The Application of a Multi-Attribute Group Decision Making Model Based on Linguistic Extended VIKOR for Quantifying Risks in a Supply Chain Under a Fuzzy Environment

Muhammad Nazam, Liming Yao, Muhammad Hashim, Sajjad Ahmad Baig and Muhammad Kashif Khan
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Muhammad Nazam: University of Agriculture, Faisalabad, Pakistan
Liming Yao: Sichuan University, Chengdu, China
Muhammad Hashim: National Textile University, Pakistan
Sajjad Ahmad Baig: National Textile University, Faisalabad, Pakistan
Muhammad Kashif Khan: Coventry University, UK

International Journal of Information Systems and Supply Chain Management (IJISSCM), 2020, vol. 13, issue 2, 27-46

Abstract: In this research, the quantification of supply chain risk has been investigated from the perspectives of the buyer. The objective of this research article is to investigate numerous risks that affect the aviation supply chain (ASC) performance and to develop a proposed framework to measure the overall supply chain risk index by considering the importance of each risk. Aviation industry is at higher level of risks, that's why the researchers have identified the most serious 10 supply chain risks (SCRs) in this industry. In order to tackle with this key problem, an indexing system for comprehensive risk evaluation has been developed, which captures the ratings and weights for risk factors, namely occurrence probability, severity of impact, and ease of mitigation, encountered by a holistic supply chain in a given scenario. In this context, to quantify the risks in a supply chain, the researchers have formulated as a fuzzy multi-attribute group decision making (FMAGDM) model, based on fuzzy FMEA and extended VIKOR methods to analyze the most feasible solution according to the selected risk parameters as per situation. The results of the present study depicted that disruption risk, financial risk, and legal/political regulations are the most significant and typical risk (ASC). A practical case study in an aviation sector of Pakistan is piloted to check the practicability of the suggested methodology. Finally, the effectiveness of the proposed model has been discussed by ranking risk alternatives in descending order. In order to check the robustness of results, a sensitivity analysis has been conducted.

Date: 2020
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