IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v62y2024i16p5676-5696.html
   My bibliography  Save this article

ChatGPT and generative artificial intelligence: an exploratory study of key benefits and challenges in operations and supply chain management

Author

Listed:
  • Samuel Fosso Wamba
  • Cameron Guthrie
  • Maciel M. Queiroz
  • Stefan Minner

Abstract

ChatGPT and generative artificial intelligence (Gen-AI) are transforming firms and supply chains. However, the empirical literature reporting the benefits, challenges, and outlook of these nascent technologies in operations and supply chain management (OSCM) is limited. This study surveys current projects and perceptions of these technologies in US (n = 119) and UK (n = 181) supply chains. We found that projects range from proof-of-concept to full implementation, with a main focus on operational gains, such as improved customer satisfaction, cost minimisation, and process efficiencies. The main challenges concern data, technological and organisational issues. Expected benefits are dominated by cost savings and enhanced customer experience, but also include increased automation and sustainability. Industries were found to cluster around six groups according to perceived benefits and implementation challenges. Our findings contribute to the emerging literature on Gen-AI use in OSCM, and to management practice by mapping the benefits, challenges, outlook, and maturity level of Gen-AI projects in supply chains.

Suggested Citation

  • Samuel Fosso Wamba & Cameron Guthrie & Maciel M. Queiroz & Stefan Minner, 2024. "ChatGPT and generative artificial intelligence: an exploratory study of key benefits and challenges in operations and supply chain management," International Journal of Production Research, Taylor & Francis Journals, vol. 62(16), pages 5676-5696, August.
    • Handle: RePEc:taf:tprsxx:v:62:y:2024:i:16:p:5676-5696
    DOI: 10.1080/00207543.2023.2294116
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2023.2294116
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2023.2294116?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.

    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:taf:tprsxx:v:62:y:2024:i:16:p:5676-5696. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    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.