Multi-Layer Fuzzy Sustainable Decision Approach for Outsourcing Manufacturer Selection in Apparel and Textile Supply Chain

Abstract

The apparel and textile industry are known as a key sector in the structure of many economies around the world. In particular, the influence of foreign outsourcing manufacturers on textile supply chains has been recognized for decades. The outsourcing manufacturers are multi-criteria selected and changed by supply chain managers from time to time in search of the most efficient state for the entire supply chain. This is a known concern with the community and there is large interest in studying the apparel and textile outsourcing manufacturer problems. Aiming at reinforcing the selection methods, this study develops a three-layer fuzzy multiple criteria decision-making approach that leverages the strengths from the original methods. In turn through the layers, the hierarchy and weights of criteria and sub-criteria, which includes sustainability factors, are determined by the fuzzy analytic hierarchy process (FAHP) method. Next, the results from the fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) process determine the outsourcing manufacturer’s performance via expert linguistics judgments. Then, data envelopment analysis (DEA) models are applied for the purpose of evaluating the outsourcing manufacturer’s overall performance along with other quantitative effectiveness. This approach is applied to the problem of selecting the apparel and textile outsourcing manufacturers in Vietnam, one of the places that makes the necessity of this problem grow. The third position in the world apparel and textile export ranking, as well as the trend of shifting labor-intensive production systems to Southeast Asia make the necessity of Vietnam outsourcing manufacturer selection problem grow. The results of this study also classified manufacturers into groups as a support for selection decisions. Analysis of quantitative uncertainties using simulation tools and forecasting techniques can strengthen the solutions in future related studies.

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