• Proceedings of the Technology Innovation Conference
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• 2005.08a
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• pp.113-137
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• 2005

본 연구는 각 프로세스가 조직에 미치는 상대적 영향을 고려한 조직 전체의 효율성을 평가하고, 조직의 효율성을 개선하기 위하여 조직을 구성하는 여러 프로세스 중에서 어떤 프로세스를 우선적으로 개선해야 하는지를 결정하며, 더 나아가 비효율적인 프로세스를 개선하기 위하여 어떤 프로세스를 벤치마킹해야 하는지를 결정할 수 있는 체계적인 방법을 제공한다. 이를 위하여 본 연구는 다음과 같은 중요한 세 가지 과정을 따른다. 첫째, DEA(data envelopment analysis)의 CCR 모형을 이용하여 프로세스의 투입요소와 산출 요소를 바탕으로 개별 프로세스의 효율성을 평가한다. 둘째, 도출된 개별프로세스의 효율성을 Lovell과 Pastor의 순수 산출요소(또는 투입요소) DEA 모형의 산출요소로 이용하여 서비스 단위 조직 전체의 효율성을 평가한다. 셋째, 앞서 도출된 개별프로세스의 효율성과 서비스 단위 조직의 전체 효율성을 각각 DT(decision tree)의 예측변수와 목표변수로 활용하여 각 서비스 단위 조직의 특성 및 상황에 따라 개선해야 할 프로세스를 선택하는 규칙을 생성한다. 제안한 방법을 통하여 기업은 비효율적 조직과 프로세스를 발견하고 조직의 효율성을 개선하기 위하여 어떤 프로세스를 우선적으로 개선해야 하는지를 결정할 수 있다. 이를 통하여 기업은 오늘날 기업 경쟁력의 핵심인 프로세스를 좀 더 효과적으로 평가 및 관리할 수 있을 것으로 기대된다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.11a
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• pp.191-196
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• 2004

This paper is to measure and ealuates the technical efficiency, pure technical efficiency and scale efficiency with three inputs and two outputs with the use of DEA(data envelopment analysis) in Korean RCC(Rescue Co-ordination Center/RSC(Rescue Sub-Center). Several conclusion emerge. first the average efficiency of overall technical efficiency measure about $91.03\%$ and pure technical efficiency $96.80\%$ is much large then scale efficiency $93.83\%$. It means that inefficiency has much more to do whit the inefficient utilization of resources rather then the scale of production. second, DRS(decreasing return to scale) is Tongyeong and IRS(increasing return to scale) is Incheon, Taean, Gunsan, Yeosu, Ulsan, Donghae in RCC/RSC. finally, inefficiency RCC/RSC. have to benchmarking with reference sets.

• Journal of Navigation and Port Research
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• v.29 no.3 s.99
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• pp.215-220
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• 2005

This paper aim, to measure and evaluates the technical efficiency, pure technical efficiency and scale efficiency with two inputs and four outputs with the use of DEA(Data Envelopment Analysis) in Korean RCC(Rescue Co-ordination Center)/RSC(Rescue Sub-Center). Several conclusion emerge. first the average efficiency of overall technical efficiency measure about $91.03{\%}$ and pure technical efficiency $96.80{\%}$ is much large then scale efficiency $93.83{\%}$. It means that inefficiency has much more to do whit the inefficient utilization of resources rather then the scale of production. second, DRS(decreasing return to scale)is Tongyeong and IRS(increasing return to scale) is Incheon, Taean, Gunsan, Yeosu, Ulsan, Donghae in RCC/RSC finally, inefficiency RCC/RSC have to benchmarking with reference sets.

• International Area Studies Review
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• v.12 no.3
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• pp.341-365
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• 2008

This paper investigates the properties and relative importance of Apparel Textile industry of Busan in terms of both the number of employees and the value-added, and then analyzes the relative technical efficiencies from the perspectives of space closeness such as consumption market, suppliers, living standards and clustering environments using the DEA model. The main outcomes are as follows. The Apparel Textile industry is still a key industry in Busan with relatively high intensity and high LQ (greater than one) in spite of the recent decrease between 2000 and 2005 both in the number of employees and the value-added. Those firms with consumption market showed higher efficiency in all kinds of efficiencies than the firms without the market, and further relatively higher efficiency than the firms with suppliers. In addition, the firms located in the higher living standards also showed higher values of efficiency compared with the firms in lower living standards. The policy implication from the results is that the strategic and selective investment should be made according to the relative efficiency and thus the traditional industry should be transformed to the high value added one with additional investment to utilize the scale efficiency.

• Environmental and Resource Economics Review
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• v.8 no.2
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• pp.149-174
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• 1999

본 연구는 1990년대 초 중반의 한국의 화력발전소 자료를 이용하여 발전부문의 생산성 변화를 분석하되, 생산성 변화를 발전소간의 효율성 격차에 의한 부문과 발전부문 전체의 생산함수 이동을 통해 나타나는 기술변화로 나누어 분석하였다. 분석결과, 화력발전소간에는 의미있는 정도의 효율성 격차가 존재하는 것으로 나타났으며, 분석에 포함된 기간 동안 연평균 약 5% 정도의 생산성 증가가 발생하였고, 이 가운데 효율성 격차의 해소를 통한 생산성 증가가 약 2%, 그리고 기술진보에 의한 생산성 증가가 약 3%를 차지하였다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.2
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• pp.161-167
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• 2017

This paper measured and evaluated the technical efficiency, pure technical efficiency and scale efficiency of port labor via three inputs and three outputs using Data Envelopment Analysis (DEA). First, the average overall technical efficiency measured about 94.18 %, with 100 % pure technical efficiency being larger than 94.18% scale efficiency. As a result, 7 ports were identified as efficient, and 4 ports were identified as inefficient. Among the 4 ineffiecient ports, Donghae had the highest Increasing Return to Scale (IRS). However, the inefficient ports were all found to be less efficient in terms of resource operation than production scale. On the other hand, the efficient ports established priorities using the super efficiency method. As a result, port efficiency was highest for Incheon, Mokpo, Pyeongtaek-Dangjin, Masan, Yeosu Gwangyang, Jeju, and Gunsan-Daesan Ports, in order. In particular, Busan Port, found to be inefficient, would benefit from using Mokpo Port and Incheon Port as benchmarks to increase trade volume and modernize equipment for full-scale commercialization to improve labor efficiency.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2005.10a
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• pp.521-532
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• 2005

IT 기업의 기술적 효율성의 정도를 추정하고, 그 결정요인을 알아보기 위해 Coelli(1995)의 확률적프론티어(Stochastic Frontier model) 중 초월대수 변경생산함수(translog stochastic frontier production function)를 설정한다. 분석견과 연구개발투자, 기업의 재무구조, 설비투자효율, 노동소득분배율 등은 기업의 비효율을 감소시키는 역할을 하는 반면, 기업규모, 재고자산증가율, 자기자본증가율 등은 기업의 비효율성을 더 높여주는 것으로 나타났다. 1990년부터 2004년까지 국내 제조업 전체의 생산의 기술적 효율성은 평균 0.5311로 이는 생산효율성이 $53.11\%$임을 의미하고, 비효율성은 $46.89\%$에 달한다고 볼 수 있다. IT 기업의 기술적 효율성은 0.5337회 제조업과 비슷하지만, IT 대기업은 0.61, IT 중소기업은 0.511로 대기업과 중소기업의 격차가 크게 발생하고 있다.

• Proceedings of the Korea Contents Association Conference
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• 2016.05a
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• pp.337-338
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• 2016

녹색기술정보포털 기사 정보와 회원정보를 이용하여 전체 녹색기술 분야에 대한 패스파인더 네트워크와 최근접 이웃 중심성을 도출하고 분야별 융합 동향을 분석하였다. 분석결과, 전체 녹색기술 분야 중에서 실리콘 태양전지 및 고효율 2차전지 등의 에너지 고효율화 분야에 대해 회원들의 관심이 높은 것으로 나타났으며, 실리콘 태양전지 분야에서 다른 녹색기술과의 융합이 가장 활발하게 이루어지는 것으로 분석되었다.

• Environmental and Resource Economics Review
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• v.14 no.3
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• pp.595-626
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• 2005

The purpose of this paper is to estimate technical efficiency, scale efficiency, and environmental efficiency by income level of Korean farms, and analyze the factors to decide three efficiencies. Depending on the non-parametric methods, we estimate technical using inputs and outputs of total farms without assuming of goods or behavior of optimization. The average technical efficiency of total firms under constant return to scale and strong disposability is 0.437. The technical inefficiency was caused by 47.7% in pure technical inefficiency, 11.3% in scale failure, and 3.2% in environmental inefficiency. The number of firms under increasing return to scale occupied almost 70% and 27% of total firms respectively. Higher are income class, middle debt & long debt per asset, and N effluents per cultural land, higher technical efficiency. The increases of BOD discharges per cultural land and machines per cultural land deteriorate environmental efficiency.

• Journal of Korea Port Economic Association
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• v.32 no.2
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• pp.123-135
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• 2016

This study uses Data Envelopment Analysis(DEA) to investigate the management efficiency of Korean shipping companies based on business administration costs such as corporate entertainment, advertisement, and labor costs. We analyze shipping enterprises listed on the Korean stock market of the period of 2010-2014. Corporate entertainment, advertisement and labor costs are used as input variables and sales and net income are used as output variables. We use technical efficiency, pure technical efficiency, scale efficiency and returns to scale to propose a plan to improve the efficiency of inefficiency decision-making units (DMUs). The results of the efficiency analysis show that six of the DMUs in the technical efficiency of CCR model and eight of the DMUs in the pure technical efficiency of BCC model are in efficient state. In terms of return to scale, six of the DMUs(24% of all DMUs) show increasing returns to scale, while 13 DMUs(52% of all DMUs) showdecreasing returns to scale. Because multiple efficient state for DMUs exist in the technical efficiency analysis, we conduct a super efficiency analysis. The results show that the efficient state of the twomost efficient DMUs are 1.314 and 1.243, respectively. This implies that these DMUs could maintain their current levels of the efficiency if they increase the amount spent on advertisements, corporate entertainment and labor costs by 31.4% and 24.3%. respectively. We conclude this study by providing the efficiency states of each DMU and target for improving the inefficiencies in each case.