Measuring accuracy requires choosing what to compare. A revenue forecast made in January and one made in November will score very differently against December actuals, so teams track error by forecast lag, such as accuracy at three months out. Bias matters as much as error size: a forecast that's always 5% low signals sandbagging, while one that's always high signals optimism baked into the process.
The practical use is improvement, not scorekeeping. Reviewing which lines missed and why, such as a driver assumption or a one-time event, tells finance where to change the model. Teams that don't measure accuracy at all can't tell whether their forecast process is getting better.
In software: Pigment and Anaplan let teams snapshot forecast versions and report error against actuals by period and lag. Anaplan's PlanIQ applies machine learning to generate baseline forecasts, and Prophix tracks versioned forecasts against actuals in its reporting layer.
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