IDEAS home Printed from https://ideas.repec.org/h/spr/lnopch/978-3-032-14489-8_9.html

Generative Artificial Intelligence for Reverse Logistics and Closed-Loop Supply Chains

In: Proceedings of the International Conference on Industrial Logistics (ICIL) 2025

Author

Listed:
  • Gerald Schneikart

    (Institute for Digital Transformation and Strategy, FHWien der WKW)

  • Walter Mayrhofer

    (Institute for Digital Transformation and Strategy, FHWien der WKW)

Abstract

Generative artificial intelligence (gen. AI) is quickly improving its capabilities and constantly extends its areas of application, one of which is logistics. Major issues in logistics are a high environmental burden due to CO2 emissions and waste production from one-way transport items, e.g., cardboard-based boxes. While environmentally friendly circular economy solutions based on returnable transport items (RTI) exist, the adoption of suitable reverse logistics processes is sluggish. There is considerable potential to expedite a transition to RTI-based logistics, using gen. AI tools for planning and data analysis. This review paper provides an overview of the most recent scientific publications about the application of gen. AI in reverse logistics and closed-loop supply chains. It will identify the most promising gen. AI use cases for further consideration in research and development and provides a critical assessment of their potential impact.

Suggested Citation

  • Gerald Schneikart & Walter Mayrhofer, 2026. "Generative Artificial Intelligence for Reverse Logistics and Closed-Loop Supply Chains," Lecture Notes in Operations Research, in: U. Aytun Ozturk & Petri T. Helo (ed.), Proceedings of the International Conference on Industrial Logistics (ICIL) 2025, pages 86-93, Springer.
    • Handle: RePEc:spr:lnopch:978-3-032-14489-8_9
    DOI: 10.1007/978-3-032-14489-8_9
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;

    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:spr:lnopch:978-3-032-14489-8_9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.