• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.4
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• pp.48-54
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• 2012

In this paper, quantitative and systematic procedures for establishing Key Performance Indicators (KPI's) of R&D departments are presented. The proposed methodology is composed of 4 steps : 1) identification of critical success factors, 2) identification of potential KPI's, 3) determination of KPI's and 4) monitoring and execution. A Strategy Map has been presented to better align KPI's with a company's competitive strategies. Also, Analytical Hierarchy Planning (AHP) is used to determine weights of KPI's and Data Envelopment Analysis (DEA) is used to analyze the effectiveness of R&D departments. To demonstrate its validity of the proposed method, it has been applied to the R&D divisions of a semiconductor company.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.2
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• pp.51-60
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• 2008

A solution method for fuzzy linear programs is proposed. A fuzzy linear program is converted to a crisp linear program with average indices being applied to the objective function and constraints. A comparative analysis between the proposed average index approach and the possibilistic approach is given. As an application example, the proposed method is applied to the linear programming model for fuzzy data envelopment analysis, and the result is compared with that of the possibilistic approach.

• Journal of the Korea Safety Management & Science
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• v.15 no.1
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• pp.249-258
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• 2013

Data Envelopment Analysis (DEA) is a useful tool to analyze the relative efficiency of decision making units (DMU) characterized by multiple inputs and multiple outputs. This method has been popularly used as an analytical tool to suggest some strategic improvement. To do this, the results of DEA provide decision makers with a single efficiency score, efficient frontier, return to scale, benchmarking decision making units, etc. The purpose of this paper is to evaluate research performance of 38 universities and provide an inefficient university with the way of organizational changes to be an efficient university by using DEA. Various input and output variables are used to identify technical and scale inefficiency. Additionally, we analyze how an inefficient DMU could be changed an efficient DMU based on a case university. This result will give an insight of constructive directions for increasing of research performance to university decision makers.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.730-736
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• 2009

The objective of this paper is to measure the relative technical efficiency of Korean railway transport service. Previous studies on the efficiency in Korean railway service have carried out before the structural reform of Korean railway industry in 2004, whereas this study used the latest data which reflected the impact of reform. We analyzed the efficiency in Korean railway transport by means of measuring the technical efficiencies of other countries, these were estimated with data envelopment analysis. DEA has gained great popularity in measuring efficiency in transport industry, school, government, hospital and so on in recent years. This analysis could serve as peer to help improve performance of the railway management and operation.

• Journal of the Korea Society of Computer and Information
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• v.5 no.2
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• pp.158-164
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• 2000

The purpose of this study is concerned with evaluating management performance in electronics and communication companies using DEA(Data Envelopment Analysis) DEA is a linear programming based technique for measuring the relative performance of organizational units where the Presence of multiple inputs and outputs makes comparisons difficult. In this research. input variables are raw-material costs, number of employees, and production capacity Real production, sales revenue and net earning are suggested as output variables. Management performance of most companies are increased or equal during 97 and 98 fiscal year and input & output variables show high correlation.

• 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.

• ETRI Journal
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• v.33 no.3
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• pp.443-453
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• 2011

This paper evaluates the efficiency of mobile content firms through a hybrid approach combining data envelopment analysis (DEA) to analyze the relative efficiency and performance of firms and principal component analysis (PCA) to analyze data structures. We performed a DEA using the total amount of assets, operating costs, employees, and years in business as inputs, and revenue as output. We calculated fifteen combinations of DEA efficiency in the mobile content firms. We performed a PCA on the results of the fifteen DEA models, dividing the mobile content firms into those having either 'asset-oriented' or 'manpower and experience-oriented' efficiency. Discriminant analysis was used to validate the relationship between the efficiency models and mobile content types. This paper contributes toward the construction of a framework that combines the DEA and PCA approaches in mobile content firms for use in comprehensive measurements. Such a framework has the potential to present major factors of efficiency for sustainable management in mobile content firms and to aid in planning mobile content industry policies.

• East Asian Economic Review
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• v.19 no.2
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• pp.167-188
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• 2015

The objective of this study is to obtain a better understanding of the current rice production condition in Myanmar through efficiency analysis, especially, to examine the impact of farm mechanization on Myanmar rice production efficiency. For representation of efficiency and the determinants, this paper uses both the data envelopment analysis (DEA) and the stochastic frontier approach (SFA) with variable returns to scale on Myanmar rice production. The efficiency of the rice production was estimated and subsequently the determinants factors were investigated based on the estimated efficiency level of these sample farmers. The empirical evidence finds that farm mechanical tools significantly improve the Myanmar rice production efficiency.

• International Journal of Railway
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• v.1 no.2
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• pp.37-44
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• 2008

Safe, fast, efficient and effective railway is a main factor of economic development level of every country. This paper uses the DEA method to evaluate and compare the efficiency of 12 Regions of RAI. In addition, we introduce the reference(s) unit(s) for every inefficient region, and determine the amount of input decrease and/or output increase need to become them efficient. Findings indicate that in 2006, 4 regions of 12 are in Constant Return to Scale (CRS) status and 7 of them in Variable Return to Scale (VRS), and the average efficiency is 0.730 and 0.888, respectively. In other words, RAI works 27 percent under its capacity. More over results indicate that Hormozgan, Khorasan, Tehran and Isfahan Regions have the most efficiency respectively. The results show that the 8 regions, have been working in Increaser Return to Scale and 4 reminder Regions in Decrease Return to Scale. According to this results, we submit the suitable suggestion for improve the efficiency of the inefficient regions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.2
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• pp.1-6
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• 2006

This study proposes a new approach which combines Data Envelopment Analysis(DEA) and the Analytic Hierarchy Process(AHP) techniques to effectively evaluate Decision Making Units(DMUs). While DEA evaluates a quantitative data set, employs linear programming to obtain input and output weights and ranks the performance of DMUs, AHP evaluates the qualitative data retrieved from expert opinions and other managerial information in specifying weights. The objective of this research is to design a decision support process for managers to incorporate positive aspects of DEA's absolute numerical evaluations and AHP's human preference structure values. It is believed that a pragmatic manager will be more receptive to the results that include subjective opinions incorporated into the evaluation of the efficiency of each DMU efficiency. The WPDEA method provides better discrimination than the DEA method by reducing the number of efficient units.