§ 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.
The AI Strategy Gap in Supply Chain: Why 77% of Organizations Lack a Formal Plan and How to Build a Balanced Investment Portfolio
· Machine learning, generative AI, agentic AI
AI for Supply Chain Recall Management at Walmart
· Computer vision, Predictive modeling, Agentic AI
How AI video surveillance cuts cargo theft in warehouse yards
· computer vision
Why Logistics Needs AI for Wildfire Smoke Risk
· machine learning
How AI Turns CSRD Supply Chain Reporting Into a Strategic Asset
· machine learning classification
What the Madewell Recall Reveals About AI Textile Inspection
· Computer Vision
Machine Learning in Logistics ROI: Benchmarks Across 9 Application Areas
· Machine learning
How ML Risk Models Quantify Red Sea Maritime Disruptions
· Random forest and AIS analysis