Port operational efficiency is considered as one of the most important competitive factors and plays a critical role in the port development all over the world, especially container ports. Haiphong Port, which is in the northern of Vietnam, is planned to become one of the national and regional ports. To do this objective, it is important to analyse the operational efficiency of its container terminals. The paper aims to comparatively analyse the operational efficiency of 16 container terminals in Haiphong Port from 2016 to 2022 by basic and Malmquist DEA models. With 112 observations collected and calculated in R software, DEA models have five inputs (container yard area, number of quay crane, berth draft, berth length, labour force) and one output (annual cargo throughput). Consequently, Hai An, Tan Vu, and Vip Greenport are more efficient terminals over the 7-year period, whereas Transvina and MIPEC have lower efficiency. Paper contributions are the literature review about port operational efficiency and references to propose resolutions in next author’s research as well as masterplans to develop Vietnam seaport’s system. Besides, the limitations are discussed as the number of observations and environmental factors in ports.

PURPOSES : This study aims to analyze the efficiency of the safety and management of private highways. METHODS : Variables were selected based on the data and performance related to the safety of 18 private highways. The appropriateness of operations management was reviewed using Data Envelopment Analysis (DEA) analysis. Items with a scope for improvement were reviewed and adjustment measures were presented. RESULTS : To increase safety management efficiency, the degree of reduction in personnel and operating expenses was presented based on the relative efficiency group. CONCLUSIONS : It is necessary to adjust the appropriate management organization and operating costs according to the characteristics of each route. Moreover, the limitations of the study and possible improvements were presented.

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.

Scientific and technological performances (e.g., patents and publications) made through R&D play a pivotal role for national economic growth. National governments encourage academia-industry cooperation and thereby pursue continuous development of science technology and innovation. Increasing R&D-related investments and manpower are crucial for national industrial development, but evidence of poor performance in business performance, efficiency, and effectiveness has recently been found in Korea. This study evaluates performance efficiency of the 6T sector (Information Technology, Bio Technology, Nano Technology, Space Technology, Environment Technology, Culture Technology), which is considered a high-potential promising industry for the next generation growth and currently occupies two thirds of the national R&D projects. The study measures the relative efficiency of R&D in a comparative perspective by employing the Data Envelopment Analysis (DEA) method. The result reveals overall low efficiency in basic R&D (0.2112), applied R&D (0.2083), development R&D (0.2638), and others (0.0641), confirming that economic performance and efficiency were relatively poor compared to production efficiency. Efficient R&D needs policy makers to create strategies that can increase overall efficiency by improving productivity performance and quality while increasing economic performance.

The purpose of this study is to understand the production efficiency of individual fishing communities and provide directions for improvement. The subject of the study is aquaculture type Ochon-Gye in Goheung-gun. The analysis method used bootstrap-DEA to overcome the statistical reliability problem of the traditional DEA analysis technique. In addition, data mining-GIS was applied to identify the spatial productivity of fishing communities. The values of technology efficiency, pure technology efficiency, and scale efficiency were estimated for 32 aquaculture-type fishing villages. Then, using the benchmarking reference set and weights, the projection was presented through adjustment of the input factor excess, and furthermore, the confidence interval of the efficiency values considering statistical significance was estimated using bootstrap.

본 연구에서는 우리나라 대형통신기업을 대상으로 2000년에서 2019년까지의 경영효율성을 측정하고 비교하였다. 그리고 도출된 경영효율성지수와 경기변동간의 상관관계에 대하여 실증분석을 하였다. 발견 된 사실들을 정리하면 다음과 같다. 1)우리나라 대형통신기업은 지난 20년간에 걸쳐 경영효율성수준이 하락하는 경향을 보여주었다. 2)통신 3사 중에서 SK텔레콤이 가장 높은 경영효율성을 보여주고 있고 KT 가 가장 낮은 효율성을 보여주고 있다. LG유플러스는 중간수준의 경영효율성을 보여주었다. 3)우리나라 통신3사는 규모의 수익 면에서 수익감소적인 경향을 보여주고 있으며 2010년 이후로 이러한 경향은 더욱 뚜렷해지고 있다. 4)통신3사의 경영효율성과 경기변동과의 상관관계에서는 매출역모형에서는 순경기순 환적으로 나타나고 있으나 부가가치모형에서는 역경기순환적으로 나타나고 있다. 5)통신기술의 도입과 통신시장 세계화에 대비하여 대형통신기업들의 경영효율성을 높일 수 있는 정책대안을 시급하게 강구해야할 것으로 생각된다.

The production of abalone seed has grown and been specialized since the 2000s with the growth of the abalone farming industry. Despite the increase in the production of abalone seeds, the sales volume of abalone seeds remained flat and competition among producers increased. This paper will analyze the management efficiency of abalone seed production fishery to diagnose the management status and improve the abalone seed production efficiency. In addition, this study is the result of the basic research on the abalone seed industry and it is meaningful to prepare a platform for further research since the management status survey and the management efficiency survey of abalone seed production fishery have not been conducted until now. The data on the farmed fish prices of abalone seeds were collected from surveys of sample fish as part of the fish seed observation project conducted by the Fisheries Outlook Center (FOC) of Korea Maritime and Fisheries Development Institute (KMI). Management efficiency analysis utilizes DEA (Data Envelopment Analysis) model. The DEA model was analyzed by classifying into CCR (Super-CCR), BCC, and SBM (Super-SBM) models according to the assumptions taking into account the characteristics of the industry. The slack considered in the SBM model was judged as possible decreases in input variables and increase in output variables. The average efficiency from the CCR model was analyzed to be 69%. The BCC model was classified into input and output orientations, and the average efficiency was 79% and 75%, respectively. There were seven production fisheries with an SE value of 1 or more, which remained unchanged in terms of size and could be benchmarked. The average efficiency of the SBM model was 59% for CRS and 66% for VRS. Under the VRS assumptions, the variable increase/decrease efficiency analysis shows that labor costs can be reduced by 37.3%, facility capacity by 18.8%, and operating costs by 8.5%. In order to improve management efficiency, Wando needs to reduce labor and management costs. In Jindo region, sales increase as well as labor cost reduction is urgent. In other regions, reduced facilities and increased sales are recommended.

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.

This study aims to objectively measure the efficiency of nanotechnology R&D programs by systematically evaluating the inputs and outputs of nanotechnology R&D activities and to find implications for improving the efficiency of nanotechnology R&D programs. Data on input factors such as R&D investment, R&D manpower, R&D period, and output factors such as paper, patent, and commercialization for R&D projects which started from 2008 or afterwards and ended by 2011 are gathered through National Science and Technology Knowledge Information Service, which are used for efficiency evaluation. In this study, we analyzed R&D efficiency in detailed technology units in depth. The process taken in this study is as follows. First, the basic statistics of input and output factors to compare and analyze R&D investment, R&D manpower, R&D period, paper, patent, and commercialization status by technology unit are analyzed. Next, DEA models are utilized to derive the overall efficiency, pure technology efficiency, and scale efficiency by conducting the efficiency evaluation for each technology unit, from which implications for strategic budget allocation are derived. In addition, partial efficiency evaluation is conducted to identify advantages and disadvantages of each technology unit. In turn, cluster analysis is performed to identify similar technology units, from which implications for efficiency improvement are derived.

The purpose of this study is to analyze the efficiencies of project management offices in large information system construction projects using the data envelopment analysis. In addition, we tried to estimate the confidence interval of those efficiencies using bootstrap DEA to give a statistical meaning. The efficiency by the CCR model is analyzed as eight project management offices are fully efficient and 22 project management offices are inefficient. On the other hand, there are 15 project management offices are fully efficient, but 15 project management offices are inefficient in the BCC model. As the result of the scale efficiencies, of the inefficient project management offices, 13 project management offices are inefficient in scale. It is possible to eliminate the inefficiency in the CCR model by improving their project performances. And, the nine project management offices showed that the inefficiency was due to pure technical efficiency, and these companies should look for various improvements such as improvement of project execution system and project management process. In order that the inefficient project management offices be efficient, it is analyzed that more efforts must be made for on-budget and on-time as a result of examining the potential improvement potentials of inefficient project management offices.

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.

Improving efficiency of the telecommunication is crucial to the development and growth of Korean economy. Recently, it has become important with the huge development of information technology and its greater potential for extensive impact on the rest of the economy. Hence, it is useful to determine the factors that help enhance efficiency in telecommunication and consider them in improving the evaluation model. This study applies DEA (data envelopment analysis) to evaluate the relative efficiency of 51 branches of a Korean telecommunication company. Using the super-efficiency approach, we tested outliers which may affect the results and ranked the efficient branches. A method of deriving key variables applied to business operation is proposed to identify the key performance indicators for evaluation that takes environmental (non-discretionary) factors into account. We used the extended CCR model proposed by Banker and Morey to investigate the influence of non-discretionary factor. The information provided by the model (slacks, weights) and the sensitivity analysis shows that the most important indicator that affects the branch performance is operating cost. The results of sensitivity analysis show that average efficient score decreases from 0.972 (base case) to 0.863 for CASE2-COST. The average score of the data proves the priority of operating cost over other indicators. The effect of environmental (non-discretionary) variable was found to be significant. The population effect was positive and improved overall efficiency by 0.91% on average. Non-discretionary factor plays a meaningful role explaining the performance of branches. The performance optimization report can help a manager of an inefficient branch to develop branch strategies. Managers can identify the top-performing units, study best practices and adopt the strategy to the organization.

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.