§ 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.
What AI Route Optimization Delivers for Air Cargo Today
· machine learning
Closing the Security Gap in World Cup Logistics with AI
· computer vision
How AI Sensors Make Cold Chain Monitoring Predictive
· Machine learning (anomaly detection, predictive modeling)
AI Turns Supply Chain Traceability into Active Investigation
· Machine learning
How AI Helps Supply Chains Plan for Tropical Storm Disruptions
· Probabilistic forecasting, machine learning
AI That Predicts Tropical Storm Flooding at Building Level
· physics-informed AI
Autonomous Procurement AI Supplier Risk Scoring: Use Case Overview
· Machine learning, NLP, agentic AI, predictive modeling, graph-based network mapping
How Safe Are Autonomous Supply Chain Vehicles?
· Computer vision
The Supply Chain Case for AI Pricing in Grocery
· machine learning forecasting
Why supply chain cybersecurity needs layered AI
· Machine learning, graph-ML, large language models