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PES2020-Prosj.etabl.støtte H2020

Web of Innovation Value Chains of Additive Manufacturing under consideration of RRI

Awarded: NOK 79,999

Additive Manufacturing (AM) or 3D?Printing consists in the ?process of joining materials to make objects from 3D model data, usually layer upon layer?. It is a technology that falls under the category of ?Advanced Manufacturing Technologies?. It is therefore part of the group of Key Enabling Technologies which are crucial for the industrial renewal of Europe. The challenges of AM do not only refer to technology innovation but also economic and social innovation since new business models have to be built. New business models in AM induce a radical change in innovation value chains and offer new options for collaboration between European players. The project develops and investigates a model for webs of innovation value chains in the field of AM including the perspectives of all types of stakeholders and especially testing the model for AM in the field of medical application, mechanical engineering (tooling, automotive, aerospace) and consumer products. The dynamic model shall strengthen the knowledge base for policy orientation regarding promoting innovation. The model will develop an understanding of these webs of innovation and open the gates for good practices of RRI. Based on established model, procedures the current innovation system of AM will be investigated and analysed. We define the boundary of the system as well as what is in and what is outside this system. All components and elements of the system has to be identified and named. The relationship of all elements are described. Cause?effective analysis and impact analysis will reveal information flows also regarding their strengths. The theoretical model will simulated the impact and role of the different type of stake holders, so the innovation system of AM can be understood more clearly and it can be learned what is the driving mechanisms for the system and where are the best opening for RRI. At the end the model is tested with empirical data for fine tuning.

Funding scheme:

PES2020-Prosj.etabl.støtte H2020