• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.224-229
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• 2009

This study measures the relative efficiency and productivity change of the Korean Non-Life Insurance Firms using DEA model and Malmquist Index for 2004-2007. The main results of this study can by summarized as follows. First, in case of efficiency of CCR for 2004-2007, the number of efficient firms(CCR value is one) are one firm, one firm, one firm, one firm respectively. Second, in case of efficiency of BCC for 2004-2007, the number of efficient firms(BCC value is one) are five firms, five firms, six firms, six firms respectively. Third, In case of return to scale for 2004-2007, DRS are five firms, DRS are five firms, DRS are six firms, DRS are six firms respectively, Fourth, Malmquist Index representing productivity change for 2004-2007 are 0.99 in 2004-2005, 1.04 in 2005-2006, 1.06 in 2006-2007.

• The Journal of the Korea Contents Association
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• v.10 no.10
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• pp.456-463
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• 2010

This study measured the relative efficiency and productivity change of the Korean professional sport teams using DEA model and Malmquist Index for 2006-2009. The main results of this study can be summarized as follows. First, in case of efficiency of CCR for 2006-2009, the number of efficient professional sport teams(CCR value is one) are two(Daejeon Citizen, Incheon United), three(Daejeon Citizen, Incheon United, FC Seoul), two(Daejeon Citizen, Incheon United), three(Kyungnam FC, Incheon United, FC Seoul) respectively. Second, Malmquist Index representing productivity change for 2006-2009 are 1.035, 0.9658, 1.5331 for Kyungnam FC, 1.2514, 0.8948, 0.9056 for Daegu FC, 1.1768, 0.8620, 0.9722 for Daejeon Citizen, 0.9368, 1.2609, 0.7714 for Incheon United, 1.0000, 1.0000, 1.0000 for FC Seoul, respectively. This result will be used by inefficient professional sport teams to improve inefficiency.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.6
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• pp.1389-1398
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• 2009

This research suggests the calculation and analytic method of determining the proper numbers of manual workers with simulation and DEA methods. For this, first of all we designed and analysed the simulation model of real facility being built in Dea-Jun. Secondly, we find the solution with DEA among various alternatives built based on the results of the simulation analysis. In the simulation process, the waiting time, the number of painted trains, and utilization of main equipment are considered as main response variables. After simulation, we built DMUs(Decision Making Unit) consisting of combined results with the manpower assignment policies, the numbers of workers, and the rate of skilled operators, and calculated the efficiency of DMUs with the DEA method. Among 27 DMUs, 4 DMUs turned out efficient technically and on scale. In conclusion, through the suggested procedure, this research shows the way that decision makers can solve the problems with various factors they should concern, along the scientific process, when building a new facility.

• Information Systems Review
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• v.9 no.2
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• pp.109-127
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• 2007

In a buyer seller transaction process, "value for money" is one of the most important criteria for a buyer's purchasing decision. The terms "value" and "money" represent a composite measure of what a buyer receives from goods and/or services and a measure of what he/she pays for them, respectively. The purpose of this paper is to help buyers select the best seller in terms of value for money. We suggest DEA models for buyer seller transactions and apply them to the case of an Internet comparison shopping site in Korea. We expect our DEA models to provide valuable information for rational buyers who want to pay the least price for high quality products/services. Moreover, we expect that our models can help sellers be more competitive by showing them how to attract buyers.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.121-128
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• 2017

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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.3
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• pp.53-60
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• 2020

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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.117-131
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• 2015

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.

• The Journal of the Korea Contents Association
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• v.17 no.2
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• pp.316-326
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• 2017

In this research, in order to analyze the efficiency of vocational education and training public services, we analyzed 27 projects conducted in Chungcheongnamdo province in 2014 using DEA. Also we compared the efficiency of 2014 projects with 2013 projects, and verified 3 representative DMU's inefficiency to suggest how to improve the efficiency. The DEA examined the efficiency using CCR model, BCC model and SE(CCR / BCC). As a result of this analysis, the total project costs and the number of projects were increased in 2014 from 2013, but it turned out that the number of inefficient projects were increased and the overall efficiency is decreased. Therefore, in order to improve efficiency, we proposed to allocate labor costs and direct project costs appropriately. In the case of large-scale projects, it was confirmed that reducing the scale of the project is important to increase the overall efficiency. Based on this research, we are presenting a research model that analyzes the efficiency of vocational education and training, and expanding the research area of performance management into the area of various public services.

• Korean Journal of Construction Engineering and Management
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• v.13 no.5
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• pp.94-102
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• 2012

Recently, the global financial crisis and the increasing number of unsold houses in Korea are construction companies to assess their efficiency. The most important factor in analyzing the efficiency of a company is the input-output variable. However, systematic stud the core input-output variables, which have a great influence on the efficiency analysis. Thus, to the core input-output variables for efficiency analysis of construction companies, this study propose a model that includes all combinations of input-output variables and to find the core input-output variables using the Data Envelopment Analysis(DEA) model and Principal Component Analysis(PCA). Existing research and theories were studied variables and 21 models were established to measure efficiency. were obtained that the core input and output variable in 2006 the number of employees and sales. For 2008, the core input variable was capital stock and the core output variable was quarterly net profit. For 2010, the core input variable was fixed asset and the core output variable was sales. Through obtaining the variables that greatly affect the efficiency of construction companies, it is considered that individual construction companies will be able to prepare a priority strategy to enhance efficiency.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.4
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• pp.87-101
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• 2024

This study aims to provide insights for the efficient utilization of resources by analyzing the entrepreneurship support efficiency of Korean universities. To identify the factors influencing the number of entrepreneurs, which is the primary goal of university entrepreneurship support, a multiple regression analysis was conducted, identifying five effective independent variables. Using these five identified independent variables as input variables and the number of entrepreneurs as the output variable, the DEA method was used to analyze the efficiency of entrepreneurship support for each university as of 2023. The analysis of 150 four-year universities in Korea showed that nine universities exhibited complete efficiency in both CCR and BCC models. Among the remaining 141 universities that showed inefficiency, the cause was scale for five universities, technology for two universities, and both scale and technology for 134 universities. Regarding the returns to scale, nine universities exhibited CRS, 79 exhibited IRS, and 62 exhibited DRS. Additionally, reference groups that could serve as benchmarks for improving the efficiency of inefficient universities were identified, and target values(projections) for each variable to achieve efficiency were also presented. Despite the limitations of the DEA model, this study helps each university identify the causes of inefficiency in their entrepreneurship support and derive specific improvements to enhance efficiency. This facilitates more efficient resource management and can positively impact the ultimate goals of university entrepreneurship support, such as regional economic development and job creation.