{"title":"Prediction of Raw Material Price Using Autoregressive Integrated Moving Average","authors":"N. Hankla, Ganda Boonsothonsatit","doi":"10.1109/IEEM45057.2020.9309847","DOIUrl":null,"url":null,"abstract":"In a highly competitive manufacturing industry, it is necessary to reduce logistics cost for remaining competitiveness and increasing business profitability. One of several causes primarily influencing logistics cost is inventory to support fluctuation of raw material price and decision makers when and how much raw material is purchased. These hence require time-series prediction of raw material price. For a small-sized manufacturing case, its main raw material of copper is predicted using Autoregressive Integrated Moving Average (ARIMA). It returns Mean Absolute Percentage Error (MAPE) less than 5 percent.","PeriodicalId":226426,"journal":{"name":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEEM45057.2020.9309847","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In a highly competitive manufacturing industry, it is necessary to reduce logistics cost for remaining competitiveness and increasing business profitability. One of several causes primarily influencing logistics cost is inventory to support fluctuation of raw material price and decision makers when and how much raw material is purchased. These hence require time-series prediction of raw material price. For a small-sized manufacturing case, its main raw material of copper is predicted using Autoregressive Integrated Moving Average (ARIMA). It returns Mean Absolute Percentage Error (MAPE) less than 5 percent.