Artificial neural networks for predicting the demand and price of the hybrid electric vehicle spare parts

The hybrid electric vehicles (HEVs) market has grown tremendously in the past few years which, as a result, has led to an exponential growth in the spare parts (SPs) market. Therefore, there is a strong need, nowadays, to predict the demand as well as the price of these SPs. However, ascertaining such an aim is not as easy as it may seem, this being due to the facts that (i) the demand is highly uncertain as it depends on many uncertain variables, and (ii) the price does not follow the normal value chain methods. In this research work, the artificial neural network (ANN) is utilized to develop models that can map 15 vehicles and SPs-related variables to the demand and the price of the HEV SPs. It has been demonstrated that the ANN models have the ability to predict both the demand and the price of the HEV SPs. In addition, the developed ANN models outperform the linear regression models by minimizing the root mean square error values by approximately 4 and 5 times for the demand and the price, respectively. Neural network-based models have been employed to accurately predict the demand as well as the price of the HEV SPs by mapping them to 15 vehicles and SPs-related variables.