• Journal of Distribution Science
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• v.16 no.11
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• pp.21-27
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• 2018

Purpose - This study investigates the financial ratio of savings banks and the effect of the ratio having influence upon bankruptcy by quantitative empirical analysis of forecast model to give material of better management and objective evidence of management strategy and way of advancement and risk control. Research design, data, and methodology - The author added two growth indexes, three fluidity indexes, five profitability indexes, and four activity indexes CAMEL rating to not only the balance sheets but also the income statement of thirty savings banks that suspended business from 2011 to 2015 and collected fourteen financial ratio indexes. IBMSPSS VER. 21.0 was used. Results - Variables having influence upon bankruptcy forecast models included total asset increase ratio and operating income increase ratio of growth index and sales to account receivable ratio, and tangible equity ratio and liquidity ratio of liquidity ratio. The study selected total asset operating ratio, and earning and expenditure ratio from profitability index, and receivable turnover ratio of activity index. Conclusions - Financial supervising system should be improved and financial consumers should be protected to develop saving bank and to control risk, and information on financial companies should be strengthened.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.303-313
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• 2018

This study considers time series models to forecast drug expenditures in national health insurance. We adopt autoregressive error model (ARE) and transfer function model (TFM) with segmented level and trends (before and after 2012) in order to reflect drug price reduction in 2012. The ARE has only a segmented deterministic term to increase the forecasting performance, while the TFM explains a causality mechanism of drug expenditure with closely related exogenous variables. The mechanism is developed by cross-correlations of drug expenditures and exogenous variables. In both models, the level change appears significant and the number of drug users and ratio of elderly patients variables are significant in the TFM. The ARE tends to produce relatively low forecasts that have been influenced by a drug price reduction; however, the TFM does relatively high forecasts that have appropriately reflected the effects of exogenous variables. The ARIMA model without the exogenous variables produce the highest forecasts.

• Electronics and Telecommunications Trends
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• v.28 no.6
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• pp.13-27
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• 2013

SDN(Software Defined Network) 기술이 미국을 중심으로 먼저 출발하였으며 NFV(Network Function Virtualization) 기술은 유럽을 중심으로 한 SDN의 경쟁적인 기술로 초기에는 인식이 되었으나, 두 기술의 상호 시너지 효과를 고려하여 2013년 하반기부터는 다양한 형태의 협력 방안들이 표준단체, 산업체 및 캐리어들로부터 소개되고 있다. 두 기술이 지향하는 가장 큰 목표는 하드웨어의 의존성을 배제하고 네트워크 및 서비스를 추상화함으로써 캐리어들이 신규 서비스를 Time-to-Market에 맞게 그러면서도 유연하게 출시하고 제어 및 관리를 중앙집중 방식으로 제공하여 CAPEX(Capital Expenditure) & OPEX(Operating Expense)를 최소화 하는데 있다. 본고에서는 이 두 기술의 표준화 및 기술 동향, 그리고 아직은 태동기인 두 기술이 지향하는 다양한 Use Case들과 적용사례를 캐리어 환경 중심으로 살펴보고 향후 상용화 및 산업화에 대한 장 단기 발전 전망에 대해 기술 중심으로 예측해 본다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1999.10a
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• pp.19-33
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• 1999

This paper is concerned with developing a standard costing model based on the case study of PECT and GCT and analysing their profitability in order to improve operation efficiency and design business strategy. In doing so, the model can be a useful tool to analyze current calculation system of lease charge at the two terminals and to judge whether the level of lease charge currently applied to them is justifiable for their profitability. This paper also deals with break-even analysis of container terminal operating companies on the basis of the model and forecast of their profitability. On the top of that, it tries to look into the arguments and to suggest proposals for improving their profitability.

• Journal of the Korea Institute of Building Construction
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• v.18 no.6
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• pp.621-631
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• 2018

The repair and maintenance cost of domestic public rental housing is an issue of considerable interest and growing financial concern. This paper suggests a quantity-based model as an alternative method for predicting costs, instead of the conventional model which is based on actual cost data. Furthermore, this paper provides a forecast of the repair costs incurred each year during the multi family house's maintenance phase (40 years). The recently changed the long-term repair plan and quality-improved interior materials were considered into the research. In order to estimate the cost of maintenance work, 5 sample apartments were selected and analyzed. The repair and maintenance cost from the case studies was converted to cost per household and per floor area for general use. On the other hand, the net present value method was applied to reflect the effect of time. We expect that the results will help to establish expenditure plans that are more effective for public rental housing in the maintenance stage.

• Korean Journal of Construction Engineering and Management
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• v.4 no.4 s.16
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• pp.114-122
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• 2003

Dynamic and fragmented characteristics are two of the most significant factors that distinguish the construction industry from other industries. Previous forecasting techniques have failed to solve the problems derived from the above characteristics, and do not provide considerable support This paper deals with providing a more precise forecasting by applying Case-based Reasoning (CBR). The newly developed model in this study enables project managers to forecast monthly expenditures with less time and effort by retrieving and referring only projects of a similar nature, while filtering out irrelevant cases included in database. For the purpose of accurate forecasting, the choice of the numbers of referring projects was investigated. It is concluded that selecting similar projects at $5{\~}6{\%}$ out of the whole database will produce a more precise forecasting. The new forecasting model, which suggests the predicted values based on previous projects, is more than just a forecasting methodology; it provides a bridge that enables current data collection techniques to be used within the context of the accumulated information. This will eventually help all the participants in the construction industry to build up the knowledge derived from invaluable experience.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.117-124
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• 2004

Dynamic and fragmented characteristics ale two of the most significant factors that distinguish the construction industry from other industries. Previous forecasting techniques have failed to solve the problems derived from the above characteristics and do not provide considerable support. This paper deals with providing a more precise forecasting by applying Case-based Reasoning (CBR). The newly developed model in this study enables project managers to forecast monthly expenditures with less time and effort by retrieving and referring only projects of a similar nature, while filtering out irrelevant cases included in database. For the purpose of accurate forecasting. the choice of the numbers of referring projects was investigated. it is concluded that selecting similar projects at $5\~6\;\%$ out of the whole database will produce a more precise forecasting. The new forecasting model. which suggests the predicted values based on previous projects, is more than just a forecasting methodology it provides a bridge that enables current data collection techniques to be used within the context of the accumulated information. This will eventually help all the participants in the construction industry to build up the know ledge derived from invaluable experience.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.72-80
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• 2014

This study is to estimate a demand of coffee in Korea. And based on assumed data, this study is to investigate price elasticity, income elasticity and cross-price elasticity of coffee demand. The data used in this study is the household income and expenditure survey micro data (2003~2012) provided by the National Statistical Office. And LA/AIDS model and SUR method were utilized in order to forecast coffee demand. As a result, price elasticity and income elasticity are found to be correspond with economic theory as they were assumed to -0.259, 0.455 respectively. Meanwhile, it indicated characteristic of essential good by showing negative (-) income odds ratio estimate. When it comes to cross-price elasticity of coffee and cigarette, it was found to be a complementary relation as its cross-price elasticity was assumed to -0.121. Besides, it was found that male consume more coffee than female, while people in their age of 50s consumes mostly. In conclusion, this study suggests necessity of reconsidering coffee as important goods when Consumer Price Stabilization Policy is determined, as coffee shows characteristic of essential goods which is inelastic.

• Korean Journal of Construction Engineering and Management
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• v.7 no.2 s.30
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• pp.138-147
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• 2006

The objective of this research is to explore a more precise forecasting method by applying Case-based Reasoning (CBR). The newly suggested method in this study enables project managers to forecast monthly expenditures with less time and effort by retrieving and referring only projects of a similar nature, while filtering out irrelevant cases included in database. For the purpose of accurate forecasting, 1) the choice of the numbers of referring projects and 2) the better selection among three levels ? which include a 20-work package level, a 7-major work package level, and a total sum level analysis, were investigated in detail. It is concluded that selecting similar projects at $12{\sim}19%$ out of the whole database will produce a more precise forecasting. The new forecasting model, which suggests the predicted values based on previous projects, is more than just a forecasting methodology; it provides a bridge that enables current data collection techniques to be used within the context of the accumulated information. This will eventually help all the participants in the construction industry to build up the knowledge derived from invaluable experience.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.253-259
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• 2017

Recent development in science and technology has modernized the weapon system of ROKN (Republic Of Korea Navy). Although the cost of purchasing, operating and maintaining the cutting-edge weapon systems has been increased significantly, the national defense expenditure is under a tight budget constraint. In order to maintain the availability of ships with low cost, we need accurate demand forecasts for spare parts. We attempted to find consumption pattern using data mining techniques. First we gathered a large amount of component consumption data through the DELIIS (Defense Logistics Intergrated Information System). Through data collection, we obtained 42 variables such as annual consumption quantity, ASL selection quantity, order-relase ratio. The objective variable is the quantity of spare parts purchased in f-year and MSE (Mean squared error) is used as the predictive power measure. To construct an optimal demand forecasting model, regression tree model, randomforest model, neural network model, and linear regression model were used as data mining techniques. The open software R was used for model construction. The results show that randomforest model is the best value of MSE. The important variables utilized in all models are consumption quantity, ASL selection quantity and order-release rate. The data related to the demand forecast of spare parts in the DELIIS was collected and the demand for the spare parts was estimated by using the data mining technique. Our approach shows improved performance in demand forecasting with higher accuracy then previous work. Also data mining can be used to identify variables that are related to demand forecasting.