A NEURAL NETWORK APPROACH FOR EARLY COST ESTIMATION OF PACKAGING PRODUCTS

Product costs need to be identified early, i.e., during the design stage, where they can be controlled best. This implies the need to estimate the productʹs cost without full knowledge of the manufacturing process plans. In this paper, a feature-based cost estimation using a back-propagation neural network is proposed and a prototype system has been developed for estimating the costs of packaging products based on design information only. All the cost-related features of a product design were extracted and quantified according to their cost effects. The correlation between these cost-related features and the final cost of the product was established by training a back-propagation neural network using historical cost data. The extraction of cost-related features and the construction, training and validation of the neural network are described. The performance of the trained neural network based on a set of testing samples is also given.