• Industrial Engineering and Management Systems
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• v.14 no.2
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• pp.196-209
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• 2015

Retail sales forecast is a special area of forecasting. Its unique characteristics call for unique data models and treatment, and unique forecasting processes. In this paper, we will address lessons learned and challenges encountered in retail sales forecast from a practical and technical perspective. In particular, starting with the data models of retail sales data, we proceed to address issues existing in estimating and processing each component in the data model. We will discuss how to estimate the multi-seasonal cycles in retail sales data, and the limitations of the existing methodologies. In addition, we will talk about the distinction between business events and forecast events, the methodologies used in event detection and event effect estimation, and the difficulties in compound event detection and effect estimation. For each of the issues and challenges, we will present our solution strategy. Some of the solution strategies can be generalized and could be helpful in solving similar forecast problems in different areas.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.31
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• pp.1-10
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• 1994

The purpose of this study is to search for the most effective forecasting model for condenser with independent demand among the quantitative methods such as Brown's exponential smoothing method, Box-Jenkins method, and multiple regression analysis method. The criterion for the comparison of the above models is mean squared error(MSE). The fitting results of these three methods are as follows. 1) Brown's exponential smoothing method is the simplest one, which means the method is easy to understand compared to others. But the precision is inferior to other ones. 2) Box-Jenkins method requires much historic data and takes time to get to the final model, although the precision is superior to that of Brown's exponential smoothing method. 3) Regression method explains the correlation between parts with similiar demand pattern, and the precision is the best out of three methods. Therefore, it is suggested that the multiple regression method is fairly good in precision for forecasting our item and that the method is easily applicable to practice.

• Korea Real Estate Review
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• v.27 no.3
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• pp.41-50
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• 2017

Previous studies, especially that by Lee (2014), showed how time series volatility models can be applied to the house price series. As the regional housing market trends, however, have shown significant differences of late, analysis with national data may have limited practical implications. This study applied volatility models in analyzing and forecasting regional house prices. The estimation of the AR(1)-ARCH(1), AR(1)-GARCH(1,1), and AR(1)-EGARCH(1,1,1) models confirmed the ARCH and/or GARCH effects in the regional house price series. The RMSEs of out-of-sample forecasts were then compared to identify the best-fitting model for each region. The monthly rates of house price changes in the second half of 2017 were then presented as an example of how the results of this study can be applied in practice.

• Journal of Preventive Medicine and Public Health
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• v.21 no.1 s.23
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• pp.195-206
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• 1988

Two major issues of the blood bank management are quality assurance and inventory control. Recently, in Korea blood donation has gained popularity increasingly to allow considerable improvement of the quality assurance with respect to blood collection, transportation, storage, component preparation skills and hematological tests. Nevertheless the inventory control, the other issue of blood bank management, has been neglected so far. For the supply of blood by donation barely meets the demand, the blood bank policy on the inventory control has been 'the more the better.' The shortage itself by no means unnecessitate inventory control. In fact, in spite of shortage, no small amount of blood is outdated. The efficient blood inventory control makes it possible to economize the blood usage in the practice of state-of-the-art medical care. For the efficient blood inventory control in Korean hospitals, this tudy is to develop formulae forecasting the standard blood inventory level and suggest a set of policies improving the blood inventory control. For this study informations of $A^+$ whole bloods and packed cells inventory control were collected from a University Hospital and the Central Blood Bank of the Korean Red Cross. Using this informations, 1,461 daily blood inventory records were formulated.48 varieties of blood inventory control environment were identified on the basis of selected combinations of 4 inventory control variables-crossmatch, transfusion, inhospital donation and age of bloods from external supply. In order to decide the optimal blood inventory level for each environment, simulation models were designed to calculate the measures of performance of each environment. After the decision of 48 optimal blood inventory levels, stepwise multiple regression analysis was started where the independent variables were 4 inventory control variables and the dependent variable was optimal inventory level of each environment. Finally the standard blood inventory level decision rule was developed using the backward elimination procedure to select the best regression equation. And the effective alternatives of the issuing policy and crossmatch release period were suggested according to the measures of performance under the condition of the standard blood inventory level. The results of this study' were as follows ; 1. The formulae to calculate the standard blood inventory level($S^*$)was $S^*=2.8617X(d)^{0.9342}$ where d is the mean daily crossmatch(demand) for a blood type. 2. The measures of performace - outdate rate, average period of storage, mean age of transfused bloods, and mean daily available inventory level - were improved after maintenance of the standard inventory level in comparison with the present system. 3. Issuing policy of First In-First Out(FIFO) decreased the outdate rate, while Last In-First Out(LIFO) decreased the mean age of transfused bloods. The decrease of the crossmatch release period reduced the outdate rate and the mean age of transfused bloods.

• Knowledge Management Research
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• v.23 no.1
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• pp.89-109
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• 2022

Due to COVID-19 and soaring participation of individual investors, large-scale transactions exceeding system capacity limits have been reported frequently in the capital market. The capital market IT systems, which the impact of system failure is very critical, have encountered unexpectedly tremendous transactions in 2020, resulting in a sharp increase in system failures. Despite the fact that many companies maintained large-scale system capacity planning policies, recent transaction influx suggests that a new approach to capacity planning is required. Therefore, this study developed capital market IT system capacity planning models using machine learning techniques and analyzed those performances. In addition, the performance of the best proposed model was improved by using sentiment index that can promptly reflect the behavior of investors. The model uses empirical data including the COVID-19 period, and has high performance and stability that can be used in practice. In practical significance, this study maximizes the cost-efficiency of a company, but also presents optimal parameters in consideration of the practical constraints involved in changing the system. Additionally, by proving that the sentiment index can be used as a major variable in system capacity planning, it shows that the sentiment index can be actively used for various other forecasting demands.