§ 40 — Use Cases

Use Cases

Pattern-level analyses of what works and what fails when supply-chain AI is applied to specific functions and techniques (demand forecasting, inventory optimization, procurement automation, warehouse computer vision), synthesized from the Post-Mortems and corroborated by cited external research (e.g., MIT CTL, SupplyChainBrain, peer-reviewed studies) rather than recycled unsourced industry statistics. Every claim carries a named source and date. Distinct from Post-Mortems (single-deployment accounts) and Readiness (task checklists): this section answers whether a category of AI application actually delivers, and under what conditions it fails, at a pattern level.

AI-Driven Dynamic Routing Optimization for Last-Mile Delivery

· DVRP optimization (DVRPTW-TA, stochastic DVRP, time-dependent VRP); metaheuristic solvers (ALNS, Tabu Search, ACO); CNN-based ML traffic surrogate models; reinforcement learning dispatch; rollout-based real-time decision-making

How AI Tackles the Transformer Bottleneck in Grid Supply Chains

· machine learning forecasting

How AI Flood Risk Forecasts Transform Supply Chain Planning

How AI Enables Precision Recall Management in Food Supply Chains

· machine learning

How AI Fraud Prevention Works in Retail Supply Chains

· supervised machine learning, unsupervised anomaly detection, computer vision, agentic decisioning

How AI Predicts Geomagnetic Storms Days Before They Disrupt Logistics

· machine learning forecasting

Why AI ROI in Logistics Remains Unclear and How to Fix It

· machine learning

Beyond the Copilot: How B2B Manufacturers Cross the AI Order Management Execution Gap