Type:

Year:

Editor:

Author:

Yevgeni Paliyenko; Christopher Langner; Benedikt Muller; Valesko Dausch; Daniel Roth; Matthias Guertler; Matthias Kreimeyer

Series:

Institution:

Page(s):

DOI number:

Abstract:

The use of Data-Driven Design (DDD) plays a pivotal role in transforming the design and development processes within various industries through the extensive use of data to extract valuable insights, thereby encouraging innovation. This paper explores the widespread issue of data management throughout the product lifecycles, emphasizing the resulting underuse of data and lost innovation opportunities. Our research focuses on the challenges in incorporating data-driven in multidisciplinary development processes, aiming to assist companies in effectively navigating, choosing, and implementing data-driven strategies within product development. Through a systematic literature review and industry insights, we present twelve data-driven applications, classified by lifecycle phases and linked with relevant data sources. Then, a guiding framework utilizing data science and AI is proposed, enhancing data empowerment and efficiency in industrial practices. Finally, our findings are validated through a case study, providing a structured path from theoretical exploration to practical implementation of DDD.

Keywords:

data-driven design, support methodology, artificial intelligence, product development, systems engineering