A sporting-goods manufacturer monitors weekly demand for hundreds of stock-keeping units (SKUs). The supply-chain manager asks you to build an analytical model that will
randomly generate thousands of possible demand scenarios for the next four weeks based on historical variation, and
for each scenario, determine the safety-stock level that minimizes expected lost-sales cost while staying within the warehouse's capacity. Your final deliverable must be a recommended reorder quantity for each SKU that management can act on immediately.
Which type of statistical/analytic method best satisfies this request?
The task goes beyond forecasting future demand; it also seeks the optimal reorder quantity that balances cost and capacity constraints. Methods that recommend an action-often by combining simulation (e.g., Monte Carlo) with optimization algorithms-fall under prescriptive analytics. Predictive analytics would stop at forecasting demand, descriptive analytics would simply summarize historical demand, and inferential statistics would test hypotheses about a population. Only prescriptive analytics is designed to recommend a specific course of action based on modeled scenarios and constraints.
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CompTIA Data+ DA0-002 (V2)
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