IDEAS home Printed from https://ideas.repec.org/a/aac/ijirss/v8y2025i5p2083-2096id9431.html
   My bibliography  Save this article

The impact of AI-powered robotics and workforce dynamics on productivity in the logistics industry

Author

Listed:
  • Usha A/P Periasamy
  • Abdul Rahman Bin S Senathirajah
  • Karim Soliman
  • Rasheedul Haque
  • Tippawan Lertatthakornkit

Abstract

This study examines the impact of AI-powered robotics and workforce dynamics on productivity in the logistics industry, drawing on insights from existing literature. The logistics sector is well-suited for AI integration due to its reliance on repetitive tasks, supply chain efficiency, and operational scalability. Early findings suggest that AI-driven robotics can enhance productivity by improving inventory management accuracy, streamlining transportation, and reducing operational costs. However, workforce dynamics also play a crucial role, as employees must adapt to new technological roles, acquire new skills, and collaborate with robotic systems, which can impact job satisfaction, motivation, and overall efficiency. Additionally, employee adaptability acts as a moderating factor, determining how effectively companies can leverage AI technologies to boost productivity. This literature review highlights the need for further empirical research to validate these relationships and provide actionable insights for logistics companies aiming to optimize operations, enhance human-robot interactions, and improve productivity outcomes.

Suggested Citation

  • Usha A/P Periasamy & Abdul Rahman Bin S Senathirajah & Karim Soliman & Rasheedul Haque & Tippawan Lertatthakornkit, 2025. "The impact of AI-powered robotics and workforce dynamics on productivity in the logistics industry," International Journal of Innovative Research and Scientific Studies, Innovative Research Publishing, vol. 8(5), pages 2083-2096.
  • Handle: RePEc:aac:ijirss:v:8:y:2025:i:5:p:2083-2096:id:9431
    as

    Download full text from publisher

    File URL: https://ijirss.com/index.php/ijirss/article/view/9431/2107
    Download Restriction: no
    ---><---

    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:aac:ijirss:v:8:y:2025:i:5:p:2083-2096:id:9431. 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.