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Introduction
The supply chain management program evaluation and monitoring is dependent on the development and application of performance measures. This evaluation and the set of performance dimensions become more complex when considering supply chain sustainability as environmental and social responsibility dimensions are added (Ashby et al. , 2012). Sustainable supply chain management performance measurement can be used for multiple purposes such as supplier selection, performance monitoring and development (Gimenez and Tachizawa, 2012; Hervani et al. , 2005).
Investigation of performance measurement systems is needed for advancing supply chain management sustainability. A critical aspect of sustainable supply chain performance measurement systems is the identification of key performance indicators (KPI) (Bai et al. , 2012; Bai and Sarkis, 2012; Chae, 2009). Hundreds of measures for traditional business and operational supply chain evaluations may exist (Gunasekaran and Kobu, 2007). This number of performance measures increases greatly when additional environmental and social sustainability dimensions are included in supply chain evaluations. Thus, the need to identify KPI becomes more critical when such a large set of sustainable supply chain performance measures are used (Zhu et al. , 2010; Dotoli et al. , 2006).
KPI may or may not provide similar amounts of information when compared to the complete performance measure indicator set. The use of information theory tools relying on information entropy measures[1] such as the rough set theory are examined to see if they are valuable for determining a usable subset of KPI for sustainable supplier evaluation with minimal information loss. Using this reduced KPI set, benchmarking tools, such as data envelopment analysis (DEA), can then be used to evaluate sustainable supplier performance (Talluri and Sarkis, 2002). DEA's application to sustainable supplier evaluation is limited; the objective of this paper is to further integrate DEA as a tool for performance measurement for sustainability in supply chains after reduction of the data sets using rough set approaches.
DEA is dependent on systemic information, inputs and outputs, related to a unit of analysis such as suppliers, where a primary concern of DEA applications is the effective selection of the input and output performance measures (Sarkis, 2007). DEA results are sensitive to the input and output data used in the evaluation. The large set of potential performance data that may be used...