5G-INDUCE relied on the deployment of an open ETSI NFV compatible 5G orchestration platform for the deployment of advanced 5G NetApps. The project focused on the Industry 4.0 vertical sector, as one of the fastest growing and most impactful sectors in European economy with high potentials for service development SMEs and with the capability to tackle all diverse cases of service requirements. The platform was integrated over 3 5G Experimentation Facilities in Spain, Greece, and Italy, and extended with links towards specific Industries, for the showcasing of NetApps in real 5G environment. The platform’s unique features provided the capability to the NetApp developers to define and modify the application requirements while the underlay intelligent OSS could expose the network capabilities to the end users on the application level without revealing any infrastructure related information. This process enabled an application-oriented network management and optimization approach that was in line with the operator’s role as manager of its own facilities, while it offered the operational environment to any developers and service providers through which tailored made applications could be designed and deployed, for the benefit of vertical industries and without any indirect dependency through a cloud provider.
UPTIME aimed to design a unified predictive maintenance framework and an associated unified information system in order to enable the predictive maintenance strategy implementation in manufacturing industries. The UPTIME predictive maintenance system extended and unified the new digital, e-maintenance services and tools and incorporated information from heterogeneous data sources, e.g. sensors, to more accurately estimate the process performances.UPTIME enabled manufacturing companies to reach Gartner’s level 4 of data analytics maturity (“optimized decision-making”) in order to improve physically-based models and to synchronise maintenance with quality management, production planning and logistics options.
Despite the indisputable benefits of AI, humans typically have little visibility and knowledge on how AI systems make any decisions or predictions due to the so-called “black-box effect” in which many of the machine learning/deep learning algorithms are not able to be examined after their execution to understand specifically how and why a decision has been made. The inner workings of machine learning and deep learning are not exactly transparent, and as algorithms become more complicated, fears of undetected bias, mistakes, and miscomprehensions creeping into decision making, naturally grow among manufacturers and practically any stakeholder.
In this context, Explainable AI (XAI) is today an emerging field that aims to address how black box decisions of AI systems are made, inspecting and attempting to understand the steps and models involved in decision making to increase human trust.
XMANAI aimed at placing the indisputable power of Explainable AI at the service of manufacturing and human progress, carving out a “human-centric”, trustful approach that is respectful of European values and principles, and adopting the mentality that “our AI is only as good as we are”. XMANAI, demonstrated in 4 real-life manufacturing cases, helped the manufacturing value chain to shift towards the amplifying AI era by coupling (hybrid and graph) AI “glass box” models that were explainable to a “human-in-the-loop” and produced value-based explanations, with complex AI assets (data and models) management-sharing-security technologies to multiply the latent data value in a trusted manner, and targeted manufacturing apps to solve concrete manufacturing problems with high impact.
Solid urban waste (SUW) is an abundant source for circular products production, but it is generally not exploited. In fact, over 500 kg of municipal waste per capita were generated in the EU in 2020, while only 45% was recycled. The proximity of resources and people, a sufficient scale for effective markets and the ability to shape urban planning and policy are key factors for cities to achieve advancements in this area. REDOL was conceived to take advantage of this scenario and transform cities into hubs for circularity that implement zero residues strategies while fostering industrial-urban symbiosis (I-US) approaches among local and regional actors.
To this end, REDOL redesigned 5 value chains for SUW (packaging, plastics, CDW, textiles, WEEE) ending-up in the production of 12 circular products. Along the value chains a range of new solutions were implemented for 1) upgrading management technologies to collect, sort and classify SUW, 2) enhancing the processing routes of sorted materials to avoid landfilling and 3) applying cutting-edge digital tools to optimize value chains and interaction among key players. Moreover, REDOL provided the required organizational procedures, business models and social innovation actions required for the establishment of successful I-US interactions and hubs for circularity at local level. Such an approach resulted in the development of guidelines and recommendations for major decision-making bodies and achieved improved citizens’ perception on SUW as a local resource and on recycled products, thus increasing their participation in separate collection schemes.
REDOL was implemented in Aragon, with Zaragoza in the center of the hub for circularity. This way, REDOL supported its transition towards a zero residues city by 2040. This implied 144.720 tons SUW/year being re-used, valorized or transformed into secondary raw materials, leveraging economic and GHG emissions savings over 14B€ and 280 ktCO2/year.
The AI REGIO project aimed at filling 3 major gaps currently preventing AI-driven DIHs from implementing fully effective digital transformation pathways for their Manufacturing SMEs: at policy level, the Regional vs. EU gap; at technological level, the Digital Manufacturing vs. Innovation Collaboration Platform gap; at business level, the Innovative AI (Industry 5.0) vs Industry 4.0 gap.
POLICY. Regional smart specialization strategies for Efficient Sustainable Manufacturing and Digital Transformation (VANGUARD initiative for Industrial Modernisation) are so far insufficiently coordinated and integrated at cross-regional and pan-EU level. SME-driven AI innovations cannot scale up to become pan-EU accessible in global marketplaces as well as SME-driven experiments remain trapped into a too local dimension without achieving a large scale dimension. Regional vs. EU Gap.
TECHNOLOGY. Digital Manufacturing Platforms DMP and Digital Innovation Hubs DIH play a fundamental role in the implementation of the Digital Single Market and Digitsing European Industry directives to SMEs, but so far such initiatives, communities, innovation actions are running in a quite independent if not siloed way, where very often Platform-related challenges are not of interest for DIHs and Socio-Business impact not of interest for DMP. DMP vs. DIH Gap.
BUSINESS. Many Industrial Data Platforms based on IOT Data in Motion and Analytics Data at Rest have been recently developed to implement effective Industry 4.0 pilots (I4MS Phase III platforms). The AI revolution and the new relationship between autonomous systems and humans (Industry 5.0) has not been properly addressed in I4MS so far. AI I5.0 vs. I4.0 Gap.
AI REGIO followed the 4 steps for VANGUARD innovation strategy (learn-connect-demonstrate-commercialize) by constantly aligning its methods with the AI DIH Network initiative and its assets with I4MS/DIH BEinCPPS Phase II and MIDIH / L4MS Phase III projects. AI REGIO: Industry 5.0 for SMEs
Suite5 Role in the Project: Suite5 as an AI technology provider mainly contributed to the AI4Manufacturing Toolkit and the Data4AI Platform for Industrial IoT. In addition, Suite5 was heavily involved in the AI DIH Collaborative Intelligence and Industry 5.0 approach, the AI DIH Data Sovereignty Solutions and the AI DIH Data Quality and Cleansing Mechanisms for AI Applications.
Circular TwAIn lowered the barriers for all the stakeholders in manufacturing and process industry circular value chains to adopt and fully leverage of trusted AI technologies, in ways that enabled end-to-end sustainability, i.e. from eco-friendly product design to the maximum exploitation of production waste across the circular chain. To this end, the project researched, developed, validated and exploited a novel AI platform for circular manufacturing value chains, which supported the development of interoperable circular twins for end-to-end sustainability. Circular TwAIn unlocked the innovation potential of a collaborative AI-based intelligence in production based on the use of cognitive digital twins. Moreover, based on the use of trustworthy AI techniques, Circular TwAIn enabled human centric sustainable manufacturing, fostering the transition towards Industry 5.0. Furthermore, Circular TwAIn enabled the integration and combination of different data from various sources over entire product life cycle considering sustainability aspects. The goal was to create and deliver innovative services among the members of the data ecosystem; these services were embedded in AI-based Digital Twins, supporting an unambiguous communication when realizing complex services for sustainable manufacturing. The ambition of Circular TwAIn was to unleash the sustainability potential of AI technologies in circular manufacturing chains through: (i) Introducing AI optimizations in stages where AI was still not used (e.g. AI-based product design); (ii) Using AI for multi-stage and multi-objective circular optimizations that could improve sustainability performance. In this direction, the project leveraged information from a circular manufacturing dataspace that provided access to the datasets needed for multi-stage and multi-objective optimizations.
Europe’s manufacturing base is under pressure from global competition, skills shortages, fragile supply chains and tightening regulation — and the AI it answers with has to be trustworthy, sovereign and built around people. euroFMX sets out to establish the first European ecosystem of generative AI and autonomisation frontier models fit for manufacturing, reinforcing Europe’s competitiveness and strategic autonomy in the digital age.
The project works along four strategic pillars. The first delivers industrial AI that acts autonomously, systemically and at scale, setting the path towards a manufacturing-friendly artificial general intelligence — from factory automation towards the autonomy of automation. The second strengthens the future workforce, bridging AI talent and industrial know-how through large-scale skilling, co-creation and open knowledge communities. The third reclaims European digital and AI sovereignty through industrial-grade, privacy-preserving data spaces and HPC-based AI factories, democratising access to resources so far concentrated in a handful of non-European companies. The fourth optimises frontier generative AI models for manufacturing, introducing physics-informed graph neural networks, multimodal reasoning and agentic orchestration platforms for trustworthy, interpretable and regulation-ready deployment.
euroFMX validates its breakthroughs in industrial pilots across machining, robotics, automotive, circular manufacturing and human–AI collaboration, and opens its framework to third parties through a series of open calls. The approach is a EURO4EURO one: a manufacturing GenAI built in Europe, by Europeans, with European open-source software technology and knowledge, for European industry, and adhering to European values and open standards.
The I4MS program in H2020 has been a great success for the Digital Transformation of European Manufacturing SMEs. Phase IV of the program focused on DIHs and on highly innovative technologies like Digital Twins and AI. In particular, the AI REGIO Innovation Action developed a virtuous alliance between Regions, DIHs, AI solution providers and Manufacturing SMEs, which is materialised by a new methodology for DIHs service portfolio and customer journey analysis, an AI4EU -oriented toolkit of Data and AI resources, a network of Didactic Factories and their TEchnology and REgulatory SAndboxes (TERESA) and an ecosystem of SME-driven experiments and their Digital Transformation pathways.
Time came to align these important outcomes to the evolution of Manufacturing towards Industry 5.0 the evolution of cloud AI Technologies to AI-at-the-Edge, the evolution of H2020 to Horizon and Digital Europe programmes e.g. to EDIH, Data Spaces and AI TEFs (Testing and Experimentation Facilities) for Manufacturing. Some of the AI REGIO I4MS Phase IV motivations have now evolved: it was time for AI REDGIO 5.0 to keep momentum of AI technologies adoption in Manufacturing SMEs.
AI REDGIO 5.0 aimed at renovating and extending the H2020 I4MS AI REGIO alliance between Vanguard EU regions and DIHs for a competitive AI-at-the-Edge Digital Transformation of Industry 5.0 Manufacturing SMEs. AI REGIO outcomes such as methods and tools for DIHs governance and cross-DIH collaboration, Data Space and AI for Manufacturing toolkit, Didactic Factories network and TERESA facilities, SME-driven experimentations in 14 Vanguard regions would be:
i) extended to the I5.0 principles
ii) enabled by the newest trusted technologies along the edge-to-cloud continuum
iii) supported by European open-source Hardware and Software reference implementations, preserving EU values and ethical principles
iv) interconnected with the EDIH network in DEP as well as with the AI TEF nodes and the Data Spaces deployment program.
AI-DAPT aims to deliver an innovative and impactful research agenda that will provide tangible benefits to a variety of stakeholders that struggle with making AI services. Seeking to reinstate the pure data-related work in its rightful place, and reinforcing the generalizability, reliability, trustworthiness, and fairness of Al solutions, AI-DAPT vision relies on the implementation of an AIOps framework to support and automate AI pipelines that continuously learn and adapt based on their context. It enables proper purposing, collection, documentation, (bias) valuation, annotation, curation and synthetic generation of data, while keeping humans-in-the-loop across five axis: (i) Data Design for AI, (ii) Data Nurturing for AI, (iii) Data Generation for AI, (iv) Model Delivery for AI, (v) Data-Model Optimization for AI.