• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.201-212
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• 2017

This study presented the new evaluation index which can evaluate the discrimination of DEA models. To evaluate the discrimination of DEA models, data were analyzed using importance index as suggested in previous study and the coefficient of variation as suggested in this study for the discrimination evaluation. This study selected the CCR-DEA, BCC-DEA, entropy, bootstrap, super efficiency, and cross efficiency DEA model for the discrimination evaluation and accomplished empirical analysis. In order to grasp the rank correlation of the models, this study implemented the rank correlation analysis between the efficiency of CCR model and BCC model and entropy, bootstrap, super efficiency, and efficiency of the cross efficiency model. The obtained results of this study are as follows. First, the discrimination rank of models using the importance index and the coefficient of variation was shown to be identical. Therefore, the coefficient of variation can be used the discrimination evaluation index of DEA model. Second, the discrimination of the super efficiency model was found to be the highest rank among 4 models according to the analysis of this present study. Third, the highest rank correlation with CCR model was the super efficiency model. In addition, the super efficiency model was found to be the highest rank correlation with BCC model.

• Journal of Distribution Science
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• v.15 no.11
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• pp.49-57
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• 2017

Purpose - In Korea, general super markets have a great impact on the market performance of traditional markets. We propose a modified two stage DEA model for evaluating the performance of traditional markets in Incheon, Korea by identifying the influence of external environmental factors including the presence of general super markets as non-discretionary variables in DEA. Research design, data, and methodology - After obtaining bias-corrected estimates of original DEA efficiency scores using the input and output data of 49 traditional markets, we regress them on several external environmental factors by bootstrap-truncated regression. Results - We obtain bias-corrected efficiency scores from the original DEA efficiency scores by bootstrap and among the five environmental factors, the residential population and the presence of general super markets or SSMs can be considered as the driving forces influencing bias-corrected efficiency scores, positively and negatively, respectively. Conclusions - When DEA efficiency scores tend to be overestimated, we need to use a biased-corrected efficiency score by bootstrap. It is important to note that the efficiency of traditional markets can be largely influenced by external environmental factors such as the presence of general super markets or SSMs that traditional markets can not control. Therefore, it is desirable to consider such environmental factors appropriately for a reasonable performance evaluation.

• Journal of Korea Port Economic Association
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• v.18 no.1
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• pp.3-26
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• 2002

The purpose of this paper is to Investigate the productive efficiency and competitive strength of world container ports using the DEA, Super-efficiency, and FDH methods with the raw data from previous research by Jun et al.(1993). The super-efficiency measure examines the maximal radial change In input, outputs for an observation to remain efficient. Therefore, it provides a means of distinguishing between efficient observations, which would otherwise seem identical. FDH provides a good test mechanism for examining the practical implications of the choice available among alternative efficiency measures and orientations, because of the lack of convexity of its production possibility set. Both methods are complementary to DEA. This paper follows the traditional productivity analysis method overcoming the limitation of previous studies by using the DEA, FDH and Super-efficiency methods, and proposing in measure the relative competitive strength of worldwide container ports. The main empirical results of this paper are as follows: Firstly the ports of Singapore, Hongkong, Kilrung, Busan, Tokyo. and Longbeach were found to be efficient In the CCR model. The ports of Felixstowe, Bangkok, Singapore, Hongkong, Kilung, Busan, Tokyo, and Longbeach were found to be efficient in the BCC model. Secondly, super. efficiency rankings under CRS and input-oriented model are as follows: Longbeach, Keelung, Singapore, Busan, Tokyo, and Honkong. However, it was difficult In differenciate the rankings under the VRS and input-oriented model. due to major difficulties posed by the ports of Singapore, Hongkong, and Longbeach. Thirdly, the FDH method shows that the inefficient ports are Bremerhaven, Antwerp, Le Havre, Kobe, Seattle, New York The policy Implications of this study are as follows: Firstly, when port authorities want to measure the international competitive strength of container ports and enhance their productive efficiency, they should consider the traditional method as well as introducing the Super-efficiency and FDH methods. Secondly, according to the analysis results of the super-efficiency and FDH methods, poll authorities should recommend benchmarks ports and dominated ports as reference ports in order to enhance the productive efficiency of their container ports that have an efficiency rating of less than 1. Efficient ports whose efficiency ratings are over 1 in the Input-oriented Super-efficiency model should also consider the usage of input and output elements used by more efficient ports.

• The Journal of Fisheries Business Administration
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• v.42 no.2
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• pp.15-30
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• 2011

This study is designed to measure the relative efficiency of regional fishery cooperatives based on Data Envelopment Analysis(DEA) methods. Selecting 40 regional fishery cooperatives in Busan as Decision Making Units (DMUs), the study uses their panel data from 2007 to 2008 to rank the relative efficiency of the DMUs. First, the efficiency score of the DMUs are calculated using CCR, SBM, and super-SMB model. Within the model, input variables are the number of employees and area of fishery cooperatives. Output variables are the amount of deposit money, loan and profit. Based on the efficiency scores calculated from super-SMB model, the efficiency ranking of the DMUs is determined. Second, the differences in average efficiency calculated from the three DEA models are tested using a pair-wise mean comparison test. The results based on the efficiency scores evaluated from super-SMB model show that seven out of the forty DMUs are efficient; among the efficient DMUs, the DMUs that can be benchmarked for inefficient DMUs through the frequency analysis of reference set being identified. Third, the differences in average efficiency of the three DEA models between 2007 and 2008 are tested using pair-wise mean comparison test and the study estimates the efficiency change of the DMUs between 2007 and 2008 using Malmquist productivity index(MPI). Finally, the paper suggests an improved composite DMU superior to the inefficient DMUs evaluated by Super-SBM model.

• Journal of the military operations research society of Korea
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• v.33 no.2
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• pp.61-73
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• 2007

We examined technological forecasting of extended TFDEA(Technological Forecasting with Data Envelopment Analysis) and thereby apply the extended method to the technological forecasting problem of main battle tank. The TFDEA has the possibility of using comparatively inefficient DMUs(Decision Making Units) because it is based on DEA(Data Envelopment Analysis), which usually leads to multiple efficient DMUs. Therefore, TFDEA may result in incorrect technological forecasting. Instead of using the simple DEA, we incorporated the concept of Super-efficiency, Cross-efficiency, and CCCA(Constrained Canonical Correlation Analysis) into the TFDEA respectively, and applied each method to the case study of main battle tank using verifiable practical data sets. The comparative analysis shows that the use of CCCA with TFDEA results in very comparable prediction accuracies with respect to MAE(Mean Absolute Error), MSE(Mean Squared Error), and RMSE(Root Mean Squared Error) than using the concept of Super-efficiency and Cross-efficiency.

• 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.

• Korean Management Science Review
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• v.31 no.3
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• pp.1-12
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• 2014

This study suggests the two types DEA models such as DEA CCR model and Super Efficiency model to evaluate the value of a company and to apply them for the investments. 14 kinds of real data of companies such as EV/EBITDA, EPS growth rate, PCR, PER, dividend yield, PBR, stock price/net current asset, debt ratio, current ratio, ROE, operating margin, inventory turnover, accounts receivable turnover, and sales growth ratio were used as input variables of DEA models. 12 year data from December 30, 2000 up to December 30, 2012 were collected, and the data with negative, missing and 0 values were removed reflecting the characteristics of the DEA. In order to verify the effectiveness of the models, we compared the historical variability and rate of return of both models those of the market. Study results are as follows. First, two DEA models are more stable than market in terms of rate of return because the historical variability of both models are less than that of market. Second, Super Efficiency model is more stable than CCR model. Lastly, the cumulative rate of return of Super Efficiency model (434%) is greater than that of the CCR model (420%) and that of the market (269%).

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.309-321
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• 2021

While total sales of broadcasting business have been increasing, terrestrial broadcasters' sales are stagnant and comprehensive programming program providers(PPs)' sales are increasing. The purpose of this study is to derive implications by analyzing the relative efficiency and super-efficiency of domestic broadcasters in such an asymmetric situation. In this study, we use input and output data of three central terrestrial broadcasters and four comprehensive programming PPs from 2012 to 2019. For the analysis, Additive DEA model and Additive super-efficiency DEA model are used with the assumption of VRS condition since the broadcasting industry is characterized by economies of scale. As a result, we find out that the input excess of tangible assets is the most decisive cause of inefficiency that reduces the efficiency of central terrestrial broadcasters. On the other hand, for the comprehensive programming PPs, the input excess of tangible assets and the output shortfall of total sales are important factors of inefficiency.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.198-200
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• 2022

With the expansion of the global supply chain, the efficiency of maritime logistics is considered a crucial factor for countries' trade and competitiveness. Nevertheless, prior research has not thoroughly evaluated the efficiency of maritime logistics, including countries' ports and shipping capacities. Accordingly, this study examines integrated maritime logistics efficiency at the national level using DEA-CCR, BCC, and super-efficiency DEA. Furthermore, this study identifies a difference between the selected countries' maritime logistics efficiency and LPI (Logistics Performance Index) through Spearman's correlation test as an ad-hoc analysis. From this, Asian countries showed higher efficiency and European countries showed higher LPI scores. These results might be derived from this difference in port-city development patterns. Additionally, the main cause of inefficiency in Europe and Japan might be attributed to high fleet capacity of control. Consequently, this study can provide valuable implications for coastal countries to set more efficient directions for maritime logistics investment and policy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.130-139
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• 2020

The defense industry is a future-oriented industry, in which high-tech technologies are concentrated. In addition, it is a key industry for maintaining national security, and the R&D of the defense industry is very important. With the introduction of a competitive system by defense industry companies, the necessity of improving the efficiency and productivity of defense industry companies is emerging. In this study, the efficiency of the defense industry was measured using the CCR and BCC model. In addition, super efficiency was derived using the Super-SBM model. For this study, the 2019 defense industry management analysis data of the Korea Defense Industry Association (KDIA) was used, and the analysis was performed by setting the number of researchers and investments of domestic defense industry companies as the input variables and sales as the output variables. Through this study, it is expected that the defense industry's R&D efficiency will be grasped, which will help establish a policy for fostering the defense industry in the future.