School of Computer and Information Engineering, Zhejiang Gongshang Uiniversity, Hangzhou, China

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Abstract—Supplier evaluation is an important process in supply chain. To our best knowledge, we firstly report a study on supplier classification problem for efficiency and performance in the meanwhile, which is to aim at reducing the risk of enterprises and finding the suppliers with both high efficiency and performance. This paper proposed an integrated model, which hybridized data envelopment analysis (DEA) and support vector machine (SVM) together, to predict the four-class problem according to their efficiency and performance. The proposed approach is a two-step process. The first step groups them into the efficient and the inefficient according to a new metric (i.e., efficient score) computed by DEA. Then the second step will use efficient score as a new feature introduced into the data set to train SVM model and further to forecast new supplier’s classification. The proposed approach shows comparable performance when compared with several existing approaches.

Index Terms—Supplier evaluation; classification; support vector machine(SVM); efficiency and performance; data envelopment analysis(DEA).

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Cite: Bo Jiang, Wei Chen, Hua Zhang, Weifeng Pan, "Supplier’s Efficiency and Performance Evaluation using DEA-SVM Approach," Journal of Software vol. 8, no. 1, pp. 25-30, 2013.