IDEAS home Printed from https://ideas.repec.org/a/alk/jcsts0/v7y2025i2id9277.html

AI-Driven Enterprise Supply Chain Intelligence: A Technical Deep Dive

Author

Listed:
  • Karthikeyan Selvarajan

    (University of Illinois Urbana-Champaign, USA)

Abstract

This article explores the transformative impact of AI-powered data platforms on enterprise supply chain management, focusing on architecture, implementation strategies, and performance optimization. The article examines how modern enterprises are leveraging artificial intelligence to enhance their supply chain operations through advanced analytics, cloud integration, and machine learning capabilities. The article presents a comprehensive analysis of key technical components, including real-time data processing, predictive analytics, and security frameworks, while evaluating their effectiveness in improving operational efficiency and decision-making processes. Through examination of implementation cases and performance metrics, the article demonstrates how AI-driven platforms are revolutionizing supply chain optimization, risk management, and compliance monitoring across various industries.

Suggested Citation

  • Karthikeyan Selvarajan, 2025. "AI-Driven Enterprise Supply Chain Intelligence: A Technical Deep Dive," Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 7(2), pages 612-617, March.
    • Handle: RePEc:alk:jcsts0:v:7:y:2025:i:2:id:9277
    DOI: 10.32996/jcsts.2025.7.2.66
    as

    Download full text from publisher

    File URL: https://al-kindipublisher.com/index.php/jcsts/article/view/9277
    File Function: Abstract page
    Download Restriction: no
    File URL: https://al-kindipublisher.com/index.php/jcsts/article/download/9277/7939
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32996/jcsts.2025.7.2.66?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
    ---><---

    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:alk:jcsts0:v:7:y:2025:i:2:id:9277. 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: Al-Kindi Center for Research and Development (email available below). General contact details of provider: https://al-kindipublisher.com/index.php/jcsts .

    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.