IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-031-94046-0_6.html
   My bibliography  Save this book chapter

Industrial AI-Driven Support to OT Logistics

In: Artificial Intelligence for Logistics 5.0

Author

Listed:
  • Bernardo Nicoletti

    (Temple University)

Abstract

This chapter focuses on the integration of AI and FMs in industrial logistics. It highlights the pivotal role of OT in monitoring and controlling physical processes, tracing its evolution from isolated systems to integrated enterprise networks. Integrating AI with OT revolutionizes warehouse automation and transportation systems, equipping them with advanced capabilities in predictive maintenance, resource optimization, and automated decision-making. The implementation framework centers on four key components: AI-driven digital twins, Industrial Internet of Things (IIoT) integration, natural language interfaces, and comprehensive data analytics [Möller et al., 2021 IEEE International Conference on Electro Information Solutions (EIT) (pp. 413–418). IEEE. https://doi.org/10.1109/EIT51626.2021.9491874 (2021)]. This framework enables real-time monitoring, control, and optimization of logistics processes. The chapter also addresses the significant challenges in cyber security and underscores the importance of protecting against data breaches, hostile attacks, and logistics vulnerabilities. It outlines strategic approaches to implementing FMs in organizations and emphasizes the importance of ethical leadership, change management, and ongoing maintenance. It recommends a hybrid approach that combines proprietary and public data for optimal model training. Implementing AI in logistics requires careful planning, significant investment in solutions and skilled personnel, and robust cybersecurity measures to protect sensitive operational data.

Suggested Citation

  • Bernardo Nicoletti, 2025. "Industrial AI-Driven Support to OT Logistics," Springer Books, in: Artificial Intelligence for Logistics 5.0, chapter 0, pages 163-177, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-94046-0_6
    DOI: 10.1007/978-3-031-94046-0_6
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:sprchp:978-3-031-94046-0_6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.