IDEAS home Printed from https://ideas.repec.org/a/igg/jban00/v4y2017i4p48-59.html
   My bibliography  Save this article

Supplier Performance Prediction for Future Collaboration: Based on Markov Chain Model

Author

Listed:
  • Mohammad Azadfallah

    (Saipayadak, Tehran, Iran)

Abstract

Today, long-term relationship plays a vital role in supplier selection for supply chain management. The main reason is that long-term relationships can act as a mechanism for shifting the chains strategic focus from price to value and priorities long-term benefit over short-term gains. Since, in this paper we tried to address a method for optimal long-term alternative prediction and selection, focusing on purchase volume factor. For this, Markov chain model had been used and the final result showed improved effectiveness.

Suggested Citation

  • Mohammad Azadfallah, 2017. "Supplier Performance Prediction for Future Collaboration: Based on Markov Chain Model," International Journal of Business Analytics (IJBAN), IGI Global, vol. 4(4), pages 48-59, October.
    • Handle: RePEc:igg:jban00:v:4:y:2017:i:4:p:48-59
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJBAN.2017100103
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Seyed Hossein Razavi Hajiagha & Jalil Heidary-Dahooie & Ieva Meidutė-Kavaliauskienė & Kannan Govindan, 2022. "A new dynamic multi-attribute decision making method based on Markov chain and linear assignment," Annals of Operations Research, Springer, vol. 315(1), pages 159-191, August.

    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:igg:jban00:v:4:y:2017:i:4:p:48-59. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.