Fundamentals of Supply Chain Theory
John Wiley & Sons Inc (Verlag)
978-1-119-02484-2 (ISBN)
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
| Verlagsort | New York |
|---|---|
| Sprache | englisch |
| Maße | 185 x 257 mm |
| Gewicht | 1837 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Angewandte Mathematik |
| Technik ► Maschinenbau | |
| Wirtschaft ► Betriebswirtschaft / Management ► Logistik / Produktion | |
| ISBN-10 | 1-119-02484-6 / 1119024846 |
| ISBN-13 | 978-1-119-02484-2 / 9781119024842 |
| Zustand | Neuware |
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