Supplier evaluation and selection: an augmented DEA approach

Abstract

Evaluating and selecting suppliers is an essential part of effectively managing today's dynamic and global supply chains. In this paper, we propose a supplier evaluation and selection methodology based on an extension of data envelopment analysis (DEA) that can evaluate suppliers in an efficient manner. Through the incorporations of a range of virtual standards, the proposed methodology termed augmented DEA, has enhanced discriminatory power over basic DEA models to rank suppliers. In addition, weight constraints are introduced to reduce the possibility of having inappropriate input and output factor weights. We demonstrate the application of augmented DEA with comparison experiments and find that the augmented DEA model has advantages over the basic DEA model as well as the cross-efficiency and super-efficiency models. Finally, we present a case application with data obtained from a communication and aviation electronics company to demonstrate the applicability and use of augmented DEA.