Back propagation neural network based product cost estimation at an early design stage of passenger vehicles

The lack of effective cost estimation method is the bottleneck for product cost control at the early design stage. In this article, the product costs of sedan, sports utility vehicle and multi-purpose vehicle – the three major categories of passenger vehicles in the Chinese market are studied. A cost estimation architecture is proposed and a back propagation neural network based cost estimation method is developed for the early design stage of passenger vehicles. Users can change parameters to check corresponding influences on product cost or to compare the costs of different manufacturers. As the confidential cost information is inaccessible, the product cost is calculated backward from the price with the product cost ratio which is estimated by a panel of experts with Delphi method. Rather than introducing pilot data, real world data is adopted in this study. The prices and specifications of passenger vehicles are retrieved through the internet while the back propagation neural network is trained with the neural network toolbox of Matlab(TM) 7.1. Two neural network models are evaluated and the test reveals that the model selection has strong relation with the training data set. A case study on how this method is applied in a Chinese automobile company is also given.