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.
Author(s): Işık Biçer
Series: Springer Texts in Business and Economics
Publisher: Springer
Year: 2023
Language: English
Pages: 322
City: Cham
Preface
Acknowledgements
Contents
1 Introduction and Risk Analysis in Supply Chains
1.1 Internet Era and Shift in Supply Chains
1.2 Three Challenges of Modern Supply Chains
1.3 Supply Chain Management Framework
1.4 Supply Chain Integration
1.5 Supply Chain Analytics at the Interface of Supply Chain Management and Data Analytics
1.6 Case Study: Kordsa Inc.
1.7 Chapter Summary
1.8 Practice Examples
1.9 Exercises
References
2 Analytical Foundations: Predictive and Prescriptive Analytics
2.1 Predictive Analytics
2.1.1 Bias-Variance Trade-Off
2.1.2 Linear Models
2.1.3 Matrix Formation
2.1.4 Generalized Least Squares (GLS)
2.1.5 Dealing with Endogeneity Problems
2.1.6 Regularization Methods
2.1.7 Classification Methods
2.1.8 Time Series Analysis
2.2 Prescriptive Analytics
2.2.1 Taylor's Expansion
2.2.2 Convexity
2.2.3 Newton's Method
2.2.4 Gradient Descent Method
2.2.5 Lagrange Optimization
2.3 Chapter Summary
2.4 Practice Examples
2.5 Exercises
References
3 Inventory Management Under Demand Uncertainty
3.1 Inventory Productivity and Financial Performance
3.2 Single-Period Models
3.2.1 Single-Period Example
3.3 Multi-period Models
3.3.1 Multi-period Example
3.4 Reorder Model with Continuous Review
3.4.1 Reorder Model Example
3.5 Monte Carlo Simulation for Inventory Models
3.6 Chapter Summary
3.7 Practice Examples
3.8 Exercises
3.9 Appendix to Chap.3
3.9.1 Analytical Solution to the Newsvendor Problem
3.9.2 Analytical Solution to the Base Stock Problem
References
4 Uncertainty Modelling
4.1 Uncertainty Modelling Versus Demand Forecasting
4.2 Evolutionary Dynamics of Uncertainty
4.2.1 Additive Demand Models
4.2.2 Multiplicative Demand Models
4.3 Integration of Uncertain Elements in a Unified Model
4.3.1 Inventory Management with the Additive Integration of Uncertain Elements
4.3.2 Inventory Management with the Multiplicative Integration of Uncertain Elements
4.3.3 A Commonly Used Multiplicative Model: Jump Diffusion Process
4.3.4 Example
4.4 Demand Regularization
4.5 Chapter Summary
4.6 Practice Examples
4.7 Exercises
References
5 Supply Chain Responsiveness
5.1 Lead Time Reduction
5.2 Multiple Sourcing
5.3 Quantity Flexibility Contracts
5.4 Multiple and Sequential Ordering Problems
5.5 Multiple Ordering in a Multi-echelon Model
5.6 Chapter Summary
5.7 Practice Examples
5.8 Exercises
5.9 Appendix to Chap.5
5.9.1 Derivation of Q*1 and K*
References
6 Managing Product Variety
6.1 Mean-Variance Analysis for Product Selection
6.2 Resource Allocation and Capacity Management
6.3 Multiple Ordering Model with Multiple Products
6.4 Product Proliferation Model
6.5 Operational Excellence
6.6 Chapter Summary
6.7 Practice Examples
6.8 Exercises
6.9 Appendix to Chap.6
6.9.1 Mean-Variance Analysis Derivations
6.9.2 Multi-product Newsvendor Model
References
7 Managing the Supply Risk
7.1 Type-1 Disruption Risk
7.2 Type-2 Disruption Risk
7.3 Implications of the Shipment Ownership for Global Trade
7.4 Type-3 Risk: Delivery Shortfalls
7.5 Chapter Summary
7.6 Practice Examples
7.7 Exercises
References
8 Supply Chain Finance
8.1 Early Payment Scheme
8.2 Reverse Factoring
8.3 Letter of Credit
8.4 Dynamic Discounting
8.4.1 Market Mechanism of Dynamic Discounting
8.5 Chapter Summary
8.6 Practice Examples
8.7 Exercises
References
9 Future Trends: AI and Beyond
9.1 Artificial Neural Networks
9.2 Activation Functions
9.3 Model Training
9.4 ANNs in Inventory Management
9.5 Chapter Summary
References
A Introduction to Python Programming for Supply Chain Analytics
A.1 NumPy
A.2 SciPy
A.3 Pandas
A.4 Matplotlib
A.5 General Programming Concepts
Reference
Index
