Closest targets and strong monotonicity on the strongly efficient frontier in DEA

The determination of closest efficient targets has attracted increasing interest of researchers in recent Data Envelopment Analysis (DEA) literature. Several methods have been introduced in this respect. However, only a few attempts exist that analyze the implications of using closest targets on the technical inefficiency measurement. In particular, least distance measures based on Hölder norms satisfy neither weak nor strong monotonicity on the strongly efficient frontier. In this paper, we study Hölder distance functions and show why strong monotonicity fails. Along this line, we provide a solution for output-oriented models that allows assuring strong monotonicity on the strongly efficient frontier. Our approach may also be extended to the most general case, i.e. non-oriented models, under some conditions of regularity.     

Non-radial profit performance: An application to Taiwanese banks

This research modifies the directional Russell measure (DRM) of Fukuyama and Weber (2009) [1] to decompose the Nerlovian profit efficiency in Chambers et al. (1998) [2] so as to obtain a generalized measure that completely excludes technical inefficiency from allocative inefficiency. Based on such a decomposition, we further develop a new slack-based and profit-oriented productivity indicator, combining the Nerlovian profit measure with the conventional Luenberger productivity indicator (LPI), in order to provide a full picture of the sources of productivity change. Productivity change, based on the profit boundary, is decomposed into four components: the change in technical efficiency; the change in allocative efficiency; the shift of technology; and the price effect from outputs and inputs. This decomposition provides a more complete picture of the sources of productivity change. The above indicator is used herein to measure the productivity change of Taiwanese banks in terms of profit.     

Profit-oriented productivity change

This study develops an applicable profit-oriented productivity indicator when producers pursue profit maximization and can recognize input and output prices. We define the indicator, inspired by the Luenberger indicator and the Nerlovian efficiency measurement, in terms of both quantity distance functions and profit. Hence, the study׳s first stage decomposes the profit-oriented productivity change into two terms: profit efficiency change and profit technology change. Second, we decompose profit efficiency change into the changes in technical efficiency and allocative efficiency. Finally, profit technology change is separated into two components for capturing the shifts of technology and relative output/input prices. These decompositions provide a more complete picture of the sources of productivity change. We illustrate them with a sample of Taiwanese banks and compute the results using the models of directional distance functions.     

Technological forecasting of supercomputer development: The March to Exascale computing

Advances in supercomputers have come at a steady pace over the past 20 years. The next milestone is to build an Exascale computer however this requires not only speed improvement but also significant enhancements for energy efficiency and massive parallelism. This paper examines technological progress of supercomputer development to identify the innovative potential of three leading technology paths toward Exascale development: hybrid system, multicore system and manycore system. Performance measurement and rate of change calculation were made by technology forecasting using data envelopment analysis (TFDEA.) The results indicate that the current level of technology and rate of progress can achieve Exascale performance between early 2021 and late 2022 as either hybrid systems or manycore systems.     

An effective total ranking model for a ranked voting system

In this paper an effective model to rank candidates in a preferential election is proposed. It is an extension and simplified form of a recently proposed model for ranking efficient candidates. The model consists of fewer constraints and can be used for ranking inefficient as well as efficient candidates. Some techniques are introduced to decrease the complexity of the proposed model by obtaining some of the results by inspection.     

Centralized resource allocation based on efficiency analysis for step-by-step improvement paths

The existing centralized resource allocation models often assume that all of the DMUs are efficient after resource allocation. For the DMU with a very low efficiency score, it means the dramatic reduction of the resources, which can cause the organizational resistance. In addition, in reality, it is particularly difficult for the DMUs to achieve their target efficiencies in a single step, especially when they are far from the efficient frontier. Thus, gradual progress towards benchmarking targets is gaining importance. In this paper, we present a new approach for resource allocation based on efficiency analysis under a centralized decision-making environment. Through our approach, the central decision-maker can obtain a sequence of intermediate benchmark targets based on efficiency analysis, which provide a level-wise improvement path to direct the DMUs to reach their ultimate targets on the efficient frontier in an implementable and realistic manner. Numerical examples are presented to illustrate the application procedure of the proposed approach.     

Returns to scale vs. damages to scale in data envelopment analysis: An impact of U.S. clean air act on coal-fired power plants

This study proposes a use of Data Envelopment Analysis (DEA) to measure Returns to Scale (RTS) of US coal-fired power plants. The power plants produce not only desirable outputs (e.g., electricity) but also undesirable outputs (e.g., CO₂ and NO_x) as a result of their plant operations. Therefore, the proposed use of DEA also measures a new concept, or “DTS: Damages to Scale” (corresponding to RTS for undesirable outputs). Both scale measures discussed in this study are a quick-and-easy approach for assessing RTS and DTS, but not an exact method, because it does not consider a direct linkage between the two measures. This study applies the proposed approach to examine the legal validity of U.S. Clean Air Act (CAA). We find that the CAA has been legally effective and influential on the operation of coal-fired power plants in the United States because their plant operations belong to increasing RTS on a desirable output and increasing DTS on three undesirable outputs. The increasing DTS indicates a need for managerial improvement and/or engineering innovation such as advanced clean coal technology. This empirical result implies that U.S. federal and state governments need to expand the legal scope of CAA to the emission control of CO₂ because the current CAA excludes the regulation on CO₂ emission that is now considered as a main source of the global warming and climate change.     

Efficiency measurement for hierarchical network systems

The conventional data envelopment analysis (DEA) models for measuring the relative efficiency of a set of decision making units (DMUs), without considering the operations of the component processes, often produce misleading results, and network models have thus been recommended. This paper discusses the development of a network DEA model for systems with a hierarchical structure. It is shown that the hierarchical structure is equivalent to a parallel structure, with the components being the units at the bottom of the hierarchy. Due to the characteristics of a parallel system, the efficiency of a hierarchical system is thus a weighted average of those of the units at the bottom of the hierarchy. A hypothetic example shows that the proposed model is able to distinguish the order of the efficient DMUs evaluated by the conventional DEA model. Moreover, it provides the efficiencies of the functions of the DMU, which enables managers to identify areas of weakness, and thus better focus efforts to improve overall performance.     

A generalisation of the Farrell cost efficiency measure applicable to non-fully competitive settings

This paper enhances cost efficiency measurement methods to account for situations where the input prices can depend on negotiation or tend to qualitatively differentiate the resources available at each decision making unit. In these circumstances, there are some shortcomings in the cost efficiency measure described in the data envelopment analysis (DEA) literature. This paper proposes new models and measures for cost efficiency evaluation that overcome the limitations of the existing DEA models. The applicability of the measures developed is illustrated in the context of the analysis of bank branch performance.     

R&D project evaluation: An integrated DEA and balanced scorecard approach

We present and demonstrate a multi-criteria approach for evaluating R&D projects in different stages of their life cycle. Our approach integrates the balanced scorecard (BSC) and data envelopment analysis (DEA) and develops an extended DEA model. The input and output measures for the integrated DEA–BSC model are grouped in “cards” which are associated with a “BSC for R&D projects”. The BSC is embedded in the DEA model through a hierarchical structure of constraints that reflect the BSC balance considerations. We illustrate the proposed approach with a case study involving an industrial research laboratory that selects and executes dozens of R&D projects every year.     

Efficiency analysis of university departments: An empirical study

Universities play an important role in the development of a country in this age of the knowledge economy. As government subsidies to universities have been reducing in recent years, the more efficient use of resources becomes an important issue for university administrators. This paper applies data envelopment analysis (DEA) to assess the relative efficiency of the academic departments at National Cheng Kung University in Taiwan. The outputs considered are total credit-hours, publications, and external grants; and the inputs utilized by the departments are personnel, operating expenses, and floor space. An assurance region is constructed by the top administrators of the university to confine the flexibility in selecting the virtual multipliers in DEA. Four groups of departments of similar characteristics are categorized via an efficiency decomposition and cluster analysis. The aggregate efficiency indicates whether the resources have been utilized efficiently by a department and the efficiency decomposition helps identify the weak areas where more effort should be devoted so that the efficiency of the department can be improved.     

Non-oriented slack-based decompositions of profit change with an application to Taiwanese banking

A change in profit can originate from the output side and the input side. In the spirit of work by Tone [1] and follows Grifell-Tatjé and Lovell's [2], we propose a non-oriented slacks-based measure (SBM) model to decompose the change in the operating profit into various meaningful components: quantity effect and a price effect. The quantity effect can be decomposed into a productivity effect and an activity effect. The productivity effect is further decomposed into a technical effect and an operating efficiency effect. Both of them include an output side, which will result in a change in revenue and an input side which will result in a change in cost. The activity effect can be decomposed into a product mix effect, a resource mix effect and a scale effect. We illustrate our decompositions to the Taiwanese banking sector during the period 1994-2002 using the average of the base and current prices to evaluate these contributions. We find ignoring input side effects on the decomposition of profit changes would cause misleading results in managerial issues.     

Taiwan quality indicator project and hospital productivity growth

The Taiwan Quality Indicator Project (TQIP) is a quality management program that measures and monitors the healthcare quality of hospitals in Taiwan. This paper examines the impact of TQIP participation on hospital productivity growth with the application of the Malmquist productivity change index based on data envelopment analysis (DEA). Analyzing operations data from 31 TQIP regional hospitals over the period 1998–2004, we find that TQIP hospitals improved their productivity in the post-TQIP period. This improvement is attributable to quality change and relative efficiency progress. The simultaneous enhancement in both quality and relative efficiency coincides with the philosophy of total quality management (TQM) spirit, and confirms the efficiency improvement and quality assurance functions of TQIP.     

How do Greek banking institutions react after significant events?—A DEA approach

The efficiency of Greek commercial banks is considered through the period 1995–2003 using the data envelopment analysis technique. Two approaches are used to measure efficiency: one using financial ratios as outputs only and the other viewing banks as credit generation and transaction institutions. The empirical results are used to examine the reaction of banking institutions after significant events such as M&As, privatizations and the crisis of the Athens Stock Exchange in 1999. In most cases performance deteriorates for the next 1 to 2 years, while increases thereafter, forming specific patterns of efficiency. In the last part we introduce an index to measure the management's efficiency through a process of change. The results suggest that the Greek banking sector operated efficiently on average during the destabilization periods. The contribution of the article is that it comprises the first study which examines empirically the performance behavior of banking institutions within the scope of change management theory.     

A survey of DEA applications

The literature of data envelopment analysis (DEA) encompasses many surveys, yet all either emphasize methodologies or do not make a distinction between methodological and application papers. This study is the first literature survey that focuses on DEA applications, covering DEA papers published in journals indexed by the Web of Science database from 1978 through August 2010. The results show that on the whole around two-thirds (63.6%) of DEA papers embed empirical data, while the remaining one-third are purely-methodological. Purely-methodological articles dominated the first 20 years of DEA development, but the accumulated number of application-embedded papers caught up to purely-methodological papers in 1999. Among the multifaceted applications, the top-five industries addressed are: banking, health care, agriculture and farm, transportation, and education. The applications that have the highest growth momentum recently are energy and environment as well as finance. In addition to the basic statistics, we uncover the development trajectory in each application area through the main path analysis. An observation from these works suggests that the two-step contextual analysis and network DEA are the recent trends across applications and that the two-step contextual analysis is the prevailing approach.     

Models for measuring and benchmarking olympics achievements

As demonstrated in several recent studies, data envelopment analysis (DEA) is a useful tool for evaluating and comparing the performance of nations competing in the Olympic Games. Assurance regions (ARs) have been used to further refine the DEA results. These AR DEA models assume that ARs apply uniformly across all nations. Such models can have shortcomings in the sense that different nations may impose different ARs, as nations may value gold, silver, and bronze medals differently. This paper extends previous DEA studies by incorporating multiple sets of nation-specific ARs into the DEA. By doing so, we establish fair models for measuring and benchmarking the performance of nations at six summer Olympic Games.     

Data envelopment analysis 1978–2010: A citation-based literature survey

This study surveys the data envelopment analysis (DEA) literature by applying a citation-based approach. The main goals are to find a set of papers playing the central role in DEA development and to discover the latest active DEA subareas. A directional network is constructed based on citation relationships among academic papers. After assigning an importance index to each link in the citation network, main DEA development paths emerge. We examine various types of main paths, including local main path, global main path, and multiple main paths. The analysis result suggests, as expected, that Charnes et al. (1978) [Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. European Journal of Operational Research 1978; 2(6): 429–444] is the most influential DEA paper. The five most active DEA subareas in recent years are identified; among them the “two-stage contextual factor evaluation framework” is relatively more active. Aside from the main path analysis, we summarize basic statistics on DEA journals and researchers. A growth curve analysis hints that the DEA literature’s size will eventually grow to at least double the size of the existing literature.     

Presenting DEA graphically

This paper introduces a methodology that permits presentation of the results of data envelopment analysis (DEA) graphically. A specialized form of multi-dimensional scaling, Co-Plot, enables presentation of the DEA results in a two-dimensional space, hence in a clear, understandable manner. When plotting ratios rather than original data, DEA efficient units can be visualized clearly, as well as their connections to specific variables and/or ratios. Furthermore, Co-Plot can be used in an exploratory data analysis to identify outliers, whose data require additional scrutiny, and potentially inconsequential variables that could be aggregated or removed from the analysis with little effect on the subsequent DEA results.     

Benchmarking of maintenance and outage repair in an electricity distribution company using the value-based DEA method

Benchmarking of electricity distribution utilities has been widely used as a means to contribute for the adoption or reinforcement of enhanced competitiveness and innovation practices to optimize costs, increase customer satisfaction, improve corporate image and maximize profits. The purpose of this paper is to present a benchmarking study for the maintenance and outage repair activity carried out by a Portuguese electricity distribution company, EDP Distribuição (EDP-D), using the value-based DEA method, which builds on links between data envelopment analysis (DEA) and multiple criteria decision analysis (MCDA). This study illustrates the impact of the incorporation of managerial preferences in the classification and ranking of 40 network areas served by EDP-D, confronting the results with a previous study based on a BCC DEA model. In order to deal with the underlying uncertainty, the value-based DEA method for performance evaluation is adapted to include the concept of super-efficiency. Besides identifying best practices, sources of inefficiency, gaps relatively to best practices and opportunities for improvement, this analysis supports the introduction of corrective measures and informs decisions about future goals.