A new fuzzy network slacks-based DEA model for evaluating performance of supply chains with reverse logistics

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Supply chain performance evaluation problems are complex problems with multiple criteria and multi-layered internal linking activities. Data Envelopment Analysis (DEA) has been used to evaluate the relative performance of organizational units called Decision Making Units (DMUs). However, the conventional DEA models cannot take into account the complex nature of supply chains with internal linking activities. Although network DEA models are used to address this drawback, most of them use Farrell radial measures of efficiency and ignore input slacks and/or output slacks and are not suitable for measuring efficiencies when inputs and outputs may change non-proportionally. In response, network DEA models using Slacks-Based Measures (SBMs) of efficiency are used when inputs and outputs are non-radial. Furthermore, crisp input and output data are fundamentally indispensable in a conventional DEA evaluation process. However, the input and output data in real-world problems are often imprecise or ambiguous. Fuzzy DEA models are used to address the impreciseness and ambiguity associated with the input and output data. Finally, conventional supply chain performance evaluation models primarily consider forward logistics dealing with the flow of products from manufacturing to customers. We propose a new Network SBM (NSBM) model in the fuzzy environment. The proposed fuzzy NSBM model considers non-radial measures of efficiency in a unified framework for evaluating performance of supply chain networks with forward and reverse logistics. A case study is presented to demonstrate the applicability of the proposed fuzzy NSBM model and exhibit the efficacy of the procedures in evaluating the performance of a supply chain in the semiconductor industry.




Momeni, E., Tavana, M., Mirzagoltabar, H. and Mirhedayatian, S.M. (2014) ‘A New Fuzzy Network Slacks-Based DEA Model for Evaluating Performance of Supply Chains with Reverse Logistics,’ Journal of Intelligent and Fuzzy Systems, Vol. 27, No. 2, pp. 793-804.