IDEAS home Printed from https://ideas.repec.org/a/ids/ijmtma/v37y2023i3-4p315-333.html
   My bibliography  Save this article

A performance evaluation of supply chain management based on factor clustering analysis

Author

Listed:
  • Yehui Dong
  • Jiawei Gao
  • Shanyin Yao

Abstract

In order to overcome the problems of low significance coefficient and low evaluation accuracy of current methods, a performance evaluation method of supply chain management based on factor clustering analysis was proposed. Firstly, SPSS software is used to standardise the data of influencing factors of supply chain management performance, and the factors that are not suitable for factor molecules are removed. Then, a factor analysis model was built, and the evaluated eigenvalue, eigenvalue contribution and cumulative contribution after model rotation were calculated. The clustering analysis of multiple common factors and the distance between samples were combined with the contribution calculation results. Finally, the correlation between supply chain management performance evaluation indicators is analysed, the weight of evaluation indicators is calculated, and the performance evaluation results are obtained. Experimental results show that the significance coefficient of the proposed method is always higher than 0.8, and the accuracy is up to 98%.

Suggested Citation

  • Yehui Dong & Jiawei Gao & Shanyin Yao, 2023. "A performance evaluation of supply chain management based on factor clustering analysis," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 37(3/4), pages 315-333.
    • Handle: RePEc:ids:ijmtma:v:37:y:2023:i:3/4:p:315-333
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=133473
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    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:ids:ijmtma:v:37:y:2023:i:3/4:p:315-333. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=21 .

    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.