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.