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1 Journal of Building Chemistry

1 2016

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Modelling the demand for cement: The case of Poland and Spain

Wiliński D., Kantorowicz J., Wierzchoń S. T.

Journal of Building Chemistry

2016

Vol. 1, iss. 1

69-83

The paper develops a new tool for forecasting the demand for cement and tests it on the data from Poland and Spain. Predicting the demand for cement is a key issue from the perspective of the cement manufacturers. Forecasting this demand helps businesses determine, among others, the level of production, future revenue stream and purchase of raw materials. The hybrid models employed in this paper consists of Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model and Artificial Neural Network (ANN). The SARIMAX model was initially used to forecast the demand for cement. The resulting forecasting errors were further corrected with ANN, which was built to account for the nonlinear tendencies that the SARIMAX technique could not identify. The forecasting errors from the hybrid model were compared with the errors from ARIMA-type and the ANN models working separately. The results indicate that the hybrid models outperform of the models used separately. If implemented, this methodology may become a powerful decisionmaking tool for cement industry.