• Health Policy and Management
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• v.6 no.2
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• pp.1-13
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• 1996

To evalate the efficiency of public and private hospitals, the author used Data Envelopment Analysis(DEA), a mathematical linear programming method calculating the of ficiency of a unity(DMU: Decision Making Unit) in relation to the other units in analysis. DEA was applied to thirty three (10 public and 23 private) general hospitals wiwith 160 to 299 beds. In respect to productivity, public hospitals appeared to be a little more efficient than private ones, even though it's statisticansignificant. However, the efficiency score for profitability conversed that these contrary results were due to the caring of more medical protection patients in public hospitals, who brought less revenlue to te hospital than other patients. Public hospitals' superiority to private counterparts in productivity, which are aguged mainly based on cared patients, suggests that the former contributes so much positively to social utility. In particular, the fact that public hospitals are caring more medical protection patients, namely the poverty group whom the society should bear a burden of by all means, seems to be desirable in respect of role of publi hospitals.

• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.33-46
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• 2019

Purpose: This study proposed a DEA (Data Envelopment Analysis)-based stepwise benchmarking target selection for inefficient DMU (Decision Making Unit) to improve its efficiency gradually to reach most efficient frontier considering resource (DEA inputs and outputs) improvement preferences. Methods: The proposed method proceeded in two steps. First step evaluates efficiency of DMUs by using DEA, and an evaluated DMU selects benchmarking targets of HCU (Hypothesis Composit Unit) or RU (Real Unit) considering resource improvement preferences. Second step selects stepwise benchmarking targets of the inefficient DMU. To achieve this, this study developed a new DEA model, which can select a benchmarking target of an inefficient DMU in considering inputs or outputs improvement preference, and suggested an algorithm, which can select stepwise benchmarking targets of the inefficient DMU. Results: The proposed method was applied to 34 international ports for validation. In efficiency evaluation, five ports was evaluated as most efficient port, and the remaining 29 ports was evaluated as relative inefficient port. When port 34 was supposed as evaluated DMU, its can select its four stepwise benchmarking targets in assigning the preference weight to inputs (berth length, total area of pier, CFS, number of loading machine) as (0.82, 1.00, 0.41, 0.00). Conclusion: For the validation of the proposed method, it applied to the 34 major ports around the world and selected stepwise benchmarking targets for an inefficient port to improve its efficiency gradually. We can say that the proposed method enables for inefficient DMU to establish more effective and practical benchmarking strategy than the conventional DEA because it considers the resource (inputs or outputs) improvement preference in selecting benchmarking targets gradually.

• Journal of Korean Society for Quality Management
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• v.32 no.2
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• pp.77-92
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• 2004

It is difficult but very necessary to measure the productivity of container terminals as logistics service provider. It is meaningful to find the appropriate inputs and outputs of the logistics service delivery systems and to measure the relationship between these inputs and outputs. This study proposes a model of evaluating the efficiency of container terminals. The evaluation consists of three phases. First, DEA(Data Envelopment Analysis) phase, determines the efficiency score and weights of DMUs(Decision Making Unit). This phase performs through four steps : selection of DMU, selection of DEA model, determination of input and output factors, calculation of efficiency score and weights for each DMU. Secondly, CEM (Cross Evaluation Model) phase, is to calculate the cross-efficiency scores of DMUs. This phase performs through three steps: selection of CEM, determination of cross-efficiency score for each DMU and development of cross-efficiency matrix. Finally, average cross-efficiency analysis phase is to compute the average cross-efficiency score. The proposed model discriminates among DMUs and ranks DMUs, whether they are efficient or inefficient.

• Journal of applied mathematics & informatics
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• v.14 no.1_2
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• pp.173-183
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• 2004

Data envelopment analysis(DEA) is a mathematical programming approach to asses s relative efficiency of a group of decision-making units. In view of the defects of existing models in evaluating efficiency of the system with P independent subsystems, Yang et al. [10] introduced YMK model with the assumption that decision-making unit(DMU) is independent of each other. But in some production systems, decision-making units usually have some relationships in this way or that. In this paper, DEA model is given by assuming that DMUs can cooperate with others in its subgroups. Some property and the efficiency relationship of the whole system and its subsystems are given.

• Journal of Technology Innovation
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• v.17 no.2
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• pp.131-158
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• 2009

We examine the effects of environmental or organizational factors on the performance of TLOs(technology transfer offices) in the PRIs(Public research institutes) using SFA(Stochastic Frontier Analysis), a technique for estimating the efficiency of DMUs(decision making units). In SFA, independent variables are assumed to determine the efficient production technique(production frontier) or affect the efficiency of DMUs. Previous researchs show that input variables such as number of personnel, R&D expenditure affect the production frontier while environmental or organizational variables affect the efficiency. We tried to estimate various types of models to find out whether environmental or organizational variables affect output variables differently from the previous research. Main empirical findings are as follows. First, R&D expenditure tends to increase all output variables considered. Second, environmental factors such as type of institutions and location of institutions affect the level of outputs. Third, organizational factors such as reward system for technology transfer also appear to affect the output variables. Fourth, environmental or organizational variables affect the production frontier directly rather than affect the efficiency of DMUs. Lastly, the efficiency of each DMU appear to be 1 or near to 1. Since almost all DMUs are equally efficient, it may not be effective to evaluate technology transfer activities of PRIs by efficiency criteria. We believe that this research should be complemented by additional data. More general types of production function need to be considered, and new techniques with concepts like output distance functions need to be developed to analyse multiple outputs simultaneously.

• Journal of Korean Society of Forest Science
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• v.103 no.4
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• pp.639-645
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• 2014

This study was conducted to evaluate the management efficiency of oak mushroom farms in Korea using the Data Envelopment Analysis (DEA), which is one of the non-parametric estimation methods. The data that was analyzed in this study was from the result of 2013 survey entitled "Standard Diagnostic Table for Oak Mushroom Management", which was conducted from March 2012 to October 2012. This survey was based on the inputs and outputs of 20 oak mushroom farms. Specifically, this study analyzed the technical efficiency, pure-technical efficiency and scale efficiency using CCR and BCC model of the DEA methods. Furthermore, this study compares the management efficiency between the full time oak mushroom production farms and part time oak mushroom production farms. Results showed that mean value for the technical efficiency was 0.655 which is considered as inefficient in general. For the pure-technical efficiency and scale efficiency, the mean values were 0.830 and 0.747, respectively which showed that inefficiency in the management was observed in the mushroom farms. Results also showed that there were seven farms with a total efficiency of 1, namely Decision Making Unit(DMU)2, DMU5, DMU6, DMU8, DMU10, DMU15 and DMU20. The management efficiency of DMU7 specifically the inputs for production was analyzed and compared to DMU5 and DMU6 and results showed that the DMU7 had an excessive inoculation and site development cost. Lastly, it was also observed that the full time mushroom production farms were more efficient as compared to the part time mushroom farms because of the lower scale efficiency value or smaller area for mushroom production allotted in the part time farms.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.4
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• pp.298-312
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• 2013

This paper compares efficiency results and performance targets for academic departments in a local private college using DEA (Data Envelopment Analysis). Because of an aging society, a smaller school-age population entering colleges, and enhanced accreditation standards by the government, colleges and universities are not recruiting and retaining sufficient students and therefore are struggling for survival. In contrast to popular four-year undergraduate universities concentrated in Seoul and its satellite cities, retaining students is critical for the survival of local private colleges in poor or remote regions. Therefore, it is very important to identify the factors involved in the retention of students in the various departments of a college. However, given the different characteristics of the departments, it is difficult to identify one unique or robust set of standards to evaluate their performance. The purpose of this paper is to maximize student retention capabilities by ensuring that additional resources are assigned to efficient DMUs, while, inefficient DMUs are given benchmarked targets. Based on previous studies and college accreditation standards, this paper presents indices to be used in evaluating the efficiency of academic departments in a college. In evaluating relative efficiency, this paper uses the output-oriented BCC model. To define target levels to be achieved for efficient DMU, a multi-stage DEA procedure is used.

• Journal of Technology Innovation
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• v.13 no.2
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• pp.207-222
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• 2005

A prerequisite for making R&D more productive is to able to measure its productivity. Most of the previous studies on this topic have attempted to measure R&D productivity at the firm or industry levels. In this study, however, R&D productivity is measured at the national level to provide R&D policy implications, particularly for Asian countries. Contrary to the previous studies where total factor productivity was adopted, this study employs the data envelopment analysis (DEA) approach to measure R&D productivity. DEA is a multi-factor productivity analysis model for measuring the relative efficiency of each Decision Making Unit (DMU). In addition to the basic DEA model that includes all inputs and outputs, five additional models are constructed by combining single input with all outputs and single output with all inputs in order to measure specialized R&D efficiency. In this study, the twenty-seven countries are classified into four clusters based on the output-specialized R&D efficiency: inventors, merchandisers, academicians, and duds. Then, the characteristics of the Asian countries with respect to R&D efficiency are identified. It is found that Singapore ranks high in total efficiency, and Japan in patent-oriented efficiency. Meanwhile, China, Korea, and Taiwan are found to be relatively inefficient in R&D. We expect that the findings from this study will be able to provide directions for R&D policy-making of the Asian countries.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.2
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• pp.190-204
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• 2008

Recently, it has been strongly required to establish a systematic and sustainable performance investigation and evaluation framework on governmental funding projects for IT small and medium-sized enterprises. In this paper, Data Envelopment Analysis (DEA) models are adopted for performance evaluation on governmental funding projects for IT small and medium-sized enterprises. A new data structure is proposed for the DEA performance evaluation. Generally, in using DEA models, DEA multipliers restriction is critical to achieve the reliability of DEA optimal solutions. Based on the outputs and inputs considered in this study, Acceptance Region (AR) constraints are generated and incorporated into the DEA models so as to improve the reliability of DEA efficiency scores. Associated with AR Type I (AR-I), AR Global Model (ARGM) constraints, DEA/ (AR-I, ARGM) models are designed and then sensitivity analysis follows investigating the robustness of DEA efficiency scores relating to AR constraints adjustment. Finally, a performance evaluation is illustrated regarding governmental direct funding projects from Ministry of Information and Communication (MIC) in Korea where each project unit (i.e. Decision Making Unit (DMU)) is determined whether it is efficient or not. By using DEA/(AR-I, ARGM) models designed in this paper, robustly efficient DMUs are gradually identified according to the successive AR constraints adjustment. Among 25 DMUs, results show that 6 DMUs such as B, E, G, Q, S, Y are determined as robustly efficient against AR constraints intermediate adjustment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.8-14
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• 2019

Most of the data envelopment analysis (DEA) models evaluate the relative efficiency of a decision making unit (DMU) based on the assumption that inputs in a specific period are consumed to produce the output in the same period of time. However, there may be some time lag between the consumption of input resources and the production of outputs. A few models to handle the concept of the time lag effect have been proposed. This paper suggests a new multi-period input DEA model considering the consistent time lag effects. Consistency of time lag effect means that the time delay for the same input factor or output factor are consistent throughout the periods. It is more realistic than the time lag effect for the same output or input factor can vary over the periods. The suggested model is an output-oriented model in order to adopt the consistent time lag effect. We analyze the results of the suggested model and the existing multi period input model with a sample data set from a long-term national research and development program in Korea. We show that the suggested model may have the better discrimination power than existing model while the ranking of DMUs is not different by two nonparametric tests.