The following research is aimed at evaluating the performance of Saudi Arabian domesticairports with the application of DataEnvelopmentAnalysis (DEA), based on multiple inputs, utilized by airports and multiple outputs they produce. DEA is a wide-spread nonparametric method, which was originally designed to measure productive efficiency of decision-making units. Different types of DEA models were developed to ensure quality analysis of the efficiency of different DMUs. In this research several DEA models were used. Moreover, the super efficiency model was used to ensure not only distinguishing efficient airports, but to rank them. Modern level of the development of computer technologies allows researchers to use special software to solve DEA models. Current research uses the Excel Solver to solve DEA models. The usage of Excel Solver and specially-designed code allows automating the process of DMUs’ efficiency analysis and analyze large numbers of DMUs with the application of multiple inputs and outputs.
views in this regard. The traditional view focuses solely on the work of the past period and is shaped by the requirements of the past. In this view, the time and space conditions of the system are ignored and may cause deviations as a result of work. A new view has targeted education, growth and development of evaluated capacities and performance improvements. This approach identifies the weaknesses and strengths of the systems. In the new view, the problem is studied in the context of time and a systemic attitude is dominant. Organizational units are only a part of the whole system. As a result, a new view leads to growth and development, improvement of performance, and the realization of the goals of the organization. In recent years, several models and approaches have been proposed for measuring efficiency, based on two general parametric and non-parametric methods. In this research, the DataEnvelopmentAnalysis (DEA) is used as a nonparametric approach. This method selects the efficient units and provides the efficiency frontier. This frontier is a criterion for the evaluation of other units. In this paper, we will measure the performance by using the dataenvelopmentanalysis method for the following five reasons. First, it evaluates the performance of the organization on the basis of a logical model with a flexible structure. Second, it detects inefficient units. Third, the degree of inefficiency of the units is determined. Fourth, there is no prior standard level and the comparison criterion is another unit that operates under the same conditions. Fifth, DEA determines the patterns and references for the inefficient units among of the efficient units.
ABSTRACT: The aim of this study is to develop a performance model for measuring relative efficiency and potential improvement capabilities of Nepali banks by scrutinizing intermediation aspects. For measuring the efficiency and performance, this paper uses a relatively new frontier approach known as DataEnvelopmentAnalysis (DEA). The paper uses two basic DEA models to fulfill its objectives. This paper seeks to measure and analyze the efficiency levels of banks in Nepal during 2007-08 to 2010-11. The study reveals that efficiency level is relatively stable and has increased on overall. Additionally, it also breaks down the overall efficiency of banks into technical and scale efficiency. This study found no significant relationship with efficiency level and ownership structure of banks and there were no notable differences in the efficiency levels of banks according to their asset size.
Prior research on incineration covers issues related to technical, political, social and envi- ronmental, and public health. Studies on performance measurement of incineration plants consider incineration as only heat and power generation. Social and environmental aspects of incineration are somewhat ignored. It’s worth considering both desirable and undesirable outputs for measuring incineration pants’ performance so as to maximize desirable output and minimize undesirable output. Overall performance depends on not only higher heat and power production but also minimum environmental and social impacts. Prior studies on per- formance analysis of incineration plants using DEA emphasize on improving less performing units. The study by Chen et al. (2010) propose a DEA-based model for incineration plants’ performance measurement using waste consumption and energy production as outputs, and come out with suggestions on improving inefficient units’ performance. Marques and Simões (2009) compare efficiency of public and private solid waste management services and find no difference. They consider waste amount and cost as criteria for the analysis. Chen and Chen (2012) also consider cost and waste amount as criteria for analysis and reveal that composting and incineration contributes positively to achieve efficiency. Chen et al. (2012) use network DEA to reveal that emission reduction and better resource allocation enhance overall per- formance of incineration. Benito-Lopez et al. (2011) finds that strong regulatory measures enhance efficiency of incineration. Chang and Yang (2011) derives that build operate trans- fer contract is more effective to implement incineration projects than any other contracts. Chen et al. (2014) reveals that effort in cost reduction, capacity utilization, ownership and most appropriate location are the critical success factors for enhancing incineration plants’ performance. Although the above studies are significant and address specific issue
Abstract—Total Productive Maintenance (TPM) has been widely accepted as a strategic tool for succeeding manufacturing performance and also it has been effectively implemented in many organizations. The evaluation of TPM efficiency can make a great contribution to companies in advancing their operations across a variety of dimensions. This study aims to propose a new framework for evaluation TPM performance. Proposed TPM effectiveness system can be divided into three stages: (i) the design of the new performance measures, (ii) the evaluation of the new performance measures, and (iii) the use of the new performance measures to evaluate TPM effectiveness. Finally, proposed fuzzy DEA method is used to evaluate TPM performance with newly developed performance measures using real manufacturing case. In this study, the fuzzy utility degrees achieved from fuzzy COPRAS are integrated with fuzzy DEA in order to determine efficient and inefficient TPM performance.
While the efficiency of China’s HEIs have been the focus of a number of empirical studies, few of these use DEA as a tool of analysis, preferring instead to base their findings on single output to single input indices, such as cost per student (Ng & Li 2000; Liu 2001). Ng & Li (2000) use DEA in an attempt to examine the effectiveness of the education reforms of the mid-1980s in China by focusing on the research performance of 84 key Chinese HEIs from 1993 to 1995. Using three inputs and five outputs, the authors find mean efficiency in the Chinese higher education sector to be around 76-80% over the three year period. Variations in efficiency levels between the three geographical regions of China (coastal, Central & Western) are also found, but these results are mixed: the HEIs in the Central zone perform best, on average, in 1993 and 1995, but it is the Western zone which has the highest mean efficiency in 1994. Liu (2001), in contrast, performs a DEA of 312 Chinese universities in total (55 comprehensive and 257 engineering) using 14 inputs and 3 outputs. Efficiency is found to be very high amongst the comprehensive universities
Dataenvelopmentanalysis (DEA) has become a popular tool for measuring the ef ﬁ ciency of non-pro ﬁ t institutions such as hospitals, schools and universities. Its popularity in these contexts derives from the fact that it is based on a distance function approach and hence can handle multiple outputs and multiple inputs; it does not assume any speci ﬁ c behavioural assumptions of the ﬁ rm (e.g. cost minimization or pro ﬁ t maximization); it makes no assumption regarding the distribution of ef ﬁ ciencies; and it requires no a priori information regarding the prices of either the inputs or the outputs. Despite there being a plethora of studies which examine the ef ﬁ ciency of the higher education sectors of various countries such as the UK, the USA, Canada, Finland, Israel and Australia (Abbott & Doucouliagos, 2003; Ahn, Arnold, Charnes & Cooper, 1989; Arecelus & Coleman, 1997; Athanassopoulos & Shale, 1997; Avkiran, 2001; Breu & Raab, 1994; Coelli, Rao & Battese, 1998; El Mahgary & Lahdelma, 1995; Flegg & Allen, 2007; Friedman & Sinuany-Stern, 1997; Haksever & Muragishi, 1998; Johnes, 2006a; Worthington & Lee, 2008), little work has been done on measuring the ef ﬁ ciency in producing any of the outputs of higher education institutions (HEIs) in China. Recent studies by Ng and Li (2000) and Liu (2001) are exceptions but are based on data for the 1990s. A more up-to-date analysis of the Chinese higher education sector is therefore overdue.
Licensed under Creative Common Page 52 Dataenvelopmentanalysis (DEA) without difficulty comprises a couple of inputs and outputs besides the requirement for a frequent denominator of measurement. This makes it specifically appropriate for inspecting the effectively of hospitals as they use a couple of inputs to produce many outputs. Furthermore, it offers unique input and output aims that would make an inefficient medical institution surprisingly efficient. It additionally identifies environment-friendly peers for those hospitals that are now not efficient. This helps the inefficient hospitals to emulate the functional organization of their peers so as to improve their efficiency (Silwal & Ashton, 2017). Research activities on dataenvelopmentanalysis (DEA) have grown rapidly recently. As it has proved its importance and ability to measure efficiency in various sectors, especially in the health sector, DEA is a methodology for performance evaluation and benchmarking where multiple performance measures are present. DEA, first delivered by means of Charnes et al., (1978), is a non-parametric programming approach to measuring the relative efficiency of peer decision-making gadgets (DMUs) with more than one inputs and outputs (Wang et al., 2015). In healthcare, the first introduced of DEA dates to 1983, in the work of Nunamaker and Lewin, who measured pursuits nursing service efficiency. Since then dataenvelopment evaluation has been used broadly in the assessment of health center technical affectivity in the United States as properly as around the world at exclusive tiers of decision-making units. For example, Sherman (1984) was first in the usage of DEA to evaluate basic health center effectivity (Li & Dong, 2015).
Wanke and Barros (2014) measured efficiency in Brazilian banking using a two-stage process where in the first stage, the number of branches and employees were used to attain a certain level of administrative and personnel expenses per year. In the productive efficiency stage, these expenses permitted the consecution of two important net outputs including equity and permanent assets. They applied the network-DEA centralized efficiency model to optimize both stages, simultaneously. They reported that Brazilian banks were heterogeneous, with some concentrating on cost efficiency and others on productive efficiency. In addition, cost efficiency was described by marketing and administration (M&A) as well as size, while productive efficiency was described by M&A and public status. Liu et al. (2009) applied DEA technique to measure the relative efficiencies on a bank in Taiwan and studied the performance and productivity changes when banks implement financial electronic data interchange. They included 18 branches of the performance for implementation of financial electronic data interchange of the overall efficiency, pure technical efficiency, scale efficiency, analysis of reference groups and the potential to improve the value of analysis for different branch performance assessments. The empirical results shown that case bank could adopt the DEA evaluation model as references to upgrade the overall operating performance effectively for creating competitive advantages. Wang et al. (2014) utilized network DEA method to evaluate efficiencies of the Chinese commercial banks.
Many literatures have discussed the performance evaluation usingDataEnvelopmentAnalysis (DEA) at the university and schools. Research on performance of different colleges or universities, and research comparing the performance of teaching and research in a university department has been made. But as of today, there is no research on performance appraisal usingDataEnvelopmentAnalysis at the Teachers’ Training Institute.
There is no formal selection process agreed among researchers as to what input and output variables should be in included in a DEA model (Callen 1991; Charnes, Cooper & Rhodes 1981; Cholos 1997; Watson, Wickramanayke & Premachandra 2011). Variable selection methods that have been adopted in the past include expert judgement, principal components analysis, a step-wise approach to input-output variable selection or a combination of all the above (Adler & Golany 2001; Norman & Stoker 1991). For this research, variable selection was based on principal components analysisusing APRA data (APRA 2013b), current issues identified in the literature and analysis of operating characteristics and performance indicators of superannuation funds (expert judgement). Inputs and outputs are performance measures, and thus, if correctly selected, can provide useful insights to managers and/or regulators. Within the context of the productivity concept and DEA model, efficiency is enhanced by reducing inputs while maintaining the current level of outputs or increasing outputs while maintaining the current level of inputs (Galagedera & Silvapulle 2002).
that the DEA applications involve a wide range of contexts such as banking (Tsolas and Charles 2015; Sahin et al. 2016; Stewart et al. 2016), transportation (Chang et al. 2013; Cui and Li 2014; Ji et al. 2015), health care (Torres-Jiménez et al. 2015; Shwartz et al. 2016), education (Fuentes et al. 2016; Lee and Worthington 2016), and agriculture (Kocisova 2015; Shrestha et al. 2016) and such previous DEA studies provide useful managerial information on improving the productivity. The DEA is a non-parametric ap- proach to evaluate the performance that was originally developed by Charnes et al. (1978) and is based on the technological assumptions of CRS and later was ex- tended to accommodate the technologies that exhibit variable returns to scale (VRS) by Banker et al. (1984). The most important point for analysisusing the DEA is its management tool; it is designed to construct specific benchmarks for evaluating the performance of the individual DMUs (Coelli et al. 2005). The DEA is an excellent empirical model that compares a deci- sion unit with an efficient frontier using the perfor- mance indicators. It further enables the extension of the single-input/single-output technical efficiency measure to the multiple-input/multiple-output case to evaluate the relative efficiency of peer units with respect to multiple performance measures (Charnes et al. 2013). Unlike the parametric methods, which require a detailed knowledge of the processes under investigation, the DEA does not require an explicit functional form relating inputs and outputs (Cooper et al. 2006; Cook and Seiford 2009) for the evaluation of the theoretical foundations and development in the DEA approach. Although the DEA can evaluate the relative efficiency of a set of the individual DMUs, it cannot identify the source of inefficiency in each DMU because the conventional DEA models view each DMU as a black box that consumes a set of inputs to produce a set of outputs (Avkiran 2009; Tavana and Khalili-Damghani 2014). Fried et al. (2002) proposed multistage input-oriented DEA models to differentiate the possibly uncontrollable effects of the environment on the firm performance. The models can be used to distinguish the pure management inefficiency from the inefficiencies resulting from external variables in forms of data, area characteristics, labour relativity, and government regulations (Fried et al. 1999). In addition, Rho and An (2007) showed that the use of the single-stage DEA might result in the inaccurate efficiency evaluation. Thus, there is a need for a fur- ther development of simulation methods to extend the variety of DEA developments and the scope of
The present paper examined the review of literature related to measuring relative efficiency of banks usingdataenvelopmentanalysis (DEA). The efficiency of banks is measure through the ability of the individual bank to maximise output given a certain level of input. By measuring its efficiency, it can serves as early warning or benchmark of its performance and it can define future improvement in various area such as managerial, technology or socio-economic. DEA is comprises of two basic model that are DEA Charnes-Cooper-Rhodes model with constant return to scale assumption and DEA Banker-Charnes-Cooper model with variable return to scale assumption. In banking industry, DEA is using two approaches that are production or intermediation approach. The former highlights banks as delivering services in the form of transaction and the later assumes banks intermediate funds between surplus units to deficit unit. The study of efficiency in banks with similar economic and political condition is important as banks operate in parallel. Keywords: DataEnvelopmentAnalysis, Efficiency, Banking
Dataenvelopmentanalysis (DEA) and multilevel modelling (MLM) are applied to a data set of 54578 graduates from UK universities in 1993 in order to assess the teaching performance of universities. A methodology developed by Thanassoulis & Portela (2002) allows each individual's DEA efficiency score to be decomposed into two components: one attributable to the university at which the student studied, and the other attributable to the individual student. From the former component a measure of each institution's teaching efficiency is derived and compared to the university effects from various multilevel models. The comparisons are made within four broad subjects: pure science; applied science; social science and arts. The results show that the rankings of universities derived from the DEA efficiencies which measure the universities' own performance (i.e. having excluded the efforts of the individuals) are not strongly correlated with the university rankings derived from the university effects of the multilevel models. The data were also used to perform various university-level DEAs. The university efficiency scores derived from these DEAs are largely unrelated to the scores from the individual-level DEAs, confirming a result from a smaller data set (Johnes 2003). However, the university-level DEAs provide efficiency scores which are generally strongly related to the university effects of the multilevel models.
Furthermore, DEA became more popular when introduced by Charnes et al. (1978) in order to estimate Ψ allowing for constant returns to scale (CRS model). Later, Banker et al. (1984) introduced a DEA estimator allowing for variable returns to scale (VRS model). In our case, when evaluating journals’ citation performance input orientation of DEA models have been applied due to the fact that input quantities appear to be the primary decision variables (Coelli and Perelman, 1999; Coelli et al., 2005; Halkos and Tzeremes, 2010). The quality of the papers appeared in a journal but also the number of the papers to be published (i.e. the number of issues and volumes) is subject to the editors’ decision. Therefore the decision makers have most control over the input compared to the outputs used. Furthermore, the CRS model developed by Charnes et al. (1978) can be calculated as:
Johnes et al. (2012) compared, using DEA approach, the performance of 210 conventional and 45 Islamic banks from 19 countries for the period 2004-2009. They found out that there was no significant difference in mean efficiency between the two types of banks when efficiency is measured relative to a common frontier. A meta-frontier analysis, however, revealed some fundamental differences between the two bank groups. They also emphasized that the Islamic banks was less efficient than the conventional one. Managers of Islamic banks made up for this as mean efficiency in Islamic banks was higher than in conventional banks when efficiency was measured relative to their own bank type frontier.
In addition to this, several studies have proven the powerful and the superiority of DEA compared with other techniques. Ronald et al. , for example, studied the comparative performance assessment in managing care. They reviewed three comparative performance methodologies with a comprehensive explanation of DEA. They used a ratio analysis, regression analysis, and dataenvelopmentanalysis. From ratio analysis and regression analysis, they found that the result provide some interesting insights into the relative strengths and weaknesses and they concluded that the ratio analysis and regression analysis provides almost endless possibilities for debate. From dataenvelopmentanalysis, they said the DEA is a special of linear programming and they concluded that DEA is superior to simple ratio analysis and regression analysis in that it incorporates an optimizing principle rather than an averaging principle. It is also produces improvement targets for inefficient providers and identifies beast practice providers that can be used as model for operational improvement.
The highly valued industry of every state is crystalized in industrial statesthat results in the development of the community. Evaluating the performance of industrial towns and measuring their efficiency is very important. Assessment of Industrial Estates purpose is to become aware of the quality and performance of them and can compare them and this process could take a step towards the continuous improvement of the town. Using basic and advanced techniques in order to achieve better performance can be one of the main goals of any organization that can more exploit the situation. The organization will be able to step to improve the weakness points using these basic techniques and bring the ship of goals in the raging sea of changes to the best possible beach by the most use of the capabilities and strengths. (Ghafourian, 1383). There is an attempt in the present study that also to identifying and analyzing measures of performance) efficacy (an industrial suburb of Tehran ,appropriate model for evaluating the performance of these towns using one of the techniques of operations research (DEA) is designed and compared their
DEA approach for the first time was introduced by Charnes et al. (1978) as a method for functional analysis for the performance of homogeneous decision-making units / organizations . DEA has widely used to compare the performance of several competitive organizations within an industry. In fact, DEA has been added to the literature of economy by integrating Farrel's (1957) method , in such a way that the characteristics of the production process included several factors of production (input) and multiplicity (output) . In DEA using a set of observations, an experimental production function is constructed based on observed data. It is called "frontier analysis" because this method offers a boundary function that includes all the data and in other words, envelops all the data. Since the DEA method is based on a set of optimizations and there is no parameter for estimation, it is a nonparametric method . In other words, DEA tool is a non-parametric boundary evaluation model used for the relative measurement and the performance of a set of comparable entities (called decision-maker units: DMU) in converting inputs to outputs. In the literature of DEA, the basic assumption is the homogeneity of DMUs. This means that DMUs have the same inputs and outputs for measuring . In fact, this approach measures the efficiency of a DMU compared to other DMUs within an organization or in a similar industry. For this reason, the performance score obtained by DEA is called a relative efficiency.
DOI: 10.4236/jtts.2019.93018 292 Journal of Transportation Technologies nals, in particular, terminal operations involve the number of cranes available for use (how much loading/unloading is operationally possible) and crane per- formance (number of movements per crane per hour). For shipping lines, termi- nal operations are crucial to their overall operations since it affects the time ships spend at the port. Another component of performance includes the movement of cargo within the container yard. In container yard, operations are based on the terminal capacity, containers are stacked and container density is checked at a particular rate. For an efficient container yard performance, the appropriate space and equipment are essential as well as a proper organization because inef- ficient yard operations can affect the time duration trucks will spend at the yard. 2.3. Efficiency and Productivity Measurement in Port Sector Several methodologies have been used with the aim of analyzing port efficiency and providing useful information for port development planning and strategy. The most widely used productivity and efficiency measurement approaches in- clude the DataEnvelopmentAnalysis (DEA). The application of the DEA me- thod to the port industry is not new. Different variations of the DEA technique have been used to analyze port production in various regions worldwide. The advantages of the DEA method is that multiple inputs and outputs can be added to the model, and therefore has the capability of providing an overall evaluation of port performance (Wang T.F., et al. , 2003) .