Diagnose Forecast Bias Before Replenishment
Work through Bias and inventory consequence with a concrete demand forecasting method.
Bias and inventory consequence. Current forecast: 120, 140, 160, 180 units. Actuals: 100, 0, 150, 220 units. A seasonal naive baseline for the same periods would have forecast 110, 20, 145, 190 units. Baseline comparison -> error metric -> bias check -> decision implication The common shortcut is to choose the number with the most familiar dashboard metric, especially MAPE. That is dangerous for intermittent or low-volume demand because percentage errors can explode near zero and reward behavior that does not actually improve replenishment decisions. Step 1 Compute absolute errors for the current forecast: 20, 140, 10, 40, for total…
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