Supply Chain Management (SCM) has received recently considerable attention in both academia and industry. The major aims of SCM are to reduce supply chain (SC) risk, reduce production costs, maximize revenue, improve customer service, optimize inventory levels, business processes, and cycle times, and resulting in increased competitiveness, customer satisfaction and profitability (Chou and Chang, 2008, Ha and Krishnan, 2008, Heizer and Render, 2004, Monczka et al., 2001, Simchi-Levi et al., 2003 and Stevenson, 2005).
One of the important activities for SC success is an effective purchasing function (Cakravastia and Takahashi, 2004, Chou and Chang, 2008, Giunipero and Brand, 1996 and Porter and Millar, 1985). The purchasing function has received a great deal of attention in the SCM due to factors such as globalization, increased value added in supply and accelerated technological change. The purchasing function involves buying the raw materials, supplies and components for the organization. The most important activity of the purchasing function is the selection of appropriate supplier, since it brings significant savings for the organization (Haq & Kannan, 2006).
One of the well known studies on supplier selection belongs to Dickson (1966) who identified 23 important evaluation criteria for supplier selection. Weber, Current, and Benton (1991) reviewed and classified 74 articles addressed the supplier selection problem. de Boer, Labro, and Morlacchi (2001) identified four stages for supplier selection including definition of the problem, formulation of criteria, qualification, and final selection, respectively. They reviewed and classified MCDM approaches for supplier selection.
Several methodologies have been proposed for the supplier selection problem. The systematic analysis for supplier selection includes categorical method, weighted point method (Timmerman, 1986 and Zenz, 1981), matrix approach (Gregory, 1986), vendor performance matrix approach (Soukup, 1987) vendor profile analysis (VPA) (Thompson, 1990), analytic hierarchy process (AHP) (Barbarosoglu and Yazgac, 1997, Narasimhan, 1983 and Nydick and Hill, 1992), analytic network process (ANP) (Sarkis & Talluri, 2000), mathematical programming (Chaudhry et al., 1993, Pan, 1989, Rosenthal et al., 1995, Sadrian and Yoon, 1994 and Weber and Current, 1993) and multiple objective programming (MOP) (Buffa and Jackson, 1983, Feng et al., 2001, Ghoudsypour and O’Brien, 1998, Sharma et al., 1989 and Weber and Ellram, 1992).
Most of these methods do not seem to address the complex and unstructured nature and context of many present day purchasing decisions (de Boer, Van der Wegen, & Telgen, 1998). In many existing decision models in the literature, only quantitative criteria have been considered for supplier selection. Several influence factors are often not taken into account in the decision-making process, such as incomplete information, additional qualitative criteria and imprecision preferences (Chen et al., 2006 and Zhang et al., 2009). Therefore, fuzzy set theory (FST) has been applied to supplier selection recently. Li et al., 1997 and Holt, 1998 discussed the application of FST in supplier selection. Chen et al. (2006) extended the concept of TOPSIS method to develop a methodology for solving supplier selection problems in fuzzy environment. Haq and Kannan (2006) presented a structured model for evaluating the supplier selection for the rubber industry using AHP and the model is verified with the fuzzy AHP. Bayrak, Çelebi, and Taşkin (2007) presented a fuzzy multi-criteria group decision-making approach to supplier selection based on fuzzy arithmetic operation. Chou and Chang (2008) presented strategy-aligned fuzzy simple multi-attribute rating technique (SMART) approach for solving the supplier selection problem from the perspective of strategic management of the SC. Chan, Kumar, Tiwari, Lau, and Choy (2008) presented fuzzy AHP to efficiently tackle both quantitative and qualitative decision factors involved in the selection of global supplier. Önüt, Kara, and Işık (2009) developed a supplier evaluation approach based on ANP and TOPSIS methods for the supplier selection.
This paper proposes an intuitionistic fuzzy multi-criteria group decision making with TOPSIS method for supplier selection problem. The importance of the criteria and the impact of alternatives on criteria provided by decision makers are difficult to precisely express by crisp data in the selection of supplier problem. Intuitionistic fuzzy sets introduced by Atanassov (1986) are suitable way to deal with this challenge and applied many decision-making problem under uncertain environment. In group decision-making problems, aggregation of expert opinions is very important to appropriately perform evaluation process. Therefore, IFWA operator is utilized to aggregate all individual decision makers’ opinions for rating the importance of criteria and the alternatives. The TOPSIS method considering both positive-ideal and negative-ideal solution is one of the popular methods in multi-attribute decision-making problem. Therefore, TOPSIS method combined with intuitionistic fuzzy set has enormous chance of success for supplier selection process.
Rest of this paper is organized as follows. In Section 2, brief description of intuitionistic fuzzy sets is given. Section 3 presents detailed description of intuitionsitic fuzzy TOPSIS method. In Section 4, a numerical example is demonstrated. Finally conclusion of this paper is presented in Section 5.
