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.
