تجزیه و تحلیل راه حل DEA از طریق تکنیک تجسم اطلاعات و داده کاوی: چارچوب DEA هوشمند

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. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA results are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the results of basic DEA models. The paper formally shows how the results 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. An innovative and convenient DEA solver, SmartDEA, is designed and developed in accordance with the proposed analysis framework. The developed software provides DEA results which are consistent with the framework and are ready-to-analyze with data mining tools, thanks to their specially designed table-based structures. The developed framework is tested and applied in a real world project for benchmarking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework.

Data envelopment analysis (DEA) is a widely used method in performance evaluation and benchmarking of a set of entities. The popularity of DEA can be easily confirmed in the article of Emrouznejad, Parker, and Tavares (2008) that has summarized previous DEA contributions during the past three decades. Its convenience in assessing the multiple input and output variables of these entities by not requiring congruity and an apriori relationship makes it a very popular management tool in many application areas. Another reason for its wide use is the managerial insights that come up with the solution of a DEA model. For instance, DEA assigns a peer group or reference set for an inefficient entity. The entity can take the entities in this reference set as role models in accordance with the assigned weights. Another important DEA result is the target values or projections for the input and output variables of an inefficient entity to achieve full efficiency. DEA thus provides significant amount of information from which analysts and managers derive insights and guidelines to enhance their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA solutions is very critical (El-Mahgary and Lahdelma, 1995, Emrouznejad and De Witte, 2010 and Yadav et al., 2010). The main contribution in this study is the development of a general framework that enables DEA analysts to extract the most important and interesting insights in a systematic manner. In order to do so, a computer science and data mining perspective is adopted for designing the structure of the DEA results. Various data mining and information visualization techniques can be appropriate for the analysis of different types of DEA models (Lin et al., 2008 and Seol et al., 2011). The paper provides a fundamental basis for the implementation of these techniques in the DEA solutions. A convenient and general notation is proposed for the DEA data included in the model, the other data and the results data generated by DEA solvers. The ultimate goal of the study is then to build a structure framework for the analysis of DEA results, enabling researchers and practitioners to make analytical benchmarking and performance evaluations. In accordance with the proposed framework, a user-friendly and convenient DEA software, SmartDEA, is designed and developed. The software generates DEA solutions with a structure consistent with the framework. The analysis steps performed by a DEA analyst are importing the model data, analyzing the data by solving the appropriate DEA model, and making analytical inquires on the generated solution data to evaluate and benchmark the entities assessed in DEA model. The solution data generated by the software allows analysts to integrate the results and many of the data mining and information visualization techniques in a convenient and effective manner. The rest of this paper is organized as follows: after a brief introduction to the DEA, the theory of the basic DEA models are explained in Section 2, providing a fundamental background for DEA. Since it is critical to know when and where DEA is an appropriate method, advantages and drawbacks of DEA are also explained in the same section. Proposed framework, which is based on the integration of DEA results with data mining and information visualization techniques, is presented in Section 3. An overview of existing DEA software is followed by the presentation of the developed DEA solver, SmartDEA, in Section 4. The testing of the framework and SmartDEA with the real world data of an automotive company is illustrated in Section 5. Concluding remarks are summarized in Section 6.

In this study, a software framework is proposed to represent the results of any DEA study in a formal manner, enabling the analysts to make analytical benchmarking between different DMUs. In accordance with this framework, an innovative DEA solver, SmartDEA, is developed and tested in a real world project for benchmarking the vendors of a Turkish automotive company. The framework allows analysts to identify hidden patterns and derive managerial insights by integrating the results of any DEA study with various types of information visualization, data mining and OLAP technologies in the implementations of DEA studies. To summarize, DEA results are examined in a computer science oriented perspective and data mining point of view. This study provides a strong fundamental background to start any kind of analytical benchmarking in DEA by developing a formal way of representation for DEA results. One of the future work can be to devise and develop an analytical analysis framework, taking the proposed framework in this paper as a basis. Associated with this analysis framework, the ABC’s DEA results can be analyzed by integrating them with data mining and information visualization techniques analytically. In addition, the software framework can be extended to allow many sub-categories of these techniques. As noticed, the proposed framework can be applied in any application area. Another future work can be focusing on any specific area and building domain specific frameworks to analyze DEA results. The developed software can be improved by adding help, information or tips for those not related with DEA concepts.