• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.1-23
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• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.

• Journal of the Korean Institute of Navigation
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• v.23 no.1
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• pp.85-97
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• 1999

Vessels chartering has contributed to developing Korean shipping industry over the past three decades. Vessels chartered by shipping companies have been employed in cross trades as well as in the Korean foreign trade to achieve their business strategy. Meanwhile, some problems such as speculation, sales-oriented expansion with poor margin, poor forecasting capacity, over excessive competition in cross trades among Korean owners, and lack of speciality in decision-making, have played adverse roles in promoting chartering business in Korea. There(ore, this paper aims to discuss motives for vessel chartering business and its significance in the Korean shipping industry and to identify problems in the business.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2006.05a
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• pp.235-240
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• 2006

The existing inventory managements bear a relation to forecasting or assumptions. So these methods become more complicated and more expensive systems as time goes. This paper developed a practical inventory system which is called DCC(demand control chart). DCC does not 'forecast' but 'control' the trend of demand without assumptions. According to the trend of sales, DCC adjusts an order quantity considering the capacity of shelf in a store. Specially, DCC is a useful method under FRID system. Besides, this paper introduces EPFR(Every Period Full Replenishment) policy for reducing stocks.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.953-959
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• 2002

계절유행상품의 수요는 연중 성수기가 길지 않고 매년 유행과 제품디자인 변화가심한 경향이 있어 수요예측에 과거의 판매정보의 유용성이 크지 않다. 성수기 초반의 수요가 연간 수요결정에 매우 중요하며 후반부수요가 급격히 감소하는 특성이 있다. 반면 이월상품의 잔존가치가 매우 낮지만 매출마진이 높아 수요예측의 정확도에 따라 수익률이 큰 영향을 받는다. 이러한 이유로 기존의 수요예측방법을 계절상품에 적용하기에 무리가 따르며 예측오차의 비용이 매우 커서 계절상품 관리에 이용할 수 없다. 본 연구에서 성수기를 하위기간으로 구분하여 시즌 초반부 수요발생시점을 측정하여 초반부 기간별수요량을 구하고 이를 근거로 기간 누적수요비율을 quantile regression에 의거 추정하여 기간별 수요량과 전제 수요량을 예측하는 방법을 제시하고 모의자료를 사용하여 이 모형의 우수성을 평가하였다.

• IE interfaces
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• v.20 no.2
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• pp.133-141
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• 2007

As the sales of the Sports Toto, the Korean lottery on sports games, have increased significantly in recent five years, interest in predicting the various results of sports matches has also been raised. Dixon and Coles (1997) proposed a bivariate Poisson model to predict the results of English soccer league matches. In this paper, we pay attention to the physical condition of players that may affect soccer match results and revise Dixon and Coles' model to consider probable fatigue due to the players' short rest followed by their frequent matches. We observed the fatigue effect in the match results, and found positive betting returns available when using our prediction model. Furthermore, the validity of probability-based odds in European and Korean betting markets is analyzed.

• Journal of Distribution Research
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• v.6 no.1
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• pp.22-35
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• 2001

The focus of Business Process Re-engineering (BPR) has been extended to inter-business process that cuts across independent companies. Combined with Supply Chain Management (SCM), inter-business process reengineering (IBPR) focuses on synchronization of business activities among trading partners to achieve performance improvements in inventory management and cycle time. This paper reviews the business process reengineering movement from the historical perspective and presents a case of inter-business process reengineering using the latest internet-based Business-to- Business (B2B) technology based on Collaborative Planning, Forecasting, and Replenishment (CPFR). The case demonstrates how CPFR technology reengineers the distribution process between Heineken USA and its distributors. As world's first implementor of web-based collaborative planning system, Heineken USA reduces cycle time from determining the customer need to delivery of the need by 50% and increases sales revenue by 10％. B2B commerce on the internet is predicted to grow from $90 billion in 1999 to $2.0 trillion in 2003. This paper provides the management with the bench-marking case on inter-business process reengineering using B2B e-commerce technology.

• The Korean Journal of Applied Statistics
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• v.23 no.1
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• pp.63-71
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• 2010

Business entrepreneurs reflect their views of domestic and foreign economic activities on their operation for the growth of their business. The decision, forecasting, and planning based on their economic sentiment affect business operation such as production, investment, and hiring and consequently affect condition of national economy. Business survey index(BSI) is compiled to get the information of business entrepreneurs' economic sentiment for the analysis of business condition. BSI has been used as an important variable in the short-term forecasting models for business cycle analysis, especially during the the period of extreme business fluctuations. Recent financial crisis has arised extreme business fluctuations similar to those caused by currency crisis at the end of 1997, and brought back the importance of BSI as a variable for the economic forecasting. In this paper, the meaning of BSI as an economic sentiment index is reviewed and a GUIDE regression tree is constructed to find out the factors which affect on BSI. The result shows that the variables related to the stability of financial market such as kospi index(Korea composite stock price index) and exchange rate as well as manufacturing operation ratio and consumer goods sales are main factors which affect business entrepreneurs' economic sentiment.

• The Korean Journal of Financial Management
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• v.25 no.4
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• pp.1-24
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• 2008

We empirically examined the forecasting abilities of analysts in the Korean stock market with regard to their earnings estimates, and the impacts of their reports on stock prices. Further, we also examine if there is any difference in analysts' forecasting accuracy and stock prices impacts depending upon the geographical distance between analysts and companies they follow. We found the following interesting empirical results. First, analysts have tendency to overestimate sales, operating income, and net income, consistent with the previous literature. Second, the degree of overestimation depends upon the geography of companies. That is, it is smaller for companies headquartered in Seoul than companies in local provinces. Third, analysts' earnings estimates are also more accurate for companies located in Seoul. So, we conjecture that analysts have easier access to the information for the companies. Fourth, when analysts downgrade target prices, companies in Seoul are less negatively affected than those in local provinces. Even when analysts revise downward stock recommendations, stock prices of companies in Seoul go up. Overall, analysts' price impacts are more favorable for Seoul-located companies. Last, but not least, when foreign ownership is higher, investors react less negatively to downward revisions of stock recommendation, but react more negatively to downward revisions of target prices.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.119-138
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• 2024

Text indicators are increasingly valuable in economic forecasting, but are often hindered by noise and high dimensionality. This study aims to explore post-processing techniques, specifically noise filtering and dimensionality reduction, to normalize text indicators and enhance their utility through empirical analysis. Predictive target variables for the empirical analysis include monthly leading index cyclical variations, BSI (business survey index) All industry sales performance, BSI All industry sales outlook, as well as quarterly real GDP SA (seasonally adjusted) growth rate and real GDP YoY (year-on-year) growth rate. This study explores the Hodrick and Prescott filter, which is widely used in econometrics for noise filtering, and employs sufficient dimension reduction, a nonparametric dimensionality reduction methodology, in conjunction with unstructured text data. The analysis results reveal that noise filtering of text indicators significantly improves predictive accuracy for both monthly and quarterly variables, particularly when the dataset is large. Moreover, this study demonstrated that applying dimensionality reduction further enhances predictive performance. These findings imply that post-processing techniques, such as noise filtering and dimensionality reduction, are crucial for enhancing the utility of text indicators and can contribute to improving the accuracy of economic forecasts.

• Culinary science and hospitality research
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• v.15 no.2
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• pp.231-242
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• 2009

One of the most crucial information at the hotel banquet is revenue data. Revenue forecast enables cost reduction, increases staffing efficiency, and provides information that helps maximizing competitive advantages in unforeseen environment. This research forecasts the hotel banquet revenue by utilizing ARIMA Model which was assessed as the appropriate forecast model for international researches. The data used for this research was based on the monthly banquet revenue data of G hotel at Seoul. The analysis results showed that SARIMA(2, 1, 3)(0, 1, 1) was finally presumed. This research implied that the ARIMA model, which was assessed as the appropriate forecast model, was applied for analyzing the monthly hotel banquet revenue data. Additionally, the research provides beneficial information with which hotel banquet professionals can utilize as a reference.