Supporting product design by anticipating the success chances of new value profiles

Companies willing to introduce radical innovations have to face the tough task of correctly evaluating manifold aspects concerning the lifecycle of the new products to be launched. In such a circumstance severe difficulties arise because, at the very beginning of the design process, project teams own limited and unreliable information about the performances viable to positively impact value for customers and consequently the commercial success. The present paper suggests an original approach for the anticipatory assessment of the expected market appraisal of a new product profile. The proposed “Value Assessment Metrics” (VAMs) is a tool to estimate the success potential of a new artefact through a balance of its functionalities and features with respect to the alternatives existing in the market. The metrics are defined through an induction process from a large collection of successful innovations and market failures. After reporting the methodological approaches adopted to build the VAMs, the first based on Logistic Regression, the second on Neural Networks, the paper presents their preliminary validation and two example applications to the proposition of an innovative lipstick and a concealed hinge.

► Shortcomings of present value-based decision support systems.
► Analysis of 92 examples concerning market successes and failures.
► A model to anticipate the success likelihood of new product profiles.
► Predictive model based on logistic regression and neural networks.
► Industrial applications show the consistency of the proposed forecasting method.