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Measuring the Quality of Intermittent-Demand Forecasts: ItÕs Worse than WeÕve Thought!

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  • Steve Morlidge

Abstract

In this eye-opening article, Steve Morlidge shows that when our demand histories are intermittent, we should rethink the use of our most common accuracy metrics for selecting a best forecast method. The problem is acute because many software applications use these metrics for performance evaluation and method selection; in doing so, they potentially provide us with poor feedback and inferior models, resulting in harmful consequences for inventory management. Copyright International Institute of Forecasters, 2015

Suggested Citation

  • Steve Morlidge, 2015. "Measuring the Quality of Intermittent-Demand Forecasts: ItÕs Worse than WeÕve Thought!," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 37, pages 37-42, Spring.
    • Handle: RePEc:for:ijafaa:y:2015:i:37:p:37-42
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    Cited by:

    1. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    2. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    3. Kolassa, Stephan, 2016. "Evaluating predictive count data distributions in retail sales forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 788-803.

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