Prediction of raw material price using autoregressive integrated moving average

Conference proceedings article

Authors/Editors

GANDA BOONSOTHONSATIT

Strategic Research Themes

Other (Strategic Research Themes)

Publication Details

Author list: Hankla, Nutthaya; Boonsothonsatit, Ganda

Publisher: IEEE Computer Society

Publication year: 2020

Start page: 220

End page: 224

Number of pages: 5

ISBN: 9781540000000

ISSN: 21573611

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099771353&doi=10.1109%2fIEEM45057.2020.9309847&partnerID=40&md5=3de95b9cd9e4282e3f57901d5dde39ef

Languages: English-Great Britain (EN-GB)

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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. © 2020 IEEE.

Keywords

ARIMA, Raw material price