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.
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).
Opportunity: The Case for Using Fit and Error
Understanding Forecast Fit
- Are the forecasts of good quality?
- Considering the number of algorithms most packages offer, which will likely provide the most accurate forecasts going forward?
Understanding Forecast Error
Putting it All Together: Applying Fit and Error in the Real World
Common Measures of Error and Fit
- Mean Squared Error (MSE)
- Standard Error (SE)
- Coefficient of Determination or R-Squared (R2)
- Mean Absolute Deviation (MAD) or Mean Absolute Error (MAE)
- Mean Absolute Percentage Error (MAPE)
- Symmetric Mean Absolute Percentage Error (SMAPE)
Improved Forecasting: The Benefits of Fit and Error
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