Demand Forecast Review
Examines forecast error by direction rather than magnitude, since consistent bias is fixable and noise is not.
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Running it, start to finish
- Plot error by direction to establish bias against noise.
- Check who is incentivised for the number to be high or low.
- Bias deliberately towards whichever error is cheaper.
What you get back
The output this produces, every time.
- Separates correctable bias from irreducible noise, which need opposite responses.
- Examines who is rewarded for the forecast being high or low, which explains more error than method.
- Recommends deliberate bias towards the cheaper error, which is a legitimate decision.
Getting better results
Where this usually goes wrong, and how to avoid it.
- Separate the forecast from the target. The same person forecasts differently depending on what the number is used for, and combining the purposes corrupts both.
- Break error down by segment. Accuracy in aggregate can hide large offsetting errors, and operational decisions are made at segment level.
- Invest in responsiveness for noisy demand. Where demand is genuinely unpredictable, shorter lead times beat any amount of forecasting effort.
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Written for The AI University. Every prompt in this library is original work — authored, tested and revised here, not collected from elsewhere. 365 of them, free with an account.