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What is forecast bias and how do I detect it?
Forecast bias is a consistent tendency to over-forecast or under-forecast. You detect it by summing signed errors (forecast minus actual) over many periods: if the total is persistently positive or negative rather than hovering around zero, the forecast is biased. Bias is often more costly than random error because it compounds in stock and cash.
Accuracy metrics like MAPE or MAE use absolute errors, so they cannot tell you the direction. A forecast can look reasonably accurate while always being too high.
Typical causes include sales targets influencing forecasts, optimism about promotions, ignoring stock-outs, and models trained on unrepresentative periods.
Track bias as a percentage of total actuals, by product group and by forecaster. Once identified it is often straightforward to correct, either by fixing the process or adjusting the model.
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