses in supply chain management in engineering and business schools, Fundamentals of Supply Chain Theory, Second Edition will also appeal to anyone interested in quantitative approaches for studying supply chains.

Lawrence V. Snyder, PhD, is Professor in the Department of Industrial and Systems Engineering and Co-Director of the Institute for Data, Intelligent Systems, and Computation at Lehigh University. He has written numerous journal articles and tutorials on optimization models for supply chains and other infrastructure systems, with a focus on decision-making under uncertainty.

Zuo-Jun Max Shen, PhD, is Professor in the Department of Industrial Engineering and Operations Research and the Department of Civil and Environmental Engineering at the University of California at Berkeley. He is an INFORMS Fellow and has published and consulted extensively in the areas of integrated supply chain design and management, data driven decision making, and systems optimization.

List of Figures xxi

List of Tables xxvii

List of Algorithms xxix

Preface xxxi

1 Introduction 1

1.1 The Evolution of Supply Chain Theory 1

1.2 Definitions and Scope 2

1.3 Levels of Decision Making in Supply Chain Management 4

2 Forecasting and Demand Modeling 5

2.1 Introduction 5

2.2 Classical Demand Forecasting Methods 6

2.3 Forecast Accuracy 15

2.4 Machine Learning in Demand Forecasting 17

2.5 Demand Modeling Techniques 23

2.6 Bass Diffusion Model 24

2.7 Leading Indicator Approach 30

2.8 Discrete Choice Models 33

Case Study: Semiconductor Demand Forecasting at Intel 38

Problems 39

3 Deterministic Inventory Models 45

3.1 Introduction to Inventory Modeling 45

3.2 Continuous Review: The Economic Order Quantity Problem 51

3.3 Power of Two Policies 57

3.4 The EOQ with Quantity Discounts 60

3.5 The EOQ with Planned Backorders 67

3.6 The Economic Production Quantity Model 70

3.7 Periodic Review: The Wagner–Whitin Model 72

Case Study: Ice Cream Production and Inventory at Scotsburn Dairy Group 76

Problems 77

4 Stochastic Inventory Models: Periodic Review 87

4.1 Inventory Policies 87

4.2 Demand Processes 89

4.3 Periodic Review with Zero Fixed Costs: Base-Stock Policies 89

4.4 Periodic Review with Nonzero Fixed Costs: (s; S) Policies 114

4.5 Policy Optimality 123

4.6 Lost Sales 136

Case Study: Optimization of Warranty Inventory at Hitachi 138

Problems 140

5 Stochastic Inventory Models: Continuous Review 155

5.1 (r; Q) Policies 155

5.2 Exact (r; Q) Problem with Continuous Demand Distribution 156

5.3 Approximations for (r; Q) Problem with Continuous Distribution 161

5.4 Exact (r; Q) Problem with Continuous Distribution: Properties of Optimal r and Q 170

5.5 Exact (r; Q) Problem with Discrete Distribution 177

Case Study: (r; Q) Inventory Optimization at Dell 180

Problems 182

6 Multiechelon Inventory Models 187

6.1 Introduction 187

6.2 Stochastic-Service Models 191

6.3 Guaranteed-Service Models 203

6.4 Closing Thoughts 217

Case Study: Multiechelon Inventory Optimization at Procter & Gamble 222

Problems 223

7 Pooling and Flexibility 229

7.1 Introduction 229

7.2 The Risk-Pooling Effect 230

7.3 Postponement 236

7.4 Transshipments 237

7.5 Process Flexibility 243

7.6 A Process Flexibility Optimization Model 253

Case Study: Risk Pooling and Inventory Management at Yedioth Group 257

Problems 259

8 Facility Location Models 267

8.1 Introduction 267

8.2 The Uncapacitated Fixed-Charge Location Problem 269

8.3 Other Minisum Models 295

8.4 Covering Models 305

8.5 Other Facility Location Problems 314

8.6 Stochastic and Robust Location Models 317

8.7 Supply Chain Network Design 321

Case Study: Locating Fire Stations in Istanbul 332

Problems 335

9 Supply Uncertainty 355

9.1 Introduction to Supply Uncertainty 355

9.2 Inventory Models with Disruptions 356

9.3 Inventory Models with Yield Uncertainty 365

9.4 A Multisupplier Model 372

9.5 The Risk-Diversification Effect 384

9.6 A Facility Location Model with Disruptions 387

Case Study: Disruption Management at Ford 395

Problems 396

10 The Traveling Salesman Problem 403

10.1 Supply Chain Transportation 403

10.2 Introduction to the TSP 404

10.3 Exact Algorithms for the TSP 408

10.4 Construction Heuristics for the TSP 416

10.5 Improvement Heuristics for the TSP 436

10.6 Bounds and Approximations for the TSP 442

10.7 World Records 452

Case Study: Routing Meals on Wheels Deliveries 453

Problems 455

11 The Vehicle Routing Problem 463

11.1 Introduction to the VRP 463

11.2 Exact Algorithms for the VRP 468

11.3 Heuristics for the VRP 475

11.4 Bounds and Approximations for the VRP 495

11.5 Extensions of the VRP 498

Case Study: ORION: Optimizing Delivery Routes at UPS 501

Problems 502

12 Integrated Supply Chain Models 511

12.1 Introduction 511

12.2 A Location–Inventory Model 512

12.3 A Location–Routing Model 529

12.4 An Inventory–Routing Model 531

Case Study: Inventory–Routing at Frito-Lay 534

Problems 535

13 The Bullwhip Effect 539

13.1 Introduction 539

13.2 Proving the Existence of the Bullwhip Effect 541

13.3 Reducing the Bullwhip Effect 552

13.4 Centralizing Demand Information 555

Case Study: Reducing the Bullwhip Effect at Philips Electronics 556

Problems 559

14 Supply Chain Contracts 563

14.1 Introduction 563

14.2 Introduction to Game Theory 564

14.3 Notation 565

14.4 Preliminary Analysis 566

14.5 The Wholesale Price Contract 568

14.6 The Buyback Contract 574

14.7 The Revenue Sharing Contract 578

14.8 The Quantity Flexibility Contract 581

Case Study: Designing a Shared-Savings Contract at McGriff Treading Company 584

Problems 586

15 Auctions 591

15.1 Introduction 591

15.2 The English Auction 593

15.3 Combinatorial Auctions 595

15.4 The Vickrey–Clarke–Groves Auction 599

Case Study: Procurement Auctions for Mars 608

Problems 610

16 Applications of Supply Chain Theory 615

16.1 Introduction 615

16.2 Electricity Systems 615

16.3 Health Care 625

16.4 Public Sector Operations 632

Case Study: Optimization of the Natural Gas Supply Chain in China 639

Problems 641

Appendix A: Multiple-Chapter Problems 643

Problems 643

Appendix B: How to Write Proofs: A Short Guide 651

B.1 How to Prove Anything 651

B.2 Types of Things You May Be Asked to Prove 653

B.3 Proof Techniques 655

B.4 Other Advice 657

Appendix C: Helpful Formulas 661

C.1 Positive and Negative Parts 661

C.2 Standard Normal Random Variables 662

C.3 Loss Functions 662

C.4 Differentiation of Integrals 665

C.5 Geometric Series 666

C.6 Normal Distributions in Excel and MATLAB 666

C.7 Partial Expectations 667

Appendix D: Integer Optimization Techniques 669

D.1 Lagrangian Relaxation 669

D.2 Column Generation 675

References 681

Subject Index 712

Author Index 725

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