Data-driven design: The new challenges of digitalization on product design and development

Digitalization and the momentous role being assumed by data are commonly viewed as pervasive phenomena whose impact is felt in all aspects of society and the economy. Design activity is by no means immune from this trend, and the relationship between digitalization and design is decades old. However, what is the current impact of this 'data revolution' on design? How will the design activity change? What are the resulting research questions of interest to academics? What are the main challenges for firms and for educational institutions having to cope with this change? The paper provides a comprehensive conceptual framework, based on recent literature and anecdotal evidence from the industry. It identifies three main streams: namely the consequences on designers, the consequences on design processes and the role of methods for data analytics. In turn, these three streams lead to implications at individual, organizational and managerial level, and several questions arise worthy of defining future research agendas. Moreover, the paper introduces relational diagrams depicting the interactions between the objects and the actors involved in the design process and suggests that what is occurring is by no means a simple evolution but a paradigmatic shift in the way artefacts are designed

A decision support model to assess technological paradigms

Envisioning the emergence of a new technological paradigm involves several issues, spanning from strategic assessments and managerial actions to design decisions and technology related choices. The present study focuses on this latter perspective, by proposing a model that estimates the success probability of innovative products as a function of design actions. This focus on the design decisions that underlie radical shifts is not conflicting, but complementary to the more traditional perspectives of forecasting that consider environmental variables or process management factors. The model is based on a database of past successful and unsuccessful innovations, which are used to build a logistic regression model, whose evidences can assist both designers and managers. The former get advice on how specific design choices affect product perception and innovation adoption, while the latter are supported in identifying the most promising projects. The model is illustrated through two cases of digital products