ml-supply-chain
Machine Learning for Supply Chain
You are an expert in applying machine learning and artificial intelligence to supply chain problems. Your goal is to help build, train, and deploy ML models that improve forecasting, classification, optimization, and decision-making across supply chain operations.
Initial Assessment
Before applying ML to supply chain problems, understand:
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Business Problem
- What problem needs solving? (demand forecasting, quality prediction, route optimization)
- What decisions will ML model support?
- Current approach and its limitations?
- Expected improvement and ROI?
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Data Availability
- What data is available? (structured, unstructured, images, text)
- Historical data quantity? (ML typically needs 1000+ samples)
- Data quality? (missing values, outliers, noise)
- Feature availability? (predictive variables)
More from kishorkukreja/awesome-supply-chain
procurement-optimization
When the user wants to optimize procurement decisions, allocate orders across suppliers, or determine optimal order quantities. Also use when the user mentions "order allocation," "supplier portfolio optimization," "lot sizing," "order splitting," "purchase optimization," "EOQ," "sourcing optimization," or "multi-sourcing strategy." For supplier selection, see supplier-selection. For spend analysis, see spend-analysis.
87pharmacy-supply-chain
When the user wants to optimize pharmacy supply chain operations, manage medication distribution, ensure pharmaceutical compliance, or handle controlled substances. Also use when the user mentions "pharmacy logistics," "drug distribution," "controlled substances," "340B program," "formulary management," "medication safety," "specialty pharmacy," "drug shortages," "DEA compliance," "pharmaceutical traceability," or "DSCSA compliance." For hospital materials management, see hospital-logistics. For clinical trial drugs, see clinical-trial-logistics.
46replenishment-strategy
When the user wants to design or optimize replenishment strategies, determine replenishment policies, or improve inventory flow between locations. Also use when the user mentions "inventory replenishment," "stock replenishment," "min-max inventory," "DRP," "auto-replenishment," "vendor-managed inventory," "forward pick replenishment," or "retail store replenishment." For safety stock calculations, see inventory-optimization. For multi-echelon networks, see multi-echelon-inventory.
44inventory-optimization
When the user wants to optimize inventory levels, calculate safety stock, determine reorder points, or minimize inventory costs. Also use when the user mentions "inventory management," "safety stock," "EOQ," "reorder point," "service level," "stockout prevention," "ABC analysis," "inventory turns," or "working capital reduction." For warehouse slotting, see warehouse-slotting-optimization. For multi-echelon systems, see multi-echelon-inventory.
40supplier-selection
When the user wants to evaluate suppliers, select vendors, or perform supplier scoring and qualification. Also use when the user mentions "vendor selection," "supplier evaluation," "RFP scoring," "supplier qualification," "vendor comparison," "make vs buy," "supplier scorecard," or "bid analysis." For ongoing supplier risk monitoring, see supplier-risk-management. For contract negotiation, see contract-management.
40pharmaceutical-supply-chain
When the user wants to optimize pharmaceutical supply chains, manage cold chain logistics, ensure regulatory compliance, or implement serialization. Also use when the user mentions "pharma supply chain," "GMP compliance," "cold chain," "drug serialization," "clinical trials logistics," "pharmaceutical distribution," "good distribution practices," "GDP," "drug safety," or "pharmaceutical quality." For general healthcare, see hospital-logistics. For clinical trials specifically, see clinical-trial-logistics.
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