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§ 40Use 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 Improves Disruption Planning for Hormuz Tanker Crises
· machine learning and digital twin modeling
How AI Makes Nearshoring Supply Chain Planning Work
· demand forecasting, inventory optimization, scenario simulation
How AI Improves Drug Launch Supply Chain Planning
· Monte Carlo simulation, digital twin simulation
Does AI Predictive Maintenance Actually Improve Airline Safety?
· Machine Learning (Anomaly Detection, Virtual Sensors)
Why Supply Chain Problem Solving Needs AI Reasoning Now
· causal AI, LLM reasoning, agentic AI
How AI Traceability Bridges Cyclospora's Detection Gap
· Machine learning, computer vision
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