IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v280y2025ics0925527324003542.html
   My bibliography  Save this article

Application of analytics in food retailing to improve online order picking time estimations

Author

Listed:
  • Vazquez-Noguerol, Mar
  • Prado-Prado, J. Carlos

Abstract

Offering a good online grocery shopping service is becoming an increasingly crucial aspect for food retailers due to the highly competitive nature of this sector. To avoid large investments, many supermarket chains have adopted an in-store strategy, where online orders are picked in traditional stores. In order to successfully implement this omnichannel strategy, retailers need to plan shared organisational structures and methods correctly. The purpose of this study is to determine the extent to which certain characteristics of store and online orders influence the order picking time. Through an empirical analysis conducted in a Spanish e-grocer, a multiple linear regression model is proposed to estimate online order picking time. The research method proposed herein enables e-grocers to plan fulfilment operations, organise resources properly, and define and adjust their capacity to respond efficiently to market demand. We present an innovative research application that deeply integrates analytics into retail operations to improve industry practice and strengthen the theoretical foundation. Our contribution serves to support and evaluate the informed decision making process along the supply chain by optimising the use of resources and facilitating more efficient online order picking scheduling.

Suggested Citation

  • Vazquez-Noguerol, Mar & Prado-Prado, J. Carlos, 2025. "Application of analytics in food retailing to improve online order picking time estimations," International Journal of Production Economics, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:proeco:v:280:y:2025:i:c:s0925527324003542
    DOI: 10.1016/j.ijpe.2024.109497
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527324003542
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2024.109497?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:eee:proeco:v:280:y:2025:i:c:s0925527324003542. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    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.