National University 3.0 credits
Prerequisite: MGT 451 End‑to‑end overview of supply chain strategy and processes (plan–source–make–deliver–return) with an applied Python lab each week. Students build small analytics artifacts, cleaning transactional data, descriptive KPI dashboards, baseline forecasting (moving average, exponential smoothing), and a mini ML forecast using scikit‑learn/Prophet. Coverage includes demand shaping vs. demand sensing, service‑cost trade-offs, digital thread & data pipelines, and responsible use of AI in planning. Culminates in a “rapid response” simulation where students use data to rebalance service, cost, and working capital.
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