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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.

What Cloud Infrastructure Does AI Supply Chain Planning Actually Need
· machine learning forecasting, probabilistic modeling, optimization, simulation
Comparing AI Platforms for Supply Chain Flood Risk Management
· machine learning forecasting, digital twin, predictive ML
How AI Cuts Maritime Disruption Recovery from Weeks to Days
· machine learning, natural language processing
Using AI to Manage Airline Passenger Disruptions
· machine learning, optimization
AI Traceability Turns FSMA 204 Compliance into an Operational Advantage
· Computer Vision, IoT Analytics, Machine Learning
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