Abstract
Article history: Received February 9, 2015 Received in revised format: May 12, 2015 Accepted June 2, 2015 Available online June 3 2015 Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). Traditional DEA models deal with measurements of relative efficiency of DMUs regarding multiple-inputs vs. multiple-outputs. One of the drawbacks of these models is the neglect of intermediate products or linking activities. Recently, DEA has been extended to examine the efficiency of network structures, where there are lots of sub-processes that are linked with intermediate parameters. These intermediate parameters can be considered as the outputs of the first stage and simultaneously as the inputs for the second stage. In contrast to the traditional DEA analysis, network DEA analysis aims to measure different sub-processes’ efficiencies in addition to the total efficiency. Lots of network DEA technique has been used recently, but none of them uses Analytic Hierarchy Process (AHP) in network DEA for assessing a network’s efficiency. In this paper, AHP methodology is used for considering the importance of each sub-process and network DEA is used for measuring total and partial efficiencies based on the importance of each department measured from AHP methodology. In this regard, the case of Iranian Handmade Carpet Industry (IHCI) is used. Growing Science Ltd. All rights reserved. 5 © 201
Highlights
Data envelopment analysis (DEA) is a method for measuring relative efficiency of peer decision making units (DMUs) with multiple inputs and outputs (Charnes et al, 1978)
Analytic Hierarchy Process (AHP) methodology is used for considering the importance of each sub-process and network DEA is used for measuring total and partial efficiencies based on the importance of each department measured from AHP methodology
Network data envelopment analysis focuses on intermediate parameters and aims to measure the efficiency of all sub-processes in addition to the whole efficiency of DMUs
Summary
Data envelopment analysis (DEA) is a method for measuring relative efficiency of peer decision making units (DMUs) with multiple inputs and outputs (Charnes et al, 1978). To overcome this, Kao (2008) introduced a linear formulation of the two-level DEA model. This model transforms the nonlinear model into a linear one using a variable substitution technique (Kao 2008). For network structures with more than two stages, Kao (2009) proposed the relational network DEA model. Hiseh and Lin (2010) utilized relational network DEA to construct a model to analyze the efficiency and effectiveness of international tourist hotel. More valuable research in the field of supply chain efficiency measurement by means of network DEA has been accomplished by Chang et al (2011) and Vaz et al (2010)
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