IDEAS home Printed from https://ideas.repec.org/a/for/ijafaa/y2014i34p39-46.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://foresight.forecasters.org/shop/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wellens, Arnoud P. & Boute, Robert N. & Udenio, Maximiliano, 2024. "Simplifying tree-based methods for retail sales forecasting with explanatory variables," European Journal of Operational Research, Elsevier, vol. 314(2), pages 523-539.
    2. 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.
    3. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:for:ijafaa:y:2014:i:34:p:39-46. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Michael Gilliland (email available below). General contact details of provider: https://edirc.repec.org/data/iiforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.