• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.2
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• pp.18-26
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• 2005

The balanced scorecard(BSC) overcomes the limit of traditional financial statement that focuses on only financial performance. BSC is widely used in government and industry because of the clear representation of the relationship and logic between the key performance indicators(KPI) of 4 perspectives - financial, customer, internal process, and loaming and growth. However, traditional BSC does not consider evaluating the difference between the results measured by BSC. By using relatively small number of inputs and outputs In comparing decision-making units, data envelopment analysis(DEA) can aggregate multiple performance measures. In this research, we propose a methodology named CDB(Combined DEA and BSC) to evaluate the performance of organization considering financial and non-financial perspectives. CDB uses KPI of cause-and-effect relationship on BSC as inputs and outputs of DEA method. In addition, this research proposes a method of converting the KPI of BSC to the input and output variables of DEA, and enhancing discrimination power using the limit number of variables. We illustrate the methodology by giving an example of evaluating aquisition-unit efficiency in a supply chain.

• Journal of Technology Innovation
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• v.15 no.2
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• pp.83-102
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• 2007

This study extends the existing method of Technology Forecasting with Data Envelopment Analysis (TFDEA) by incorporating a ranking method into the model so that we can reduce the required number of DMUs (Decision Making Units). TFDEA estimates technological rate of change with the set of observations identified by DEA(Data Envelopment Analysis) model. It uses an excessive number of efficient DMUs(Decision Making Units), when the number of inputs and outputs is large compare to the number of observations. Hence, we investigated the possibility of incorporating CCCA(Constrained Canonical Correlation Analysis) into TFDEA so that the ranking of DMUs can be made. Using the ranks developed by CCCA(Constrained Canonical Correlation Analysis), we could limit the number of efficient DMUs that are to be used in the technology forecasting process. The proposed hybrid model could establish technology frontiers with the efficient DMUs for each generation of technology with the help of CCCA that uses the common weights. We applied our hybrid model to forecast the technological progress of main battle tank in order to demonstrate its forecasting capability with practical application. It was found that our hybrid model generated statistically more reliable forecasting results than both TFDEA and the regression model.

• Ocean and Polar Research
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• v.28 no.4
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• pp.439-449
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• 2006

This study was aimed at measuring the fishing capacity of Powered Anchovy Drag Net Fisheries (PADNF) in Korea using Peak-to-Peak(PTP) and Data Envelopment Analysis(DEA) methods recommended by FAO. In the analysis, both fishing capacities of total PADNF and individual PADNF vessels were measured with time series data and cross sectional data, respectively. In addition, the results of the DEA measurement were analyzed in order to determine reduction levels of fishing capacity. In case of total PADNF, the results by rn and DEA methods showed a similar rate of capacity utilization (79%), indicating the capacity was not utilized enough. In addition, the sensitivity analysis suggested that the number of vessels should be reduced by 20%, and the gross tonnage and the horse power should be reduced by 20% and 21%, respectively if the current catch is to stay at the 2004 level. The DEA results on individual PADNF vessels indicated the capacity utilization was 75% on average, showing some differences in capacity utilization among vessels (31%-100%). The results of the study would be useful for measuring production efficiency in PADNF. They would also provide good policy information for efficient use of resources and capacity reduction levels, which are useful far vessel buyback programs of coastal and offshore fisheries.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.2
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• pp.899-904
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• 2013

This paper proposes an efficient portfolio selection methodology for the listed construction corporations in KOSPI and KOSDAQ. For the construction industrial sector classified by KRX(Korea Exchange), the proposed method carries out an efficiency analysis using DEA (Data envelopment analysis) approach and for the efficient corporations filtered by DEA, construct portfolio using Markowitz's Model. In order to show the effectiveness of the proposed method, we constructed annually portfolios for 5 years (2007-2011) out of 53 listed corporations in KOSPI and KOSDAQ, and proved that our portfolios are superior to benchmark portfolios in terms of rate of returns.

• Journal of Korean Society for Quality Management
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• v.32 no.4
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• pp.92-102
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• 2004

In this era of intense competition, TQM has become the key program in organizations as they strive for a competitive advantage. It has been applied to manufacturing and service sector since BNQA model was established in 1987. TQM literature for manufacturing sector abounds with empirical studies on the critical dimensions of TQM, but there is few empirical studies on the TQM evaluation for service sector. In this paper, two methodologies are applied to evaluate the TQM activities of service companies comparatively One of them is the traditional scoring system(TSS) by analytic hierarchy process(AHP). The other is the efficiency measuring system(EMS) by data envelopment analysis(DEA). DEA outperformed other alternative methods to measure the efficiency and it can be applied to evaluate the TQM activities. The objective of this paper is to evaluate TQM activities of domestic service companies by applying TAE(Total quality management Activities Evaluation) model to them. The result of this study is that TSS scores are not significantly correlated with EMS scores. It means that service organizations must not only make efforts to get the higher scores in terms of TSS but also take necessary steps to enhance their efficiencies.

• 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 the Korean Society of Manufacturing Technology Engineers
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• v.22 no.6
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• pp.921-930
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• 2013

From an economic perspective, an enterprise's business activity depends on the efficient use of corporate resources for generating profits. However, on the enterprise side, it is difficult to measure and evaluate the effective use of each resource. This paper suggests an alternative for eliminating process inefficiencies in the consolidation of competitive power in auto parts manufacturing company A. Multitudinous process variables from company A's raw materials-to-shipment process are configured as input resources, and a Data Envelopment Analysis(DEA) is carried out to determine economical benefit of said resources' operation, as well as how products are manufactured. The DEA model offers a non-parametric approach to measuring relative efficiency using input and output factors. Furthermore, AHP is used for logically deciding the importance of each evaluation factor. In general, DEA models have been used for measuring efficiency of the service and public sectors. However, this study focused on measuring the efficiency of SMEs production lines.

• Journal of Technology Innovation
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• v.12 no.1
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• pp.25-48
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• 2004

The relative efficiency of loan projects of information technology promotion fund is measured using Data Envelopment Analysis. Information technology promotion project is supervised by the Ministry of Information and Communication and is managed by the Institute of Information Technology Assessment. Among all the projects of information technology supported by this fund, this study deals with the themes that have been completed from 2000 to 2002. With multiple input and output data including the amount of fund, the period of study, the rate of increase in revenue, the increase in the amount of export and the increase in the number of patent, the relative efficiency scores of all the 119 subjects were calculated in CCR and BCC models of DEA. From the reference sets of some inefficient Decision Making Units, the causes of their inefficiency were analyzed. To compare the relative efficiencies among various DMUs, Super-Efficiency Ranking Method and Logistic Regression Model were used. As the result of this study, it was shown that W promotion funds in the fields related to mobile technology, visual equipment and communication device were used most efficiently.

• Environmental and Resource Economics Review
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• v.9 no.2
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• pp.349-371
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• 2000

Data Envelopment Analysis model is a linear programming based technique for measuring the relative performance of organizational units where the presence of multiple inputs and outputs makes comparison difficult. A common measure for relative efficiency is weighted sum of outputs divided by weighted sum of inputs. DEA model allows each unit to adopt a set of weight that shows it in the most favorable light in comparison to the other unit. In this paper, we present the mathematical background and characteristics of DEA model, and give a short case study where we apply the DEA model to evaluate the relative efficiencies of 51 global electricity companies. The technical efficiency and scale efficiency are also to be investigated. Generating capacity and the number of employees are used for input data, and revenue, net profit and electricity sales are used for output data. We find that the companies with 100% relative efficiency are only 9 among 51 electricity companies. And the technical and scale efficiency of KEPCO is 98.7% and 78.89%, respectively. This means that the inefficiency of KEPCO is caused by the scale inefficiency. The analysis shows that the employees should be decreased by 15% at minimum to get the 100% efficiency. The result suggests that KEPCO needs the structural reform to improve the efficiency.

• Journal of Korean Institute of Industrial Engineers
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• v.43 no.3
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• pp.154-163
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• 2017

Research collaboration is regarded as core source to lead various innovations in all countries. This paper compares and analyzes the performance of Industry-University-Government Research Institutes (GRI) collaboration based on the four types of research collaborations; GRI-GRI, Industry-GRI, University-GRI and Industry-University-GRI. So this paper will show which collaboration type has the best work on each R&D step. We use four R&D steps; research, development, commercialization and overall. We also evaluate the performance of research collaboration of GRIs based on the collaboration types. In order to evaluate the performance of research collaboration, Data Envelopment Analysis (DEA) is employed for measuring the efficiency of GRIs in this paper. DEA is a non-parametric approach to measuring the relative efficiency of decision-making units (DMUs) with multiple inputs and outputs. The empirical results represent that the performance of collaboration with industry is generally superior to other collaboration types. These findings from this paper are expected to provide basic information for national collaboration strategy making.