• Journal of Korean Academy of Nursing
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• v.45 no.3
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• pp.429-438
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• 2015

Purpose: This study was conducted to develop key performance indicators (KPIs) for home care nursing (HCN) based on a balanced scorecard, and to construct a performance prediction model of strategic objectives using the Bayesian Belief Network (BBN). Methods: This methodological study included four steps: establishment of KPIs, performance prediction modeling, development of a performance prediction model using BBN, and simulation of a suggested nursing management strategy. An HCN expert group and a staff group participated. The content validity index was analyzed using STATA 13.0, and BBN was analyzed using HUGIN 8.0. Results: We generated a list of KPIs composed of 4 perspectives, 10 strategic objectives, and 31 KPIs. In the validity test of the performance prediction model, the factor with the greatest variance for increasing profit was maximum cost reduction of HCN services. The factor with the smallest variance for increasing profit was a minimum image improvement for HCN. During sensitivity analysis, the probability of the expert group did not affect the sensitivity. Furthermore, simulation of a 10% image improvement predicted the most effective way to increase profit. Conclusion: KPIs of HCN can estimate financial and non-financial performance. The performance prediction model for HCN will be useful to improve performance.

• Korean Journal of Construction Engineering and Management
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• v.16 no.4
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• pp.50-59
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• 2015

While the international construction industry for Korean companies have grown in market size exponentially in the recent years, the profit rate of small and medium sized construction companies (SMCCs) are incomparably lower than those of large construction companies. Furthermore, small and medium size companies, especially subcontractor, lacks the judgement of project involvement appropriateness, which leads to an unpredictable profit rate. Therefore, this research aims to create a profit rate prediction model for the international construction project focusing on SMCCs. First, the factors that influence the profit rate and the area of profit zone are defined by using a total of 8,637 projects since the year 1965. Seconds, an extensive literature review is conducted to derive 10 influencing factors. Multiple regression analysis and corresponding judgement technique are used to derive the weight of each factor. Third, cased based reasoning (CBR) methodology is applied to develop the model for profit rate analysis in the project participation review stage. Using 120 validation data set, the developed model showed 11% (14 data sets) of error rate for type 1 and type 2 error. In utilizing the result, project decision makers are able to make decision based on authentic results instead of intuitive based decisions. The model additionally give guidance to the Korean subcontractors when advancing into the international construction based on the model result that shows the profit distribution and checks in advance for the quality of the project to secure a sound profit in each project.

• Health Policy and Management
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• v.20 no.3
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• pp.123-137
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• 2010

The main purpose of this article is for deriving functions related to the prediction of the closure of the hospitals, and finding out how the discriminant functions affect the closure of the hospitals. Empirical data were collected from 3 years financial statements of 41 private hospitals closed down from 2000 till 2006 and 62 private hospitals in business till now. As a result, the functions related to the prediction of the closure of the private hospital are 4 indices: Return on Assets, Operating Margin, Normal Profit Total Assets, Interest expenses to Total borrowings and bonds payable. From these discriminant functions predicting the closure, I found that the profitability indices - Return on Assets, Operating Margin, Normal Profit Total Assets - are the significant affecting factors. The discriminant functions predicting the closure of the group of the hospitals, 3 years before the closure were Normal Profit to Gross Revenues, Total borrowings and bonds payable to total assets, Total Assets Turnover, Total borrowings and bonds payable to Revenues, Interest expenses to Total borrowings and bonds payable and among them Normal Profit to Gross Revenues, Total borrowings and bonds payable to total assets, Total Assets Turnover, Total borrowings and bonds payable to Revenues are the significant affecting factors. However 2 years before the closure, the discriminant functions predicting the closure of the hospital were Interest expenses to Total borrowings and bonds payable and it was the significant affecting factor. And, one year before the closure, the discriminant functions predicting the closure were Total Assets Turnover, Fixed Assets Turnover, Growth Rate of Total Assets, Growth Rate of Revenues, Interest expenses to Revenues, Interest expenses to Total borrowings and bonds payable. Among them, Total Assets Turnover, Growth Rate of Revenues, Interest expenses to Revenues were the significant affecting factors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4950-4966
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• 2015

Cloud providers now face the problem of estimating the amount of computing resources required to satisfy a future workload. In this paper, a virtual machine provisioning (VMP) mechanism is designed to adapt workload fluctuation. The arrival rate of forthcoming jobs is predicted for acquiring the proper service rate by adopting an exponential smoothing (ES) method. The proper service rate is estimated to guarantee the service level agreement (SLA) constraints by using a diffusion approximation statistical model. The VMP problem is formulated as a facility location problem. Furthermore, it is characterized as the maximization of submodular function subject to the matroid constraints. A greedy-based VMP algorithm is designed to obtain the optimal virtual machine provision pattern. Simulation results illustrate that the proposed mechanism could increase the average profit efficiently without incurring significant quality of service (QoS) violations.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.98-103
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• 2021

Recently, as the need for food resources has increased both domestically and internationally, support for the agricultural sector for stable food supply and demand is expanding in Korea. However, according to recent media articles, the biggest problem in rural communities is the unstable profit structure. In addition, in order to confirm the profit structure, profit forecast data must be clearly prepared, but there is a lack of auxiliary data for farmers or future returnees to predict farm income. Therefore, in this paper we analyzed data over the past 15 years through time series analysis and proposes an artificial intelligence farm income prediction algorithm that can predict farm household income in the future. If the proposed algorithm is used, it is expected that it can be used as auxiliary data to predict farm profits.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.2
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• pp.35-54
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• 2009

The recent economic crisis not only reduces the profit of department stores but also incurs the significance losses caused by the increasing late-payment rate of credit cards. Under this pressure, the scope of credit prediction needs to be broadened from the simple prediction of whether this customer has a good credit or not to the accurate prediction of how much profit can be gained from this customer. This study classifies the delinquent customers of credit card in a Korean department store into homogeneous clusters. Using this information, this study analyzes the repayment patterns for each cluster and develops the credit prediction system to manage the delinquent customers. The model presented by this study uses Kohonen network, which is one of artificial neural networks of data mining technique, to cluster the credit delinquent customers into clusters. Cox proportional hazard model is also used, which is one of survival analysis used in medical statistics, to analyze the repayment patterns of the delinquent customers in each cluster. The presented model estimates the repayment period of delinquent customers for each cluster and introduces the influencing variables on the repayment pattern prediction. Although there are some differences among clusters, the variables about the purchasing frequency in a month and the average number of installment repayment are the most predictive variables for the repayment pattern. The accuracy of the presented system leaches 97.5%.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.157-165
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• 2022

As the stock market is affected by various circumstances including economic and political variables, predicting the stock market is considered a still open problem. When combined with corporate financial statement data analysis, which is used as fundamental analysis, and technical analysis with a short data generation cycle, there is a problem that the time domain does not match. Our proposed method, LSTE the operating profit and market outlook of a petrochemical company and estimates the sales and operating profit of the company, it was possible to solve the above-mentioned problems and improve the accuracy of stock price prediction. Extensive experiments on real-world stock data show that our method outperforms the 8.58% relative improvements on average w.r.t. accuracy.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.213-216
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• 2015

Despite the significant increase of Korean contractors in the international construction market, many SMCCs (Small & Medium Construction Companies) have suffered in the global financial crisis, and some of them have been kicked out of the international market after experiencing huge losses on projects. SMCCs face obstacles in the international market, such as an insufficient ability to gather information and inappropriate management of associated risks, which lead to difficulties in establishing effective business strategies. In other words, making immature decisions without an effective business strategy may cause not only the failure of one project but also the bankruptcy of the SMCC. To overcome this, the research presented herein aims to propose a decision support system for SMCCs, which would screen projects and make a go/no-go decision at the early stages of international projects. The proposed system comprises a double axis: (1) a profit prediction model, which evaluates 10 project properties using an objective methodology based on a historical project performance database and roughly suggests expected profit rate, and (2) a feasibility assessment model, which evaluates 17 project environment factors in a subjective and quantitative methodology based on experience and supervision. Finally, a web-based system is established to enhance the practical usability, which is expected to be a good reference for inexperienced SMCCs to make proper decisions and establish effective business strategies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.313-320
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• 2021

This study develops a demand and supply model and price model for Miryang sesame leaf cultivation and predicts the optimal production level to maximize total profit for Miryang sesame leaf farms. We used time series data from 1996 to 2017, which are related to Miryang sesame leaf cultivation. For the analysis, we estimated the demand function and average cost function, calculated the optimal production level and price, and derived the optimal profit. In addition, we predicted the optimal production level, price, total revenue, total cost, and profit until the year 2030 through scenario analysis. The results show that the optimal production level until the year 2030 is between 10 and 12.5 thousand tons, while the production volume was 7 thousand tons in 2017, and total profit for Miryang sesame leaf farms is estimated at 13.3 to 21.3 billion Korean won in 2030. The producer group needs to maintain the optimal production level to maximize total profit for farmers, as suggested in this study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.3
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• pp.954-972
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• 2012

Content Delivery Networks (CDN) server loads that fluctuant necessitate CDN to improve its service scalability especially when the peak load exceeds its service capacity. The peer assisted scheme is widely used in improving CDN scalability. However, CDN operators do not want to lose profit by overusing it, which may lead to the CDN resource utilization reduced. Therefore, improving CDN scalability moderately and guarantying CDN resource utilization maximized is necessary. However, when and how to use the peer-assisted scheme to achieve such improvement remains a great challenge. In this paper, we propose a new method called Dynamic Moderate Peer-assisted Method (DMPM), which uses time series analysis to predict and decide when and how many server loads needs to offload. A novel peer-assisted mechanism based on the prediction designed, which can maximize the profit of the CDN operators without influencing scalability. Extensive evaluations based on an actual CDN load traces have shown the effectiveness of DMPM.