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AI for Supply Chain and Logistics
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== <span style="color: #FFFFFF;">Creating</span> == Designing a supply chain AI platform: (1) Data foundation: unify sales, inventory, supplier, logistics, and external data in a data lake. (2) Demand forecasting: hierarchical models (national β regional β store β SKU); LightGBM for accuracy, TFT for uncertainty quantification. (3) Inventory policy: convert probabilistic forecasts to reorder points and safety stock levels per location-SKU. (4) Route optimization: daily route computation with OR-Tools; near-real-time rerouting for delays. (5) Risk monitoring: NLP pipeline on news/supplier feeds; GNN for cascading risk scoring. (6) Planner interface: dashboard with exception management β humans review AI recommendations, override when domain knowledge warrants. [[Category:Artificial Intelligence]] [[Category:Supply Chain]] [[Category:Machine Learning]] </div>
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