DEA(data envelopment analysis) is a technique for evaluation of relative efficiency of decision making units (DMUs) that have multiple input and output. A DEA model measures the efficiency of a DMU by the relative position of the DMU’s input and output in the production possibility set defined by the input and output of the DMUs being compared. In this paper, we proposed several DEA models measuring the multi-period efficiency of a DMU. First, we defined the input and output data that make a production possibility set as the spanning set. We proposed several spanning sets containing input and output of entire periods for measuring the multi-period efficiency of a DMU. We defined the production possibility sets with the proposed spanning sets and gave DEA models under the production possibility sets. Some models measure the efficiency score of each period of a DMU and others measure the integrated efficiency score of the DMU over the entire period. For the test, we applied the models to the sample data set from a long term university student training project. The results show that the suggested models may have the better discrimination power than CCR based results while the ranking of DMUs is not different.

This study analyzed the relationship between efficient pitchers and teams advancing to the postseason in Korean professional baseball through DEA. A total of 1,133 pitchers who threw more than one inning from the 2014 season to the 2018 season were selected for this study. For DEA analysis, input variables were selected as annual salary and inning output variables as Wins, Saves, and Holds and the number of efficient pitchers for each season was classified using the input-oriented BCC model. After that, it was divided into two groups based on joining the postseason or not, and the number of efficient pitchers was compared through a prop test. As a result of the analysis, the groups that advanced to the postseason in the rest of the season except for the 2014 and 2017 seasons had more efficient pitchers. Considering that the 2014 season recorded the highest WAR (Wins Above Replacement) at 183.56 compared to other seasons, most pitchers threw well, and in the 2017 season, they made more mistakes in pitching than in other seasons, but they performed well in batters. The results of this study have expanded the research field using efficiency analysis in professional baseball and can be used as useful data for practical research.

COVID-19가 전 세계를 강타하면서 각 국가는 대혼란에 빠졌다. 전 세계 화물교역은 80 % 이상이 해상운송을 통해 이루어지고 있어 화물과 여객을 포함한 해상운송업은 COVID-19의 큰 영향을 받는 산업으로 예측되었다. 따라서 본 연구의 목적은 코로나 팬데믹 (Coronavirus Pandemic) 발생 전후로 아시아 주요 항만 컨테이너 항구의 팬데믹 전후 운영효율성을 분석하는 것이다. 항만의 운영효율성을 분석하기 위해서 자료포락분석(DEA)을 이용하였다. 본 연구의 분석 기간은 5년(2016~2020년)으로 2016년, 2017년, 2018년, 2019년을 코로나 이전으로 하고, 2020년을 포스트 코로나 시대로 설정하였다. 또한, 분석 대상으로는 아시아 상위 10개 항구 중 동종 DMU의 DEA 요건을 충족시킨 상하이, 광저우, 선전, 닝보-저우산, 부산 및 싱가포르 총 7개 항구를 선택하였다. DEA의 CCR 및 BCC 모델의 결과는 몇 가지 비효율성이 확인되었음에도 COVID-19 팬데믹 발생 시점에서 몇 개월 이후부터는 전반적으로 운영효율성이 코로나 이전 몇 년 동안보다 상대적으로 높았음을 확인하였다. 하지만 일부 항만 (부산, 광저우)의 경우에는 더욱 나은 운영효율성을 위해서 항만의 규모와 운영의 기술적 능력 등을 제고 할 필요가 있다.

It is highly challenging to measure the efficiency of electric vehicle charging stations (EVCSs) because factors affecting operational characteristics of EVCSs are time-varying in practice. For the efficiency measurement, environmental factors around the EVCSs can be considered because such factors affect charging behaviors of electric vehicle drivers, resulting in variations of accessibility and attractiveness for the EVCSs. Considering dynamics of the factors, this paper examines the technical efficiency of 622 electric vehicle charging stations in Seoul using data envelopment analysis (DEA). The DEA is formulated as a multi-period output-oriented constant return to scale model. Five inputs including floating population, number of nearby EVCSs, average distance of nearby EVCSs, traffic volume and traffic congestion are considered and the charging frequency of EVCSs is used as the output. The result of efficiency measurement shows that not many EVCSs has most of charging demand at certain periods of time, while the others are facing with anemic charging demand. Tobit regression analyses show that the traffic congestion negatively affects the efficiency of EVCSs, while the traffic volume and the number of nearby EVCSs are positive factors improving the efficiency around EVCSs. We draw some notable characteristics of efficient EVCSs by comparing means of the inputs related to the groups classified by K-means clustering algorithm. This analysis presents that efficient EVCSs can be generally characterized with the high number of nearby EVCSs and low level of the traffic congestion.

Evaluating the operational efficiency of electric vehicle charging stations (EVCSs) is important to understand charging network evolution and the charging behavior of electric vehicle users. However, aggregation of efficiency performance metrics poses a significant challenge to practitioners and researchers. In general, the operational efficiency of EVCSs can be measured as a complicated function of various factors with multiple criteria. Such a complex aspect of managing EVCSs becomes one of the challenging issues to measure their operational efficiency. Considering the difficulty in the efficiency measurement, this paper suggests a way to measure the operational efficiency of EVCSs based on data envelopment analysis (DEA). The DEA model is formulated as constant returns of output-oriented model with five types of inputs, four of them are the numbers of floating population and nearby charging stations, distance of nearby charging stations and traffic volume as desirable inputs and the other is the traffic speed in congestion as undesirable one. Meanwhile, the output is given by the charging frequency of EVCSs in a day. Using real-world data obtained from reliable sources, we suggest operational efficiencies of EVCSs in Seoul and discuss implications on the development of electric vehicle charging network. The result of efficiency measurement shows that most of EVCSs in Seoul are inefficient, while some districts (Nowon-gu, Dongdaemun-gu, Dongjak-gu, Songpa-gu, Guro-gu) have relatively more efficient EVCSs than the others.

Korea’s R&D investment has significantly increased in recent years and the quantitative outputs such as number of papers and patents have also increased with the investment. However, the quality of R&D outputs has not been fully addressed. In particular, quality of technical performance, such as the quality of patents, has attracted little attention. In this paper, a Data Envelopment Analysis (DEA) method was used to construct models for efficiency analysis of R&D investment, focused on quality of technical performance. Indices were proposed to analyze the quantitative and qualitative efficiencies of R&D investment. In order to effectively analyze R&D efficiencies, the measurement units of the input and output variables were standardized. Based on cases of livestock quarantine R&D projects of Korea, the quantitative and qualitative efficiencies of national R&D projects were analyzed and factors that would influence R&D efficiencies were identified. This paper suggests that both quantitative and qualitative efficiencies should be considered when measuring R&D efficiency. Also, it is recommended to carefully consider the characteristics of R&D projects during project selection stage.

Most of the data envelopment analysis (DEA) models evaluate the relative efficiency of a decision making unit (DMU) based on the assumption that inputs in a specific period are consumed to produce the output in the same period of time. However, there may be some time lag between the consumption of input resources and the production of outputs. A few models to handle the concept of the time lag effect have been proposed. This paper suggests a new multi-period input DEA model considering the consistent time lag effects. Consistency of time lag effect means that the time delay for the same input factor or output factor are consistent throughout the periods. It is more realistic than the time lag effect for the same output or input factor can vary over the periods. The suggested model is an output-oriented model in order to adopt the consistent time lag effect. We analyze the results of the suggested model and the existing multi period input model with a sample data set from a long-term national research and development program in Korea. We show that the suggested model may have the better discrimination power than existing model while the ranking of DMUs is not different by two nonparametric tests.

PURPOSES: This study evaluates the efficiency of snow removal operation resources using data envelopment analysis (DEA). The results of this study can help decision-making strategies, especially for resource allocation for snow removal works on national highways. METHODS: First, regional road management offices (DMUs) for efficiency evaluation were set up, and a database (for years 2012-2016) for analysis was formed. Second, DEA was carried out by selecting input and output variables based on the constructed database. Lastly, based on the results of the DEA, the efficiency of each regional road management office was evaluated. In addition, the potential for future improvement was determined. RESULTS: The results showed that there was a large variation in efficiency of snow removal operation resources by regional offices. CONCLUSIONS: The results of this study imply that the evaluation of efficiency for snow removal operation resources is important when decisions related to snow-removal strategies are made by road management offices.

We studied the efficiency of service quality of loan consultants contracted to a bank in Korea. Since the consultant is not an employee of the bank, he/she is paid solely in proportion to how much he/she sell loans. In this study, a consultant is considered as a decision making unit (DMU) in the DEA (Data Envelopment Analysis) model. We use a principal component analysis-data envelopment analysis (PCADEA) model to evaluate quality efficiency of the consultants. In the first stage, we use PCA to obtain 6 synthetic indicators, including 4 input indicators and 2 output indicators, from survey results in which questionnaire items are constructed on the basis of SERVQUAL model. In the second stage, 3 DEA models allowing negative values are used to calculate the relative efficiency of each DMU. An example illustrates the proposed process of evaluating the relative quality efficiency of the loan consultants.

Loan consultants assist clients with loan application processing and loan decisions. Their duties may include contacting people to ask if they want a loan, meeting with loan applicants and explaining different loan options. We studied the efficiency of service quality of loan consultants contracted to a bank in Korea. They do not work as a team, but do work independently. Since he/she is not an employee of the bank, the consultant is paid solely in proportion to how much he/she sell loans. In this study, a consultant is considered as a decision making unit (DMU) in the DEA (Data Envelopment Analysis) model. We use a principal component analysis-data envelopment analysis (PCA-DEA) model integrated with Shannon’s Entropy to evaluate quality efficiency of the consultants. We adopt a three-stage process to calculate the efficiency of service quality of the consultants. In the first stage, we use PCA to obtain 6 synthetic indicators, including 4 input indicators and 2 output indicators, from survey results in which questionnaire items are constructed on the basis of SERVQUAL model. In the second stage, 3 DEA models allowing negative values are used to calculate the relative efficiency of each DMU. In the third stage, the weight of each result is calculated on the basis of Shannon’s Entropy theory, and then we generate a comprehensive efficiency score using it. An example illustrates the proposed process of evaluating the relative quality efficiency of the loan consultants and how to use the efficiency to improve the service quality of the consultants.

This paper was to evaluate social enterprises’ management efficiency with Data Envelope Analysis (DEA). The data was based on the 168 social enterprises’ of annual performance reports published in 2015. The research focused on to measure both financial efficiency and social impact of the companies simultaneously. To apply DEA, the paper classified the enterprises into seven types based on types of socal impacts which each company provides before the estimation of the efficiency. The research results showed that group D, which employes disadvantaged people, provides social services and shares resources was the most efficient group and had higest net worths in Pure Technical Efficiency. In contrast, Group B, which only employs social advantage people and provides social service, was the least efficient one. The research suggests a practical and efficient framework in measuring social enterprises’ management efficiency, including both the financial performance and social impacts simultaneously with their self-publishing reports. Because the Korea Social Enterprise Promotion Agency does not open business reports which social enterprises submit each year, there are basic limitations on researchers attempting to analyse with data from all social enterprises in Korea. Thus, this study dealt with only 10% of the social enterprises which self-published their performance report on the Korea Social Enterprise Promotion Agency’s web site. Regardless of these limitations, this study suggested substantial methods to estimate management efficiency with the self-published reports. Because self-publishing is increasing each year, it will be the main source of information for researchers in examining and evaluating social enterprises’ financial performance or social contribution. The research suggests a practical and efficient framework in measuring social enterprises’ management efficiency, including both the financial performance and social impacts simultaneously with their self-publishing reports. The research results suggest not only list of efficient enterprises but also methods of improvement for less efficient enterprises.

The fisheries industry has led the Korean economy, and has been achieving high-level position in the world. However, this industry meets aging, low growth and profit. In order to overcome this critical situation, it is needed to understand the overall status of industry. In industry level, most of previous researches focused on ocean industry rather than fisheries. In addition, scholars have been getting a lot of attention about fisheries cooperatives, fishing-ports, methods of fishery, and manufacturing process in fisheries sector. The aim of this research is analysis of domestic fisheries industry’s managerial performance using data envelopment analysis(DEA) considering operating and scale view. Furthermore, the comparative analysis is performed by firm size, and industry type. In results, fisheries industry’s managerial performance is not high, overall. In more detail, most of big size firms are under decreasing returns to scale(DRS) status. Fishery processing industry’s performance is low, and fishery distribution industry has the best performance. This paper suggests that transferring operating capability from big firms to small firms, and policy supports and firm’s activities should be accompanied for high-value added in fisher, and fishery processing industries.

The present study has aimed at analyzing the technical and scale efficiencies of credit utilization by the farmer-borrowers in Chittoor district of Andhra Pradesh, India. DEA approach was followed to analyze the credit utilization efficiency and to analyze the factors influencing the credit utilization efficiency, log-linear regression analysis was attempted. DEA analysis revealed that, the number of farmers operating at CRS are more in number in marginal farms (40%) followed by other (35%) and small (17.5%) farms. Regarding the number of farmers operating at VRS, small farmers dominate the scenario with 72.5 per cent followed by other (67.5%) and marginal (42.5%) farmers. With reference to scale efficiency, marginal farmers are in majority (52.5%) followed by other (47.5%) and small (25%) farmers. At the pooled level, 26.7 per cent of the farmers are being operated at CRS, 63 per cent at VRS and 32.5 per cent of the farmers are either performed at the optimum scale or were close to the optimum scale (farms having scale efficiency values equal to or more than 0.90). Nearly 58, 15 and 28 percents of the farmers in the marginal farms category were found operating in the region of increasing, decreasing and constant returns respectively. Compared to marginal farmers category, there are less number of farmers operating at CRS both in small farmers category (15%) and other farmers category (22.5%). At the pooled level, only 5 per cent of the farmers are operating at DRS, majority of the farmers (73%) are operating at IRS and only 22 per cent of the farmers are operating at CRS indicating efficient utilization of credit. The log-linear regression model fitted to analyze the major determinants of credit utilization (technical) efficiency of farmer- borrowers revealed that, the three variables viz., cost of cultivation and family expenditure (both negatively influencing at 1% significant level) and family income (positively influencing at 1% significant level) are the major determinants of credit utilization efficiency across all the selected farmers categories and at pooled level. The analysis further indicate that, escalation in the cost of cultivation of crop enterprises in the region, rise in family expenditure and prior indebtedness of the farmers are showing adverse influence on the credit utilization efficiency of the farmer-borrowers.

India is largest producer of banana in the world producing 29.72 million tonnes from an area of 0.803 million ha with a productivity of 35.7 MT ha-1 and accounted for 15.48 and 27.01 per cent of the world’s area and production respectively (www.nhb.gov.in). In India, Tamil Nadu leads other states both in terms of area and production followed by Maharashtra, Gujarat and Andhra Pradesh. In Rayalaseema region of Andhra Pradesh, Kurnool district had special reputation in the cultivation of banana in an area of 5765 hectares with an annual production of 2.01 lakh tonnes in the year 2012-13 and hence, it was purposively chosen for the study. On 23rd November 2003, the Government of Andhra Pradesh has commenced a comprehensive project called ‘Andhra Pradesh Micro Irrigation Project (APMIP)’, first of its kind in the world so as to promote water use efficiency. APMIP is offering 100 per cent of subsidy in case of SC, ST and 90 per cent in case of other categories of farmers up to 5.0 acres of land. In case of acreage between 5-10 acres, 70 per cent subsidy and acreage above 10, 50 per cent of subsidy is given to the farmer beneficiaries. The sampling frame consists of Kurnool district, two mandals, four villages and 180 sample farmers comprising of 60 farmers each from Marginal (<1ha), Small (1-2ha) and Other (>2ha) categories. A well structured pre-tested schedule was employed to collect the requisite information pertaining to the performance of drip irrigation among the sample farmers and Data Envelopment Analysis (DEA) model was employed to analyze the performance of drip irrigation in banana farms. The performance of drip irrigation was assessed based on the parameters like: Land Development Works (LDW), Fertigation costs (FC), Volume of water supplied (VWS), Annual maintenance costs of drip irrigation (AMC), Economic Status of the farmer (ES), Crop Productivity (CP) etc. The first four parameters are considered as inputs and last two as outputs for DEA modelling purposes. The findings revealed that, the number of farms operating at CRS are more in number in other farms (46.66%) followed by marginal (45%) and small farms (28.33%). Similarly, regarding the number of farmers operating at VRS, the other farms are again more in number with 61.66 per cent followed by marginal (53.33%) and small farms (35%). With reference to scale efficiency, marginal farms dominate the scenario with 57 per cent followed by others (55%) and small farms (50%). At pooled level, 26.11 per cent of the farms are being operated at CRS with an average technical efficiency score of 0.6138 i.e., 47 out of 180 farms. Nearly 40 per cent of the farmers at pooled level are being operated at VRS with an average technical efficiency score of 0.7241. As regards to scale efficiency, nearly 52 per cent of the farmers (94 out of 180 farmers) at pooled level, either performed at the optimum scale or were close to the optimum scale (farms having scale efficiency values equal to or more than 0.90). Majority of the farms (39.44%) are operating at IRS and only 29 per cent of the farmers are operating at DRS. This signifies that, more resources should be provided to these farms operating at IRS and the same should be decreased towards the farms operating at DRS. Nearly 32 per cent of the farms are operating at CRS indicating efficient utilization of resources. Log linear regression model was used to analyze the major determinants of input use efficiency in banana farms. The input variables considered under DEA model were again considered as influential factors for the CRS obtained for the three categories of farmers. Volume of water supplied (X1) and fertigation cost (X2) are the major determinants of banana farms across all the farmer categories and even at pooled level. In view of their positive influence on the CRS, it is essential to strengthen modern irrigation infrastructure like drip irrigation and offer more fertilizer subsidies to the farmer to enhance the crop production on cost-effective basis in Kurnool district of Andhra Pradesh, India. This study further suggests that, the present era of Information Technology will help the irrigation management in the context of generating new techniques, extension, adoption and information. It will also guide the farmers in irrigation scheduling and quantifying the irrigation water requirements in accordance with the water availability in a particular season. So, it is high time for the Government of India to pay adequate attention towards the applications of ‘Information and Communication Technology (ICT) and its applications in irrigation water management’ for facilitating the deployment of Decision Supports Systems (DSSs) at various levels of planning and management of water resources in the country.

Conventional data envelopment analysis (DEA) models require that inputs and outputs are given as crisp values. Very often, however, some of inputs and outputs are given as imprecise data where they are only known to lie within bounded intervals. While a typical approach to addressing this situation for optimization models such as DEA is to conduct sensitivity analysis, it provides only a limited ex-post measure against the data imprecision. Robust optimization provides a more effective ex-ante measure where the data imprecision is directly incorporated into the model. This study aims to apply robust optimization approach to DEA models with imprecise data. Based upon a recently developed robust optimization framework which allows a flexible adjustment of the level of conservatism, we propose two robust optimization DEA model formulations with imprecise data; multiplier and envelopment models. We demonstrate that the two models consider different risks regarding imprecise efficiency scores, and that the existing DEA models with imprecise data are special cases of the proposed models. We show that the robust optimization for the multiplier DEA model considers the risk that estimated efficiency scores exceed true values, while the one for the envelopment DEA model deals with the risk that estimated efficiency scores fall short of true values. We also show that efficiency scores stratified in terms of probabilistic bounds of constraint violations can be obtained from the proposed models. We finally illustrate the proposed approach using a sample data set and show how the results can be used for ranking DMUs.

Due to the severe market conditions, pre-entrepreneur seeks to start their business through franchise company. This paper, using the data envelopment analysis(DEA) method, examines efficiency of a group of franchise company in order to provide efficiency information with pre-entrepreneur. Output-oriented DEA model is applied in the investigation of efficiency, and the overall efficiency score is decomposed into pure technical efficiency and scale efficiency. The input variables selected to evaluate the efficiency are franchise deposit, franchise contribution cost and the output variables are sales and number of franchises, and length of business. The results of this paper show franchise industry have the low level of overall efficiency and the main sources of inefficiency is found technical rather than scale. As a result, this paper provides not only the current status of efficiency information of a franchise with pre-entrepreneur but also give warning when they sign-up with franchise business.

정보화 사회의 도래에 따른 정보통신기술의 발전과 활용이 국가 경제구조 및 성장에 급격한 영향을 미치고 있는 추세에 맞춰 본 연구는 자료포락분석과 맘퀴스트지수를 활용하여 국가별 정보통신기술의 활용성과를 상대적 효율성 및 생산성의 관점에서 접근하고자 하였다. 투입요소로 ICT환경과 ICT이용준비도 그리고 산출물로 ICT활용도를 이용하여 총 28개 국가를 대상으로 2008년부터 2011년 동안 정보통신기술 활용성과를 진단한 결과, 자료포락분석에서는 전체적인 ICT 효율성이 감소한 것으로 나타나 외형적 성장에 비해 실질적인 ICT 활용 부문은 부진한 것으로 판단되었고, 맘퀴스트지수 분석결과에서도 전체적인 ICT 생산성은 지난 3개년 구간동안 개선되지 않은 것으로 분석되었다. 이러한 분석결과를 종합적으로 고려해 볼 때, ICT 활용성과를 제고하기 위해서는 지금까지 추진해온 물리적 요소의 양적 투입에 의존하는 외형적 개발정책보다는 투입요소와 산출물을 합목적적으로 연계시키고 ICT 활용 효율성을 증진시킬 수 있는 다각적인 운영 합리화 방안이 필요하다고 본다.

Due to the competition between the various professional events, it is imperative for the team's management to improve efficiency by removing the inefficiencies of the professional team in order to gain a competitive edge. This study use different Data Envelopment Analysis (DEA) models to measure the efficiency of professional sport organizations. In this mathematical-analytical study, this study first reviews the related literature to analyze the input/output variables. In the end, the variables were detected and the data for this study were gathered from the Korean Basketball League (KBL). While previous studies examine relative efficiency of Korean Professional baseball teams by using CCR model, this study fully utilize the DEA method to investigate Korean professional sports organizations' operating problem. Thus, this study propose full results of DEA analysis such as efficiency score (overall, technical, and scale efficiency), slacks in inputs and outputs of inefficient organizations, Malmquist index) As a result, this study provides not only the exact productivity information of a team and a way of improving a firm's productivity with a decision maker.

Data Envelopment Analysis (DEA) is a useful tool to analyze the relative efficiency of decision making units (DMU) characterized by multiple inputs and multiple outputs. This method has been popularly used as an analytical tool to suggest some strategic improvement. To do this, the results of DEA provide decision makers with a single efficiency score, efficient frontier, return to scale, benchmarking decision making units, etc. The purpose of this paper is to evaluate research performance of 38 universities and provide an inefficient university with the way of organizational changes to be an efficient university by using DEA. Various input and output variables are used to identify technical and scale inefficiency. Additionally, we analyze how an inefficient DMU could be changed an efficient DMU based on a case university. This result will give an insight of constructive directions for increasing of research performance to university decision makers.