§ 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.
From Dashboards to Decisions: How Agentic AI Is Shifting Machine Learning from Prediction to Autonomous Execution in Supply Chains
· agentic AI, machine learning
How AI Predicts Hospital Construction Supply Chain Delays
· machine learning forecasting
How AI Cuts Offshore Wind Logistics Costs by 10–36%
· Machine learning forecasting
How AI Traceability Solves Converging Agricultural Policy Changes
· Machine Learning
The Cost of Inaction: Building a Business Case for Predictive Analytics in Logistics
· predictive analytics
How USPS Deploys AI Supply Chain Planning for Election Ballot Delivery
· Machine learning forecasting, computer vision
Agentic AI in Procurement: What Works in Production in 2026 — Use Cases, Benchmarks, and ROI from Live Deployments
· agentic AI, multi-agent orchestration
How AI spare parts forecasting optimizes Boeing 737 MRO
· Machine learning forecasting (gradient boosting, LSTM, XGBoost)
AI drone attacks create a new risk category for oil supply chains
· computer vision