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.
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Lawrence V. Snyder
Zuo-Jun Max Shen
University of California, Berkeley
This edition first published 2019
© 2019 John Wiley & Sons, Inc.
John Wiley & Sons, Inc. (1e, 2011)
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Library of Congress Cataloging-in-Publication Data
Names: Snyder, Lawrence V., 1975- author. | Shen, Zuo-Jun Max, 1970- author.
Title: Fundamentals of supply chain theory / Lawrence V. Snyder, Lehigh University, Zuo-Jun Max Shen, University of California, Berkeley.
Description: Second edition. | Hoboken, New Jersey : John Wiley & Sons, Inc.,  | Includes bibliographical references and index. |
Identifiers: LCCN 2019015579 (print) | LCCN 2019018047 (ebook) | ISBN 9781119024866 (Adobe PDF) | ISBN 9781119024972 (ePub) | ISBN 9781119024842 (hardcover)
Subjects: LCSH: Business logistics.
Classification: LCC HD38.5 (ebook) | LCC HD38.5 .S6256 2020 (print) | DDC 658.701-dc23
LC record available at https://lccn.loc.gov/2019015579
Cover Design: Wiley
Cover Image: © RomoloTavani/Getty Images
To Suzanne, Coralie, and Matilda
-L. V. S.
To Irene, Michelle, and Jeffrey
-Z.-J. M. S.
Table 2.1 Monthly historical demand of books and CDs for Examples 2.1–2...
Table 2.2 Demands (
), forecasts (
), and forecast errors (
Table 2.3 Snippet of historical data on demand for baseball jerseys for...
Table 2.4 Bass model parameters. Adapted with permission from Lilien an...
Table 2.5 Estimated utilities
for uPhone models for Example 2.11.
values for Example 2.11.
Table 2.7 Choice probabilities
and segment sizes for Example 2.11.
Table 4.1 Sample demands and stockouts.
for Example 4.7.
Table 4.3 Demand for in‐flight meals for Problem 4.3.
Table 4.4 Probability distribution of TV show duration for Problem 4.7(...
Table 5.1 Meanand maximum error of
for Poisson(3) demand with
Table 6.1 Stochastic‐service model notation summary.
Table 7.1 Demand mean and standard deviation at DCs in Example 7.1.
Table 7.2 DVR parameters for Problem 7.6.
Table 8.1 Greedy algorithm costs: Iteration 1.
Table 8.2 Greedy algorithm costs: Iteration 2.
Table 8.3 Paper‐company data for Problem 8.19.
Table 9.1 pmf, cdf, and costs of supplier disruptions in Example 9.3.
Table 9.2 Key quantities for suppliers in Example 9.9.
Table 9.3 Disruption costs for optimal DCs. Reprinted by permission, Sn...
Table 10.1 Node coordinates for Problems 10.5 and 10.6.
Table 11.1 All positive entries of sorted savings list for VRP instance...
Table 11.2 Node coordinates for Problem 11.1.
Table 11.3 Node coordinates and demands for Problem 11.2.
Table 11.4 Node coordinates and demands for Problem 11.3.
Table 11.5 Partial savings list for Problem 11.4.
Table 11.6 Partial savings list for Problem 11.5.
Table 12.1 Costs for optimal and UFLP‐based solution to 88‐node LMRP in...
Table 13.1 Bounds on variability increase: Decentralized vs. centralize...
Table 13.2 Data for Problem 13.2.
Table 14.1 Payoffs for a sample game.
Table 14.2 Payoffs after implementing a contract.
Table 14.3 Contracting notation summary.
Table 15.1 Valuations that induce nontruthful bidding.
Table 15.2 Single‐item VCG auction for Example 15.1.
Table 15.3 Two‐item VCG auction for Example 15.1.
Table 15.4 Valuations that induce zero revenue in the VCG auction.
Table 15.5 Valuations that induce nonmonotonicity in the VCG auction.
Table 15.6 Valuations that induce collusion in the VCG auction.
Table 15.7 Noncollusive bids that fail to game the VCG auction.
Table 15.8 Valuations that induce multiple identities in the VCG auctio...
Table 15.9 Valuations for English auction in Problem 15.1.
Table 15.10 Transaction costs for double auction in Problem 15.6.
Table 15.11 Valuations for VCG auction in Problem 15.7.
Table A.1 Demand for finished compost for Problem A.2(b).
values for Problem A.9.
2.1 Leadingindicator identification
3.1 Wagner-Whitin algorithm
4.1 DP for finitehorizon inventory problem
4.2 Exact algorithm for periodicreview (s, S) policies with discrete demand distribution (Zheng and Federgruen 1991)
5.1 Iterative algorithm for EIL approximation for (r, Q) policy
5.2 Exact algorithm for continuousreview (r, Q) policy with continuous demand distribution
5.3 Exact algorithm for continuousreview (r, Q) policy with discrete demand distribution (Federgruen and Zheng 1992)
6.1 Relabel stages
6.2 DP algorithm for tree SSSPP
8.1 Solve (UFLP-Lr
8.2 Get feasible solution for UFLP from solution to (UFLP-LR
8.3 Lagrangian relaxation algorithm for UFLP
8.4 Dualascent procedure for DUALOC algorithm
8.5 Dualadjustment procedure for DUALOC algorithm
8.6 Greedyadd heuristic for UFLP
8.7 Swap heuristic for pMP
8.8 Neighborhood search heuristic for pMP
8.9 SCLPbased algorithm for pCP
8.10 1-Center on a tree
8.11 2-Center on a tree
10.1 Nearest neighbor heuristic
10.2 Nearest insertion heuristic
10.3 Cheapest insertion heuristic
10.4 GENI heuristic
10.5 MST heuristic
10.6 Christofides' heuristic
11.1 Set coveringbased algorithm for VRP
11.2 Clarke-Wright savings heuristic
11.3 Randomized Clarke-Wright savings heuristic
11.4 Sweep heuristic
11.5 Locationbased heuristic for VRP
12.1 Solve (P'
D.1 Lagrangian relaxation
In the past few decades, the study of supply chain management has evolved into a cohesive body of knowledge—not merely a haphazard collection of models, algorithms, and theorems, but a rich theory whose components intersect and inform each other. We wrote this book to help codify the foundations of this emerging supply chain theory and to demonstrate how recent developments build upon the classical models. Our focus is primarily on the seminal models and algorithms of supply chain theory—the building blocks that underlie much of the supply chain literature. We believe that an understanding of these models provides researchers with a sort of guidebook to the literature, as well as a toolbox to draw from when developing new models. We also discuss some more recent models that demonstrate how the classical models can be extended and applied in richer settings. These models provide graduate students and other new researchers in the field with some examples of the trajectory of research on supply chain theory—how the building blocks can be assembled to create something more complex, interesting, or useful.
Studying supply chain theory as a whole allows us the luxury of gaining some perspective on the field, a perspective that is not always evident when we immerse ourselves deeply in the literature on a particular topic. To that end, wherever possible, we have attempted to highlight the connections among supply chain models—for example, the conceptual similarities among different supply chain pooling models, the ways that inventory and location models can be combined, or the ways that inventory theory interacts with game theory to produce supply chain coordination models.
This book was written for anyone who is interested in quantitative approaches for studying supply chains. This includes people from a wide range of disciplines, such as industrial engineering/operations research, mathematics, management, economics, computer science, and finance. This also includes students (primarily graduate students), faculty, researchers, and practitioners of supply chain theory. And it includes scholars who are new to supply chain theory and want a gentle but rigorous introduction to it, or scholars who are well versed in the field and want a refresher or a reference for the seminal models. Finally, since you are holding this book, it most likely includes you.
One of the hallmarks—and, in our opinion, the great pleasures—of supply chain theory is that it makes use of a wide variety of the tools of operations research, mathematics, and computer science. In this book, you will find mathematical programming models (linear, integer, nonlinear, conic, stochastic, robust), duality theory, optimization techniques (Lagrangian relaxation, column generation, dynamic programming, line search, plus optimization by calculus and finite differences), heuristics and approximations, probability, stochastic processes, game theory, combinatorics, simulation, and complexity theory.
To make use of this book, you need not be an expert in all of these. (We are not.) We assume that you are familiar with basic optimization theory—that you know how to formulate a linear program and its dual, that you know how branch‐and‐bound works, and that you can perform a simple line search method such as bisection search. We also assume that you understand probability distributions and know how to compute expectations of random variables and functions thereof. We assume that your calculus is in good working order, that you can compute derivatives and integrals, including ones that involve multiple variables or other derivatives or integrals. We assume you have met Markov chains before, but we don't require you to remember much about them. For just about everything else, we will start from the ground up and tell you (or remind you of) what you need to know in order to understand the topic at hand. For some topics, you will find a useful reference in Appendix C, which lists formulas for calculating expectations, loss functions, geometric series, and some tricky derivatives and integrals. Because Lagrangian relaxation and column generation play a role in several chapters of this book, we have included a brief primer on those topics in Appendix D.
Probably the single most important prerequisite for this book is a high level of general mathematical maturity. We discuss a lot of mathematical proofs, and ask you to write your own in the homework problems. If you do not have much experience in this area, you may find the proofs to be the most challenging aspect of this book. To help you out, we have included in Appendix B a short guide to proof‐writing. We hope this appendix will familiarize you with some of the basic principles of proof‐writing, as well as some of the finer points of proof style and syntax. But, proof‐writing is perhaps more art than science, and the appendix will only get you so far. You will learn to be a good proof‐writer mainly by practicing the craft.
Our intention in writing this book was to cover a broad range of topics in supply chain theory, even if that meant that we could not cover some topics as deeply as we might have liked. Most of the material in this book is derived from earlier papers, and of course we have cited those papers carefully so that readers can delve deeper into any topics they wish. We have also cited important related references, and review articles where possible, so that readers can find more information about topics that interest them.
Most of this book (Chapters 2–12) deals with centralized supply chain models, in which all of the decision variables are under the control of a single decision‐maker. Most classical supply chain models, such as those for optimizing inventories and facility locations, are centralized models. In contrast, the decentralized models of Chapters 13–15 involve multiple parties with independent, conflicting objectives and the autonomy to choose their decision variables to optimize those objectives. The bullwhip effect (Chapter 13) is an example of a result of this decentralization, while the models of Chapters 14 and 15 discuss strategies for mitigating the negative financial effects of decentralization.
This chapters of this book are as follows:
(“Introduction”) gives an overview of supply chain management and defines terms that we will use throughout the book.
(“Forecasting and Demand Modeling”) discusses classical and machine‐learning–based forecasting methods, as well as three approaches—the Bass diffusion model, leading indicators, and choice models—that have been used more recently to predict demand. We refer to these latter approaches as “demand modeling” to differentiate them from classical forecasting techniques and to emphasize the fact that they aim to provide a model of the demand itself and not merely of its statistical properties.
We discuss classical single‐location inventory models in Chapters
(“Deterministic Inventory Models”),
(“Stochastic Inventory Models: Periodic Review”), and
(“Stochastic Inventory Models: Continuous Review”). For most of these models, we discuss how to formulate the objective function as well as how to optimize it—exactly or heuristically, in closed form or using algorithms—by our choice of inventory parameters. We also explore the theoretical properties of some of these models, including the optimality of inventory policies and the worst‐case performance of heuristics.
(“Multiechelon Inventory Models”), we discuss multiechelon inventory models, including both stochastic‐service models (including the Clark–Scarf model for serial systems and the Shang and Song approximation) and guaranteed‐service models (also known as strategic safety stock placement problems).
(“Pooling and Flexibility”) discusses risk pooling, as well as other techniques, such as postponement, transshipments, and process flexibility, that can provide similar pooling benefits.
(“Facility Location Models”), we turn our attention to facility location models. We present the classical uncapacitated fixed‐charge location problem (UFLP) in some detail, including its formulation as an integer programming problem and its solution by Lagrangian relaxation. We then discuss other classical location models such as the
‐median problem and covering models, as well as stochastic versions of the UFLP. Finally, we cover network design problems, including both problems in which we make yes/no decisions on the nodes and those in which we do the same for the arcs.
(“Supply Uncertainty”), we consider randomness in the availability or quantity of supply and develop models for coping with this uncertainty in inventory and facility location models.
(“The Traveling Salesman Problem”) discusses perhaps the most famous supply chain problem, the traveling salesman problem (TSP). We discuss both exact and heuristic solution methods for the TSP, as well as theoretical properties of the model and the algorithms. We conclude with a digression on TSP “world records.”
(“The Vehicle Routing Problem”), we extend the TSP to consider the more practical problem of routing multiple vehicles simultaneously to deliver to many customers, a problem known as the vehicle routing problem (VRP). We present algorithms, focusing mainly on heuristics for this very difficult computational problem. We discuss theoretical properties of the problem, as well as some of the many extensions that have been proposed to add more practical features to the classical model.
(“Integrated Supply Chain Models”) discusses models that combine multiple types of models discussed earlier in the book. In particular, we include location–inventory, location–routing, and inventory–routing models.
(“The Bullwhip Effect”), we discuss a phenomenon of demand variability amplification known as the bullwhip effect. The bullwhip effect can occur because of irrational or suboptimal behavior on the part of supply chain managers, but it can also occur as the result of rational, optimizing behavior. We cover mathematical models for proving that the bullwhip effect occurs as a result of the latter type.
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