§ 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 Scenario Planning for Red Sea Supply Chain Disruption
· Digital twin
Comparing Machine Learning Use Cases Across Supply Chain Functions: ROI, Maturity, and Adoption Priorities
· Machine learning, generative AI
Predictive Analytics in Supply Chain: Five Functions Where It Delivers Measurable ROI
· machine learning forecasting, predictive analytics
Python Hunting AI Forecast: Supply Chain Resource Allocation Lessons
· Generalized additive mixed models (GAMM)
Why Most Retail Predictive Analytics Fail at the Data Layer — and How to Fix It
· machine learning forecasting
What the $53 Billion Agentic AI Forecast Means for Supply Chain Leaders
· multi-agent orchestration
AI Bridge Risk Screening for Supply Chain Route Intelligence
· surrogate neural networks
AI Demand Forecasting in 2026: Hype vs. Reality
· Machine learning forecasting
How AI Plans Drone Swarm Logistics in Contested Environments
· reinforcement learning
AI Early Warning Systems for Supplier Bankruptcy Risk
· Machine Learning
AI earthquake damage assessment cuts post-earthquake decision time
· computer vision