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Forecast Quality in the Supply Chain

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  • Steve Morlidge

Abstract

Building on his two previous publications in Foresight on the measurement of forecastability, Steve shows how forecast quality can be objectively measured using the relative absolute error (RAE) metric and how this metric can be used to reveal the potential for improvements in forecast accuracy. He presents compelling evidence that many companies fail to achieve levels of relative error that are better than a simple ?same as last period? na‹ve forecast, and that around 50% of individual forecasts fail to meet this benchmark. He makes it clear that, while there is a great need for improvement in forecast quality, there is the potential for forecasters to accomplish just such improvement. Copyright International Institute of Forecasters, 2014

Suggested Citation

  • Steve Morlidge, 2014. "Forecast Quality in the Supply Chain," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 33, pages 26-31, Spring.
    • Handle: RePEc:for:ijafaa:y:2014:i:33:p:26-31
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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.

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