IDEAS home Printed from https://ideas.repec.org/a/for/ijafaa/y2019i55p10-18.html
   My bibliography  Save this article

Forecasting at Scale: The Architecture of a Modern Retail Forecasting System

Author

Listed:
  • Phillip Yelland
  • Zeynep Erkin Baz
  • David Serafini

Abstract

In this first of a three-part article, Phillip Yelland, Zeynep Erkin Baz, and David Serafini, technical leads in the Data Science/AI team at Target, describe their team's efforts to construct a demand forecasting system capable of efficiently generating the nearly one billion weekly forecasts required by the Target Corporation. They highlight the interplay of challenges arising in the contexts of statistical modeling, software engineering, and business practice and explain how the team surmounted obstacles in these three fields of knowledge. Subsequent parts of the article will address the process of implementing the forecasting system and its maintenance in production.

Suggested Citation

  • Phillip Yelland & Zeynep Erkin Baz & David Serafini, 2019. "Forecasting at Scale: The Architecture of a Modern Retail Forecasting System," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 55, pages 10-18, Fall.
    • Handle: RePEc:for:ijafaa:y:2019:i:55:p:10-18
    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. Hoeltgebaum, Henrique & Borenstein, Denis & Fernandes, Cristiano & Veiga, Álvaro, 2021. "A score-driven model of short-term demand forecasting for retail distribution centers," Journal of Retailing, Elsevier, vol. 97(4), pages 715-725.
    4. Kolassa, Stephan, 2022. "Commentary on the M5 forecasting competition," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1562-1568.

    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:2019:i:55:p:10-18. 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.