IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-124-1_58.html

Application of Machine Learning in Supply Chain Management

In: Proceedings of the 2022 3rd International Conference on Big Data Economy and Information Management (BDEIM 2022)

Author

Listed:
  • Jiaming Luo

    (Southwestern University of Finance and Economics)

Abstract

With the continuous development of information technology, machine learning, and other artificial intelligence technology has gradually developed and perfected. Supply chain management is an important link in business, its importance is self-evident. Supply chain management is to make the supply chain operation achieve optimization, with the least cost, so that the supply chain from procurement to meet the final customer all the process. It is closely connected with China’s economy and society and develops rapidly. This article will explore the convergence of machine learning techniques and supply chain management. After reviews of machine learning techniques, this paper introduces several commonly used machine learning techniques, and then studies the application of support vector machines and decision trees in the field of supply chain management, and enumerates the corresponding successful cases. Finally, the possible future development direction of machine learning technology is proposed. In this paper, the machine learning technology and its application are summarized and the future development of this technology prospects.

Suggested Citation

  • Jiaming Luo, 2023. "Application of Machine Learning in Supply Chain Management," Advances in Economics, Business and Management Research, in: Seifedine Kadry & Yingchen Yan & Junjie Xia (ed.), Proceedings of the 2022 3rd International Conference on Big Data Economy and Information Management (BDEIM 2022), pages 489-498, Springer.
  • Handle: RePEc:spr:advbcp:978-94-6463-124-1_58
    DOI: 10.2991/978-94-6463-124-1_58
    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
    for a similarly titled item that would be available.

    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:spr:advbcp:978-94-6463-124-1_58. 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.