Supplier selection is the most critical activity of purchasing and influences both the quality of manufactured products and the buyer's performance. In the literature, dozens of multicriteria methods have been explored to support decision making for supplier selection. Among such methods, TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and Fuzzy-TOPSIS (an adaptation of the first) have been often used because of their simplicity of usage and ability to simultaneously evaluate an unlimited number of alternatives and criteria. Although many authors have suggested the adoption of TOPSIS and Fuzzy-TOPSIS to deal with the supplier selection problem, advantages of usage and limitations of both methods (when they are applied to this problem domain) have not been discussed in the literature. To fill such a gap, this study compares TOPSIS and Fuzzy-TOPSIS methods concerning computational complexity, structure of the algorithms and results from their application to the same real case of supplier selection. The methods were implemented through MATLAB(r) and applied to supplier selection in an automotive company. The results show that TOPSIS requires less effort for data collection and computer processing. However, Fuzzy-TOPSIS does not suffer ranking reversal and is suitable to model qualitative and imprecise information. The results can guide researchers and practitioners in choosing the most appropriate method to support the supplier selection problem at hand.
Supplier Selection; TOPSIS; Fuzzy-TOPSIS; Multicriteria Decision Making Methods
