• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.504-511
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• 2020

This paper studies the operational efficiency of the new sesame variety "da u" farmed by the Rural Development Administration. In the survey area--Gyeonggi-do Province, where sesame cultivation was the most intensive. Thirty farmers were surveyed by rural enterprises in the Yangpyeng area where 'da u' was planted. The efficiency of the operation was analyzed by studying the data of 30 farmers. In order to analyze the operational efficiency of the farmers, this survey used the DEA model for analysis in order to determine the technical efficiency of farmers. The result shows that the DEA technical efficiencies of most farmers are efficient, and only 6 farmers are inefficient. The reason for the inefficiency is the high cost of input factors. To analyze the determinants of efficiency through the Tobit model, reducing the pesticides and general fertilizers led to increased technical efficiency. This is inefficient use of pesticides and general fertilizers relative to the output factor, and efficient methods should reduce the cost issues.

• The Korean Journal of Health Service Management
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• v.7 no.3
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• pp.225-235
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• 2013

This study purposed to analyze the productivity of regional medical capacity in attracting medical tourist between 2009 and 2011. Data envelopment analysis (DEA) and Malmquist productivity index (MPI) were applied to test the productivity and changes in study periods. The DEA model included a number of doctors, nurses, and beds as input factors, and a number of inpatient and outpatient, and hospital income as output factors. The result of the study were as follow. Jeju had efficiency value of 1 for three years. Seoul, Busan, Daegu, Daejeon, Ulsan, Gyeonggi, Gangwon, Jeonnam, Gyeongbuk, and Gyeongnam showed an increasing trend of efficiency value over three years. Seoul had the efficiency value, 1, in 2011. But, Incheon, Gwangju showed decreasing efficiency score during the study period. MPI showed overall productivity decrease during the period. Further studies will be needed by collecting more time-series data.

• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.292-302
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• 2018

This study proposes a Bayesian stochastic frontier model that is well-suited to productivity/efficiency analysis particularly using panel data. A unique feature of our proposal is that both production frontier and efficiency are estimable for each individual firm and their linkage to various firm characteristics enriches our understanding of the source of productivity/efficiency. Empirical application of the proposed analysis to Human Capital Corporate Panel data enables identification and quantification of the effects of Human Resource factors on firm efficiency in tandem with those of firm types on production frontier. A comprehensive description of the Markov Chain Monte Carlo estimation procedure is forwarded to facilitate the use of our proposed stochastic frontier analysis.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.188-195
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• 2013

Construction operations generate a significant amount of air pollutant emissions, including carbon emissions. The environmental performance of construction operations is closely relevant to the operational efficiency of each resource employed, which indicates how efficiently each resource (e.g., construction equipment) is utilized. In this context, monitoring the operational efficiency of construction equipment provides key information in managing and improving the environmental performance and productivity of construction operations. In this paper, we report our efforts to measure the operational efficiency of construction equipment, using low-cost accelerometers. An experimental study and real-world case studies are conducted to demonstrate the feasibility of the proposed approach. The results have shown the potential of this approach as an economically feasible means of monitoring the environmental performance of construction operations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.65-73
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• 2018

The dynamic capabilities of sensing market signals, creating new opportunities and reconfiguring resources and capabilities to new opportunities in a rapidly changing economic environment determines the competitiveness of the enterprise to create added value and survival. This study conceptualized a two-stage performance measurement framework based on the casual model of resource (input)-process-performance (output). We have developed a 'Process capability index' that reflect the dynamic capabilities factors as a key intermediary product linking resource inputs and performance outputs in enterprise performance measurement. The process capability index consists of four elements : manpower (level of human resource), operation productivity, structure and risk management. The DEA (Data Envelopment Analysis) model was applied to the developed performance indicators to analyze the branch office performance of a telecom company. Process capability efficiency (stage 1) uses resource inputs to reach a certain level of process capabilities. In performance result efficiency (stage 2), the process capabilities are used to generate sales revenues and subscribers. The two-stage DEA model derives intermediate output values that optimize the individual stages simultaneously. Some branch offices in the telecom company have focused on process capability efficiency or some other branch offices focused on performance result efficiency. Positioning map using two-stage efficiency decomposition and benchmarking can help identify the sources of inefficiencies and visualize strategic directions for performance optimization. Applications of two-stage DEA in conjunction with the case study that are meaningfully used in performance measurement areas have been scarce. In particular, this paper has the contribution to present a new performance measurement model considering the organization theory, the dynamic capabilities.

• 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 the Korea Academia-Industrial cooperation Society
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• v.11 no.10
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• pp.3779-3788
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• 2010

In this study, We found approach to improve the efficiency of human resource policies for small-medium enterprises(SMEs) in the regional strategic industries. Especially, we intended to identify practical and policy implications, through analyzing to the supply and demand side of human resource at the same time. In the supply side, we analyzed workers, supply, and support policy in regional strategic industry. In the demand side, we analyzed the demands from 201 SMEs in regional strategic industry. We found the demand differences in strategic industries. we found the efficiency improvement and directions of human resource policies. Thus, these results imply the necessity of creating a customizing policy for SMEs. In conclusion, the human resource policy should be reflected the central government's policy direction and the demands of SMEs.

• Journal of Digital Convergence
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• v.14 no.7
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• pp.93-102
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• 2016

The purpose of this study is to examine the effects of the Mobile Smart-Work IT resources on the work efficiency based on moderating effects of the perceived usefulness in the Resource Based View. The study has academic meanings and practical implications that studied widely based on moderating effects of the perceived usefulness through the Hierarchical regression furthermore on the existing study on hypothesis by standard multiple regression. The results showed that IT Infrastructure in the Mobile Smart-work IT resources by moderating effects of the perceived usefulness was found to bring positive effect on individual work efficiency and teamwork efficiency. On the other hand Business Application was found to bring positive effect on teamwork efficiency. Therefore, we expect that business firm and employees actively use the effects of the perceived usefulness with the trust that Mobile Smart-work IT resources bring positive effects on the work efficiency. It can improve this work efficiency and lead to strengthening the firm competitiveness.

• Journal of Environmental Science International
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• v.22 no.3
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• pp.281-289
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• 2013

For the purpose of evaluating the eco-efficiency(EE) on surplus heat generated from industrial process, techniques of life cycle assessment are adopted in this study. Because it can be indicated both environmental impacts and economic benefits, EE is well known as a useful tool for symbiosis network on the sustainable development of new projects and businesses. To evaluate environmental impacts, the categories were divided into two areas of resource depletion and global warming potential. It can be seen that environmental impact increased a little but much higher economic benefit on the company, environmental performance and economic value were improved on the apartment by the district heating, respectively. In result, eco-industrial park(EIP) project on surplus heat should be found sustainable new business because the EE was in the area of fully positively eco-efficiency and, moreover resource depletion was taken place than the reduction of greenhouse gas.

• 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.