Taming Intermittent Demand Forecasting With AI

A Turkish automotive spare-parts case study shows how intermittent and lumpy demand can be tamed with AI. We compare the old cross-method approach with exponential smoothing to an ensemble of models, including RNNs, and a linear-regression meta-learner that blends their forecasts. The result: dramatically reduced inventory costs and fewer shortages, offering a glimpse into a future of anticipatory logistics.

Note:  This podcast was AI-generated, and sometimes AI can make mistakes.  Please double-check any critical information.

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