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Forecast Fit vs. Forecast Error: Clarifying the Concepts, Understanding the Value
Anyone in a position to improve supply chain operations by influencing demand planning must understand the technical and functional implications of these terms
After years of working in supply chain operations — first in industry, then as a consultant helping corporations and clients better manage their demand planning processes and technologies — it's still surprising to hear so many demand planners misuse the terms fit and error.
The concepts are relatively straightforward, yet they're quite distinct, and using one in place of the other is incorrect. Anyone in a position to improve supply chain operations by influencing demand planning should understand the difference between fit and error and be mindful of both the technical and functional implications these terms have in the world of forecasting.
By Definition...
Forecast fit describes the relative difference between actual historical data and a hypothetical forecast generated by a statistical model (or algorithm) using that same historical data as input. It's quite literally a backward-looking assessment of how closely a forecast created by any one of various statistical models would stack up against — or "fit" when compared to — actual historical demand.
Planners use forecast fit to project the suitability of one or more statistical forecasting algorithms to accurately forecast future demand (see Figure 1).
Figure 1: Forecast Fit — After running statistical forecasting models using actual historical demand data as input, planners analyze the "fit" of the resulting fitted forecasts to determine which model will likely yield forecasts that most closely match past demand patterns.
Forecast Error
Forecast error is defined by APICS as "the difference between actual and forecast demand, stated as an absolute value or as a percentage." Forecast error is a postmortem benchmark of the variance between demand that was projected and actual demand that subsequently occurred (see Figure 2).
Figure 2: Forecast Error — The variance, expressed as an absolute value or a percentage, between forecast demand and actual demand.
The concepts are relatively straightforward, yet they're quite distinct, and using one in place of the other is incorrect. Anyone in a position to improve supply chain operations by influencing demand planning should understand the difference between fit and error and be mindful of both the technical and functional implications these terms have in the world of forecasting.
By Definition...
Forecast fit describes the relative difference between actual historical data and a hypothetical forecast generated by a statistical model (or algorithm) using that same historical data as input. It's quite literally a backward-looking assessment of how closely a forecast created by any one of various statistical models would stack up against — or "fit" when compared to — actual historical demand.
Planners use forecast fit to project the suitability of one or more statistical forecasting algorithms to accurately forecast future demand (see Figure 1).
Figure 1: Forecast Fit — After running statistical forecasting models using actual historical demand data as input, planners analyze the "fit" of the resulting fitted forecasts to determine which model will likely yield forecasts that most closely match past demand patterns.
Forecast Error
Forecast error is defined by APICS as "the difference between actual and forecast demand, stated as an absolute value or as a percentage." Forecast error is a postmortem benchmark of the variance between demand that was projected and actual demand that subsequently occurred (see Figure 2).
Figure 2: Forecast Error — The variance, expressed as an absolute value or a percentage, between forecast demand and actual demand.
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