Supply Chain Analytics:
An Uncertainty Modeling Approach
Photo by Alfons Morales
Summary
Supply Chain Analytics: An Uncertainty Modeling Approach blends a unified supply chain management framework with the uncertainty modeling approach. There is no doubt that this cannot happen without the use of algorithms and computing. Therefore, we complement the book with an interactive web tool built on the Python programming language. The Python codes are also shared so those readers who are interested can use the codes to run the experiments on their own computers.
Link to the publisher's book website: https://link.springer.com/book/10.1007/978-3-031-30347-0
Book Outline
- Chapter 1: Introduction and Risk Analysis in Supply Chains
- Chapter 2: Analytical Foundations: Predictive and Prescriptive Modeling
- Chapter 3: Inventory Management under Demand Uncertainty
- Chapter 4: Uncertainty Modeling
- Chapter 5: Supply Chain Responsiveness
- Chapter 6: Managing Product Variety
- Chapter 7: Managing the Supply Risk
- Chapter 8: Supply Chain Finance
- Chapter 9: Future Trends: AI and beyond
- Appendix: Introduction to Python Programming for Supply Chain Analytics
- Bibliography
- Index
Web Tool
An interactive web application was developed to demonstrate many of the algorithms covered in the textbook. For each algorithm, users can change input values and see the corresponding changes in the output. All algorithms were implemented in Python using common scientific computing libraries, with all the required code and dependencies provided.
If the link doesn't work, please write https://scanalytics.pythonanywhere.com to your browser!