Supply Chain Optimization Version 1

About This Book

Chapter 1 Python Foundations for Supply Chain Analytics 1.1 Why Python for Supply Chain? 1.2 Setting Up Your Environment 1.3 NumPy for Numerical Operations 1.4 Pandas for Supply Chain Data 1.5 Matplotlib for Visualization 1.6 Building Your First Cost Model Chapter 2 Mathematical Optimization — From Theory to Code 2.1 What Is an Optimization Problem? 2.2 Decision Variables, Objectives & Constraints 2.3 Linear Programming Theory 2.4 Integer & Binary Programming 2.5 Nonlinear Programming 2.6 Introducing PuLP and SciPy Chapter 3 Procurement Optimization 3.1 The Supplier Selection Problem 3.2 Total Cost of Ownership (TCO) 3.3 Building a Multi-Supplier MIP in PuLP 3.4 Price Breaks and MOQ Constraints 3.5 Supplier Risk Scoring 3.6 Sensitivity Analysis in Procurement Chapter 4 Inventory Optimization 4.1 Why Inventory Matters 4.2 The EOQ Model — Step-by-Step Derivation 4.3 Safety Stock and Service Levels 4.4 Reorder Points and Cycle Stock 4.5 Multi-Echelon Inventory Systems 4.6 Simulation-Based Inventory Optimization Chapter 5 Transportation and Network Flow Optimization 5.1 The Classical Transportation Problem 5.2 Solving Transportation LP with PuLP 5.3 Network Flow Optimization 5.4 The Assignment Problem 5.5 Shortest Path Algorithms Chapter 6 Vehicle Routing Problem (VRP) with OR-Tools 6.1 Why VRP? The Last-Mile Problem 6.2 Google OR-Tools Architecture 6.3 Solving Basic VRP 6.4 Capacitated VRP (CVRP) 6.5 VRP with Time Windows (VRPTW) 6.6 Multi-Depot and Fleet Planning Chapter 7 Supply Chain Network Design 7.1 Strategic Network Design Decisions 7.2 The Facility Location Problem 7.3 Building a Full Network Design MIP 7.4 Multi-Commodity Network Design 7.5 Carbon-Aware Network Optimization Chapter 8 Supply Chain Risk Modeling 8.1 Risk Taxonomy and Quantification 8.2 Monte Carlo Simulation in Python 8.3 Markov Chain Risk Models 8.4 Dual-Sourcing Optimization Under Risk 8.5 Value at Risk (VaR) and CVaR Chapter 9 Demand Forecasting with Machine Learning 9.1 Why Forecasting Is the Foundation 9.2 Classical Methods: ARIMA and Exponential Smoothing 9.3 Feature Engineering for Demand Data 9.4 Gradient Boosting (XGBoost/LightGBM) 9.5 Evaluating and Tuning Forecast Models 9.6 Connecting Forecasts to Optimization Chapter 10 Advanced Metaheuristics 10.1 When Exact Solvers Are Not Enough 10.2 Genetic Algorithms from Scratch 10.3 Simulated Annealing 10.4 Tabu Search 10.5 Applying Metaheuristics to Real Problems Chapter 11 Building End-to-End Supply Chain Dashboards 11.1 Connecting Optimization to Visualization 11.2 Plotly for Interactive Supply Chain Charts 11.3 Building a Procurement Analytics Dashboard 11.4 Inventory KPI Monitoring System Chapter 12 Complete Industry Projects 12.1 Project A: FMCG Distribution Optimization 12.2 Project B: Airline Spare Parts Inventory 12.3 Project C: E-Commerce Fulfillment Network 12.4 Project D: Supplier Risk Mitigation System