§ 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 Safety Stock Optimization Across Multi-Echelon Inventory Networks: Deployment Evidence, Vendor Fit, and Implementation Risks
· Probabilistic demand modeling, stochastic risk modeling, ML-driven safety stock recalibration, scenario simulation, real-time demand sensing
How AI Enables Surgical Pharmaceutical Recalls with DSCSA
· NLP, machine learning
How AI Traceability Prevents the Next Egg Recall Crisis
· machine learning