A Maturity Model for Rapid Diffusion of Innovation in High Value Manufacturing

In order to support accelerating the diffusion of innovations in high value manufacturing related to enabling flexible mass customization, this paper presents a research-based maturity model for forecasting the speed of innovation diffusion from ideation to market saturation. The model provides an early stage applied research view of (groups of) “game changing” variables, which accelerate diffusion of innovations to significantly reduce financial uncertainty and minimize the time to derive value from the original idea. The model is applied to multiple case studies related to the repurposing and customization of existing mass manufacturing infrastructures and processes to meet novel requirements. Case studies include among others a reference model based on a literature review, the diffusion of 3-D printing technology in manufacturing, the diffusion of novel cement manufacturing technology and the manufacturing of intensive care ventilators during the Covid-19 pandemic. The diffusion of innovation model applied is based on diffusion of innovation principles founded in the research of Everett Rodgers, the Bass Diffusion Curve and aligned to recent advances in living (eco-) systems theory. Special emphasis is placed on determining not only the relevance of “known-known” success factors for rapid innovation diffusion, but also on identifying “unknown-unknown” game changers enabling the required changes at pace. Key findings are that “game changing” factors for the innovations are primarily the interdependent availability of budget and resources to achieve market saturation, urgency of need shared by all participants, observability of impact (value creation) and compatibility with existing ways of work. Critical as well is population of all diffusion web roles with unique individuals. Further research is suggested regarding the dependency of assessed variable (groups) and the integration of Technical Readiness Level phases into the forecasting model.