NDLI: ANN, ARIMA and MA timeseries model for forecasting in cement manufacturing industry: Case study at lafarge cement Indonesia

Content Provider	IEEE Xplore Digital Library
Author	Fradinata, E. Sirivongpaisal, N. Suthummanon, S. Suntiamorntuthq, W.
Copyright Year	2014
Description	Author affiliation: Comput. Eng. Dept., Prince of Songkla Univ., Songkhla, Thailand (Suntiamorntuthq, W.) \|\| Ind. Eng. & Manage. Dept., Serambi Mekkah Univ., Banda Aceh, Indonesia (Fradinata, E.) \|\| Ind. Eng. & Syst. Dept., Prince of Songkla Univ., Hatyai, Thailand (Sirivongpaisal, N.) \|\| Ind. Engneering & Syst. Dept., Prince of Songkla Univ., Hatyai, Thailand (Suthummanon, S.)
Abstract	The accurate demand forecast method is one of the main important to industry to minimize error. In this study tried to propose the Artificial Neural Network (ANN), Arima and Moving Average (MA) to predict the condition of sale demand in cement manufacturing industry. The predicted months after the twenty two at the last months data and should be validated with the real two months data. The processes come from collecting sales real data from cement industry in aceh province. Analyzed the predicted condition and the mean square error (MSE), MAPE and SSE. Compared to the installed method in the factory should be also considered. The result of this study ANN, Arima and MA models are better than the installed method and the predicted data are better as well where the installment produce more than thirty percent errors.
Starting Page	39
Ending Page	44
File Size	524775
Page Count	6
File Format	PDF
ISBN	9781479969845
e-ISBN	9781479951000
DOI	10.1109/ICAICTA.2014.7005912
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-20
Publisher Place	Indonesia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Supplyugu chain Demand Correlation Time series analysis Arima Artificial neural networks Predictive models Time series forecasting Data models Artificial neural network Forecasting
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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