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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