• Journal of the Korea Academia-Industrial cooperation Society
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• v.1 no.1
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• pp.83-90
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• 2000

DEA is a newly developed analyzing tool to measure efficiency evaluation of decision making units (DMU). It compares DMU by radial Projection on the efficient frontier. The purpose of this study is to show reference point approach used for searching solution in multiple objective linear Programming can be usefully used to determine flexible efficient frontier of each DMU In reference point approach, the minimization of ASF Produces an efficient points in frontier and enhances the usefulness of DEA by Providing flexibility in DEA and optimally allocating resources to DMU. Various DEA models can be supported by reference point method by changing the projection direction in order to choose the targets units, standards costs and management benching-marking.

• The Journal of Information Systems
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• v.29 no.3
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• pp.145-157
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• 2020

Purpose This study proposed an approach for predicting the efficiency rating of the cultural tourism festivals using DEA and machine learning techniques. The cultural tourism festivals are selected for the best festivals through peer reviews by tourism experts. However, only 10% of the festivals which are held in a year could be evaluated in the view of effectiveness without considering the efficiency of festivals. Design/methodology/approach Efficiency scores were derived from the results of DEA for the prediction of efficiency ratings. This study utilized BCC models to reflect the size effect of festivals and classified the festivals into four ratings according the efficiency scores. Multi-classification method were considered to build the prediction of four ratings for the festivals in this study. We utilized neural networks and SVMs with OAO(one-against-one), OAR(one-against-rest), C&S(crammer & singer) with Korea festival data from 2013 to 2018. Findings The number of total visitors in low efficient rating of DEA is more larger than the number of total visitors in high efficient ratings although the total expenditure of visitors is the highest in the most efficient rating when we analyzed the results of DEA for the characteristics of four ratings. SVM with OAO model showed the most superior performance in accuracy as SVM with OAR model was not trained well because of the imbalanced distribution between efficient rating and the other ratings. Our approach could predict the efficiency of festivals which were not included in the review process of culture tourism festivals without rebuilding DEA models each time. This enables us to manage the festivals efficiently with the proposed machine learning models.

• 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.

• The Journal of the Korea Contents Association
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• v.11 no.6
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• pp.397-406
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• 2011

This paper analyzes the efficiency of Korean Financial Investment Institution using DEA model. We evaluate the CCR, BCC efficiency and RTS of 30 Financial Investment Institution. We also suggest the Financial Investment Institution which can be benchmarked based on analyzed information. The result shows that 3 Institution whose values of CCR efficiency are 1, and 7 Institution whose values of BCC efficiency are 1. RTS indicates IRS of 21 Investments, DRS of 6 Investments and CRS of 3 Investments.

• Journal of Korean Institute of Industrial Engineers
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• v.43 no.3
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• pp.203-212
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• 2017

Emissions Trading System (ETS) is utilized in many countries, including South Korea, as an efficient policy to abate GHG (Greenhouse Gas) emissions. Grandfathering on the basis of historic emissions is used as the way to allocate permits in South Korea. It, however, has caused an increase in the emission permits and lack of equity. To overcome these drawbacks, we propose an alternative DEA model for centralized allocation of emission abatement to evaluate the amount of emissions abatement by company based on the energy efficiency. In addition, an empirical analysis of 36 assigned companies for ETS in Korean metal industry is conducted to validate the feasibility of the proposed model. The result of the analysis shows that energy-efficient companies achieve reduced target of the emissions abatement and companies with low energy efficiency score are turned out to have contrary outcome, against the result of applying Grandfathering.

• Management & Information Systems Review
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• v.5
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• pp.81-101
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• 2000

The purpose of this comparative study is to compare and evaluate venture business of electronic and communication industry by Data Envelopment Analysis(DEA). DEA is a linear programming-based technique that converts multiple input and output measures into a single comprehensive measures of productive efficiency. In this paper, the CCR model and trend analysis model are used to examine the efficiency of 18 venture business. Input variables are number of employees. raw-material costs and production capability and output variables are real production, sales revenues and net income after taxes. DEA approach broad information like as efficiency level of each Decision Making Unit(DMU), reference group of efficiency improvement and trends of efficiency shift. Finally, the correlation of input and output variables are examined to examine the relationship among variables.

• Proceedings of the Safety Management and Science Conference
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• 2008.11a
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• pp.189-199
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• 2008

It is good to use DEA method as it can measure the efficiency without depending on a specific function like cost function. The method also finds out the most efficient group among the sample groups and gives us a specific number. For example, it shows what kind of factor of inefficient group gives how much input and produces how much output. Originally DEA, which was developed by Charnes, Cooper and Rhodes, allows us not only to measure the relative efficiency of Decision Making Units(DMUs) of non-profit organizations whose success cannot be measured by a single bottom-line figure such as profit but also to integrate several variables, which have different measuring scale, into a single model. Therefore we can use physical scales and financial scales simultaneously in the same model without any transformation process. In this study, price and measurable performance indexes of vehicles are used as input and outputs respectively. The purpose of this study is to propose an effective approach for evaluating the relative efficiency of vehicles and to determine the vehicles have high performance efficiency compared to product cost.

• Korean Management Science Review
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• v.19 no.1
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• pp.117-128
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• 2002

In this Paper we investigate the performance of the five efficiency estimation methods which include the stochastic frontier model estimated by maximum likelihood (SFML), the stochastic frontier model estimated by corrected ordinary least squares (SFCOLS), the data envelopment analysis (DIA) model, the combined estimation of SFML and DEA (SFML + DEA), and the combined estimation of SFCOLS arid DIA (SFCOLS+ DEA) using Monte Carlo analysis. The results include: 1) SFML provides most accurate efficiency estimates for the sample sloe 150 or over,2) SFML+DEAor SFCOLS + DIA Perform better for the cases with sample sloe 25, 50, and low random errors, 3) SFCOLS performs better for the close with sample sloe 25, 50, and very high random errors.

• Journal of Venture Innovation
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• v.1 no.2
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• pp.35-46
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• 2018

The research on the efficiency analysis of domestic auto parts companies is mostly based on the calculation of the magnitude of the performance creation such as the sales amount compared to the input assets. However, the performance indicators such as sales, operating profit, and net profit are directly related to the cost structure and This cost structure is affected by changes in the firm's asset and capital structure. As a result, it is considered that efficient capital procurement needs to be done at the same time to create efficient management performance through proper investment. This study focuses on this point and attempts to measure the efficiency of procurement capital relative to the sales and other performance indicators generated by the first 33 suppliers who supply parts to Hyundai Kia Motors. Among the methods of evaluating efficiency, the DEA model based on the linear programming method is most widely used as a nonparametric method but The efficient frontier-based DEA model has the limitation that it can not use the variables that have a downward influence on the efficiency. This is inadequate to directly consider variables such as borrowings and total liabilities related to capital procurement. In this study, the efficiency of capital procurement was measured using Worst Practice DEA and the improvement direction of the capital procurement aspect of domestic auto parts companies was suggested

• 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.