The paper presents a study run to verify the applicability of data mining algorithms as
decision support in early design stages of a product development project. The paper
describes a two-stage scenario providing the rationale for the application of data science in
engineering design. Furthermore, it describes a demonstrator showing how data can be fed
back to the early design stages and can be used to populate models to reduce uncertainty in
decision making. A wheel loader for constructions works is the reference product for the
demonstration. Data mining is applied on a dataset built on machine performances and
contextual and environmental data. The demonstrator focuses on the estimation of the fuel
consumption of alternative design concepts and estimates the performance variations given
different contextual variable. Finally, a way of visualizing the results of the data analysis in
relation to the tested and expected performances is presented.