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Supplier risk assessment based on trapezoidal intuitionistic fuzzy numbers and ELECTRE TRI-C: a case illustration involving service suppliers

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  • Kannan Govindan

    (Department of Business and Economics, University of Southern Denmark, Odense M-5230, Denmark)

  • Martin Brandt Jepsen

    (Department of Business and Economics, University of Southern Denmark, Odense M-5230, Denmark)

Abstract

Many companies today have embraced the concept of risk management, usually in the form of enterprise risk management or supply chain risk management. Both are based on a holistic view of risks. Hence, risks related to specific functions within a company must be considered more broadly than previously. Risks, however, involve uncertainty, and the less specific the context in which risks are viewed, the more uncertainty will be involved. One particular way to express uncertainty is through trapezoidal intuitionistic fuzzy numbers (TrIFNs). In this paper, risks that are relevant for supplier risk assessments are first collected from the literature. Then it is illustrated how the multi-criteria decision analysis method ELECTRE TRI-C can be used for sorting suppliers into risk categories, when the risks as well as some of the method’s parameters are expressed with TrIFNs. In order to do this, we make use of a small modification of an existing method for converting TrIFNs into crisp values. The approach is illustrated in a case problem based on a company that is looking for service providers (suppliers) of electrical maintenance. The problem involves 20 suppliers that are sorted into three risk categories based on evaluations from 27 criteria. Results from the case study point to two low risk suppliers. A further ad-hoc analysis suggests one of these to be less risky than the other.

Suggested Citation

  • Kannan Govindan & Martin Brandt Jepsen, 2016. "Supplier risk assessment based on trapezoidal intuitionistic fuzzy numbers and ELECTRE TRI-C: a case illustration involving service suppliers," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(2), pages 339-376, February.
  • Handle: RePEc:pal:jorsoc:v:67:y:2016:i:2:p:339-376
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    Citations

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    Cited by:

    1. Mir Seyed Mohammad Mohsen Emamat & Caroline Maria de Miranda Mota & Mohammad Reza Mehregan & Mohammad Reza Sadeghi Moghadam & Philippe Nemery, 2022. "Using ELECTRE-TRI and FlowSort methods in a stock portfolio selection context," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-35, December.
    2. Pelissari, Renata & Oliveira, Maria Célia & Ben Amor, Sarah & Abackerli, Alvaro José, 2019. "A new FlowSort-based method to deal with information imperfections in sorting decision-making problems," European Journal of Operational Research, Elsevier, vol. 276(1), pages 235-246.
    3. Qingyou Yan & Meijuan Zhang & Wei Li & Guangyu Qin, 2020. "Risk Assessment of New Energy Vehicle Supply Chain Based on Variable Weight Theory and Cloud Model: A Case Study in China," Sustainability, MDPI, vol. 12(8), pages 1-21, April.
    4. Tsan-Ming Choi & Kannan Govindan & Xiang Li & Yongjian Li, 2017. "Innovative supply chain optimization models with multiple uncertainty factors," Annals of Operations Research, Springer, vol. 257(1), pages 1-14, October.
    5. Lapko, Yulia & Trucco, Paolo & Nuur, Cali, 2016. "The business perspective on materials criticality: Evidence from manufacturers," Resources Policy, Elsevier, vol. 50(C), pages 93-107.

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