Artificial Neural Networks Supporting Cause-and-Effect Studies in Product–Service System Development

A data analysis method based on artificial neural networks aiming to support cause-and-effect in design exploration studies is presented. The clusters and aggregates the effects of multiple variables structural hierarchy evaluated system. proposed exemplified a case study showing that predictive capability created, clustered, dataset comparable original, unmodified, one. using coefficient determination, root mean square error, average relative error. Data approach with believed significantly improve comprehensibility relationships studying PSS concepts cross-functional team thereby assisting difficult resource-demanding negotiations process at conceptual stage design.