Comparison of product life cycle cost estimating models based on neural networks and parametric techniques—a case study for induction motors

Abstract

The cost estimation of a product’s life cycle is a key factor in the product design process. The research is based on an innovative model of artificial neural networks (ANNs) compared to a parametric estimation. Introducing modern elements of information technologies in the area of cost estimation for a production company is a vital element of its sustainability in the era of Industry 4.0. The presented modern product life cycle cost estimation tool in the form of ANN is a reliable source of forecast that is the basis for the product life cycle cost reduction program, which is a crucial element of sustainability. Research shows that ANNs are a viable alternative to parametric cost estimation. The percentage error between estimated and historical cost values is 8.05 times lower for ANN than for the parametric approach. ANN is an adequate cost estimation model for technologically complex products. The second contribution is using technical specifications required by the customer directly to estimate the cost of a product’s life cycle automatically. This can translate both into a reduction of the time needed to provide information to the client and the workload of engineers.