In the framework of the I4MS (ICT Innovation for Manufacturing SMEs) initiative, Phase IV Innovation Actions belong to the DIH (Digital Innovation Hubs) focus area. For the AI REGIO case, we are speaking of AI-driven DIHs for Manufacturing. In the recent years, there has been an ever increasing interest and turmoil in DIHs and their services but the most significant emergent characteristic was their heterogeneity: DIHs were born basically bottom-up without a precise and scientifically approved classification of their services, their services have been basically self-declarations without any precise governance, and this resulted in an extreme fragmentation of their ecosystem that the JRC DIH Catalogue is just partly solving and remedying.
Derived from the MIDIH Phase III I4MS project, AI REGIO is proposing a methodology (METHODIH) for the service characterisation and clustering of the different typologies of DIHs: those who are closely linked to regional innovation development agencies, those who are closely linked to local technology competence centres, those who are representing the needs and the requirements of Manufacturing Industry and SMEs in particular.
To put in evidence commonalities and specificities of DIHs, AI REGIO partners have adopted the D BEST methodology for service portfolio definition, which is implemented by a series of online and collaborative highly interactive workshops to let emerge the Data, Business, Ecosystem, Skills, Technology aspects of DIHs’ services and the opportunities for cross-DIH collaborations (joint provision of services, joint development of services, joint matchmaking of assets and competencies).
In this analysis, AI REGIO discovered that many of the interviewed DIHs are closely connected to (or even embed) a Didactic (Learning Teaching) Factory (DF) where Skills Development (S), Data Integration (D) and Technology (Test before Invest) services are provided and where not just Technology Users but also Students and Start-ups Customer Journeys are implemented. The assumption was to be able to characterise an AI REGIO DF from its services (especially S, D and T) setting, some sort of fingerprint and benchmark for a potential ecosystem and community of pan-EU Didactic Factories. Our ideas have been quite supported by European Commission’s new strategies, especially in the domain of AI and in the new concept of domain-specific Testing and Experimentation Facilities (TEFs). In the case of Trustworthy AI experimentations, such TEFs could assume the form of TERESA, TEchnology, REgulatory SAndboxes.
Finally, and also thanks to the COVID crisis, we discovered that a new service category is indeed unique for AI REGIO DFs: Remotisation (R), where Data, Software, Digital Twins Simulations and physical industrial assets can be accessed, used, transformed and repurposed from remote and in a FAIR way (Findable Accessible Interoperable Reusable).
More information
Karolien.debruine@oostnl.nl (06 424 70 718)
