Biçer Supply Chain Analytics

An Uncertainty Modeling Approach
Erscheinungsjahr 2023
ISBN: 978-3-031-30347-0
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 314 Seiten, eBook

Reihe: Springer Texts in Business and Economics

This textbook offers a detailed account of analytical models used to solve complex supply chain problems. It introduces a unique risk analysis framework that helps the reader understand the sources of uncertainties and use appropriate models to improve decisions in supply chains. This framework illustrates the complete supply chain for a product and demonstrates the supply chain's exposure to demand, supply, inventory, and financial risks.  Step by step, this book provides a detailed examination of analytical methods that optimize operational decisions under different types of uncertainty. It discusses stochastic inventory models, introduces uncertainty modeling methods, and explains methods for managing uncertainty. To help readers deepen their understanding, it includes access to various supplementary material including an online interactive tool in Python.This book is intended for undergraduate and graduate students of supply chain management with a focus on supply chain analytics. It also prepares practitioners to make better decisions in this field.

Zielgruppe

Graduate

Autoren/Hrsg.

Biçer, Isik

Inhaltsverzeichnis

1. Introduction and Risk Analysis in Supply Chains.- 2. Theoretical Foundations: Predictive and Prescriptive Modeling.- 3. Inventory Management under Demand Uncertainty.- 4. Uncertainty Modeling.- 5. Supply Chain Responsiveness.- 6. Managing Product Variety.- 7. Managing the Supply Risk.- 8. Supply Chain Finance.- 9. Future Trends: AI and beyond.- Appendix: Introduction to Python Programming for Supply Chain Analytics.- Bibliography.

Über Autor(innen)

Isik Bicer is an Assistant Professor of Operations Management and Information Systems at the Schulich School of Business, York University, Canada. His research focuses on supply chain analytics and supply chain finance. He uses methods from quantitative finance, optimization theory, statistics, and stochastic modeling to develop supply chain models that aim to reduce the mismatches between supply and demand. His research appeared in the top operations management journals, such as Production and Operations Management and Journal of Operations Management, and some practitioner outlets such as Harvard Business Review, California Management Review, and Forbes. The analytical tools developed as the outcome of his research have been implemented in companies in the pharmaceutical, automotive, and agriculture industries.