IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v10y2024i6id423.html

AI-Driven Incident Management in Retail : A Case Study

Author

Listed:
  • Manpreet Singh Sachdeva

Abstract

This article examines the strategic implementation of artificial intelligence to transform incident management processes and enhance operational efficiency in e-commerce operations. In response to challenges including cart abandonment, system downtimes, slow incident response, and inconsistent customer support, the organization developed a comprehensive AI-driven approach encompassing advanced monitoring, predictive maintenance, and automated incident resolution. The implementation revolutionized the technical infrastructure through machine learning algorithms, natural language processing, and advanced analytics, significantly improving system reliability, customer satisfaction, and operational efficiency. This transformation addressed immediate operational challenges and established new benchmarks for the retail industry, demonstrating the transformative potential of AI in modern e-commerce operations while providing valuable insights for organizations seeking to enhance their incident management capabilities through technological innovation.

Suggested Citation

  • Manpreet Singh Sachdeva, 2024. "AI-Driven Incident Management in Retail : A Case Study," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10(6), pages 355-363, November.
    • Handle: RePEc:jbh:ijsrcs:v10:y2024:i6:id:423
    DOI: 10.32628/CSEIT24106182
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT24106182
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/home/article/view/CSEIT24106182
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsrcseit.com/home/article/download/CSEIT24106182/CSEIT24106182
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32628/CSEIT24106182?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:jbh:ijsrcs:v10:y2024:i6:id:423. 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: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .

    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.