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

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.

[1]  Haiyan Song,et al.  Modelling and forecasting the demand for Hong Kong tourism , 2003 .

[2]  Rajesh Kumar,et al.  The Evaluation of Forecasting Methods for Sales of Sterilized Flavoured Milk in Chhattisgarh , 2014 .

[3]  A. Wold,et al.  A GENERALIZATION OF CAUSAL CHAIN MODELS (PART III OF A TRIPTYCH ON CAUSAL CHAIN SYSTEMS) , 1960 .

[4]  V. Miranda,et al.  Entropy and Correntropy Against Minimum Square Error in Offline and Online Three-Day Ahead Wind Power Forecasting , 2009, IEEE Transactions on Power Systems.

[5]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[6]  Steven C. Wheelwright,et al.  Forecasting methods and applications. , 1979 .

[7]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[8]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[9]  S. Senith,et al.  A Study on Factors Affecting Performance of Indian Cement Industry , 2013 .

[10]  Teuvo Kohonen,et al.  An introduction to neural computing , 1988, Neural Networks.

[11]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[12]  Dipti Srinivasan,et al.  Evolving artificial neural networks for short term load forecasting , 1998, Neurocomputing.

[13]  S Makeig,et al.  Spatially independent activity patterns in functional MRI data during the stroop color-naming task. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[14]  G. Box,et al.  On a measure of lack of fit in time series models , 1978 .

[15]  E. McKenzie.  General exponential smoothing and the equivalent arma process , 1984 .

[16]  Robert Hecht-Nielsen,et al.  Theory of the backpropagation neural network , 1989, International 1989 Joint Conference on Neural Networks.

[17]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[18]  Sovan Lek,et al.  Artificial neural networks as a tool in ecological modelling, an introduction , 1999 .