Supplier selection and stepwise benchmarking: a new hybrid model using DEA and AHP based on cluster analysis

Abstract

This study proposes a new comprehensive methodology for supplier evaluation, selection, and improvement. A hybrid approach for evaluation and selection is presented using an expectation maximization (EM) algorithm for clustering, data envelopment analysis (DEA) for efficiency, and analytic hierarchy process (AHP) for importance. First, industrial taxonomy and EM algorithms are used to cluster the suppliers. Then, DEA is utilized to calculate internal operation efficiency. Next, AHP is applied to assess external function importance. After the weighted efficiency and importance scores are combined, the suppliers for improvement and strategic improving direction are determined based on a quadrant and diamond graph analysis. Finally, based on the supplier clusters, the effective stepwise benchmarking paths are presented with improvement strategies, and the final target indicators are obtained through DEA projection analysis. The proposed method is successfully demonstrated on 63 tier-one suppliers in the Korean automobile industry using three-year panel data from 2012 to 2014.

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