Inventory Optimization: Models and Simulations (Paperback or Softback)

Vandeput, Nicolas

ISBN 10: 3110673916 / ISBN 13: 9783110673913

Published by de Gruyter 10/1/2020, 2020

About this Item

Inventory Optimization: Models and Simulations.

Seller Inventory # BBS-9783110673913

Bibliographic Details

• Title: Inventory Optimization: Models and Simulations (Paperback or Softback)
• Publisher: de Gruyter 10/1/2020
• Publication Date: 2020
• Language: English
• ISBN 10: 3110673916
• ISBN 13: 9783110673913
• Binding: Paperback or Softback
• Condition: New
• Weight: 1.4 pounds
• Book Type: Book

About this title

Synopsis

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, supply planners, and academics interested in gaining cost-effective solutions will benefit from the "do-it-yourself" examples and Python programs included in each chapter.

About the Author

Nicolas Vandeput is a supply chain data scientist specializing in demand forecasting and inventory optimization. He founded his consultancy company SupChains in 2016, delivering models and training courses worldwide. He co-founded SKU Science—a demand forecasting platform—in 2018. Passionate about education, Nicolas is an avid learner enjoying teaching at universities. He currently teaches forecasting and inventory optimization to master students in CentraleSupelec, Paris, France.