ASEAN Mass Rapid Transit Efficiency Assessment using Data Envelopment Analysis

In this article an application of Data Envelopment Analysis – DEA is carried out to evaluate the existence of constant or variable returns to scale in the performance of technological learning institutions with an emphasis on high school education. CCR, BCC and Guarantee Region models are tested for a set of 23 Federal Centers for Technological Education of Brazil (CEFETs). DEA is used to test the existence of constant (CCR model) or variable (BCC model) returns to scale, adopting enrollments, the number of entries and the results from the National High School Exam (ENEM) as products, and budget, faculty and faculty degrees as inputs. The main results suggest there are variable returns to scale for budget and faculty, but a constant return for degrees. The theoretical implications of the findings suggest that an analysis of each input should be carried out before modeling the virtual input and choosing the DEA model. Keywords: Performance Evaluation; DEA; Data Envelopment Analysis; Education; Returns to Scale.

Data envelopment analysis (DEA) is a non-parametric technique for measuring the efficiency of decision making units (DMU) with exact input and output terms. So far, a number of DEA models with interval data have been developed. The radial DEA model such as CCR or BCC model with interval data are well known as basic DEA models with interval data. However, these radial DEA models have some defects. In this study, we develop a new approach based upon the slacks-based measurement (SBM) of super-efficiency model for dealing with interval data in DEA. The SBM super-efficiency DEA model can not only discriminate the performance of efficient DMUs from inefficient ones, but can distinguish between the efficient DMUs. In addition, this model can overcome the drawback that the super-efficiency model can be infeasible under variable returns to scale (VRS) assumption. An illustrative example is provided to verify the idea of this paper and show its potential application and validity.

Using statistically designed experiments, 12,500 observations are generated from a “4-pieced Cobb-Douglas function” exhibiting increasing and decreasing returns to scale in its different pieces. Performances of DEA and frontier regressions represented by COLS (Corrected Ordinary Least Squares) are compared at sample sizes ofn=50, 100, 150 and 200. Statistical consistency is exhibited, with performances improving as sample sizes increase. Both DEA and COLS generally give good results at all sample sizes. In evaluating efficiency, DEA generally shows superior performance, with BCC models being best (except at “corner points”), followed by the CCR model and then by COLS, with log-linear regressions performing better than their translog counterparts at almost all sample sizes. Because of the need to consider locally varying behavior, only the CCR and translog models are used for returns to scale, with CCR being the better performer. An additional set of 7,500 observations were generated under conditions that made it possible to compare efficiency evaluations in the presence of collinearity and with model misspecification in the form of added and omitted variables. Results were similar to the larger experiment: the BCC model is the best performer. However, COLS exhibited surprisingly good performances — which suggests that COLS may have previously unidentified robustness properties — while the CCR model is the poorest performer when one of the variables used to generate the observations is omitted.

This study aimed to measure the efficiency scores of 75 dairy buffalo farms in the province of Nueva Ecija, Central Luzon, Philippines, using an input-oriented, variable-return-to-scale Data Envelopment Analysis (DEA) model. The farmer-informants or decision-making units (DMUs) were categorized as smallholders, family modules, and semi-commercial in operations. Personal interviews using structured questionnaires were done to gather various information on the socio-economic and management practices of the DMUs. Output in the form of volume and value of milk produced and inputs such as quantities and costs of biologics, feeds, forage, and labor were also collected and evaluated among individual DMUs. The efficiency scores were computed using PIM-DEA software, which identified fully efficient DMUs lying on the frontier line (scores of 1.0) and those enveloped by it (inefficient DMUs with scores of less than 1.0). The overall mean Technical Efficiency (TE), Allocative Efficiency (AE), and Economic Efficiency (EE) scores among the DMUs were 0.80, 0.81, and 0.65, respectively. Most of the inefficient DMUs were in the smallholder category. In sum, smallholder DMUs classified under low and moderate TE clusters should reduce their inputs by 53.31% and 40.01%, respectively, to become fully efficient. Likewise, higher lambda values among efficient peer DMUs indicate the best practice frontiers that the inefficient peer DMUs can benchmark with. Extension and advisory services can help promote the best management practices of the frontiers to improve the TE, AE, and EE of the inefficient DMUs.

The present study seeks to investigate efficiency of roads in provinces in Iran. Considering the resource limitation, prioritisation of roads seems necessary for better allocation of resources. Since it is possible to investigate decision-making units (DMUs) where there are several inputs and outputs without considering any weight for them in data envelopment analysis, this method is very suitable for ranking. In order to analyse efficiency, BCC model with variable return to scale was used and given that a number of DMUs become efficient after solving the model, Anderson-Peterson method was employed to determine strong efficiency. Considering that one of the advantages of data envelopment analysis is the possibility of offering a progress model for inefficient DMUs, the essential changes in inputs and outputs of inefficient DMUs for being efficient have been mentioned.

A generalized model for data envelopment analysis with interval data

This paper analyzes the cases where the hotels have increased their services and enhanced their work process through IT solutions to cope with computerization globalization. Also the cases have been studies where national hotels use the CRM solution internally to respond effectively to customers requests, increase customer analysis, and build marketing strategies. In particular, this study discusses the introduction of the CRM solutions and CRM sales business and marketing services using a process for utilizing the presumed, CRM by introducing effective DEA(Data Envelopment Analysis). First, the comparison has done regarding the relative efficiency of L Company with the CCR model, then compared L Company's restaurants and facilities' effectiveness through BCC model. L Company reached a conclusion that it is important to precisely create and manage sales data which are the preliminary data for CRM, and for that reason it made it possible to save sales data generated by POS system on each sales performance database. In order to do that, it newly established Oracle POS system and LORIS POS system concerned with restaurants for food and beverage as well as rooms, and made it possible to stably generate and manage sales data and manage. Moreover, it set up a composite database to control comprehensively the results of work processes during a specific period by collecting customer registration information and made it possible to systematically control the information on sales performances. By establishing a system which unifies database and managing it comprehensively, impeccability of data has been greatly enhanced and a problem which generated asymmetric data could be thoroughly solved. Using data accumulated on the comprehensive database, sales data can be analyzed, categorized, classified through data mining engine imbedded in Polaris CRM and the results can be organized on data mart to provide them in the form of CRM application data. By transforming original sales data into forms which are easy to handle and saving them on data mart separately, it enabled acquiring well-organized data with ease when engaging in various marketing operations, holding a morning meeting and working on decision-making. By using summarized data at data mart, it was possible to process marketing operations such as telemarketing, direct mailing, internet marketing service and service product developments for perceived customers; moreover, information on customer perceptions which is one of CRM's end-products could feed back into the comprehensive database. This research was undertaken to find out how effectively CRM has been employed by comparing and analyzing the management performance of each enterprise site and store after introducing CRM to Hotel enterprises using DEA technique. According to the research results, efficiency evaluation for each site was calculated through input and output factors to find out comparative CRM system usage efficiency of L's Company four sites; moreover, with regard to stores, the sizes of workforce and budget application show a huge difference and so does the each store efficiency. Furthermore, by using the DEA technique, it could assess which sites have comparatively high efficiency and which don't by comparing and evaluating hotel enterprises IT project outcomes such as CRM introduction using the CCR model for each site of the related enterprises. By using the BCC model, it could comparatively evaluate the outcome of CRM usage at each store of A site, which is representative of L Company, and as a result, it could figure out which stores maintain high efficiency in using CRM and which don't. It analyzed the cases of CRM introduction at L Company, which is a hotel enterprise, and precisely evaluated them through DEA. L Company analyzed the customer analysis system by introducing CRM and achieved to provide customers identified through client analysis data with one to one tailored services. Moreover, it could come up with a plan to differentiate the service for customers who revisit by assessing customer discernment rate. As tasks to be solved in the future, it is required to do research on the process analysis which can lead to a specific outcome such as increased sales volumes by carrying on test marketing, target marketing using CRM. Furthermore, it is also necessary to do research on efficiency evaluation in accordance with linkages between other IT solutions such as ERP and CRM system.

In recent years, attention to the high debt ratio in public institutions has pushed the government to make efforts in reducing the debt ratio. However, in order to stimulate the economy, the government needs drastically innovative measures that reduce debt by improving efficiency rather than moderate approaches that focus solely on debt reduction. Despite this need, no study has yet systematically analyzed the overall efficiency of domestic public institutions and identified the source of inefficiencies in each public entity. Therefore, largely two research questions are examined. First, this study compares the efficiency levels by types of public institutions. Second, this study identifies the cause of inefficiencies in each public institution and proposes directions for improving efficiency. Based on a 5-year data of 302 public institutions published in public business information systems and organizational websites from 2009 to 2013, Data Envelopment Analysis (DEA) was performed. The input variables include the number of employees and total costs while the output variables include sales and net income. Reflecting the characteristics of public institutions, the input-oriented CCR model and input-oriented BCC model were utilized. Analysis results are as follows. First, market-oriented public institutions showed the highest efficiency while fund management quasi-governmental agencies showed the highest inefficiency. Second, scale efficiency score was measured by applying the CCR model and the BCC model on the organizations with the lowest efficiency level, fund management quasi-governmental agencies. Based on these analysis results, the source of inefficiency and detailed directions for improvement were proposed for Decision Making Units (DMUs) with low CCR and BCC scores.

Efficiency of hospitals belongs among frequently discussed topics of public policy. This paper deals with a nonparametric model for efficiency evaluation of hospitals - data envelopment analysis (DEA). DEA evaluates efficiency of production units with the help of mathematical programming. DEA specifies the production frontier as the most pessimistic piecewise linear envelopment of the data. In case that a hospital is inefficient DEA determines the sources of inefficiency and defines corresponding target values. In this study, authors analyze the sample of 22 Czech acute-care hospitals with the constant-returns-to-scale model (the CCR model) and the variable-returns-to-scale model (the BCC model). The data comes from regional yearbooks of the Institute of Health Information and Statistics of the Czech Republic (ÚZIS ČR). A fundamental problem of evaluation in health care is a definition of inputs and outputs, especially a measurement of case-mix of patients. Interpretation of the results is described on one selected hospital (Hospital of Prachatice). One hospital was identified as efficient by the CCR model and six hospitals were efficient according to the BCC model.

Information technology (IT) is defined as the obtainment, procedure, storage and propagation of sounding, drawing, and textual information by combining microelectronics-based computing and telecommunications. Nowadays, IT is starting to spread further from the conventional personal computer and network technologies to integrations of other fields of technology such as the use of cell phones, televisions, automobiles, etc. In other words, IT has penetrated in daily life of human beings and become one part of the whole society. The importance of IT has become momentous. Therefore, to understand the performance of efficiency and productivity of the IT firms is critical for managers as well as for personal investors. Until now, there are very few researches tried to analyze final performance of the IT firms. As a result, this research intends to use traditional Data Envelopment Analysis (DEA) CCR or BCC models to evaluate the performance of IT firms. The Decision Making Units (DMUs) on this research are chosen from IT firms in S&P 500. However, the traditional DEA models are not fair models from the aspect of improper weight derivations. Thus, this paper intends to analyze the efficiency of IT firms in S&P 500 efficiencies by using multiple objective programming (MOP) based Data Envelopment Analysis (DEA). In a MOP based DEA approach, DMUs will be evaluated based on an equal standard and the results will be evaluated more fairly. The world’s leading IT firms in S&P 500 will be evaluated based on publicly available financial reports of the fiscal year 2010. In addition, the newly developed MOP can improve the traditional DEA’s unfair weights problems and benchmark the efficiency of IT firms in S&P 500 correctly. In the empirical study, the MOP based DEA demonstrated that F5 Networks should be the communications equipment companies of IT worthwhile to be invested. In the future, performance evaluation results can be served as foundations for investment strategies definition.KeywordsInformation Technology (IT)Standard and Poor’s 500 indexPerformance EvaluationData Envelopment Analysis (DEA)Multiple Objective Programming (MOP)

E-Government is one area of digitization that has been under way for several years in European countries. In this paper, we focus on identifying different indices that are aimed at measuring digitalization or e-Government. The results of the analysis showed that there are several indices that focus on this area within the EU, such as EGDI, EPI, LOSI, DGI, e-Government benchmark, Eurostat—Internet use, GII, DSGI, Going Digital toolkit, and DESI. Subsequently, the index areas to be used in the DEA method to measure the efficiency of e-Government-related inputs and outputs within the EU were identified. Inputs and outputs were selected logically and then verified using correlation analysis. Among the input and output indices chosen were Internet usage, DSGI, GII, e-Government benchmark, and interaction with public administration online. From the analysis, three inputs and three outputs were used and the models were output oriented. After implementing the correlation, it can be said that the values between the selected sub-variables are suitable for DEA analysis. Two models were chosen for the calculation, namely the CCR and BCC models. The CCR model found 10 states to be efficient and BCC model found 13 states to be efficient. In addition, in the close analysis, we took a closer look at the CCR model’s inference. Countries such as Denmark, Finland, Estonia, Malta, and Portugal were efficient outliers. When comparing the regions within the EU, we can conclude that the countries of Northern Europe are the most efficient in the field of digitalization (e-Government). As many as four countries out of seven are efficient.

Dear Editor, The human development index (HDI) is a composite index calculated on the basis of three socioeconomic indicators that reflect three major dimensions of human development: longevity, educational attainment and standard of living to sufficiently capture the multidimensionality of human development. Longevity is measured by life expectancy at birth (LEB); educational attainment is measured by a weighted average of the adult literacy rate (ALR) and the combined gross educational enrolment ratios (GER). An adjusted gross domestic product (GDP) per capita, converted into US dollars on the basis of the purchasing power parity exchange rate (PPP USD), is used as a measure of a decent standard of living. For the components of the HDI, except of the GDP per capita, individual indices are calculated according to the general linear transformation: Index = actual value - fixed min imumvalue / fixed max imumvalue - fixed min mumvalue To construct the income index, the following nonlinear transformation is applied on GDP per capita, taking into account diminishing returns of higher incomes (utility adjustment): Income Index = log (actual GDP for capita) - log (min imum GDP for Capita) / log ( fixed max imum value) - ( fixed min imum value) The fixed minimum and maximum values of indicators are 25 and 85 years for LEB, 0% and 100% for ALR and GER and 100 and 40000 US$ for GDP.[1][2][3][4][5] Data envelopment analysis (DEA) is the leading technique for measuring the relative efficiency of decision making units on the basis of multiple inputs and outputs. The efficiency of a unit is defined as the weighted sum of its outputs divided by a weighted sum of its inputs and it is measured on a bounded ratio scale. The weights for inputs and outputs are estimated by a linear program in the best advantage for each unit so as to maximize its relative efficiency. Basically, DEA provides a categorical classification of the units into efficient and inefficient ones by assuming either constant returns to scale (introduced by Charnes, Cooper and Rhodes named CCR model) or variable returns to scale (introduced by Banker, Charnes and Cooper named BCC model) for the inputs and outputs.[6][7][8][9] In this paper, we have been considered the assessment of Kerman Province towns technical efficiency in accessing HDI via DEA. For this objective, in the descriptive study, we applied CCR and BCC models for assessing Kerman town’s technical efficiency in accessing HDI by using DEP2 software. Each town’s HDI was considered as output and the number of physicians for 1000 people (a proxy for life expectancy), educational staff rates (a proxy for educational attainment) and the employment rate of over 10 years working workers (a proxy for GDP) was considered as inputs for calculating the efficiency. All findings of study have been summarized in Table 1. Table 1 HDI and Efficiency scores with BCC and CCR output oriented models in the years 2000 and 2007 Based on the findings of study we concluded HDI score of all towns of Kerman Province have been improved in the year 2007 relative to year 2000. All towns were in the average range of HDI (0/5 to 0.8).[2] We can conclude that middle range scores of some towns including Bardsir, Ravar and Kahnouj resulted from input shortage but other towns scores showed some inefficiencies that can improve by more efficient use of inputs.

Data envelopment analysis (DEA) is a tool for measuring the relative efficiency of decision-making units (DMUs) that perform similar tasks. One of the uses of the DEA is the establishment of benchmarks for inefficient DMUs. These benchmarks play the role of target for inefficient DMUs. However, traditional DEA (TDEA) models fail to rank the efficient DMUs, and as a result, they cannot determine a benchmark for the inefficient DMUs. To overcome this shortcoming, this paper proposes virtual network DEA (NDEA) approach. The proposed method not only ranks the whole inefficient and efficient DMUs but also establishes a virtual benchmark called ideal DMU. The case study demonstrates the applicability of the proposed model.

This study aimed to analyze the manage achievement and technical efficiency of mango farms. The Budget method for each crop, Data Envelopment Analysis (DEA), and bootstrap DEA are used for the analysis. In the case of efficiency is calculated through input-oriented CCR and BCC models. The data used for the analysis are results of an income survey conducted by visiting 19 mango farms. The main results are as follows. First, the total income is 30,885 thousand won, the management cost is 17,597 thousand won, and the income is 12,928 thousand won per unit (10a) of mango farms. Second, the average technical efficiency (TECRS) is 0.686. This means that 31.4% of the input factors are used inefficiently. Third, the average pure technology efficiency (PTEVRS) is 0.899, and the scale efficiency (SE) is 0.762. This means that 10.1% of the inefficiencies are caused by technology and 23.8% by scale. Forth, as a result of bootstrap DEA, the average bias of the CCR model is 0.185 and the BCC model is relatively low, 0.087. Fifth, most of the inefficient farms are producing in the area of increasing return to scale that can be expected to increase efficiency through scale expansion. Sixth, as a result of examining the scale and rate at which resources can be saved by benchmarking the reference group, the cost of establishing an orchard showed the highest saving rate with an average of 45.7%. On the other hand, the material cost, which has the lowest possible reduction ratio, is the largest, with an average of 15,229 thousand won.

The aim of this study is to determine the competitiveness indices of the sub regions according to NUTS2 level in Turkey and to estimate efficiency of these indices for creating GDP per capita. In this study, it has been taken into account four basic factors -economic structure, innovation, human capital, infrastructure and accessibility- that affect both the high competition and the level of GDP per capita. In this study, the methods have been followed Exploratory Factor Analysis (EFA) and Data Envelopment Analysis (DEA). Inputs of study are seventeen items related to four factors, whereas output item is per capita GDP. In the study, regional competition indices are firstly obtained with EFA. Then, by using DEA, it has been estimated efficiency of these indices for creating GDP per capita. According to the indices’ results of economic and innovative infrastructure, skilled labor infrastructure and regional basic infrastructure, TR10 İstanbul, TR31 İzmir, TR42 Kocaeli and TR51 Ankara are most competitive sub regions of Turkey. In the CCR Model, only three sub regions - TR10 Istanbul, TR42 Kocaeli and TR51 Ankara- have become efficient in generating per capita income, whereas eight sub regions -TR10 İstanbul, TR21 Tekirdağ, TR42 Kocaeli, TR51 Ankara, TR82 Kastamonu, TRA2 Ağrı, TRB2 Van and TRC2 Şanlıurfa- are efficient in the BCC Model.