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 analysis in design exploration studies is presented. The method clusters and aggregates the effects of multiple design variables based on the structural hierarchy of the evaluated system. The proposed method is exemplified in a case study showing that the predictive capability of the created, clustered, a dataset is comparable to the original, unmodified, one. The proposed method is evaluated using coefficient-of-determination, root mean square error, average relative error, and mean square error. Data analysis approach with artificial neural networks is believed to significantly improve the comprehensibility of the evaluated cause-and-effect relationships studying PSS concepts in a cross-functional team and thereby assisting the difficult and resource-demanding negotiations process at the conceptual stage of the design.
Artificial neural networks, Data analysis, Design exploration, Product-Service System (PSS)
Aeddula, Omsri, J. Wall, T. Larsson (2021) ARTIFICIAL NEURAL NETWORKS SUPPORTING CAUSE AND EFFECT STUDIES IN PRODUCT-SERVICE SYSTEM DEVELOPMENT. In: Design for Tomorrow—Volume 1: Proceedings of ICoRD 2021 / [ed] Chakrabarti, A., Poovaiah, R., Bokil, P., Kant, V. (Eds.), 2021, Vol. I, article id 132