Multi-criteria analysis of supply chain risk management using interval valued fuzzy TOPSIS

The global supply chain in the past decade is relying heavily on the outsourced supply chain partners for competitive edge. Increased supplier risk vulnerability has lead firms to give more weightage to purchasing function and its associated decision makers. Hence determining which supplier to include in supply chain has become the key strategic consideration. Supplier selection problem requires a trade-off between multiple criteria exhibiting ambiguity and vagueness with the involvement of a group of decision makers. This paper formulates a multiple criteria decision making problem with linguistic variables and their transformation to interval valued fuzzy numbers. By appropriate extension of technique for order of preference by similarity to ideal solution (TOPSIS) method with interval valued fuzzy numbers, this paper considers the proposed signed distance measure developing a model to manage decision making problem under uncertainty. The performance rating values and weights of linguistic risk criteria are expressed as triangular interval-valued fuzzy numbers in normalized mode. The best alternative is selected according to both ideal and non-ideal solutions without defuzzification. Finally, the feasibility and effectiveness of the developed method is illustrated by a case study in Electronics supply chain with six risk based criteria and four alternatives (Li-ion based battery suppliers). We compare the results with some existing methods to show the validity of the extended method. A sensitivity analysis is performed with six different sets of criteria weights for analyzing the robustness of the proposed method.