A hybrid spares demand forecasting method dedicated to mining industry

作者：Rosienkiewicz Maria^*; Chlebus Edward; Detyna Jerzy

来源：Applied Mathematical Modelling, 2017, 49: 87-107.

DOI：10.1016/j.apm.2017.04.027

• Spare parts
• Demand forecasting
• Artificial neural networks (ANN)
• Akaike's information criterion (AIC)
• Bayesian information criterion (BIC)
• Mining industry

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.

• 出版日期2017-9