IDEAS home Printed from https://ideas.repec.org/a/aac/ijirss/v8y2025i3p4571-4578id7551.html
   My bibliography  Save this article

Enhancing supply chain resilience with artificial intelligence a bibliometric analysis and systematic literature review

Author

Listed:
  • Thi Huong Tran
  • Thuc Anh Nguyen
  • Hong Quan Do
  • Sebastian Kummer

Abstract

Among many innovative technical solutions, Artificial Intelligence (AI) is considered a promising approach for fostering resilient supply chains. This study aims to investigate how AI enhances Supply Chain Resilience (SCRES) by identifying AI applications in supply chain contexts, evaluating their impacts on SCRES, and uncovering gaps and emerging trends for future research. We adopted the PRISMA procedure to collect appropriate papers and then employed VOS Viewer to uncover key insights within scholarly literature. After that, selected papers underwent systematic content analysis to synthesize key concepts and applications as well as valuable aspects of AI application in fostering SCRES. The AI-SCRES relationship is an emerging field, with a notable increase in publications, particularly driven by unprecedented events like the COVID-19 pandemic. Findings showed 11 AI techniques frequently mentioned in the literature for SCRES enhancement. Bayesian networks emerged as the most discussed and mature, followed by artificial neural networks and genetic algorithms. These technologies are predominantly used for risk prediction, automated reasoning, optimization, and decision support. While AI offers substantial benefits such as enhanced decision support and demand forecasting, it also brings challenges like the need for highly skilled personnel, investment costs, and data-related risks.

Suggested Citation

  • Thi Huong Tran & Thuc Anh Nguyen & Hong Quan Do & Sebastian Kummer, 2025. "Enhancing supply chain resilience with artificial intelligence a bibliometric analysis and systematic literature review," International Journal of Innovative Research and Scientific Studies, Innovative Research Publishing, vol. 8(3), pages 4571-4578.
    • Handle: RePEc:aac:ijirss:v:8:y:2025:i:3:p:4571-4578:id:7551
    as

    Download full text from publisher

    File URL: https://ijirss.com/index.php/ijirss/article/view/7551/1626
    Download Restriction: no
    ---><---

    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:aac:ijirss:v:8:y:2025:i:3:p:4571-4578:id:7551. 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: Natalie Jean (email available below). General contact details of provider: https://ijirss.com/index.php/ijirss/ .

    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.