IDEAS home Printed from https://ideas.repec.org/a/hin/jnddns/1223577.html
   My bibliography  Save this article

Study on Performance Evaluation of Service Supply Chain by Extension Method

Author

Listed:
  • Jun Cheng
  • Qiaoning Yang
  • Lin Lu
  • Tingsong Wang

Abstract

With the rapid development of the service industry, the service supply chain has become one of the most popular areas. With the logistics service supply chain being an important branch of the service supply chain, we should pay attention to the role of logistics service supply chain in enterprises. How to evaluate the logistics service supply chain comprehensively and rationally has become an important task for related enterprises to solve urgently. This paper, through extensive literature analysis and research on logistics service supply chain projects, established an indicator system composed of 18 secondary indicators from three perspectives of customer satisfaction, logistics service capabilities, and co-development ability, using the analytic hierarchy process (AHP) to determine the weight of each index and matter-element analysis to establish a logistics service supply chain performance evaluation model. At the end of the paper, the performance of S logistics enterprise is evaluated. The result turns out to be good, which is consistent with the actual situation, indicating that the evaluation system and method are effective and reliable.

Suggested Citation

  • Jun Cheng & Qiaoning Yang & Lin Lu & Tingsong Wang, 2021. "Study on Performance Evaluation of Service Supply Chain by Extension Method," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-11, October.
  • Handle: RePEc:hin:jnddns:1223577
    DOI: 10.1155/2021/1223577
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/ddns/2021/1223577.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/ddns/2021/1223577.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2021/1223577?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
    ---><---

    Citations

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


    Cited by:

    1. Luliang Liu & Yuanming Dou & Jiangang Qiao, 2022. "Evaluation Method of Highway Plant Slope Based on Rough Set Theory and Analytic Hierarchy Process: A Case Study in Taihang Mountain, Hebei, China," Mathematics, MDPI, vol. 10(8), pages 1-17, April.

    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:hin:jnddns:1223577. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.