§ 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.
How to Choose the Right AI in Supply Chain Course: A Decision Framework for 2026
· Machine learning, generative AI, prompt engineering
Why 77% of Supply Chain Machine Learning Deployments Have No Strategy
· Machine learning forecasting
How McDonald's Uses AI to Forecast Demand for New Menu Items
· Machine learning forecasting
The ROI of Predictive Analytics in Logistics: What the Numbers Actually Say
· machine learning forecasting, predictive analytics
Procurement AI ROI in 2026: What the Evidence Actually Shows
· generative AI, agentic AI
What Retail Supply Chain Predictive Analytics Actually Delivers: An ROI Benchmark
· Machine Learning Forecasting
Supply Chain AI ROI in 2026: Why Productivity Gains Don't Reach the P&L and How to Fix It
· machine learning forecasting
Why Most Supply Chain AI Investments Miss the P&L Impact — and Where to Invest Instead
· machine learning
Can AI Prevent Supply Chain Chaos from the Tacoma Narrows Closure?
· natural language processing, machine learning
Three Layers of AI for Outbreak-Resilient Cruise Supply Chains
· machine learning forecasting
Dynamic Slotting Delivers the Fastest Warehouse AI Returns
· machine learning
How AI optimizes airline routes for supply chain profitability
· Causal inference and ensemble modeling