Journal of Quality Engineering and Management

Abstract Abstract: Data envelopment analysis (DEA) is a nonparametric technique based on mathematical programming to evaluate performance of homogenous decision-making units (DMUs). Many DMUs have network structure where the output of one stage is the input to the next stage. A supply chain, which consists of several members such as supplier, manufacturer and customer, has multi-stage process. In this paper we, for the first time, introduce network DEA approaches to obtain the most productivity in a supply chain as a multi-stage DMU. Our models, by looking inside the internal structure of supply chain, consider the intermediate links between the stages. This perspective proposes managerial implications to improve the overall performance of a supply chain and the productivity of each member.