IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v201y2025ics1366554525002479.html
   My bibliography  Save this article

Prescriptive analytics for sustainable supply chain operations: The PASO framework for Industry 5.0

Author

Listed:
  • Choi, Tsan-Ming
  • Sun, Xuting

Abstract

Today, in supply chain operations, achieving the goal of sustainability with the use of data is critical. In the data analytics era, both companies and non-profit making organizations are extensively using data to improve decision making. As a critical stage of data analytics, to improve decision making for the unforeseeable future, prescriptive analytics aims to provide the advanced data-driven scientific decision supporting models for real world applications in supply chain operations. In this paper, we propose a novel “prescriptive analytics for sustainable operations” (PASO) framework for Industry 5.0 to provide academics and industrialists with the guidance on how to make the proper use of prescriptive analytics for sustainable supply chain operations. We start by defining the role of prescriptive analytics as the ultimate stage of data analytics. Then, we select a few important prior studies and critically examine how prescriptive analytics has been implemented. Combining the findings from the literature and some observed real-world practices, we propose and construct the PASO framework, which highlights the importance of people welfare, in addition to enjoying efficiency improvement with technologies. Finally, we establish a future research agenda.

Suggested Citation

  • Choi, Tsan-Ming & Sun, Xuting, 2025. "Prescriptive analytics for sustainable supply chain operations: The PASO framework for Industry 5.0," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 201(C).
    • Handle: RePEc:eee:transe:v:201:y:2025:i:c:s1366554525002479
    DOI: 10.1016/j.tre.2025.104206
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554525002479
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2025.104206?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:transe:v:201:y:2025:i:c:s1366554525002479. 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/wps/find/journaldescription.cws_home/600244/description#description .

    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.