최근 국민 삶의질 향상, 여가 활동 다변화, 인구구조의 변화 등으로 관광수요 증가 및 관광활동이 다양화되고 있다. 특히 연안 도시의 경우, 육상 관광 요소와 해양관광 요소가 공존하는 지역으로 다양한 요인이 관광수요에 영향을 미치고 있다. 본 연구 목적은 본 연구는 행위자 기반의 데이터를 활용하여 관광규모의 시계열 분석을 통해 예측 정확도를 향상시키고, 영향요인을 탐색하고자 한다. 연구 대상은 부산 지역 내 기초자치단체이며, 데이터는 월단위의 관광객수와 관광소비금액을 활용하였다. 연구방법으로 확정적(결정적) 모형 인 단변량 시계열 분석과 영향요인을 파악하기 위해 SARIMAX 분석을 수행하였다. 영향요인은 관광소비성향을 설정하였으며, 업종별 소 비금액과 SNS 언급량을 중심으로 설정하였다. 연구결과 COVID-19를 고려하지 않은 시계열 모형과 고려한 모형 간의 정확도(RMSE 기준) 차이가 지역별로 최소 1.8배에서 최대 32.7배 향상되었다. 또한 영향요인을 보면 관광소비업종과 SNS 트렌드가 관광객수와 관광소비금액 에 유의한 영향을 미치고 있다. 따라서 미래 수요예측을 위해서는 외적 영향을 고려하고, 관광객의 소비성향과 관심도가 지역관광 측면에 서 고려 대상이 된다. 본 연구는 연안도시인 부산 지역의 미래 관광수요 예측과 관광규모에 미치고 있는 영향요인을 파악하여 정부 관광 정책 및 관광추세를 고려한 관광수요태세 마련을 위한 정책 의사결정에 기여하고자 한다.

Numerous studies have established a correlation between sociodemographic characteristics and water usage, identifying population as a primary independent variable in mid- to long-term demand forecasting. Recent dramatic sociodemographic changes, including urban concentration-rural depopulation, low birth rates-aging population, and the rise in single-person households, are expected to impact water demand and supply patterns. This underscores the necessity for operational and managerial changes in existing water supply systems. While sociodemographic characteristics are regularly surveyed, the conducted surveys use aggregate units that do not align with the actual system. Consequently, many water demand forecasts have been conducted at the administrative district level without adequately considering the water supply system. This study presents an upward water demand forecasting model that accurately reflects real water facilities and consumers. The model comprises three key steps. Firstly, Statistics Korea’s SGIS (Statistical Geological Information System) data was reorganized at the DMA level. Secondly, DMAs were classified using the SOM (Self-Organizing Map) algorithm to consider differences in water facilities and consumer characteristics. Lastly, water demand forecasting employed the PCR (Principal Component Regression) method to address multicollinearity and overfitting issues. The performance evaluation of this model was conducted for DMAs classified as rural areas due to the insufficient number of DMAs. The estimation results indicate that the correlation coefficients exceeded 0.9, and the MAPE remained within approximately 10% for the test dataset. This method is expected to be useful for reorganization plans, such as the expansion and contraction of existing facilities.

PURPOSES : The primary purpose of this study is to develop a framework for predicting the demand and distribution of pedestrians when an open space zone is built at the top through the undergroundization of the Gyeongin Expressway. METHODS : After analyzing the current status through a survey on the number of people, students, surrounding traffic volume, and future socioeconomic indicators, the rate of change in the floating population and the rate of increase and decrease in the traffic volume of pedestrians were calculated to evaluate the effect. In addition, microscopic analysis results were derived by setting a pedestrian analysis zone (PAZ). A walking environment index (WEI) was developed that can quantitatively evaluate the degree of walking activation by indicating walking-related surrounding environmental factors. Based on this, a walking demand prediction model was developed. In addition, the results were validated by calculating the walking volume through a micro-simulation in/around the open space zone. RESULTS : The number of crosswalks and schools, transit development indicators, and pedestrian volume increased as the WEI value increased. However, the log form of the distance was observed to be a factor that reduced walking. CONCLUSIONS : This study attempted to reliably predict the demand for walking on the Gyeongin Expressway by calculating the amount of induced walking and the amount of passing walking. The pedestrian demand can be boosted by improving walking environments.

본 연구는 전주시에 위치한 기린봉, 완산칠봉, 황방산 등 3개의 산지형 근린공원을 대상으로 이용행태, 이용권의 인구 통계학적 특성, 근린공원까지의 도달거리를 사용하여 유효이용수요를 예측한 것이다. 조사대상 3개 근린공원의 이용자는 주로 40대~60대 이상이었으며, 이용방법은 주로 도보로 근린공원에 도달하였다. 방문횟수는 주간 1~2회 정도로 나타났다. 조사대상 근린공원의 유효 이용권을 1,000m로 설정하여 변형 중력모델에 의해 이용수요를 예측한 결과 기린봉 근린공원은 4,500명/일, 완산칠봉 근린공원은 3,159명/일, 황방산 근린공원은 2,961명/일이었다. 전체 유효 이용수요는 기린봉 근린공원이 가장 많이 나타났으나, 면적대비 유효이용수요는 완산칠봉 근린공원이 가장 높게 나타났다.

정부는 공유수면 매립사업의 계획적인 관리를 위해, 10년 주기의 공유수면 매립기본계획을 수립하고 있다. 그러나 수시변경을 통한 매립사업을 추진하는 경우가 상당한 비중을 차지하고 있는 것으로 나타났다. 이에 기본계획의 실효성에 대한 의문이 제기되고 있으 며, 이를 보완하기 위한 장기 매립 수요 추세 분석에 대한 필요성이 증가하고 있다. 이에 본 연구에서는 그간의 연간 매립 실적 자료를 활용하여 매립 수요 추세 분석을 수행하였다. 분석 결과, 국내 공유수면 매립 수요는 지속적으로 하락하는 추세인 것으로 나타났으며, 특 히 매립기본계획 체제로 전환된 1990년대 이후에는 그 추세가 뚜렷하게 나타나고 있는 것으로 나타났다. 또한 2021-2030년까지 총 매립 수요는 최대 13.8 km2에서 최소 1.7 km2 수준으로 산정되었다.

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.

In this study, we consider the problem of forecasting the number of inbound foreigners visiting Korea. Forecasting tourism demand is an essential decision to plan related facilities and staffs, thus many studies have been carried out, mainly focusing on the number of inbound or outbound tourists. In order to forecast tourism demand, we use a seasonal ARIMA (SARIMA) model, as well as a SARIMAX model which additionally comprises an exogenous variable affecting the dependent variable, i.e., tourism demand. For constructing the forecasting model, we use a search procedure that can be used to determine the values of the orders of the SARIMA and SARIMAX. For the exogenous variable, we introduce factors that could cause the tourism demand reduction, such as the 9/11 attack, the SARS and MERS epidemic, and the deployment of THAAD. In this study, we propose a procedure, called Measuring Impact on Demand (MID), where the impact of each factor on tourism demand is measured and the value of the exogenous variable corresponding to the factor is determined based on the measurement. To show the performance of the proposed forecasting method, an empirical analysis was conducted where the monthly number of foreign visitors in 2019 were forecasted. It was shown that the proposed method can find more accurate forecasts than other benchmarks in terms of the mean absolute percentage error (MAPE).

The fourth industrial revolution encourages manufacturing industry to pursue a new paradigm shift to meet customers' diverse demands by managing the production process efficiently. However, it is not easy to manage efficiently a variety of tasks of all the processes including materials management, production management, process control, sales management, and inventory management. Especially, to set up an efficient production schedule and maintain appropriate inventory is crucial for tailored response to customers' needs. This paper deals with the optimized inventory policy in a steel company that produces granule products under supply contracts of three targeted on-time delivery rates. For efficient inventory management, products are classified into three groups A, B and C, and three differentiated production cycles and safety factors are assumed for the targeted on-time delivery rates of the groups. To derive the optimized inventory policy, we experimented eight cases of combined safety stock and data analysis methods in terms of key performance metrics such as mean inventory level and sold-out rate. Through simulation experiments based on real data we find that the proposed optimized inventory policy reduces inventory level by about 9%, and increases surplus production capacity rate, which is usually used for the production of products in Group C, from 43.4% to 46.3%, compared with the existing inventory policy.

In this study, we proposed a model for forecasting power energy demand by investigating how outside temperature at a given time affected power consumption and. To this end, we analyzed the time series of power consumption in terms of the power spectrum and found the periodicities of one day and one week. With these periodicities, we investigated two time series of temperature and power consumption, and found, for a given hour, an approximate linear relation between temperature and power consumption. We adopted an exponential smoothing model to examine the effect of the linearity in forecasting the power demand. In particular, we adjusted the exponential smoothing model by using the variation of power consumption due to temperature change. In this way, the proposed model became a mixture of a time series model and a regression model. We demonstrated that the adjusted model outperformed the exponential smoothing model alone in terms of the mean relative percentage error and the root mean square error in the range of 3%~8% and 4kWh~27kWh, respectively. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric energy together with the outside temperature.

Intermittent demand is a demand with a pattern in which zero demands occur frequently and non-zero demands occur sporadically. This type of demand mainly appears in spare parts with very low demand. Croston’s method, which is an initiative intermittent demand forecasting method, estimates the average demand by separately estimating the size of non-zero demands and the interval between non-zero demands. Such smoothing type of forecasting methods can be suitable for mid-term or long-term demand forecasting because those provides the same demand forecasts during the forecasting horizon. However, the smoothing type of forecasting methods aims at short-term forecasting, so the estimated average forecast is a factor to decrease accuracy. In this paper, we propose a forecasting method to improve short-term accuracy by improving Croston’s method for intermittent demand forecasting. The proposed forecasting method estimates both the non-zero demand size and the zero demands’ interval separately, as in Croston’s method, but the forecast at a future period adjusted by binomial weight according to occurrence probability. This serves to improve the accuracy of short-term forecasts. In this paper, we first prove the unbiasedness of the proposed method as an important attribute in forecasting. The performance of the proposed method is compared with those of five existing forecasting methods via eight evaluation criteria. The simulation results show that the proposed forecasting method is superior to other methods in terms of all evaluation criteria in short-term forecasting regardless of average size and dispersion parameter of demands. However, the larger the average demand size and dispersion are, that is, the closer to continuous demand, the less the performance gap with other forecasting methods.

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.