文章基本信息

标题：Artificial intelligence for forecasting in supply chain management: a case study of White Sugar consumption rate in Thailand
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作者：Anirut Kantasa-ard ; Abdelghani Bekrar ; Abdessamad Ait el cadi 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：13
页码：725-730
DOI：10.1016/j.ifacol.2019.11.201
语种：English
出版社：Elsevier
摘要：This paper proposes an appropriate model to forecast the trend of white sugar consumption rate in Thailand due to the fluctuation of consumption rate nowadays. This paper will focus on two main forecasting model types, which are the regression models and neural network models. Moreover, the performance is evaluated by using Root Mean Square Error (RMSE) and Theil’U statistic value. After processing the experiments, the results demonstrate that Long Short-Term Memory (LSTM) recurrent neural network provides the best performance for the forecasting, with the condition of combination between the existing consumption rate and other relevant factors like production supply, import rate, export rate, and inventory stock. Also tuning the model’s parameters is an important issue.
关键词：KeywordsMachine LearningPhysical InternetDemand ForecastingNeural NetworkRegression