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).



Forecast Error





Opportunity: The Case for Using Fit and Error

















Understanding Forecast Fit



  1. Are the forecasts of good quality?

  2. 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













About the Author

Appendices



Appendix A



Appendix B



Appendix C



Appendix D



Appendix E

  • Enhance Your Experience.

    When you register for SDCExec.com you stay connected to the pulse of the industry by signing up for topic-based e-newsletters and information. Registering also allows you to quickly comment on content and request more infomation.

Already have an account? Click here to Log in.

Enhance Your Experience.

When you register for SDCExec.com you stay connected to the pulse of the industry by signing up for topic-based e-newsletters and information. Registering also allows you to quickly comment on content and request more infomation.

OR

Complete the registration form.

Required
Required
Required
Required
Required
Required
Required
Required
Required
Required
Required