IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v60y2022i22p6682-6690.html
   My bibliography  Save this article

Big data analytics in production and distribution management

Author

Listed:
  • Yunqiang Yin
  • Feng Chu
  • Alexandre Dolgui
  • T.C.E. Cheng
  • M.C. Zhou

Abstract

Production and distribution are two key constituents of a supply chain. In view of the growing availability of data and advances in big data analytics techniques, there have been more and more applications of data analytics to deal with the problems in production and distribution management. With this in mind, we proposed a special issue on ‘Big Data Analytics in Production and Distribution Management' to report the latest development in this field. In this editorial, we first introduce the background and examine the existing review works on the applications of data analytics to operations management. We then introduce the papers accepted in the issue, and discuss how different types of big data analytics techniques are applied to production and distribution management, including demand forecasting, production scheduling, distribution management, manufacturing management, and supply chain management. Finally, we conclude the paper with a discussion of future research.

Suggested Citation

  • Yunqiang Yin & Feng Chu & Alexandre Dolgui & T.C.E. Cheng & M.C. Zhou, 2022. "Big data analytics in production and distribution management," International Journal of Production Research, Taylor & Francis Journals, vol. 60(22), pages 6682-6690, November.
    • Handle: RePEc:taf:tprsxx:v:60:y:2022:i:22:p:6682-6690
    DOI: 10.1080/00207543.2022.2130589
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2022.2130589
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2022.2130589?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. Akhtar, Pervaiz & Ghouri, Arsalan Mujahid & Ashraf, Aniqa & Lim, Jia Jia & Khan, Naveed R & Ma, Shuang, 2024. "Smart product platforming powered by AI and generative AI: Personalization for the circular economy," International Journal of Production Economics, Elsevier, vol. 273(C).
    2. Kwabena Abrokwah-Larbi, 2024. "The nexus between customer value analytics and SME performance in emerging market: a resource-based view perspective," Journal of Global Entrepreneurship Research, Springer;UNESCO Chair in Entrepreneurship, vol. 14(1), pages 1-20, December.

    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:60:y:2022:i:22:p:6682-6690. 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.