Abstract
• A new method addressing the problem of spares lumpy demand forecasting is proposed. • The method combines information criteria, regression modeling and ANN. • An algorithm for efficiency analysis on variables selection methods was developed. • A new application domain is presented – the mining industry. • Research shows that the method is more efficient than traditional methods. The paper addresses the problem of lumpy demand forecasting which is typical for spare parts. Several prediction methods are presented in the paper - traditional techniques based on time series and advanced methods which use artificial neural networks. The paper presents a new hybrid spares demand forecasting method dedicated to mining companies. The method combines information criteria, regression modeling and artificial neural networks. The paper also discusses simulation research related to efficiency assessment of the chosen variable selection methods and its application in the newly developed forecasting method. The assessment of this method is conducted by a comparison with traditional methods and is based on selected forecast errors.
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