ExpectedOutcome:
Projects are expected to contribute to the following outcomes:
Fully reaching the opportunities of sharing and exploiting industrial data, including deep industrial data[1], requires to strike the right balance between storing and handling data centrally in the cloud or locally at the edge of industrial network. Such a balance has to take into account not only efficiency but also the real-time requirements and cybersecurity aspects as well as the ability to systemically integrate and upgrade operational technology to the innovative developments in (self-) configuration, therefore building a flexible industrial Internet for distributed control and modular manufacturing while keeping the high-level of reliability and safety required by the manufacturing sector.
Computing, storage and networking technologies will have to show also flexibility along the industrial value chains and promote the introduction of new business models, based on the availability of deep industrial data from different data sources and ontologies, within an agreed data governance, with mutual trust and adequate distribution of the value created by sharing data.
Proposals are expected to address one of the following technology areas for data-driven industrial environments:
Projects are encouraged to develop toolkits of open hardware, software and toolware, and qualify the use of these to provide opportunities to SMEs to further automate and digitalise their manufacturing, through, for example, OPC-UA and Administrative Shell (AAS) as well as further development on top of these Industrial Internet standards and there inherent cyber security demands for Operational Technology environment.
The distributed industrial computing environments will be demonstrated effectively in a minimum of two specific manufacturing applications. The topic will integrate new or existing technologies to make them practically and economically viable in the industrial world, and will encompass modern manufacturing technologies such as digital twins.
Proposals submitted under this topic should include a business case and exploitation strategy, as outlined in the introduction to this Destination.
Research must build on existing standards or contribute to standardisation. Interoperability for data sharing should be addressed. Additionally, a strategy for skills development should be presented, associating social partners when relevant.
All projects should build on or seek collaboration with existing projects and develop synergies with other relevant European, national or regional initiatives, funding programmes and platforms.
This topic implements the co-programmed European Partnership Made in Europe.
In this topic the integration of the gender dimension (sex and gender analysis) in research and innovation content is not a mandatory requirement
Specific Topic Conditions:Activities are expected to start at TRL 4 and achieve TRL 7 by the end of the project – see General Annex B.
Cross-cutting Priorities:Socio-economic science and humanities
Co-programmed European Partnerships
[1]In this context, “deep industrial data” means data available only internally in an industrial process (e.g., data used in a manufacturing machine or a logistic process), and not normally shared across the value chain.