• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.3
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• pp.219-230
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• 2015

It is difficult to make plans about the production schedule and volume of seasonal products due to the huge uncertainty in the prediction of their demands, which is why the amounts of carryover seasonal products increase after the peak season. Traditional models fail to meet the important requirements of production and stock plans related to the enhanced efficiency of logistics system due to the reduced value of carryover products by the disposal based on large discounts and deterioration, which poses considerable difficulties with actual problem solving. This study examined the stages of product storage from the specialized factory warehouses during a low season through the stores and the warehouses of local distribution centers during a high season to stock disposal and carryover product warehouses after a high season. The study developed a model for logistics rationalization plans to minimize carryover products by advance selling new products by subscription during a low season in anticipation of high season demands, increasing the accuracy of demands prediction, and making stable production plans, as well as demonstrated its excellence through numerical analysis.

• Korean Management Science Review
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• v.20 no.1
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• pp.103-124
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• 2003

The seasonal adjustment is an essential process in analyzing the time series of economy and business. There are various methods to adjust seasonal effect such as moving average, extrapolation, smoothing and X11. One of the powerful adjustment methods is X11-ARIMA Model which is popularly used in Korea. This method was delivered from Canada. However, this model has been developed to be appropriate for Canadian and American environment. Therefore, we need to review whether the Xl1-ARIMA Model could be used properly in Korea. In this study, we have applied the method to the annual sales of refrigerator sales in A electronic company. We appreciated the adjustment by result analyzing the time series components such as seasonal component, trend-cycle component, and irregular component, with the proposed method.

• Journal of Applied Reliability
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• v.3 no.1
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• pp.13-40
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• 2003

The seasonal adjustment is an essential process in analyzing the time series of economy and business. One of the powerful adjustment methods is X11-ARIMA Model which is popularly used in Korea. This method was delivered from Canada. However, this model has been developed to be appropriate for Canadian and American environment. Therefore, we need to review whether the X11-ARIMA Model could be used properly in Korea. In this study, we have applied the method to the annual sales of refrigerator sales in A electronic company. We appreciated the adjustment by result analyzing the time series components such as seasonal component, trend-cycle component, and irregular component, with the proposed method. Additionally, in order to improve the result of seasonal adjusted time series, we suggest the demand forecasting method base on autocorrelation and seasonality with the X11-ARIMA PROC.

• Natural Product Sciences
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• v.12 no.4
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• pp.241-246
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• 2006

Alstonia scholaris is known as 'Saptaparna' in Ayurvedic System of Medicines and the bark is used for the treatment of various diseases. It has various ethnomedicinal values as different traditional communities find diverse medicinal properties. The present communication deals with the seasonal variation studies of the stem bark of this plant. The bark was collected in the month of January, July and November. There is no macro-microscopical changes in all the three seasons but the ash values showed significant differences. Crude fibre content showed sharp decline from January to July to November. Total tannin percentage was found maximum in January and minimum in June. TLC finger print profile showed more concentration of constituents in January as compared to June and November. Hence, the bark may show more efficacy when collected in January.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.117-124
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• 2009

If time series data with seasonal effect exist, various statistical models like winters for successful forecasts could be used. But if the data are not enough to estimate seasonal effect, not much methods are available. This paper proposes the statistical forecasting method based on cumulative data when the data are not enough to estimate seasonal effect. We apply this method to real cosmetic sales data and show its better performance over moving average method.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.41-50
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• 2009

Chain-linking is a method for aggregating volume measures which would improve the quality of estimates of economic growth over the present fixed base in Korea. There is a risk that choice of chain-linking techniques such annual overlap, one-quarter overlap or over-the-year overlap may create an artificial seasonality to the volume series. The empirical results on Korean GDP suggest that the use of the annual overlap is recommended. And conducting seasonal adjustment after chain-linking to produce the chain-linked seasonally adjusted GDP is more appropriated in Korea.

• International Journal of Reliability and Applications
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• v.4 no.2
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• pp.79-95
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• 2003

Manufacturers collect and analyze field reliability data to enhance the quality and reliability of their products and to improve customer satisfaction. To reduce the data collecting and maintenance costs, the amount of data maintained for evaluating product quality and reliability should be minimized. With this in mind, some industrial companies assemble warranty databases by gathering data from different sources for a particular time period. This “marginal count failure data” does not provide (i) the number of failures by when the product entered service, (ii) the number of failures by product age, or (iii) information about the effects of the operating season or environment. This article describes a method for estimating age-based claim rates from marginal count failure data. It uses covariates to identify variations in claims relative to variables such as manufacturing characteristics, time of manufacture, operating season or environment. A Poisson model is presented, and the method is illustrated using warranty claims data for two electrical products.

• Proceedings of the Membrane Society of Korea Conference
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• 1997.06a
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• pp.207-231
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• 1997

The use household water purifier is continuously increasing due to the distrust of the piped, rap water, which may be caused by the deterioration of water quality and water supply facilities in Korea. The water purifier distributing in the domestic market is the membrane type that composes of membrane as a basic filter, sediment filter as a prefilter and activated carbon filter instead of one purifying water system. The membrane type using as a water cleaning system is mainly the method of micro filtration(NIF) as well as ultra filtration(UF) and reverse osmosis(RO). The types of MF and UF are using to remove suspended solids and virus, and RO is using to remove ionic compound in chemical organic compounds. At the beginning the household water purifier was imported as end-product or assembled by only importing module from small businesses in Korea. Therefore, people was hard to confide in the product quality because the sales policy and management of water purifier by small business were not effectively organized. However, the defects of water purifier having up to now ate improving by the efforts of special branches of water purifier and large enterprises, and new great products are developing and producing. To develop the best quality product of water purifier in Korea, it should be considered seasonal water characteristrics rather than any other condition. For example, the water purifier can be affected by the water temperature change, increase of suspended solids in the rainy season and inflow of raw water having concentration in a dry season. The subject we have to investigate in the future is to develop the water purifying system adaptable in Korea which can treat the water quality using close analyses of local and seasonal water characteristics.

• Journal of Dairy Science and Biotechnology
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• v.19 no.1
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• pp.30-38
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• 2001

The most important task in Korean dairy industry is to keep the seasonal and annual balance of raw milk supply and demand. Too much surplus milk supply which causes dumping sale of market milk makes dairy industries get in trouble of management, and eventually affects to farmers and consumers economically. As balancing of supply and demand is so important in the fee economic market system, the adaption of the quota system of milk production and seasonal price differentiation has been recommended very often as a method of controlling the milk supply and demand. However, this recommendation did not go through successfully due to the strong objection of dairy farmers. Recently, the voice of consumer's requirement for safer and more hygienic, and high protein, low fat level dairy product is getting stronger. By knowledge of this kind changes, quality improvement in nutrients and hygiene is the most positive way to expand the volume of milk consumption. To meet the consumer's demand, therefore, it is necessary to revise the level of milk fat content and the hygienic grading system for the payment system of raw milk.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1039-1041
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• 2003

Ocean color products derived from remote sensing satellite data are useful for monitoring the sea water quality such as the concentrations of chlorophyll, sediments and dissolved organic matter. Currently, ocean color products derived from MODIS data can be requested from NASA over the internet. However, due to the bandwidth limitation of most users in this region, and the time delay in data delivery, the products cannot be use for near-real time monitoring of sea water chlorophyll. CRISP operates a MODIS data receiving station for environmental monitoring purposes. MODIS data have been routinely received and processed to level 1B. We have adapted the higher level processing algorithms from the Institutional Algorithms provided by NASA to run in a standalone environment. The implemented algorithms include the MODIS ocean color algorithms. Seasonal chlorophyll concentration composite can be compiled for the region. By comparing the near-real time chlorophyll product with the seasonal composite, anomaly in chlorophyll concentration can be detected.