Operations & Supply Chain AI Consultant
Open a video or resource link, watch it, then check its box to track your progress and unlock what comes next.
- Model demand, uncertainty and inventory/capacity tradeoffs
- Optimize networks and test scenarios before changing operations
- Deploy agentic execution and governed operational decision systems
Benchmark: Amazon, Walmart and Apple supply-chain / applied-AI role signals (2026 sample) Mastery ladder: 2 job-market competencies per level; 2 direct VIDEO bridges per competency (12 total).
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Model demand, uncertainty and inventory/capacity tradeoffs
Forecasting, uncertainty and decision support
Coverage: Causal Inference, Forecasting & Decision Science
Inventory, capacity and cost-to-serve decisions
Coverage: Operations Management & Operating Models
Optimize networks and test scenarios before changing operations
Optimization, simulation and scenario modeling
Coverage: Operations Research, Simulation & Decision Science
Process mining and bottleneck diagnosis
Coverage: Operational Excellence Analytics: Root Cause, SPC & Process Mining
Deploy agentic execution and governed operational decision systems
Agentic planning, procurement and exception workflows
Coverage: AI Agents
Workflow controls, lineage and decision-system governance
Coverage: Workflow Automation & Data Engineering
Role-Readiness Proof / Capstone
Build an AI-enabled supply-chain decision and execution system: forecast demand with uncertainty, model inventory/capacity and cost-to-serve, run optimization/simulation scenarios, diagnose a process bottleneck, trigger an agentic or approval workflow, document lineage/controls, and quantify service, working-capital, cost and resilience impact.
Proof Rubric
- Explain the operating decision clearly
- apply forecasting, optimization and simulation to realistic supply-chain data
- compare service/cost/capital/risk tradeoffs
- show workflow, lineage and control evidence
- quantify measurable operational impact