• Journal of Navigation and Port Research
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• v.33 no.10
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• pp.757-769
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• 2009

This paper is intended to forecast the demand for tourism of fishing regions and find the public policies to activate it. The paper focuses on the east coast regions in Gangwon-do. The analysis was conducted through time series analyses and surveys of the tourists in the regions. The results of analyses showed that, while the number of tourists(both domestic and foreign) to the regions has increased, the regions have not been able to accommodate them enough to help improve economies of the regions. It was forecasted that the number of tourists will significantly increase in the future. However, that rates of increase, especially the rates of increase of foreign tourists, cannot be evaluated positively compared to those of the past. These results suggested a few local governmental policies to activate tourism in the regions.

• Journal of Korea Water Resources Association
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• v.54 no.8
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• pp.617-628
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• 2021

Drought is a major natural disaster that causes serious social and economic losses. Local drought forecasts can provide important information for drought preparedness. In this study, we propose a new machine learning model that predicts drought by using historical drought indices and meteorological data from 10 sites from 1981 to 2020 in the southeastern part of the Korean Peninsula, Busan-Ulsan-Gyeongnam. Using Bayesian optimization techniques, a hyper-parameter-tuned Random Forest, XGBoost, and Light GBM model were constructed to predict the evaporative demand drought index on a 6-month time scale after 1-month. The model performance was compared by constructing a single site model and a regional model, respectively. In addition, the possibility of improving the model performance was examined by constructing a fine-tuned model using data from a individual site based on the regional model.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.1
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• pp.35-48
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• 2023

In this study, we introduced the development status and the government policy of the Chinese automobile industry under the rapidly changing global economic environment. We conducted a consumer trend survey on automobile purchases by consumers in China. Despite the Chinese government's strong national emission control policy and stricter standards for manufacturing and selling internal combustion engine vehicles, 59.6% of respondents saying they would choose an internal combustion engine vehicle when purchasing a vehicle in the future for various reasons. It was confirmed that there is a significant gap between government policies and consumer perceptions. In addition, we have discovered the recent declining trend of automobile sales in China, and used the monthly sales volume from January 2010 to December 2020 as training set, and the sales volume from January 2021 to November 2022 as a test set. We proposed and evaluated a time-series model for predicting future automobile demand in China. Then, we showed the monthly sales forecast for 2023 when each model was applied.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.139-145
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• 1996

A forecast on the past output data sets of small bar steels is very important information to make a decision on the future production quantities. In many cases, however, it has been mainly determined by experience (or rule of thumb). In this paper, past basic data sets of each small bar steels are statistically analyzed by some graphical and statistical forecasting methods. This work is mainly done by SAS. Among various quantitative forecasting methods in SAS, STEPAR forecasting method was best performed to the above data sets. By the method, the future production quantities of each small bar steels are forecasted. As a result of this statistical analysis, 95% confidence intervals for future forecast quantities are very wide. To improve this problem, a suitable systematic database system, integrated management system of demand-production-inventory and integrated computer system should be required.

• Journal of the Korea Safety Management & Science
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• v.12 no.2
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• pp.209-216
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• 2010

We need a study of the ratio of successful applicants control methodology about the national technical qualification under the global-green industrial society and rapid change of international circumstances, infinite competition rider society under FTA aspects. It is necessary to develop of HRD Korea selfishness and increase brand value of national technical qualification. So, this study is analysed to the ratio of successful applicants of national technical qualification toward change of the 'bank of problems' control rule, various characters of candidates and the trend of demand and supply of labours instead of the absolute evaluation method. Accordingly, this study suggests to a methodology for the forecasting model of the ratio of successful applicants using the level of problems difficulty and pattern and the candidates academical carriers.

• Journal of Korean Society of Rural Planning
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• v.25 no.4
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• pp.109-117
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• 2019

The purpose of this study is to present a time series model of onion and garlic prices. After considering the various time series models, we calculated the appropriate time series models for each item and then selected the model with the minimized error rate by reflecting the monthly dummy variables and import data. Also, we examined whether the predictive power improves when we combine the predictions of the Korea Rural Economic Institute with the predictions of time series models. As a result, onion prices were identified as ARMGARCH and garlic prices as ARXM. Monthly dummy variables were statistically significant for onion in May and garlic in June. Garlic imports were statistically significant as a result of adding imports as exogenous variables. This study is expected to help improve the forecasting model by suggesting a method to minimize the price forecasting error rate in the case of the unstable supply and demand of onion and garlic.

• Korean Journal of Agricultural Science
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• v.48 no.4
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• pp.797-806
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• 2021

As the number of scaled-up ranches increased and agile responses to market changes became possible, decision-making by Hanwoo cattle farms also began to affect short-term shipments. Considering the changing environment of the Hanwoo supply market and the response speed of producers, it is necessary quickly to grasp the forecast ahead of time and to respond accordingly in an effort to stabilize supply and demand in the Hanwoo market. In this study, short-term forecasting model centered on the supply of Hanwoo was established. The analysis conducted here indicates that the slaughter of Hanwoo males increases by 0.248 as the number of beef cattle raised over 29 months of age in the previous month increases by one, and 0.764 Hanwoo females were slaughtered under average conditions for every Hanwoo male slaughtered. With regard to time, the slaughtering of Hanwoo was higher in January and August, which are months known for holiday food preparation activities for the New Year and Chuseok in Korea, respectively. Simulations indicated that errors were within 10% in all simulations performed through the Hanwoo supply model. Accordingly, it is considered that the estimation results from the supply model devised in this study are reliable and that the model has good structural stability.

• Journal of Korean Society of Transportation
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• v.36 no.2
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• pp.141-154
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• 2018

In order to meet the ever-increasing demand for international air travel, several plans are underway to open new airports and expand existing provincial airports. However, existing air demand forecasts have been based on the total air demand in Korea or the air demand among major cities. There is not much forecast of regional air demand considering local characteristics. In this study, the outbound air travel demand in the southeastern region of Korea was analyzed and the fixed-effects model using panel data was proposed as an optimal model that can reflect the inherent characteristics of metropolitan areas which are difficult to observe in reality. The results of model validation show that panel data analysis effectively addresses the spurious regression and unobserved heterogeneity that are difficult to handle in a model using only a few macroeconomic indicators with time series characteristics. Various statistical validation and conformance tests suggest that the fixed-effects model proposed in this study is superior to other econometric models in predicting demand for international demand in the southeastern region.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.93-106
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• 2020

This research explores how imported automobile companies can develop their strategies to improve the outcome of their recalls. For this, the researchers analyzed patterns of recall demand, classified recall types based on the demand patterns and examined response strategies, considering plans on how to procure parts and induce customers to visit workshops, recall execution capacity and costs. As a result, recalls are classified into four types: U-type, reverse U-type, L- type and reverse L-type. Also, as determinants of the types, the following factors are further categorized into four types and 12 sub-types of recalls: the height of maximum demand, which indicates the volatility of recall demand; the number of peaks, which are the patterns of demand variations; and the tail length of the demand curve, which indicates the speed of recalls. The classification resulted in the following: L-type, or customer-driven recall, is the most common type of recalls, taking up 25 out of the total 36 cases, followed by five U-type, four reverse L-type, and two reverse U-type cases. Prior studies show that the types of recalls are determined by factors influencing recall execution rates: severity, the number of cars to be recalled, recall execution rate, government policies, time since model launch, and recall costs, etc. As a component demand forecast model for automobile recalls, this study estimated the ARIMA model. ARIMA models were shown in three models: ARIMA (1,0,0), ARIMA (0,0,1) and ARIMA (0,0,0). These all three ARIMA models appear to be significant for all recall patterns, indicating that the ARIMA model is very valid as a predictive model for car recall patterns. Based on the classification of recall types, we drew some strategic implications for recall response according to types of recalls. The conclusion section of this research suggests the implications for several aspects: how to improve the recall outcome (execution rate), customer satisfaction, brand image, recall costs, and response to the regulatory authority.

• Management Science and Financial Engineering
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• v.14 no.2
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• pp.89-104
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• 2008

Growth curves including Bass, Logistic and Gompertz functions are widely used in forecasting the market demand. Nonlinear least square method is often adopted for estimating the model parameters but it is difficult to set up the starting value for each parameter. If a wrong starting point is selected, the result may lead to erroneous forecasts. This paper proposes a method of selecting starting values for model parameters in estimating some growth curves by nonlinear least square method through grid search and transformation into linear regression model. Resealing the market data using the national economic index makes it possible to figure out the range of parameters and to utilize the grid search method. Application to some real data is also included, where the performance of our method is demonstrated.