IDEAS home Printed from https://ideas.repec.org/a/ids/ijpmbe/v17y2024i1p109-135.html
   My bibliography  Save this article

Artificial intelligence of things and circular warehouse process management of automotive parts: conceptual framework and practice review

Author

Listed:
  • Asmae El Jaouhari
  • Jabir Arif

Abstract

In recent years, sustainable warehouse management strategies have been developed by businesses that desire to mitigate the adverse social and environmental effects within their warehouses. A circular method has been established in the warehouse management literature from this standpoint. The recycling process, knowledge, operational excellence and smart decision-making have all been enabled by circular economy models and solutions enabled by artificial intelligence and internet of things technologies. In this paper, an artificial intelligence of things (AIoT)-based circular warehouse management system (CWMS) is designed and tested within a real-world automotive supply chain to assess the sustainability performance of a CWMS for Industry 4.0. The results indicate that using AIoT technology to restructure the warehouse for a circular economy can enable CWMS. By connecting the proposed approach to the circular economy aspects of reuse, optimise, remove, recycle, and virtualise, clear benefits are provided. This study aims to enhance the current research by providing real proof of how AIoT and circular economy technology are used in practice.

Suggested Citation

  • Asmae El Jaouhari & Jabir Arif, 2024. "Artificial intelligence of things and circular warehouse process management of automotive parts: conceptual framework and practice review," International Journal of Process Management and Benchmarking, Inderscience Enterprises Ltd, vol. 17(1), pages 109-135.
    • Handle: RePEc:ids:ijpmbe:v:17:y:2024:i:1:p:109-135
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=137789
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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:ids:ijpmbe:v:17:y:2024:i:1:p:109-135. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=95 .

    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.