• Proceedings of the Korea Society for Industrial Systems Conference
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• 2006.05a
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• pp.77-88
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• 2006

Data Envelopment Analysis (DEA), a non-parametric productivity analysis tool, has become an accepted approach for assessing efficiency in a wide range of fields. Despite of its extensive applications, some features of DEA remain bothersome. DEA offers no guidelines to where relatively inefficient DMU(Decision Making Unit) improve since a reference set of an inefficient DMU consists of several efficient DMUs and it doesn't provide a stepwise path for improving the efficiency of each inefficient DMU considering the difference of efficiency. We aim to show that DEA can be used to evaluate the efficiency of System Integration Projects and suggest the methodology which overcomes the limitation of DEA through hybrid analysis utilizing DEA along with machine learning.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.3
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• pp.39-52
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• 2001

Recently, the assessment of a bank efficiency focusing on its branches has been conceived as important in developing a competitive strategy. DEA (Data Envelopment Analysis) model can be employed as an effective analysis model for such an assessment. Therefore, this paper proposes an analysis process using DEA model to conduct an efficiency assessment of bank branches. The proposed process includes a segmentation of branches considering their competitive environment and strategy for target market : this approach can help to develop effective strategies for each group of branches. The proposed DEA model can analyze efficiency in terms of not only cost but also marketing. Finally, a real case is analyzed, demonstrating the effectiveness of the proposed model and process.

• Journal of the Korean Society for Railway
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• v.16 no.2
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• pp.117-121
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• 2013

Railway line segment deterioration can be affected by rail tracks, subgrades, bridges, tunnels, and line shapes. In this paper, an evaluation method is presented for the railway line segment deterioration using the analytic hierarchy process (AHP) and data envelopment analysis (DEA). The importance weights can be assessed systematically for component facilities from numerous experts using AHP. The importance weights provided by experts may differ according to each expert; however, the DEA enables the evaluation of railway line segment deterioration that reflects the variety of expert opinions using these importance weights.

• Journal of Korea Port Economic Association
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• v.26 no.1
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• pp.131-143
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• 2010

This study aims to analyse efficiencies of international main container ports. For the purpose, it selected twenty main ports based on the volume of cargo of ports recorded in Containerisation International Yearbook and conducted BCC(efficiency analysis) on them. The variables which may influence efficiency of ports were selected based on previous studies. Berth, Depth, Crane and Total area were input variables and Total TEU was an output variables. The results are summarized as follows: First, DEA-CCR efficiency of Shenchen Port(China) was 1.0, which was the most efficient poor analysed. Second, DEA-BCC efficiency of Port of Singapore, Shenchen Port(China), Ningbo Port (China), Port of Tanjung Pelepas (Malaysia) was 1.0, which was the most efficient analysed. Third, efficient scale of Shenchen Port(China) has been 1.0 for four subsequent years from 2004 to 2007, which was the most efficient analysed.

• Journal of Technology Innovation
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• v.20 no.3
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• pp.255-285
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• 2012

As the environmental performance is getting important in global automotive industry sector, there is a need to build the intellectual capacity. Hence it is important to measure the performance of the green car patent development of global automotive companies. To do this, we propose to use Data Envelopment Analysis(DEA) Model with Analytic Network Process(ANP), which generates weight coefficients of inputs and outputs for DEA-AR(Assurance Region) model. We considered three inputs: corporate asset, R&D expenditures, number of employees, and three outputs: patent counts, patent citations and patent claims. The results showed that our model could measure the potential of green car technology, and we could see the trend of the green car industry sector.

• Journal of Korea Port Economic Association
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• v.31 no.1
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• pp.85-110
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• 2015

The purpose of this paper is to show the clustering trend and the empirical comparison and to choose the clustering ports for 3 Korean ports(Busan, Incheon and Gwangyang Ports) by using the Fuzzy(Average Index Transformation) DEA and Cross-efficiency models for 38 Asian ports during 11 years(2001-2011) with 4 input variables(birth length, depth, total area, and number of crane) and 1 output variable(container TEU). The main empirical results of this paper are as follows. First, clustering results by using Fuzzy(AIT)DEA show that 3 Korean ports[Busan(56.29%), Incheon(57.96%), and Gwangyang(66.80%) each]can increase the efficiency. Second, according to Cross-efficiency model, Busan(Hongkong, Kobe, Manila, Singapore, and Kaosiung etc.), Incheon(Aquaba, Dammam, Karachi, Mohammad Byin Oasim and Davao), and Gwangyang(Damman, Yokohama, Nogoya, Keelong, Kaosiung, and Bangkok) should be clustered with those ports in parentheses. Third, when both Fuzzy(AIT)DEA and Cross-efficiency models are mixed, the empirical result shows that 3 Korean ports[Busan(71.38%), Incheon(103.89%), and Gwangyang(168.55%) each]can increase the efficiency. The efficiency ranking comparison among the three models by using Wilcoxon Signed-rank Test was matched with the average level of 66%-67%. The policy implication of this paper is that Korean port policy planner should introduce the Fuzzy(AIT)DEA, and Cross-efficiency models with the mixed two models when clustering is needed among the Asian ports for enhancing the efficiency of inputs and outputs. Also, the results of SWOT analysis among the clustering ports should be considered.

• Journal of Korea Port Economic Association
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• v.37 no.3
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• pp.75-91
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• 2021

Due to increased competition in supply side to reduce operational costs, port professionals have experienced extreme pressure, which demanded academicians to develop the model for efficient port operations from the industry perspective. Among many ports in the world, U.S. ports are our primary interest to analyze in our study for its high volume of cargoes transacted in the U.S. ports. We primarily employed DEA (Data Envelopment Analysis) technique to research the productivity of U.S. ports and applied the algorithm of double bootstrapped DEA proposed by Simar & Wilson (2007) to further investigate the driving forces of the performance of U.S. port operations. The external variables employed in our study comprise onDock Rail, Channel Depth, Location, Area, Acres, ForeignCargoRatio, and TEUChange, out of which onDock Rail, Acres, ForeignCargoRatio, and TEUChange were significant. In order to evaluate the effects of methodology selection, we conducted the same analysis applying the Censored model (Tobit) and contrasted the outcomes drawn from the two different techniques. Based on the findings from this work we proposed managerial implications and concluded.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2406-2419
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• 2010

This study aims to elevate the usefulness of the current annual Accreditation of Hospitals. To achieve this purpose, A modeling of an efficiency analysis based on DEA and AHP to the Accreditation of Hospitals Data from 2004 to 2008. By applying to AHP and DEA_AR to the scores derived from the various domains in data, An adequate prediction model about conversion factor in fee contract is made. By summarizing information derived from DEA, factor analysis and Generalized Linear Model, The linear functions combining conversion factor and efficiency index is successfully established. The factor analysis with AHP was used to merge diverse scores from the domains of evaluation. Not only the input and output initially introduced, AHP scores, dummy variables of hospital classification, geographical location are effective variables to forecast a conversion factor. If a predicted conversion factors from efficiency is used, It will be a great contributions to the annul doctor's fee contract.

• Journal of Digital Convergence
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• v.9 no.6
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• pp.1-11
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• 2011

It is time to review the direction of R&D investment which is the source of change in knowledge information ages. The DEA(Data Envelopment Analysis) is a efficient method by using the ratio of input and output elements. In this paper, we analyzed industry relationship and efficiency by DEA in order to analyze ripple effect of the regional industry technology development project. According to these results, we found the primary affected factor in performance of this project was human resource, and the turnover of participated company was not on the rise yet. On types of the project, we could find the efficiency quotient of critical technique development project was higher than of common technique development project because project period or budget scale infected to project performance. We expect the enhancement of technique developer, tenn and budget, and it is necessary the project results should lead performance such as sales force and employee growth.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.255-262
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• 2013

Data envelopment analysis(DEA) is a linear programming procedure designed to evaluate the relative efficiency of a set of peer entities called decision making units which use the same inputs to produce the same outputs. It has been widely employed in a variety of disciplines as an efficiency or performance measurement tool for comparing a set of entities such as firms, banks, hospitals, nations and organizations. The method, however, cant's make the priority of their performance when many units have efficiency score of unity or 100 percent. In this paper, we propose a new approach which combine qualitative method(graphical approach using network analysis) and quantitative method(super-efficient analysis using DEA), and present the results of an empirical analysis using the data of the Korean professional baseball players. As a result, there were 12 DMU that priority is hardly realized through DEA. However, this problem could be solved with super-efficiency analyzing. Also, more in-depth interpretation was able through integrating results of dendrogram and super-efficiency analyzing and prospecting it in qualitative, quantitative ways.