§ 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.
AI for Tornado Disaster Recovery in Supply Chains
· Deep learning (computer vision), predictive analytics
How AI Demand Planning Software Actually Works: Techniques, Models, and Implementation Patterns
· Gradient boosting, LSTM, Transformers, hybrid models, agentic AI
Machine Learning in Logistics: A Use-Case-by-Use-Case ROI Breakdown for Supply Chain Leaders
· machine learning
From Reactive to Predictive: Building the Data Foundation for Logistics Analytics
· machine learning forecasting
The Measurable ROI of AI in Demand Forecasting: Accuracy, Inventory, and the 2–4 Year Payback Timeline
· machine learning forecasting
The Supply Chain AI Adoption Paradox
· machine learning forecasting
Can AI-Powered CT Scanners Fix Airport Baggage Issues?
· computer vision
AI Demand Forecasting in CPG and Retail: A Structured Use Case Reference
· Gradient boosting, LSTM, Transformer models, causal ML, attribute-based transfer learning, real-time signal integration