Long term supplier selection using a combined fuzzy MCDM approach: A case study for a telecommunication company - Onut, Semih; Kara, Selin Soner; Isik, Elif (PERGAMON-ELSEVIER SCIENCE LTD, 2009)
With the globalization and the emergence of the extended enterprise of interdependent organizations, there has been a steady increase in the outsourcing of parts and services. This has led firms to give more importance to the purchasing function and its associated decisions. Since these decisions require a long term investment for the telecommunication industry especially and affect the strategic positioning of the companies in the sector, the selection of the proper supplier is one of the most important problems. Supplier selection is a multi-criteria problem which includes both tangible and intangible factors. This paper develops a supplier evaluation approach based on the analytic network process (ANP) and the technique for order performance by similarity to ideal solution (TOPSIS) methods to help a telecommunication company in the GSM sector in Turkey under the fuzzy environment where the vagueness and subjectivity are handled with linguistic terms parameterized by triangular fuzzy numbers. Contrary to conventional Fuzzy ANP (FANP) methodology in the literature, we use triangular fuzzy numbers in all pairwise comparison matrices in the FANP. Hence, criteria weights are calculated as the triangular fuzzy numbers and then these fuzzy criteria weights are inserted to the fuzzy TOPSIS methodology to rank the alternatives. This approach is demonstrated with a real world case study involving six main evaluation criteria that the company has determined to choose the most appropriate supplier. The study was followed by the sensitivity analyses of the results. (C) 2008 Elsevier Ltd. All rights reserved.
Keywords: Multiple criteria decision-making (MCDM); Fuzzy sets; Fuzzy ANP; Fuzzy TOPSIS; Supplier selection; Telecommunication industry; ANALYTIC NETWORK PROCESS; DECISION-MAKING; DESIGN; MODEL; ANP; AHP; OPTIMIZATION; Computer Science; Engineering; Operations Research & Management Science