• 대한산업공학회지
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• 제40권1호
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• pp.84-99
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• 2014

In this study, determinant input-output variables are identified for calculating Data Envelopment Analysis (DEA) efficiency scores relating to evaluating the efficiency of government-sponsored research and development (R&D) projects. In particular, this study proposes a systematic framework of design and analysis of experiments, called "all possible DEAs", for pinpointing DEA determinant input-output variables. In addition to correlation analyses, two modified measures of time series analysis are developed in order to check the similarities between a DEA complete data structure (CDS) versus the rest of incomplete data structures (IDSs). In this empirical analysis, a few DEA determinant input-output variables are found to be associated with a typical public R&D performance evaluation logic model, especially oriented to a mid- and long-term performance perspective. Among four variables, only two determinants are identified : "R&D manpower" ($x_2$) and "Sales revenue" ($y_1$). However, it should be pointed out that the input variable "R&D funds" ($x_1$) is insignificant for calculating DEA efficiency score even if it is a critical input for measuring efficiency of a government-sonsored R&D project from a practical point of view a priori. In this context, if practitioners' top priority is to see the efficiency between "R&D funds" ($x_1$) and "Sales revenue" ($y_1$), the DEA efficiency score cannot properly meet their expectations. Therefore, meticulous attention is required when using the DEA application for public R&D performance evaluation, considering that discrepancies can occur between practitioners' expectations and DEA efficiency scores.

• Journal of Korea Trade
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• 제26권3호
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• pp.23-44
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• 2022

Purpose - Recent issues such as vessel enlargement, strengthening of environmental regulations, and port smartization are expected to increase costs and intensify competition in the port industry. In the new normal era, when external growth has reached its limit, the efficient operation of ports is becoming indispensable for achieving sustainable growth. This study aims to identify the determinants of inefficiency by examining the cost structure and efficiency of container terminals in Korea and furthermore propose the political implications to derive the maximization of efficiency. Design/methodology - This study estimates the cost function of container terminal operators and identifies the efficiency of container terminals using stochastic cost frontier (SCF) in the first stage. In the second step, the SCF results are compared with the data envelopment analysis (DEA). Last, this paper proposes efficiency determinants on container terminal operation to establish appropriate strategies. Out of the 29 container terminal operators in South Korea, 13 operators participated in the survey. The translog cost function was estimated utilizing a total of 116 observations collected over the 2007-2017 period. Findings - Empirical analysis shows that economies of scale exist in Korea's container ports, which provides a rationale for the government's policy to establish the global terminal operator by integrating small terminal operators to enhance competitiveness. In addition, as a result of the determinants analysis, container throughput, weight of direct employment costs, and labour cost share have positive effects on improving cost efficiency, while inefficiency increases as the length of quay increases. More specifically, cost efficiency improves as the proportion of direct employment costs to outsourcing service costs increases. Originality/value - This study contributes to analyzing the inefficiency factors of container terminals through efficiency analysis with respect to a cost function. In addition, this study proposes the practical and political implications, such as establishing a long-term manpower pool, the application of the hybrid liner terminal system, and the construction of a statistical data system, to improve the cost inefficiency of terminal operators.

• 정보관리연구
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• 제40권3호
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• pp.41-60
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• 2009

최근까지 DEA를 이용한 대학도서관 효율성 평가에 관한 선행연구들은 대부분 특정 시점의 횡단면 분석 위주였으며, 효율성 추세변화 분석과 같은 종단면 분석은 거의 수행되지 못하고 있었다. 이에 본 연구에서는 우선 선행연구들처럼 DEA를 이용하여 특정 시점(2008년) 기준 하에서 국립대학도서관의 상대적 효율 수준을 분석하고, 다음으로 DEA Window 분석 기법을 적용하여 최근 5년(2004년-2008년)간 상대적 효율 수준의 추세변화를 분석하였다. 분석 결과 첫째, 특정시점 하에서 상대적 효율은 기술 효율성 측면에서 비효율적이었으며, 이는 상대적으로 규모의 비효율에 기인하는 것으로 나타나 선행연구 결과들을 재확인하였다. 둘째, 이러한 기술 효율성의 비효율은 전반적으로 최근 5년간 지속적인 개선이 있었으나 이는 상대적으로 순수 기술 효율성의 개선에 따른 것으로 나타났다. 따라서 앞으로 국립대학도서관의 기술 효율성을 개선하기 위해서는 규모의 비효율을 개선하는 노력이 필요할 것으로 사료된다.

• The Journal of Asian Finance, Economics and Business
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• 제8권1호
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• pp.741-750
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• 2021

The research aims to analyze the level of efficiency by grouping banks during the period 2017 - 2018 into category 1 and category 2 banks and then dividing them as Regional Development Banks (BPD) and Non-BPD Conventional Commercial Banks (BUK) within each category. The research objects are banks within the categories BPD and BUK comprised 18 BPDs and 35 BUKs. The research methodology uses 3 stages, first, using Data Envelopment Analysis (DEA) we measure the level of bank efficiency; second, using the Tobit regression model we evaluate the effect of financial performance on DEA efficiency, and third, using the Mann-Whitney test we determine whether there is a difference in the efficiency of category 1 and 2 banks. The results showed that there was a decrease in the efficiency of category 1 and 2 banks but on average, the efficiency of category 1 banks is higher than category 2 banks. The estimation results of the Tobit regression model show that only the ROA variable affects the efficiency level of category 1 banks, while category 2 banks are influenced by NPL and ROA variables. In the Mann-Whitney test, it was proven that there were differences in efficiency between BUK and BPD in category 1 and 2 banks.

• 경영과학
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• 제31권2호
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• pp.49-63
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• 2014

With the huge concerns on the inefficiency of public enterprises, particularly a significant amount of debt, an increasing number of studies have been carried out to analyze the levels of inefficiency and investigate the causes of that inefficiency. However, very limited range of analytical methodologies have been used in the efficiency analysis and moreover, the effects of external factors have been little addressed. This study explores the efficiency of urban railway corporations in Korea by utilizing a method of stochastic frontier analysis (SFA). In particular, the potential effects of external factors including residential and floating populations of a station were statistically analyzed. A total of seven Korean urban railway corporations were selected to compare each other in terms of operational efficiency. The results present three important findings. First, the Cobb-Douglas model was found to be more valid for SFA compared to the Translog model. Second, the efficiencies of urban railway corporations in Seoul and Busan are relatively high whereas those of Daejeon and Gwangju are very low in efficiency in the area of sales revenue. In an aspect of number of transport of passengers, Gwangju Metro also showed the lowest efficiency. Third, the external factors are significantly associated with the efficiency, indicating that the efficiencies of Daejeon Metro and Gwangju Metro would increase while the efficiency of Seoul Metro would decreases when the external variables are excluded in the efficiency analysis. The results provide several meaningful implications for managers of the urban railway corporations as well as policy makers who are attempting to resolve the inefficiency problems of public enterprises.

• 한국항해항만학회지
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• 제44권2호
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• pp.119-127
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• 2020

In this study, the current state of management efficiency of ocean carriers in Korea and the factors affecting them were analyzed. The purpose of this research is to enhance global competitiveness of ocean carriers by presenting suggestions that can improve management efficiency based on the analysis results. The measurement of management efficiency was made using the DEA model. The results of testing the adequacy of the input and output variables used are as follows. Appropriate inputs are total assets, cost of goods sold, charter expenses, sales and general management expenses, and interest expenses. Appropriate variables are sales, operating income, and operating cash flow. According to the analysis results of the DEA model by these variables, inefficient carriers (78%) are nearly four times more than efficient carriers(22%). However, container carriers have the most improved management efficiency compared to 2016 and 2017. According to the panel regression analysis, the charter rate has the greatest negative impact on efficiency (CRS), and the debt rate has a significant negative impact. Thus, it appears that reducing the charter size and the debt-to-sale rate facilitate improvement of the management efficiency of ocean carriers. Additionally, the pre-sales tax return rate, value added rate, total asset turnover rate, and the scale variable and interest coverage rate have a positive (+) effect. Thus ocean carriers should restore their global competitiveness by improving management efficiency by securing stable cargoes increasing sales profitability from the cost management perspective, increasing productivity, and enhancing the efficiency of their total assets through efficient fleet management.

• 한국항만경제학회지
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• 제18권1호
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• pp.3-26
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• 2002

The purpose of this paper is to Investigate the productive efficiency and competitive strength of world container ports using the DEA, Super-efficiency, and FDH methods with the raw data from previous research by Jun et al.(1993). The super-efficiency measure examines the maximal radial change In input, outputs for an observation to remain efficient. Therefore, it provides a means of distinguishing between efficient observations, which would otherwise seem identical. FDH provides a good test mechanism for examining the practical implications of the choice available among alternative efficiency measures and orientations, because of the lack of convexity of its production possibility set. Both methods are complementary to DEA. This paper follows the traditional productivity analysis method overcoming the limitation of previous studies by using the DEA, FDH and Super-efficiency methods, and proposing in measure the relative competitive strength of worldwide container ports. The main empirical results of this paper are as follows: Firstly the ports of Singapore, Hongkong, Kilrung, Busan, Tokyo. and Longbeach were found to be efficient In the CCR model. The ports of Felixstowe, Bangkok, Singapore, Hongkong, Kilung, Busan, Tokyo, and Longbeach were found to be efficient in the BCC model. Secondly, super. efficiency rankings under CRS and input-oriented model are as follows: Longbeach, Keelung, Singapore, Busan, Tokyo, and Honkong. However, it was difficult In differenciate the rankings under the VRS and input-oriented model. due to major difficulties posed by the ports of Singapore, Hongkong, and Longbeach. Thirdly, the FDH method shows that the inefficient ports are Bremerhaven, Antwerp, Le Havre, Kobe, Seattle, New York The policy Implications of this study are as follows: Firstly, when port authorities want to measure the international competitive strength of container ports and enhance their productive efficiency, they should consider the traditional method as well as introducing the Super-efficiency and FDH methods. Secondly, according to the analysis results of the super-efficiency and FDH methods, poll authorities should recommend benchmarks ports and dominated ports as reference ports in order to enhance the productive efficiency of their container ports that have an efficiency rating of less than 1. Efficient ports whose efficiency ratings are over 1 in the Input-oriented Super-efficiency model should also consider the usage of input and output elements used by more efficient ports.

• 한국국방경영분석학회지
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• 제33권2호
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• pp.61-73
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• 2007

본 연구의 목적은 미래 기술예측에 사용되는 TFDEA(Technological Forecasting with Data Envelopment Analysis)의 문제점을 살펴보고 이의 개선방향을 찾아 주력전차의 기술예측 문제에 적용해 보는 것이다. 기존의 TFDEA는 복수의 DMU(Decison Making Unit)를 효율적 DMU로 판정하는 DEA(Data Envelopment Analysis)의 특성상 실제로는 그다지 효율적이지 않은 DMU까지 포함해서 기슬예측을 수행함으로써 예측 결과의 정확도가 저하될 수 있다. 본 연구에서는 DEA의 확장된 개념을 적용하여 평가 대상 DMU에 대한 순위를 산정한 후 이를 토대로 기술 예측을 시행하는 방법을 검토해 보았다. 이를 위해 일반적인 DEA기반의 순위선정 방법 중 대표적인 Super-efficiency, Cross-efficiency, CCCA(Constrained Canonical Correlation Analysis)을 TFDEA에 결합 적용하고 이들을 비교해 보았다. 제시된 방법을 주력 전차의 미래 기술 예측 문제에 적용한 결과 CCCA를 이용한 순위선정방법이 실제 실현된 기술 수준과 비교했을 때 통계적으로 가장 작은 오차율을 보였다.

• 품질경영학회지
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• 제32권2호
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• pp.77-92
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• 2004

It is difficult but very necessary to measure the productivity of container terminals as logistics service provider. It is meaningful to find the appropriate inputs and outputs of the logistics service delivery systems and to measure the relationship between these inputs and outputs. This study proposes a model of evaluating the efficiency of container terminals. The evaluation consists of three phases. First, DEA(Data Envelopment Analysis) phase, determines the efficiency score and weights of DMUs(Decision Making Unit). This phase performs through four steps : selection of DMU, selection of DEA model, determination of input and output factors, calculation of efficiency score and weights for each DMU. Secondly, CEM (Cross Evaluation Model) phase, is to calculate the cross-efficiency scores of DMUs. This phase performs through three steps: selection of CEM, determination of cross-efficiency score for each DMU and development of cross-efficiency matrix. Finally, average cross-efficiency analysis phase is to compute the average cross-efficiency score. The proposed model discriminates among DMUs and ranks DMUs, whether they are efficient or inefficient.

• 경영과학
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• 제31권4호
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• pp.135-150
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• 2014

This paper analyzes the operational efficiency of 91 individual retail stores in Seoul by a two-step procedure. In the first step, a data envelopment analysis (DEA) model is used to identify the efficiency scores. Three inputs (store size, number of items, and number of employees) and two outputs (sales and number of customers) are used for the efficiency measurement. In the second step, a Tobit regression model is used to identify the drivers of efficiency. DEA efficiency scores are used to test hypotheses on the impact of five independent variables, namely store age, number of items per store size, number of items per employee, trade area index, and number of competitors. Results of the Tobit analysis show that number of items per store size, number of items per employee, and number of competitors play a significant role in influencing the operational efficiency of retail stores. Managerial implications of the study are discussed.