Operations analytics
Supply-chain analytics should start with process decisions, not dashboard ideas.
Supply Chain Analytics maps practical analytics and AI use cases to planning, sourcing, production, risk, and delivery decisions.
- For supply-chain and operations professionals
- For managers prioritizing analytics and AI use cases
- For students and analysts learning process-first analytics
- Grounded in public GitHub teaching artifacts
What problem this topic solves
Supply-chain teams often face a long list of possible dashboards, forecasts, optimization ideas, and AI use cases. The practical challenge is prioritization: which use case is connected to a real process decision, which data is available, and which action would change after the analysis?
Planning
Forecast demand, compare scenarios, and make uncertainty visible before decisions are locked.
Production
Use bottleneck, capacity, and schedule signals to support operational trade-offs.
Risk
Identify fragile suppliers, routes, demand patterns, and process dependencies before disruption.
Delivery
Connect service-level, inventory, and logistics data to customer-facing performance.
Learning path and proof
Supply Chain AI Use-Case Map
Status: planned. This page does not promise a live product or download yet.