Definition
Forecast accuracy compares forecast to actual, usually via Mean Absolute Percentage Error (MAPE) or Weighted MAPE. It should be measured at the level you plan at: monthly for capacity, daily for scheduling, and interval level for intraday.
Direction matters as much as magnitude, so report bias, the signed average error, alongside accuracy. A forecast that is 5% out in both directions is a variance problem; one that is consistently 5% low is a systematic problem you can correct.
Interval accuracy is always worse than daily, and daily worse than monthly, because errors offset when aggregated. Comparing an interval MAPE against a monthly benchmark makes a good forecast look terrible.
Why it matters
- Forecast accuracy sets the realistic ceiling on service level performance; no roster fixes a badly wrong forecast.
- Knowing your typical error range tells you how much staffing buffer or flexibility to build in.
- Measuring AHT accuracy separately from volume accuracy usually reveals that AHT drift, not volume, caused the miss.
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