• Journal of Hospice and Palliative Care
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• v.25 no.2
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• pp.76-84
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• 2022

Purpose: We compared cost-effectiveness parameters between inpatient and home-based hospice-palliative care services for terminal cancer patients in Korea. Methods: A decision-analytic Markov model was used to compare the cost-effectiveness of hospice-palliative care in an inpatient unit (inpatient-start group) and at home (home-start group). The model adopted a healthcare system perspective, with a 9-week horizon and a 1-week cycle length. The transition probabilities were calculated based on the reports from the Korean National Cancer Center in 2017 and Health Insurance Review & Assessment Service in 2020. Quality of life (QOL) was converted to the quality-adjusted life week (QALW). Modeling and cost-effectiveness analysis were performed with TreeAge software. The weekly medical cost was estimated to be 2,481,479 Korean won (KRW) for inpatient hospice-palliative care and 225,688 KRW for home-based hospice-palliative care. One-way sensitivity analysis was used to assess the impact of different scenarios and assumptions on the model results. Results: Compared with the inpatient-start group, the incremental cost of the home-start group was 697,657 KRW, and the incremental effectiveness based on QOL was 0.88 QALW. The incremental cost-effectiveness ratio (ICER) of the home-start group was 796,476 KRW/QALW. Based on one-way sensitivity analyses, the ICER was predicted to increase to 1,626,988 KRW/QALW if the weekly cost of home-based hospice doubled, but it was estimated to decrease to -2,898,361 KRW/QALW if death rates at home doubled. Conclusion: Home-based hospice-palliative care may be more cost-effective than inpatient hospice-palliative care. Home-based hospice appears to be affordable even if the associated medical expenditures double.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.377-377
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• 2000

Current software cost estimation models, such as the 1951 COCOMO, its 1987 Ada COCOMO update, is composed of nonlinear models, such as product attributes, computer attributes, personnel attributes, project attributes, effort-multiplier cost drivers, and have been experiencing increasing difficulties in estimating the costs of software developed to new lift cycle processes and capabilities. The COCOMO II is developed fur new forms against the current software cost estimation models. This paper provides a case-based analysis result of the cost driver in the software cost models, such as COCOMO and COCOMO 2.0 by fuzzy and neural network.

• Journal of Korean Society for Quality Management
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• v.43 no.4
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• pp.533-544
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• 2015

Purpose: The purpose of this study is to analyze the cost efficiency of food industry in JeollaNamdo. And this study is focused on the correlation between the economic efficiency of food industry and its cost efficiency, based on the analysis of 372 food companies' data in JeollaNamdo in 2012. Methods: DEA cost minimization is the measurement of the cost efficiency of JeollaNamdo food industry in 2012. In this study, the CCR and BBC models have been employed to analyze the decomposing cost efficiency-technical efficiency, allocative efficiency, and scale efficiency respectively. And the Spearman rank correlation and Wilcoxon signed rank test also have been employed to check the correlation and difference between the ranking orders based on the efficiency scores respectively. Results: For the CCR model, mean cost efficiency was found to be 0.084(0.54 for allocative efficiency and 0.19 for technical efficiency). For the BCC model, mean cost efficiency was found to be 0.252(0.453 for allocative efficiency and 0.564 for technical efficiency). Average scale efficiency was found to be 0.38. In analyzing the results, this study argues that the optimal way to improve cost efficiency is by reducing inputs proportionally and changing their combination. Conclusion: The efficiency scores of the two models show high correlation, whereas, the differences between them are also found to be significant. Hence, it should be cautious to select a suitable model when we do the research.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.246-252
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• 2017

In the defense acquisition, a company's goal is to maximize profits, and the government's goal is to allocate budgets efficiently. Each year, the government estimates the ratio of indirect cost sector to defense companies, and estimates the ratio to be applied when calculating cost of the defense articles next year. The defense industry environment is changing rapidly, due to the increasing trend of defense acquisition budgets, the advancement of weapon systems, the effects of the 4th industrial revolution, and so on. As a result, the cost structure of defense companies is being diversifying. The purpose of this study is to find an alternative that can enhance the rationality of the current methodology for estimating the ratio of indirect cost sector of defense companies. To do this, we conducted data analysis using the R language on the cost data of defense companies over the past six years in the Defense Integrated Cost System. First, cluster analysis was conducted on the cost characteristics of defense companies. Then, we conducted a regression analysis of the relationship between direct and indirect costs for each cluster to see how much it reflects the cost structure of defense companies in direct labor cost-based indirect cost rate estimates. Lastly a new ratio prediction model based on regularized regression analysis was developed, applied to each cluster, and analyzed to compare performance with existing prediction models. According to the results of the study, it is necessary to estimate the indirect cost ratio based on the cost character group of defense companies, and the direct labor cost based indirect cost ratio estimation partially reflects the cost structure of defense companies. In addition, the current indirect cost ratio prediction method has a larger error than the new model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.3
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• pp.71-78
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• 2011

This parer proposes application of quality costing system in quality improvement area. Cost of quality in quality improvement area provides a valuable of both providing the need for improvement and giving a starting point for project. WQCMS(Web-based Q-Cost Management System) have ability to collect and analyze quality data generated from various different departments in the inside or outside of the enterprise without any limitations, if end-users are able to access wide area network. It provides the capability to integrate quality information from database and to generate various easy analysis reports to management's needs using built-in analysis tool modules with real-time. Web-based quality cost management system to measure the performance of quality improvement activities in the business firms. This paper proposes standard model for quality cost process in quality improvement area.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.567-573
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• 2008

Life cycle costing is one of the most effective approaches for the cost analysis of long-term life products, like as railroad vehicle. Life cycle costing includes the cost of concept design, development, manufacture, operating, maintenance and disposal. Especially, life cycle costing in the railroad industry has been focused on the maintenance cost. In this paper, we investigated the standard, guide and maintenance information of railroad vehicle. For this purpose, we suggested the cost model of railroad vehicle maintenance information. We also performed maintenance cost analysis on the some sub-system of railroad vehicle for the case study.

• Journal of the Korea Safety Management & Science
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• v.3 no.4
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• pp.1-9
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• 2001

Cost-benefit analysis was investigated to propose the analysis method of the effect of investment and the optimum investment level of safety management cost for preventing gas accident in the B governor station. From five classifications of safety management costs consisting of cost items with similar characters and potential accident costs calculated by risk assessments(FMEA/HAZOP), we found that the order of the benefit(the reduction cost of the potential accident cost) was the instrument increase and repair cost > the safety checking and inspection cost > the labor and training cost > the safety equipment and corresponding cost > the research and development cost. As the benefit was increased with increasing the investment cost, the effect of investment was increased with decreasing the Investment cost. As a result, the optimum safety management cost was estimated and the investment level was analyzed by the model of optimum investment level.

• Advances in Computational Design
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• v.3 no.4
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• pp.385-404
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• 2018

The study investigated an area of sustainable structural design that is often overlooked in practical engineering applications. Specifically, a novel method to simultaneously optimise the cost and embodied carbon performance of steel building structures was explored in this paper. To achieve this, a parametric design model was developed to analyse code compliant structural configurations based on project specific constraints and rigorous testing of various steel beam sections, floor construction typologies (precast or composite) and column layouts that could not be performed manually by engineering practitioners. Detailed objective functions were embedded in the model to compute the cost and life cycle carbon emissions of the different material types used in the structure. Results from a comparative numerical analysis of a real case study illustrated that the proposed optimisation approach could guide structural engineers towards areas of the solution space with realistic design configurations, enabling them to effectively evaluate trade-offs between cost and carbon performance. This significant contribution implied that the optimisation model could reduce the time required for the design and analysis of multiple structural configurations especially during the early stages of a project. Overall, the paper suggested that the deployment of automated design procedures can enhance the quality as well as the efficiency of the optimisation analysis.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.3
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• pp.39-52
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• 2001

Recently, the assessment of a bank efficiency focusing on its branches has been conceived as important in developing a competitive strategy. DEA (Data Envelopment Analysis) model can be employed as an effective analysis model for such an assessment. Therefore, this paper proposes an analysis process using DEA model to conduct an efficiency assessment of bank branches. The proposed process includes a segmentation of branches considering their competitive environment and strategy for target market : this approach can help to develop effective strategies for each group of branches. The proposed DEA model can analyze efficiency in terms of not only cost but also marketing. Finally, a real case is analyzed, demonstrating the effectiveness of the proposed model and process.

• KIEAE Journal
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• v.9 no.1
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• pp.107-113
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• 2009

The energy cost is resulted from the energy use. Its sources are divided into some types and depended on the building use or energy-use type. The energy cost should be affected by the amount of the energy use. The cost could be calculated to consider various factors such as the insulation, heating type, building shape and others. But it can not consider all of the affect factors to the energy cost and need to categorize the factors to the condition for estimating the cost. In this paper, it aimed at providing the estimation model in linear equation and multiple linear regression, utilizing the building exterior condition and management characteristics in apartment housing. Its survey are conducted in two parts of management characteristics and building exterior condition. The correlation analysis is conducted to get rid of the multicolinearity among the inputted factors. The number of linear equation model is 11 and includes the 1st, 2nd and 3rd equation function, power function and others. Among these, it suggested the 2nd and 3rd function and power function in terms of the statistics. In multiple linear regression model, the building volume and management area are inputted to the estimation.