CAPRI is organising a network of European DIHs focused on the Process Industry; this event has been an opportunity to present the project and the services it provides. It will develop and promote digital transformation through a Cognitive Automation Platform (CAP) involving a Reference Architecture with four levels of cognitive human-machine interaction and a set of commercial and open-source reference implementations.
To this end, CAPRI enables cognitive tools that bring operational flexibility to existing process industries, improving performance through different KPIs and state-of-the-art quality control of their products and intermediate flows.
This initiative is inspired by the DIH4INDUSTRY portal. This network will not only focus on dissemination and communication initiatives, but above all it will create opportunities for collaboration between DIHs thanks to the analysis of the D BEST portfolio of services.
In close collaboration with the European Commission's Digital Innovation Hubs tool, the initiative aims to create, nurture and steer an ecosystem of Digital Innovation Hubs with a regional smart manufacturing specialisation.
At last Friday's meeting, in addition to presenting both projects, the invited DIHs were given the opportunity to present their services, as well as their current projects. Also, areas such as knowledge and implementation of efficient business models and work ecosystems, manufacturing, creating new projects and the generation of synergies between DIHs, were discussed.
Answers were also provided to questions such as:
- How does the process industry differ from discrete manufacturing according to your experience?
- Are there specific services for the Process Industry or services with a significant difference when delivered in discrete as opposed to continuous production?
- Are there professional roles with specific needs in the PI? Are there specific competences for PI customers?
- Do you have success stories of IP SMEs benefiting from your services and what is the difference in discrete manufacturing (in terms of time, iterations, effort required, KPIs measured)?