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Using Relative Error Metrics to Improve Forecast Quality in the Supply Chain

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  • Steve Morlidge

Abstract

How can we identify our best opportunities to improve forecast accuracy? Steve Morlidge concludes his four-part Foresight series on forecast quality by offering an approach based on (a) product volumes and variability, and (b) a forecastability metric that assesses forecast accuracy in relation to the accuracy of a na•ve (i.e., no change) forecast. The metric helps supply-chain forecasters set meaningful targets for improvement, quantifies the scope for improvement, and tracks progress toward final goals. Copyright International Institute of Forecasters, 2014

Suggested Citation

  • Steve Morlidge, 2014. "Using Relative Error Metrics to Improve Forecast Quality in the Supply Chain," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 34, pages 39-46, Summer.
    • Handle: RePEc:for:ijafaa:y:2014:i:34:p:39-46
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    Cited by:

    1. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Paul Goodwin & Fotios Petropoulos & Rob J. Hyndman, 2017. "A note on upper bounds for forecast-value-added relative to naïve forecasts," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(9), pages 1082-1084, September.

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