Beschreibung:
Frontmatter -- Acknowledgments -- About the Author -- Foreword -- Contents -- Introduction -- 1 Inventory Policies -- 2 How Much Should I Order? -- 3 When Should I Order? -- 4 Safety Stocks -- 5 Inventory Policies -- 6 Stochastic Lead Times -- 7 Fill Rate -- 8 Cost and Service Level Optimization -- 9 Beyond Normality -- 10 Multi-Echelon Inventory Optimization -- 11 Newsvendor -- 12 Discrete Probabilistic Demand -- 13 Simulation Optimization -- Now It Is Your Turn! -- A Python -- B Proofs -- Bibliography -- Glossary -- List of Symbols -- Index
In this book . . . Nicolas Vandeput hacks his way through the maze of quantitative supply chain optimizations. This book illustrates how the quantitative optimization of 21st century supply chains should be crafted and executed. . . . Vandeput is at the forefront of a new and better way of doing supply chains, and thanks to a richly illustrated book, where every single situation gets its own illustrating code snippet, so could you. --Joannes Vermorel, CEO, Lokad Inventory Optimization argues that mathematical inventory models can only take us so far with supply chain management. In order to optimize inventory policies, we have to use probabilistic simulations. The book explains how to implement these models and simulations step-by-step, starting from simple deterministic ones to complex multi-echelon optimization. The first two parts of the book discuss classical mathematical models, their limitations and assumptions, and a quick but effective introduction to Python is provided. Part 3 contains more advanced models that will allow you to optimize your profits, estimate your lost sales and use advanced demand distributions. It also provides an explanation of how you can optimize a multi-echelon supply chain based on a simple—yet powerful—framework. Part 4 discusses inventory optimization thanks to simulations under custom discrete demand probability functions. Inventory managers, demand planners and academics interested in gaining cost-effective solutions will benefit from the "do-it-yourself" examples and Python programs included in each chapter