Early Product Cost Estimation by Intelligent Machine Learning Algorithms

Abstract

Predicting the total manufacturing costs of a new product early in its development is an obstacle for many businesses, especially when selecting between different product designs and their cost implications. Typically, material costs comprise a large part of total manufacturing costs, and therefore obtaining an early estimate of material costs can help businesses in predicting the total manufacturing costs more accurately. At the early stage of product development, with many imponderables and frequent design modifications, it would be impractical to obtain quotations from suppliers. We, therefore, developed a two-stage machine learning scheme estimating the material cost to guide alternative product design choices that yield a lower total manufacturing cost. Our innovative two-stage technique for cost estimation is meant to overcome this issue. In this paper, we demonstrate that neural networks, a prevalent technique in the literature, can be enhanced by adding the concept of modularity to the estimation of the pricing of technical components already during the design process of a new product.