• Journal of Korean Society for Quality Management
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• v.45 no.1
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• pp.65-92
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• 2017

Purpose: The performance of global major airports is evaluated and several research questions are examined relative to the measures characterizing airport performance. Methods: The two-stage internal structure of airport performance is considered by decomposing it into physical operations and revenue generation. In the physical operations stage, operating costs, number of runways, terminal area and number of employees are used as inputs, while passenger throughput, cargo throughput and aircraft movements are taken as outputs. Subsequently, in the revenue generation stage, the outputs from the preceding stage are taken as inputs, while revenue is used as output. Results: Based upon this two-stage modeling of airport performance, a multiplicative two-stage network data envelopment analysis model is employed to calculate the overall and stage efficiencies of 59 airports using the recent data in the 2014 Airport Benchmarking Report published by the Air Transport Research Society. Several internal and external factors are also considered such as airport size, airport geographical location, proportion of international passengers, ownership (listed or not) and management style, and statistical analysis is performed to examine their impacts on airport performance. Conclusion: It is shown that the airports exhibit statistically significant difference across regions, and also some statistically significant factors affecting airport performance are identified.

• Journal of Korean Society for Quality Management
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• v.44 no.4
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• pp.785-798
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• 2016

Purpose: This paper developed the framework for extracting strategies of research and business development (R&BD) based on the data envelopment analysis(DEA). Methods: DEA has been widely utilized in evaluating R&D efficiency. Even though, technology transfer and commercialization has been regarded as the important factors for practical R&D efficiency evaluation, most research have evaluated R&D efficiency by just using the DEA outputs such as the number of patents and papers. The technology transfer, commercialization and relations among costs and generated technology and commercialization are needed to be considered for more practical R&D efficiency evaluation. Thus, this research addressed a method on how to incorporate the commercialization factors into the R&DB efficiency evaluation, and improve the efficiency strategically in terms of R&D and B&D. To achieve this, this research utilized a two-stage network DEA model for R&BD efficiency evaluation. Results: The proposed framework was applied to the 15 public research institutes and the 34 universities for validation. R&BD efficiency for the 15 public research institutes and the 34 universities was evaluated, and the differentiated improvement strategies for the inefficient DMUs to improve their efficient were proposed. Conclusion: The R&BD efficiency would be effectively analyzed based on two-stage network DEA. It would be utilized for the effective strategy planning for cultivating R&BD.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.248-261
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• 2021

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.

• Journal of Practical Engineering Education
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• v.12 no.1
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• pp.213-230
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• 2020

Until now, various educational budgets, facilities, and programs have been put into school education, but the results have not been clearly evaluated. This study presents a model to analyze the effectiveness of educational support for students in high schools across the country. In this model, we first use EM cluster analysis to make clusters with similar inputs for school operation, and then calculate the relative efficiency in each cluster by using Network DEA analysis. The Network DEA analysis has a two-stage structure where the first stage uses six inputs in terms of school infrastructure to generate outputs such as the number of academic persistence. In the Network DEA analysis, the second stage uses 10 inputs in terms of school programs to generate outputs such as the number of enrollees to higher learning and the number of employees and per capita usage of library as the connection variable. Based on the efficiency analysis results, Tier analysis is performed by applying the Euclidean distance to select targets for benchmarking. In this study, we applied the model to analyze the efficiency of educational support by collecting data regarding student education support in general and vocational high school nationwide. The stepwise benchmarking method proposed that the target be selected for efficiency improvement step by step, taking into account inefficient school elements to complement the problem of the choice of benchmarking targets. Based on this study, it is expected that schools with low efficiency of educational support for students will be used as basic data for stepwise benchmarking for efficient operation of educational support for students.