Parametric vs. neural network models for the estimation of production costs: A case study in the automotive industry

This paper aims at illustrating the compared results of the application of two different approaches – respectively parametric and artificial neural network techniques – for the estimation of the unitary manufacturing costs of a new type of brake disks produced by an Italian manufacturing firm. The results seem to confirm the validity of the neural network theory in this application field, but not a clear superiority with respect to the more “traditional” parametric approach: in particular, the ANN seems to be characterised by a better trade-off between precision and cost of development, while a critical point – especially in the specific application context – is represented by the reduced possibility of interpreting output data (which is critical for the “optimisation” of design solutions during the new product development process).