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Integration of analytic hierarchy process and Dempster-Shafer theory for supplier performance measurement considering risk

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  • Kunal Ganguly

Abstract

Purpose - – The purpose of this paper is to provide proactive supply chain performance method considering risk which can be used during the supplier selection/assessment process. Design/methodology/approach - – In this paper, the effort is to present a model for evaluating the supply-related risk, which is based on the analytic hierarchy process (AHP) method and the Dempster-Shafer theory (DST). The proactive risk management methods used in this research is: seeking risk sources and identifying the variables to be used in the model, preprocessing the variables data to get the directions of the variables and the risk bounds, assigning variables weights via AHP method and finally evaluating the supply risk via DST method and determine the final risk degree. Findings - – The paper contributes to research in risk assessment in the specific field of supplier performance measurement. In this paper, a hybrid model using AHP and DST for risk assessment of supplier based on performance measurement is presented. An empirical analysis is conducted to illustrate the use of the model for the risk assessment in supply chain. Research limitations/implications - – This methodology can be adopted by supply chain managers to evaluate the level of risk associated with current suppliers, and to assist them in making outsourcing decisions. Originality/value - – The proposed method makes a contribution by including risk as a performance measure in supply chain. The generated proactive supply risk assessment process uses a hybrid model of AHP and DST providing a novel approach for performance measurement which will be valuable both to academics and practitioners in this field.

Suggested Citation

  • Kunal Ganguly, 2014. "Integration of analytic hierarchy process and Dempster-Shafer theory for supplier performance measurement considering risk," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 63(1), pages 85-102, January.
  • Handle: RePEc:eme:ijppmp:v:63:y:2014:i:1:p:85-102
    DOI: 10.1108/IJPPM-10-2012-0117
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    Cited by:

    1. Qazi, Abroon & Dickson, Alex & Quigley, John & Gaudenzi, Barbara, 2018. "Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks," International Journal of Production Economics, Elsevier, vol. 196(C), pages 24-42.

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