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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 optimizes flight planning for fuel savings
· Machine learning with physics-based models
How AI Builds a Flood-Resilient Water Supply Chain
· Machine learning, digital twins
AI Forecasting Models: A Five-Tier Decision Framework for Supply Chain Leaders
· Tree-based ensembles, recurrent deep learning, transformer forecasting, graph neural networks
Stage Inventory Smarter Using AI Flood Risk Intelligence
· Machine learning forecasting, AI optimization
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