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 networktechniques—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 networktheory 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).