IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v61y2023i12p4004-4021.html
   My bibliography  Save this article

Internet of things (IoT) and big data analytics (BDA) for digital manufacturing (DM)

Author

Listed:
  • Zhuming Bi
  • Yan Jin
  • Paul Maropoulos
  • Wen-Jun Zhang
  • Lihui Wang

Abstract

This paper aims to investigate the impact of enterprise architecture (EA) on system capabilities in dealing with changes and uncertainties in globalised business environments. Enterprise information systems are viewed as information systems to acquire, process, and utilise data in decision-making supports at all levels and domains of businesses, and Internet of things (IoT), big data analytics (BDA), and digital manufacturing (DM) are introduced as representative enabling technologies for data collection, processing, and utilisation in manufacturing applications. The historical development of manufacturing technologies is examined to understand the evolution of system paradigms. The Shannon entropy is adopted to measure the complexity of systems and illustrate the roles of EAs in managing system complexity and achieving system stability in the long term. It is our argument that existing EAs sacrifice system flexibility, resilience, and adaptability for the reduction of system complexity; note that higher adaptability is critical to make a manufacturing system successfully. New EA is proposed to maximise system capabilities for higher flexibility, resilience, and adaptability. The potentials of the proposed EA to modern manufacturing are explored to identify critical research topics with illustrative examples from an application perspective.

Suggested Citation

  • Zhuming Bi & Yan Jin & Paul Maropoulos & Wen-Jun Zhang & Lihui Wang, 2023. "Internet of things (IoT) and big data analytics (BDA) for digital manufacturing (DM)," International Journal of Production Research, Taylor & Francis Journals, vol. 61(12), pages 4004-4021, June.
  • Handle: RePEc:taf:tprsxx:v:61:y:2023:i:12:p:4004-4021
    DOI: 10.1080/00207543.2021.1953181
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2021.1953181
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00207543.2021.1953181?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Abderahman Rejeb & Karim Rejeb & Imen Zrelli, 2024. "Analyzing Barriers to Internet of Things (IoT) Adoption in Humanitarian Logistics: An ISM–DEMATEL Approach," Logistics, MDPI, vol. 8(2), pages 1-27, April.
    2. Qingyu Zhang & Aman Ullah & Sana Ashraf & Muhammad Abdullah, 2024. "Synergistic Impact of Internet of Things and Big-Data-Driven Supply Chain on Sustainable Firm Performance," Sustainability, MDPI, vol. 16(13), pages 1-20, July.
    3. Xuanzhu Sheng & Chao Yu & Yang Zhou & Xiaolong Cui, 2024. "Reputation-Driven Asynchronous Federated Learning for Optimizing Communication Efficiency in Big Data Labeling Systems," Mathematics, MDPI, vol. 12(18), pages 1-27, September.
    4. Chang, Qing & Wu, Mengtao & Zhang, Longtian, 2024. "Endogenous growth and human capital accumulation in a data economy," Structural Change and Economic Dynamics, Elsevier, vol. 69(C), pages 298-312.

    More about this item

    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:taf:tprsxx:v:61:y:2023:i:12:p:4004-4021. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    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.