Abstract
Ranking of efficient decision-making units (DMUs) using data envelopment analysis (DEA) results is very important for various purposes. We propose a new comprehensive ranking method using network analysis for efficient DMUs to improve the discriminating power of DEA. This ranking method uses a measure, namely dominance value, which is a network centrality-based indicator. Thus far, existing methods exploiting DMU’s positional features use either the superiority, which considers the efficient DMUs’ relative position on the frontier compared to other DMUs, or the influence, which captures the importance of the DMUs’ role as benchmarking targets for inefficient DMUs. However, in this research, the dominance value is the compounded measure of both core positional features of DMUs. Moreover, a network representation technique has been used to ensure the performance of the dominance value compared to the superiority and influence. To demonstrate the proposed ranking method, we present two examples, research and development (R&D) efficiency of small and medium-sized enterprises (SMEs) and technical efficiency of plug-in hybrid electric vehicles (HEVs). Through these two examples, we can see how the known weaknesses and the unobserved points in the existing method differ in this new method. Hence, it is expected that the proposed method provides another new meaningful ranking result that can show different implications.
Highlights
Data envelopment analysis (DEA) is a well-known linear programming-based technique for measuring the relative efficiency of homogeneous decision-making units (DMUs) [1]
Much previous work has pointed out the low discriminating power of DEA, because it is hard to distinguish the performance of efficient DMUs, and a number of ranking methods have been attempted
This study proposes a “dominance value” for ranking efficient DMUs based on the concept of centrality measures in network theory
Summary
Data envelopment analysis (DEA) is a well-known linear programming-based technique for measuring the relative efficiency of homogeneous decision-making units (DMUs) [1]. The ranking of DMUs can be used in identifying competitors with similar performance levels and determining the future direction of efficiency improvement for sustainable development, such as inefficient DMUs’ benchmarking target. It would be more reasonable to consider these two features together in the evaluation Based on this background, this study aims to develop a new ranking method to discriminate efficient DMUs by including both the superiority and influence features. This study aims to develop a new ranking method to discriminate efficient DMUs by including both the superiority and influence features To this end, we introduce quantified measures for estimating the superiority and influence features and define the “dominance value,” a compounded measure of two positional features by using the centrality of the network, a commonly used term to identify the most centric vertex in a network. We summarize our work and conclude the paper
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