An integrated approach for performance evaluation of suppliers in manufacturing industries

The supplier selection is a multi-criterion problem which includes both qualitative and quantitative factors. The model presented in this thesis aims at aiding the selection process and consists of three phases. In the first phase, criteria for evaluation of suppliers are selected based on literature and analytic hierarchy process (AHP) is applied. The AHP is used to analyse the structure of the problem and weights of each criterion are determined. The second phase involves clustering the available data set into required number of clusters by K-means algorithm. K-means is commonly preferred partitional clustering method which generates required number of clusters. The best cluster is selected based on the weighted sum of ratings of the elements. In the third phase, technique for order preference by similarity to ideal solution (TOPSIS) is performed on the selected cluster. Potential suppliers belonging to the best cluster are ranked according to performance. A sensitivity analysis is performed to determine the robustness of the model.