§ 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 AI Transforms Tornado Diagrams for Supply Chain Risk Management
· machine learning, Monte Carlo simulation
The AI ROI Playbook for Transportation and Logistics: Where the Money Actually Is
· machine learning forecasting, computer vision, reinforcement learning
The ROI of AI for Supply Chain Weather Disruption Planning
· machine learning forecasting
The Hidden Infrastructure Tax: Why 80% of Warehouses Still Haven't Deployed ML and What It Takes to Cross the Gap
· machine learning
How Machine Learning Reduces Safety Stock in Multi-Echelon Inventory
· Machine learning, probabilistic modeling
How Supply Chain Tracking Solves Multi-Brand Egg Recalls
· Natural Language Processing, Machine Learning
How AI Planning Tools Tackle NYC Flash Flood Disruptions
· LSTM forecasting, digital twin simulation
How to Operationalize AI Weather for Supply Chain Risk
· Machine learning forecasting
Why Process Discipline Matters More Than Algorithms in AI Demand Forecasting
· machine learning forecasting
What Supply Chain AI Can Learn from Rocket Launch Abort Systems
· sensor fusion, anomaly detection, reinforcement learning