DEA efficiency analysis: A DEA approach with double frontiers

Data envelopment analysis (DEA) is a method for measuring efficiency of peer decision-making units (DMUs). Conventional DEA evaluates the performance of each DMU using a set of most favourable weights. As a result, traditional DEA models can be considered methods for the analysis of the best relative efficiency or analysis of the optimistic efficiency. DEA efficient DMUs obtained from conventional DEA models create an efficient production frontier. Traditional DEA can be used to identify units with good performance in the most desirable scenarios. There is a similar approach that evaluates the performance indicators of each DMU using a set of most unfavourable weights. Accordingly, such models can be considered models for analysing the worst relative efficiency or pessimistic efficiency. This approach uses the inefficient production frontier for determining the worst relative efficiency that can be assigned to each DMU. DMUs lying on the inefficient production frontier are referred to as DEA inefficient while those neither on the efficient frontier nor on the inefficient frontier are declared DEA inefficient. It can be argued that both relative efficiencies should be considered simultaneously and any approach with only one of them would be biased. This paper proposed the integration of both efficiencies as an interval so that the overall performance score would belong to this interval. It was shown that efficiency interval provided more information than either of the two efficiencies, which was illustrated using two numerical examples.     

A comparison of DEA and the stochastic frontier model using panel data

Two competing approaches for the measurement of efficiency are the stochastic frontier model and data envelopment analysis (DEA). Previous research has established that the two models applied to cross‐sectional data are both adversely affected by measurement error. While the cross‐sectional stochastic frontier model does not effectively handle statistical noise, panel data models do. This is true because additional information from multiple time periods is incorporated into the estimation. A panel data DEA model that uses averaged data has been shown to effectively smooth out measurement error. In this paper, we compare the panel data models using simulated data.     

Expansion of the cross-efficiency evaluation by using the equations forming the efficient frontier in DEA

In this paper, we propose an algorithm to calculate cross-efficiency scores which used the equations forming the efficient frontier in data envelopment analysis (DEA). In many standard DEA models, each decision-making unit (DMU) is evaluated by using the advantageous weight for itself. Then, many DMUs are evaluated as efficient, and those efficient DMUs are not ranked by the models. The cross-efficiency evaluation is a method to rank DMUs by using the advantageous weights for all DMUs. Previously, the cross-efficiency scores based on different ideas are calculated by solving multiple linear or nonlinear programming problems. However, it is often hard to solve such a nonlinear programming problem. Therefore, by analysing the efficient frontier, we construct an algorithm to calculate alternative cross-efficiency scores.     

Measuring the efficiencies of Asia–Pacific international airports – Parametric and non-parametric evidence

The purpose of this paper is to estimate the efficiencies of, and to discuss the managerial implications for 12 international airports in the Asia–Pacific region based on data from the period 1998–2006. We applied data envelopment analysis (DEA) and stochastic frontier analysis (SFA) to compute efficiency estimates, and the empirical results are discussed in terms of management perspectives and mathematical analysis. From the management perspectives, we suggest that airports should focus more on investment than on human resources. In addition, we found that inefficiency effects associated with the production functions of airports increased over the investigated period. From the perspective of mathematical analysis, we determined that deviations from the efficient frontiers of production functions are largely attributed to technical inefficiency. Finally, the empirical results imply that employing the discretion to adjust the scale size of the production function appears to improve efficiency. The main contribution of the paper is in showing how DEA and SFA can be used together to complement each other.     

A learning performance evaluation with benchmarking concept for English writing courses

This paper adopts data envelopment analysis (DEA), a robust and reliable evaluation method widely applied in various fields to explore the key indicators contributing to the learning performance of English freshmen writing courses in a university of Taiwan from the academic year 2004 to 2006. The results of DEA model applied in learning performance change our original viewpoint and reveal that some decision-making units (DMUs) with higher actual values of inputs and outputs have lower efficiency because the relative efficiency of each DMU is measured by their distance to the efficiency frontier. DMUs may refer to different facet reference sets according to their actual values located in lower or higher ranges. In the managerial strategy of educational field, the paper can encourage inefficient DMUs to always compare themselves with efficient DMUs in their range and make improvement little by little. The results of DEA model can also give clear indicators and the percentage of which input and output items to improve. The paper also demonstrates that the benchmarking characteristics of the DEA model can automatically segment all the DMUs into different levels based on the indicators fed into the performance evaluation mechanism. The efficient DMUs on the frontier curve can be considered as the boundaries of the classification which are systematically defined by the DEA model according to the statistic distribution.     

How the environment determines banking efficiency: a comparison of banking firms in Taiwan,Hong Kong,and Mainland China

This study uses the three‐stage data envelopment analysis (DEA) model to explore the true managerial efficiency of the banking firms in Taiwan, Hong Kong, and Mainland China. The empirical results indicate that the environmental conditions have a significant impact on banking efficiency. When the country‐specific situations are important factors in explaining efficiency difference, the common frontier estimates obtained by neglecting those factors can generate biased and overestimated inefficiency levels. With findings obtained from the slack variable analyses, the current study can provide inefficient banks with ways to reduce their input waste through the adjustment of input allocations. The findings also confirm the importance of the three‐stage DEA and its applications in determining the true managerial efficiencies of banks. Without the three‐stage DEA, the management could be misguided when making strategic decisions and conducting inappropriate resource allocation.     

Benchmarking distribution centres using Principal Component Analysis and Data Envelopment Analysis: A case study of Serbia

The efficiency of distribution systems is largely affected by the performances of distribution centres. The main objective of this paper is to develop and propose a DEA model for distribution centres efficiency measuring that can help managers in decision making and improving the efficiency. Due to numerous indicators that describe DCs operating, the main problem is indicators selection. In order to improve discriminatory power of classical DEA models PCA–DEA approach is used. This paper analysis the efficiency of distribution centres of one trading company in Serbia. Proposed models integrate operational, quality, energy, utilisation and equipment warehouse and transport indicators. Several hypotheses are tested in this paper. The results showed that small distribution centres are more efficient than large.     

Computing SSA Form with Matrices

Static Single-Assignment (SSA) form is an efficient intermediate representation used in virtual machines and modern compilers. It provides data flow information that simplifies the implementation of standard program optimisations such as constant propagation, dead code elimination, and partial redundancy elimination. Constructing SSA form involves the computation of graph relations such as dominance, and non-iterated and iterated dominance frontier. Although there exist efficient graph algorithms for these relations, the algorithms are elaborate to implement. In this paper we introduce a new approach to compute the dominance relation, the dominance frontiers, and the iterated dominance frontiers based on Boolean matrix calculus. We implemented our approach in an optimising backend for LCC bytecode and compared its performance with the state-of-the-art approaches. We use the Spec95 benchmark suite for our experimental evaluation.     

Nonparametric frontier analysis models for efficiency evaluation in insurance industry: a case study of Iranian insurance market

Performance evaluation and efficiency analysis is considered to be one of the critical responsibilities of the management department. This paper investigates and assesses the efficiency and performance of Iranian insurance companies through nonparametric frontier analysis (FA) models. The two well-known nonparametric FA models, data envelopment analysis (DEA) and free disposal hull, are utilized to separate the efficient companies from the inefficient companies, and two well-known super-efficiency analysis models are utilized to rank the efficient units. For the further analysis, critical inputs are also identified for inefficient companies using DEA sensitivity analysis which is a powerful tool for planning performance improvement project, and the reference sets are also identified as the benchmark pattern for each inefficient unit. Managerial insights and implications of the study are discussed finally.     

The worst-practice DEA model with slack-based measurement

An original data envelopment analysis (DEA) model is to evaluate each decision-making unit (DMU) with a set of most favorable weights of performance indices. The efficient DMUs obtained from the original DEA construct an efficient (best-practice) frontier. The original DEA can be considered to identify good (efficient) performers in the most favorable scenario. For the purpose of identifying bad performers such as bankrupt firms in the most unfavorable (worst-case) scenario, radial worst-practice frontier DEA (WPF–DEA) model in which the “worst efficient” DMUs construct a worst-practice frontier has been proposed. To identify bad performers together with the slack values we formulate another model called WPF–SBM. Then we develop the HypoSBM model to distinguish the worst performers from the bad ones. Finally, a solution approach is suggested to fully rank worst efficiencies in the worst-case scenario.     

Measuring efficiency and productive performance of colleges at the university of Santo Tomas: a nonparametric approach

This paper estimates relative efficiency and productive performance of 13 colleges at the University of Santo Tomas (UST), using data envelopment analysis (DEA) – Malmquist indices and a multi‐stage model. DEA is a management evaluation tool that assists with identifying the most efficient and inefficient decision‐making units (DMUs) in the best practice frontier. Total factor productivity (TFP) is measured for a sample of 13 colleges at UST over the period 1998–2003. Empirical results show that the main contributing factor to TFP growth is efficiency change. That is, UST colleges are technically operating efficiently in the frontier technology; though there is a downward shift in the technological advancement. Our results further imply that with the use of output–input mix, UST colleges as a whole have recorded a higher level of technical efficiency than innovation. These new findings contribute significantly to the existing literature on efficiency and productive performance in the education sector.     

Indian thermal power plant challenges and remedies via application of modified data envelopment analysis

This paper evaluates the performance of coal‐fired thermal power plants in India for the year 2008–2009 using data envelopment analysis (DEA); subdividing the power plants into three categories depending on their scale—small, medium, and large. The classical DEA model is analyzed to identify the efficient ones from the whole gamut of plants run by various organizations of the central government, state government, and private sector. Slack analysis is carried out to explore the specific areas that need to be focused on, in quantitative terms, for the overall efficiency improvement. Further efficiency evaluation is extended from a single criterion‐based conventional approach to a multiple criteria oriented approach, and the resulting DEA models are more efficient and flexible in many aspects, particularly in discriminant and weight analysis. Results of multicriteria DEA (MCDEA) are substantiated with cross‐efficiency analysis by deploying the weights obtained by the MCDEA in the cross‐efficiency analysis. On the basis of the insights provided by the outcome of the analysis, both qualitative and quantitative measures are proposed for improvement of the plant performances. The result of this analysis may assist the management of the power plants to introspect and review their systems and processes for optimal use of resources. The methodology adopted in the present work can also be employed for deeper understanding of power plants in other parts of India as well as in other countries.     

Estimation of portfolio efficiency frontiers

Estimation of portfolio efficiency frontiers is performed here under three alternative specifications on the basis of ten mutual funds randomly selected from a set of 115 funds analysed previously by Jensen (1969) and others. The estimates show three broad results: that portfolios exist which perform much better than the market portfolio, that the balanced portfolios behave significantly differently from the growth portfolios, and that a quadratic efficiency frontier is as good as a linear risk-return relationship following from the capital asset pricing model of efficient markets.     

Assessing Asian Economies Renewable Energy Consumption Efficiency Using DEA with Undesirable Output

Along with vast non-fossil potential and significant expertise, there is a question of whether Asian nations are attaining efficient consumption and exploitation of renewable resources. From this perspective, the paper aims to evaluate the efficiency of 14 potential Asia countries in renewable energy consumption during the six-year periods (2014–2019). In analyzing the performance of the renewable energy sector, the data envelopment analysis (DEA) with an undesirable output model approach has been widely utilized to measure the efficiency of peer units compared with the best practice frontier. We consider four inputs and two outputs to a DEA-based efficiency model. Labor force, total energy consumption, share of renewable energy, and total renewable energy capacity are inputs. The outputs consist of CO₂ emissions as an undesirable output and gross domestic product as a desirable output. The results show that United Arab Emirates, Saudi Arabia, Japan, and South Korea consistently outperform in the evaluation, achieving perfect efficiency scores during the research period. Uzbekistan is found to have the lowest average efficiency of renewable energy utilization.     

Measuring the efficiencies of Asia–Pacific international airports – Parametric and non-parametric evidence

The purpose of this paper is to estimate the efficiencies of, and to discuss the managerial implications for 12 international airports in the Asia–Pacific region based on data from the period 1998–2006. We applied data envelopment analysis (DEA) and stochastic frontier analysis (SFA) to compute efficiency estimates, and the empirical results are discussed in terms of management perspectives and mathematical analysis. From the management perspectives, we suggest that airports should focus more on investment than on human resources. In addition, we found that inefficiency effects associated with the production functions of airports increased over the investigated period. From the perspective of mathematical analysis, we determined that deviations from the efficient frontiers of production functions are largely attributed to technical inefficiency. Finally, the empirical results imply that employing the discretion to adjust the scale size of the production function appears to improve efficiency. The main contribution of the paper is in showing how DEA and SFA can be used together to complement each other.     

Integrated efficiency and trade-off analyses using a DEA-oriented interactive minimax reference point approach

A DEA-oriented Interactive Minimax Reference Point (DEA-IMRP) approach was recently developed to support integrated performance assessment and target setting for consistent management control and planning. To conduct the Integrated Efficiency and Trade-off (IET) analyses using the DEA-IMRP approach, it is important to understand the characteristics of the efficiency frontier and interactive trade-off analysis process. In this paper, the features of the IET analyses are investigated in detail. Graphical and analytical methods and procedures are explored for generating and analysing data envelopes and efficient frontiers for multiple input and multiple output DEA models using the DEA-IMRP approach. This computational investigation generates useful insights into the IET analyses and leads to the definition of new efficiency measures, which are instrumental to help conduct trade-off analysis for setting realistic performance targets. A numerical example is studied to illustrate the findings graphically. A case study for UK retail banks is conducted using the new methods and procedures investigated in this paper.     

Measuring Information Technology's Indirect Impact on Firm Performance

It has been recognized that the link between information technology (IT) investment and firm performance is indirect due to the effect of mediating and moderating variables. For example, in the banking industry, the IT-value added activity helps to effectively generate funds from the customer in the forms of deposits. Profits then are generated by using deposits as a source of investment funds. Traditional efficiency models, such as data envelopment analysis (DEA), can only measure the efficiency of one specific stage when a two-stage production process is present. We develop an efficiency model that identifies the efficient frontier of a two-stage production process linked by intermediate measures. A set of firms in the banking industry is used to illustrate how the new model can be utilized to (i) characterize the indirect impact of IT on firm performance, (ii) identify the efficient frontier of two principal value-added stages related to IT investment and profit generation, and (iii) highlight those firms that can be further analyzed for best practice benchmarking.     

Using interactive multiobjective methods to solve DEA problems with value judgements

Data envelopment analysis (DEA) is a performance measurement tool that was initially developed without consideration of the decision maker (DM)'s preference structures. Ever since, there has been a wide literature incorporating DEA with value judgements such as the goal and target setting models. However, most of these models require prior judgements on target or weight setting. This paper will establish an equivalence model between DEA and multiple objective linear programming (MOLP) and show how a DEA problem can be solved interactively without any prior judgements by transforming it into an MOLP formulation. Various interactive multiobjective models would be used to solve DEA problems with the aid of PROMOIN, an interactive multiobjective programming software tool. The DM can then search along the efficient frontier to locate the most preferred solution where resource allocation and target levels based on the DM's value judgements can be set. An application on the efficiency analysis of retail banks in the UK is examined. Comparisons of the results among the interactive MOLP methods are investigated and recommendations on which method may best fit the data set and the DM's preferences will be made.