H2020
Europeo
ExpectedOutcome:Projects are expected to contribute to the following outcomes:
Establishing European industry as leader in sustainable data-driven manufacturing and process industries through efficient data processing and notably at the edge of the network, improving the environmental, economic and social sustainability of industrial production, and reinforcing European leadership in the deployment and operations of industrial network;Improving the agility of European manufacturing industry and increase its resiliency to external shocks, including with agile, secure and easy-to-implement non-public 5G systems, leading to more resilient production processes;Demonstrate the use of open systems and qualified open source software tools for data monitoring & collection as well as data analytics;Foster industrial data and distributed computing standardisation;Facilitate the development of technologies requiring only minimal training of the industrial workforce.
Scope: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...
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ExpectedOutcome:Projects are expected to contribute to the following outcomes:
Establishing European industry as leader in sustainable data-driven manufacturing and process industries through efficient data processing and notably at the edge of the network, improving the environmental, economic and social sustainability of industrial production, and reinforcing European leadership in the deployment and operations of industrial network;Improving the agility of European manufacturing industry and increase its resiliency to external shocks, including with agile, secure and easy-to-implement non-public 5G systems, leading to more resilient production processes;Demonstrate the use of open systems and qualified open source software tools for data monitoring & collection as well as data analytics;Foster industrial data and distributed computing standardisation;Facilitate the development of technologies requiring only minimal training of the industrial workforce.
Scope: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:
Development of technologies and definition of specifications and standards for data, products, and/or business processes, that can be agreed and commonly used by many industrial actors, and that have the potential for the emergence of future digital value chains, identify promising industrial areas and organisational models that facilitate cooperation and collaborative product and service design among industry players facilitating industry agreements.Quick uptake of advanced 5G technologies by European manufacturing sector to support the convergence towards greater exploitation of industrial data and increase resilience and cybersecurity by design. Private 5G networks (5G NPN) are exclusive mobile networks that manufacturers can use for a defined local production site; they can be tailored to the individual needs of the manufacturer and meet future requirements in the area of Industry 4.0. Innovative approaches to simplify the deployment and operation of such private 5G networks throughout their life cycle are needed. Implementers in industrial environments need to take a holistic view, including both the connectivity infrastructure (with 5G as a central component) and the actual production system. An important element for rapid deployment is also the development and evaluation of new business models for private 5G networks. In particular, projects should offer opportunities for new players that have their main focus on non-public (campus) networks (NGN) for connected industries and in particular automation applications. Projects will aim at "Zero-Touch Management", using network automation, AI / ML, Self-organizing Networks (SON), etc. and taking into account the specifics of 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 humanitiesCo-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.
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