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http://dx.doi.org/10.36498/kbigdt.2020.5.2.111

Demand Prediction of Furniture Component Order Using Deep Learning Techniques  

Kim, Jae-Sung (충북대학교 대학원 빅데이터학과)
Yang, Yeo-Jin (충북대학교 대학원 빅데이터학과)
Oh, Min-Ji (충북대학교 대학원 빅데이터학과)
Lee, Sung-Woong (새한)
Kwon, Sun-dong (충북대학교 경영정보학과)
Cho, Wan-Sup (충북대학교 경영정보학과)
Publication Information
The Journal of Bigdata / v.5, no.2, 2020 , pp. 111-120 More about this Journal
Abstract
Despite the recent economic contraction caused by the Corona 19 incident, interest in the residential environment is growing as more people live at home due to the increase in telecommuting, thereby increasing demand for remodeling. In addition, the government's real estate policy is also expected to have a visible impact on the sales of the interior and furniture industries as it shifts from regulatory policy to the expansion of housing supply. Accurate demand forecasting is a problem directly related to inventory management, and a good demand forecast can reduce logistics and inventory costs due to overproduction by eliminating the need to have unnecessary inventory. However, it is a difficult problem to predict accurate demand because external factors such as constantly changing economic trends, market trends, and social issues must be taken into account. In this study, LSTM model and 1D-CNN model were compared and analyzed by artificial intelligence-based time series analysis method to produce reliable results for manufacturers producing furniture components.
Keywords
Furniture Component; Oer demand Forecast; Inventory Control; ARIMA; LSTM; 1D-CNN F;
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Times Cited By KSCI : 6  (Citation Analysis)
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