The dual formulation shows that optimizing total input consumption and output production is equivalent to finding weights that maximize the relative efficiency of a virtual DMU with average inputs and outputs. The two key features of the proposed models are their simplicity and the fact that both of them project all DMUs onto the efficient frontier. In both cases, total output production is guaranteed not to decrease. One type of model seeks radial reductions of the total consumption of every input while the other type seeks separate reductions for each input according to a preference structure. Two new DEA models are presented for such resource allocation. In such intraorganizational scenario the DM has an interest in maximizing the efficiency of individual units at the same time that total input consumption is minimized or total output production is maximized. While conventional DEA models set targets separately for each DMU, in this paper we consider that there is a centralized decision maker (DM) who “owns” or supervises all the operating units. The results show the effectiveness and the efficacy of the proposed framework.

The developed framework is tested and applied in a real world project for benchmarking the vendors of a leading Turkish automotive company. The developed software provides a DEA solution which is consistent with the framework and is ready-to-analyze with data mining tools, through a table-based structure. An innovative and convenient DEA solver, SmartDEA, is designed and developed in accordance with the pro-posed analysis framework. The paper formally shows how the solutions of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the solutions of basic DEA models. Regarding to this fact, effective and methodologic analysis and interpretation of DEA solutions are very critical.

DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making.

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