• 한국항해항만학회지
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• 제46권2호
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• pp.110-120
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• 2022

Spain is Europe's second-largest country with total throughput reaching 16.7 million twenty-foot equivalent units (TEU) by 2020. The purpose of this study was to measure and compare the efficiency of 17 container terminals. As a study method, the DEA-CCR model, undesirable variable, and Malmquist Index (MI) were used for data envelopment analysis (DEA). The study results are as follow: (1) DEA-CCR is used to evaluate basic efficiency. The most efficient terminals are decision-making units DMU 1 (APM Terminals (Algeciras Port)), DMU 2 (Total Terminal International Algeciras (Algeciras Port)) and DMU 5 (Barcelona Europe South Terminal (Barcelona Port)). (2) Undesirable DEA was conducted to suggest inefficiency from the undesirable output. Overall, the efficiency scores were reduced. However, DMU 1, DMU 2, and DMU 5 maintained efficiency scores regardless of the finish factor. (3) Malmquist Index was used to observe technology and efficiency changes dynamically. The changes in TCI affected Spanish container terminals more than the Technical Efficiency Change Index (TECI) in 2018-2019. However, in 2019-2020, the TECI was 2.706, higher than the TCI value, indicating that the change in TECI had more influence on the increase in productivity. This study offers a broader understanding of Spanish container terminals.

• 한국항만경제학회지
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• 제32권2호
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• pp.123-135
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• 2016

본 연구는 자료포락분석법(DEA)을 이용하여 기업의 경영관리비용인 접대비, 광고 선전비, 인건비를 중심으로 국내 해운기업의 경영효율성을 평가하고자 하였다. 분석의 대상은 2010년부터 2014년까지 국내증시에 상장된 해운기업이다. 투입변수로 접대비, 광고 선전비, 인건비를 설정하였으며 산출변수는 매출액과 당기순이익으로 하였다. 본 연구에서는 기술효율성, 순수기술효율성, 규모효율성 및 규모수익을 도출하였으며 비효율 DMU의 효율성 개선방안을 제시하였다. 효율성 측정 결과 CCR모형의 기술효율성 분석에서 6개의 DMU와 BCC모형의 순수기술효율성 분석에서 8개의 DMU가 효율적인 상태인 것으로 나타났다. 규모의 수익측면에서는 전체 DMU의 24%인 6개의 DMU가 규모수익체증상태이며, 전체 DMU의 52%인 13개 DMU가 규모수익체감상태인 것으로 나타났다. 기술효율성 분석결과 복수의 효율적인 상태의 DMU가 존재하여 초효율성 분석을 실시한다. 본 연구에서는 초효율성 분석 결과 가장 효율성이 높은 DMU의 효율성 수준은 1.314, 다음은 1.243으로 나타났다. 이는 광고 선전비, 접대비, 인건비 투입에서 각각 31.4%, 24.3% 증가시키더라도 현재의 효율적인 상태를 유지할 수 있다는 것을 의미한다. 본 연구는 해운기업의 접대비와 광고 선전비, 인건비의 효율성을 평가하고, 초효율성 분석을 통해 의사결정단위의 순위를 파악하였으며 비효율 의사결정단위의 비효율성 개선을 위한 목표치를 제시하였다는데 의의가 있다.

• 농업과학연구
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• 제39권2호
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• pp.279-289
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• 2012

This study was conducted to survey and diagnose operation status of the agricultural machinery rental service, analyse and compare operational efficiency among 82 city and county ATDEC (agricultural technology development and extension center) using the DEA (Data Envelopment Analysis) method, and recommend future direction, for improvement of the business. Input variables were invested budget and labor, and output variable was rental return. Percentages of return to investment on the rental service were calculated as 68.3% and 63.9% when analyzed with CCR (Charnes, Cooper and Rhodes) and BCC (Banker, Charnes and Cooper) models, respectively, indicating inefficiency of the service operation. Increase of rental charge would increase efficiency by 63.9~68.3% depending on models, and decrease of financial and labor investment would improve the efficiency by about 11.3%. Technical efficiency would be more important than scale efficiency, therefore adjustment of over-invested budget and labor needed to be made together with increase of rental charge to improve the operation. Among the ATDECs providing the rental service, 6 (7.3%), 43 (52.4%), and 33 (40.2%) were in state of CRS (constant return to scale), IRS (increasing return to scale), and DRS (decreasing return to scale), respectively. These indicated public aspects of the rental system, over-investment, lack of output component for input component, meaning that scale income would be increased by qualitative expand of rental charge. Efficiency analysis of the rental system by region showed that efficient ATDECs to be benchmarked by others were in the order of DMU-70, DMU-54, DMU-29, DMU-5, DMU-22, DMU-2, and DMU-61. More comprehensive and extensive survey and analyses would be necessary in the future.

• 품질경영학회지
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• 제47권1호
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• pp.33-46
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• 2019

Purpose: This study proposed a DEA (Data Envelopment Analysis)-based stepwise benchmarking target selection for inefficient DMU (Decision Making Unit) to improve its efficiency gradually to reach most efficient frontier considering resource (DEA inputs and outputs) improvement preferences. Methods: The proposed method proceeded in two steps. First step evaluates efficiency of DMUs by using DEA, and an evaluated DMU selects benchmarking targets of HCU (Hypothesis Composit Unit) or RU (Real Unit) considering resource improvement preferences. Second step selects stepwise benchmarking targets of the inefficient DMU. To achieve this, this study developed a new DEA model, which can select a benchmarking target of an inefficient DMU in considering inputs or outputs improvement preference, and suggested an algorithm, which can select stepwise benchmarking targets of the inefficient DMU. Results: The proposed method was applied to 34 international ports for validation. In efficiency evaluation, five ports was evaluated as most efficient port, and the remaining 29 ports was evaluated as relative inefficient port. When port 34 was supposed as evaluated DMU, its can select its four stepwise benchmarking targets in assigning the preference weight to inputs (berth length, total area of pier, CFS, number of loading machine) as (0.82, 1.00, 0.41, 0.00). Conclusion: For the validation of the proposed method, it applied to the 34 major ports around the world and selected stepwise benchmarking targets for an inefficient port to improve its efficiency gradually. We can say that the proposed method enables for inefficient DMU to establish more effective and practical benchmarking strategy than the conventional DEA because it considers the resource (inputs or outputs) improvement preference in selecting benchmarking targets gradually.

• 한국재료학회:학술대회논문집
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• 한국재료학회 1999년도 춘계학술발표강연 및 눈문개요집
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• pp.142-142
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• 1999

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2005년도 제25회 추계학술대회논문집
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• pp.293-298
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• 2005

최근 활발한 연구가 진행 중인 공중발사 방식은 극소형 위성을 매우 저렴한 비용으로 발사할 수 있는 효율적인 방법이다. 본 연구에서는 비교적 단순한 임무를 수행할 수 있는 나노 위성을 공중발사 방식을 이용하여 원 궤도에 올릴 수 있는 공중발사 로켓 시스템설계와 각 단별 계통 설계를 수행하였다. 이를 위하여 공중발사 로켓 시스템의 WBS와 각 계통별 작동개념도를 정립하였고, 이를 바탕으로 시스템 세부형상과 DMU를 구현하였다.

• 대한산업공학회지
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• 제36권1호
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• pp.32-41
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• 2010

DEA(Data Envelopment Analysis) is the relative efficiency measure among homogeneous DMU(Decision- Making Units) which can be used to useful tool to improve performance through efficiency evaluation and benchmarking. However, the general case of DEA was considered as unrealistic since it consists a benchmarking regardless of DMU characteristic by input and output elements and the high efficiency gap in benchmarking for inefficient DMU. To solve this problem, stratification method for benchmarking was suggested, but simply presented benchmarking path in repeatedly applying level. In this paper, we suggest a new method that inefficient DMU can choice the optimal path to benchmark the most efficient DMU base on the similarity among the input elements. For this, we propose a route choice method that combined a stratification benchmarking algorithm and SOM (Self-Organizing Map). An implementation on real environment is also presented.

• 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.

• 에너지공학
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• 제16권3호
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• pp.93-102
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• 2007

본 논문에서는 해체 시나리오를 정량적 및 정성적 고려사항을 반영하여 평가하기 위하여 계층적분석이론(Analytic Hierarchy Process, AHP)을 이용한 평가모델을 개발하였으며 또한 해체 시나리오의 정량적인 자료산출을 위하여 해체일정, 폐기물량, 방사화 가시화, 해체비용, 작업자 피폭량 등과 같은 해체정보산출모듈을 개발하였다. 그리고 해체공정을 가상환경에서 구현하여 해체절차를 파악하기 위하여 디지털 목업(Digital Mock-Up, DMU)을 개발하였으며 DMU 시스템은 해체정보산출모듈, 해체 DB 및 해체 시나리오 평가 모듈을 통합적으로 관리하도록 개발되었다. 마지막으로 개발된 해체 DMU 시스템과 계층분석과정 모델을 연구로 1호기(Korea Research Reactor-1, KRR-1) thermal column의 플라즈마 절단 시나리오와 nibbler 절단 시나리오에 적용하여 비교 평가하였다.

• 한국전자거래학회:학술대회논문집
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• 한국전자거래학회 1999년도 학술대회지 vol.1
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• pp.277-299
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• 1999

Since Chrysler Motor Co. had experienced the digital development system in the beginning of 1990's, most of leading automobile companies are trying to apply a digital information system for their own business process reengineering based upon concurrent engineering system from product planning phase. This is called as virtual DMU(Digital Mock-Up) system instead of the traditional PMU(Physical Mock-Up) system. By using the virtual prototype, all of the design requirements and system specifications can be checked, changed and optimized more quickly and more efficiently. This paper consists of five chapters for the DMU information system. In the 1$^{st}$ chapter, the principle of digital design system is suggested by using four basic modules such as product design module, process design module, manufacturing system design module and central control module. The basic scheme of DMU is introduced with the benefits of application in the chapter 2. In the chapter 3, a digital design process of new car development is explained with the detailed DMU design and design review processes. In the chapter 4, the practical DMU manufacturing techniques and applications are introduced as CAD/CAM analyses, DPA(Digital Pre-Assembly)reviews for development, production, operation and maintenance phases, digital tolerance analyses and digital factory analyses for assembling line simulation, automated robot welding processes, production jig ＆ fixtures and painting process simulation. Finally, the activities of digital design support; CAS-styling, CAE-engineering and CAT-testing are summarized for design optimization in the chapter 5. As today's automobile manufactures and related business organizations are struggling to compete in the global marketplace, they are concentrating on efficient use of DMU information system to reduce the new car development cost, to have shorten the delivery schedule and to improve product design quality. To meet the demand of those automobile industries on digital information systems, the CALS(Computer aided Acquisition and Logistics Support) and EC(Electronic Commerce)initiative has been focused as a dominant philosophy in defense ＆ commercial industries, specially automobile industries.s.