§ 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 Weather Impacts Supply Chain Planning and Logistics
· machine learning
How AI Helps Alaska Airlines Manage Boeing 787 Delivery Delays
· Machine learning, constraint-aware AI, real-time optimization
The ROI of Artificial Intelligence in Supply Chain: Benchmarks Across Functions
· Machine learning, agentic AI
AI supplier risk scoring after the Boeing 737 Max crisis
· machine learning
Cyclospora Needs AI-Powered Supply Chain Traceability
· Machine Learning
Where AI Delivers Highest ROI in Heat Wave Supply Chains
· Digital twin, LLM
How AI Prevents Ransomware-Driven Dairy Supply Shortages
· Machine learning anomaly detection
How Ransomware Disrupts Dairy Supply Chains
· Machine learning anomaly detection
Integrated AI for Supply Chain Tropical Storm Disruption Planning
· machine learning forecasting, digital twin simulation, agentic AI
Matching AI Demand Forecasting Tools to Your Demand Pattern Profile
· Probabilistic forecasting, graph neural networks, attribute-based similarity
Panasonic toaster recall exposes supply chain traceability gaps
· machine learning
Why Predictive Analytics Projects in Supply Chain Fail — and the Data-Driven Fix for Each Failure Mode
· Machine Learning Forecasting
How to Build AI Defenses for Supply Chain Payment Fraud
· machine learning, NLP, anomaly detection