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Using non-parametric technical data envelopment analysis - DEA, for measuring productive technical efficiency

机译:使用非参数技术数据包络分析-DEA,以测量生产技术效率

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The following study is, in addition to a reassessment of literature and an analysis based on non-parametrical techniques based on linear programming. The analysis based on the Data Envelopment Analisys (DEA) technique will be used to see whether the model that we have used has a significant importance, if there are any substantial differences between the efficiency scores obtained or estimated through various methods. The theoretical part, based on the DEA technique will be analysed under the influence of both the works of Farell(1957), and also Charnes, Cooper, Rhodes(1978), Banker, Charnes, Cooper(1984) and other newer models. The dissolution of efficiency scores obtained through the CRS-DEA model has been studied for a long time into two different components: One is linked with the scale inefficiency and the other one represents the pure technical inefficiency. This dissolution can be done by using the CRS model with technology when not all the companies are operating at the optimum level, i.e. through the simultaneous application on the same set of data of the CRS and VRS models. In this study, the main non-parametrical Data Envelopment Analysis method is presented (Wu, Fan, Zhou, Zhou, 2012; Halkos, Tzeremes, 2009) and its application on a group of 42 companies (The headquarters of a top commercial bank in Romania - S.C. BRD GROUPE SOCIéTé GéNéRALE ), based on the information gained in the years 2016-2017. This paper is original because it combines the already developed method with new techniques, in order to link together economic factors and operational research and leaves more room for future researches with the purpose of further assessing and changing the performance of every decisional unit under the influence of the environmental factors.
机译:除了对文献进行重新评估和基于线性规划的非参数技术分析之外,下面的研究也是。基于数据包络分析(DEA)技术的分析将用于查看我们使用的模型是否具有重要意义,如果通过各种方法获得或评估的效率得分之间存在实质性差异。在DEA技术的基础上,理论部分将在Farell(1957)以及Charnes,Cooper,Rhodes(1978),Banker,Charnes,Cooper(1984)和其他更新模型的影响下进行分析。通过CRS-DEA模型获得的效率得分的分解已经被长期研究为两个不同的组成部分:一个与规模效率低下有关,另一个代表纯粹的技术效率低下。当并非所有公司都以最佳水平运营时,也可以通过将CRS模型与技术结合使用来解决问题,即同时应用CRS和VRS模型的同一组数据。在这项研究中,提出了主要的非参数数据包络分析方法(Wu,Fan,Zhou,Zhou,2012; Halkos,Tzeremes,2009),并将其应用于一组42家公司(一家顶级商业银行的总部)。罗马尼亚-SC BRD GROUPESOCIéTéGéNéRALE),基于2016-2017年获得的信息。本文之所以独创,是因为它结合了已经开发的方法和新技术,以便将经济因素和运营研究联系在一起,并为将来的研究留出了更多的空间,目的是进一步评估和改变决策部门的绩效。环境因素。

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