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Measuring performance with common weights: network DEA

机译:用普通重量测量性能:网络DEA

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In conventional data envelopment analysis (DEA), a production system has been seen as a black box for measuring the efficiency without any attention to what is happening inside the system. However, in practice, performance improvement often requires observing the internal structure of the producing system in order to find the sources of inefficiencies. In addition, weight flexibility as a key property of the multiplier DEA models allows a system to totally disregard an assessment factor, either input or output, from the evaluation process by assigning a value of zero or epsilon to its weight. This paper contributes to the existing literature by proposing a common-weights DEA model when the production system includes a number of interrelated processes. To this end, we propose an aggregate DEA model to calculate the most favourable common weights for determining the efficiency of all production systems and their processes at the same time. Our proposed aggregate model not only is linear for equitably evaluating the producing units on the same scale, but also enables us to deal with the mixed network structures. Furthermore, the network system is decomposed into a series system to build a relational network DEA model that emphasises separate relatedness. This greatly reduces the computational complexities for enormous volumes of data in many real applications and treat difficulties in network DEA models including the zero value and fluctuating weights and multiple solutions. Managerially speaking, this paper aims to provide the top management team of a production system with an integrated framework to shape a better strategic decision process about firm performance, which is to treat the sources of inefficiencies and ultimately take corrective actions over the long run. Put differently, the proposed framework helps top managers make proper decisions in complex situations with a view of improving a firm's efficiency in all production divisions, which can be identified as a core competency leading to competitive advantages of the organisation. In the context of performance management, our study is equipped with a simple numerical example and a case study of the non-life insurance companies to demonstrate the applicability of the proposed common-weights network model.
机译:在传统的数据包络分析(DEA)中,生产系统被视为一种黑匣子,用于测量效率而不会对系统内部发生的事情进行任何关注。然而,在实践中,性能改进通常需要观察生产系统的内部结构,以便找到效率低下的来源。此外,作为乘法器DEA模型的重量灵活性,通过将零或epsilon分配给其重量,可以完全忽略评估因子,从评估因子,要么输出。本文通过提出当生产系统包括许多相互关联的过程时,通过提出共同重量的DEA模型来促进现有文献。为此,我们提出了一个聚合DEA模型来计算用于确定所有生产系统的效率及其过程的最有利的共同重量。我们所提出的聚合模型不仅是线性的用于在相同规模上公平地评估生产单元,而且还使我们能够处理混合网络结构。此外,网络系统被分解成系列系统,以构建一个关联网络DEA模型,强调单独的相关性。这大大降低了许多真实应用中巨大数据的计算复杂性,并在包括零值和波动权重和多种解决方案的网络DEA模型中治疗困难。本文旨在为生产系统的顶级管理团队提供综合框架,以塑造更好的战略决策过程,以便对待效率低下的来源,最终会在长远来看纠正措施。拟议的框架不同,拟议的框架有助于顶级管理人员在复杂情况下做出适当的决策,了解在所有生产划分中提高公司的效率,这可以被确定为核心竞争力,从而导致本组织的竞争优势。在绩效管理的背景下,我们的研究配备了一个简单的数值例子和对非寿险公司的案例研究,以证明所提出的共同权重网络模型的适用性。

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