AN INTEGRATED IRN-BWM-EDAS METHOD FOR SUPPLIER SELECTION IN A TEXTILE INDUSTRY

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Abstract

Like all other manufacturing industries, supplier selection also plays a pivotal role in a textile industry with respect to timely and cost-effective delivery of raw materials (cotton, yarn or fabric), chemicals and dyes, machineries, spare parts and other auxiliary parts/items. An appropriately selected supplier would help the textile industry in seamless production of final or semi-finished products leading to effective deployment of supply chain management concept. Due to involvement of many competing suppliers and a set of conflicting criteria, supplier selection is often treated as a typical multi-criteria decision making problem. The process of choosing the right supplier for a given item often becomes more difficult due to presence of both quantitative and qualitative evaluation criteria. In this paper, based on six most significant criteria, an attempt is put forward to integrate interval rough number (IRN) with best worst method (BWM) and evaluation based on distance from average solution (EDAS) method to solve a supplier selection problem for a textile industry. The application of IRN helps in expressing opinions of the decision makers with respect to relative importance of the considered criteria and performance of the suppliers against each of the criteria using rough boundary intervals under group decision making environment. Later, the criteria weights are determined using IRN-BWM and the alternative suppliers are ranked from the best to the worst employing IRN-EDAS method. An IRN Dombi weighted geometric averaging (IRNDWGA) technique is considered to aggregate the opinions of the decision makers. This integrated approach identifies alternative 3 as the most apposite supplier for the textile industry under consideration.

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APA

Paul, V. K., Chakraborty, S., & Chakraborty, S. (2022). AN INTEGRATED IRN-BWM-EDAS METHOD FOR SUPPLIER SELECTION IN A TEXTILE INDUSTRY. Decision Making: Applications in Management and Engineering, 5(2), 219–240. https://doi.org/10.31181/dmame0307102022p

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