Periodic Reporting for period 1 - AI REDGIO 5.0 (Regions and (E)DIHs alliance for AI-at-the-Edge adoption by European Industry 5.0 Manufacturing SMEs)
Berichtszeitraum: 2023-01-01 bis 2024-06-30
The AI REDGIO 5.0 project, funded by the European Union, involves 43 partners from 18 countries across 15 regions, aiming to drive the digital transformation of Industry 5.0 manufacturing SMEs. Building on the Horizon 2020 I4MS – AI REGIO project, it introduces AI-at-the-Edge solutions to enhance competitiveness.
Rooted in the successes of the I4MS program, AI REDGIO 5.0 leverages the achievements of AI REGIO, fostering collaboration among regions, DIHs, and SMEs. It supports the transition from cloud AI to AI-at-the-Edge, aligning with Industry 5.0 and integrating with initiatives like European Digital Innovation Hubs (EDIHs) and AI Testing and Experimentation Facilities (TEFs).
The project connects two Vanguard Pilots "Efficient and Sustainable Manufacturing (ESM)" and the "AI" Pilot—expanding EU region and DIH participation. It drives AI adoption through SME-Driven Experiments in real industrial settings and Didactic Factory Experiments, focusing on enhancing human-machine interaction. AI REDGIO 5.0 ensures the digital transformation of European SMEs remains competitive and future-focused.
Rooted in the successes of the I4MS program, AI REDGIO 5.0 leverages the achievements of AI REGIO, fostering collaboration among regions, DIHs, and SMEs. It supports the transition from cloud AI to AI-at-the-Edge, aligning with Industry 5.0 and integrating with initiatives like European Digital Innovation Hubs (EDIHs) and AI Testing and Experimentation Facilities (TEFs).
The project connects two Vanguard Pilots "Efficient and Sustainable Manufacturing (ESM)" and the "AI" Pilot—expanding EU region and DIH participation. It drives AI adoption through SME-Driven Experiments in real industrial settings and Didactic Factory Experiments, focusing on enhancing human-machine interaction. AI REDGIO 5.0 ensures the digital transformation of European SMEs remains competitive and future-focused.
The project has advanced its SME-driven experiments, showcasing AI's practical impact on optimizing manufacturing processes in real industrial settings. Significant progress has also been made with Didactic Factory experiments, focusing on rigorous testing of AI technologies within TERESA environments for broader manufacturing application. A key achievement is the first open call, which aimed to add new use cases for AI at the Edge, strengthen the pan-European AI-on-Demand platform, enhance European Digital Innovation Hubs, and conduct test-before-invest experiments in AI Didactic Factories.
In details, each Work Package reach specific goals in the first part of the project.
- The major achievements of WP1 include:
Successful Project Setup: Establishment of the project's framework, ensuring all consortium members are informed about their rights and duties, as well as reporting, financial, and contractual modalities.
Consortium Management Bodies: Election and launch of consortium management bodies.
Collaborative Infrastructure: Implementation of a collaborative infrastructure with templates, instructions, and regulations to ensure project quality and proper handling of project results and findings.
- Major achievements in WP2 include:
Development of Requirements Engineering Methodology: Creation of a harmonized approach for collecting information related to technical work packages, resulting in D2.1 AI REDGIO 5.0 Requirements Engineering Methodology.
Analysis of Scenarios and Requirements: Detailed analysis of the AI REDGIO 5.0 experiments' scenarios and requirements, as well as the applicable European and national legislation and ethical sources. This work is documented in D2.2 Platforms and Experiments AI Scenarios and D2.3 User Requirements Specification for Edge-AI Industry 5.0.
- WP3 key achievements include:
Creation of an EDIH Ecosystem: Establishment of an ecosystem of EDIHs active in AI-at-the-edge for Manufacturing Industry 5.0.
Development of METHODIH: Refinement of the methodology for DIHs, addressing service portfolio analysis, customer journeys, and collaboration corridors among DIHs.
AI-at-the-Edge Experimental Facilities: Materialization of a network of facilities for performing experiments.
Extension of DIHIWARE Platform: Integration of the DIHIWARE platform for Industry 5.0 EDIH networks.
- Achievements in WP4 include:
Distributed Data Processing: Establishment of a distributed architecture facilitating data processing across cloud and edge layers, ensuring real-time data understanding and isolated operation capability.
Database Solutions: Implementation of a database capable of distributing storage and workload across edge architecture tiers, with real-time replication and synchronization across instances.
- Key WP5 achievements include:
Collaborative Intelligence Platform: Definition, specifications, and initial designs of the platform.
AI Pipeline Lifecycle Solution: Development and interoperability with the AIoD platform.
Open Hardware: Initial designs and specifications for open hardware solutions.
- WP6 is the core of experimentation and validation. Achievements include:
Monitoring Methodology: Development of the methodology for monitoring experiments.
Trial Handbook: Completion of Chapter 1 and Chapter 2 by most experiments.
Experiment Deployment: All experiments started on time and are running, with As-IS and To-Be scenarios depicted.
- WP7 focuses on assessing the socio-economic impact and legal/ethical considerations. Achievements include:
Regulatory and Ethical Analysis: Analysis of relevant frameworks and identification of applicable standards and certifications.
Surveys: Design and deployment of surveys to collect AS-IS situation information from project partners.
Exploitation Plan Development: Initial activities for market and competition analysis, SWOT assessment, and strategic positioning.
- WP8 is dedicated to communication, dissemination, and stakeholder engagement; a dedicated section provides all the improvements about the WP.
In details, each Work Package reach specific goals in the first part of the project.
- The major achievements of WP1 include:
Successful Project Setup: Establishment of the project's framework, ensuring all consortium members are informed about their rights and duties, as well as reporting, financial, and contractual modalities.
Consortium Management Bodies: Election and launch of consortium management bodies.
Collaborative Infrastructure: Implementation of a collaborative infrastructure with templates, instructions, and regulations to ensure project quality and proper handling of project results and findings.
- Major achievements in WP2 include:
Development of Requirements Engineering Methodology: Creation of a harmonized approach for collecting information related to technical work packages, resulting in D2.1 AI REDGIO 5.0 Requirements Engineering Methodology.
Analysis of Scenarios and Requirements: Detailed analysis of the AI REDGIO 5.0 experiments' scenarios and requirements, as well as the applicable European and national legislation and ethical sources. This work is documented in D2.2 Platforms and Experiments AI Scenarios and D2.3 User Requirements Specification for Edge-AI Industry 5.0.
- WP3 key achievements include:
Creation of an EDIH Ecosystem: Establishment of an ecosystem of EDIHs active in AI-at-the-edge for Manufacturing Industry 5.0.
Development of METHODIH: Refinement of the methodology for DIHs, addressing service portfolio analysis, customer journeys, and collaboration corridors among DIHs.
AI-at-the-Edge Experimental Facilities: Materialization of a network of facilities for performing experiments.
Extension of DIHIWARE Platform: Integration of the DIHIWARE platform for Industry 5.0 EDIH networks.
- Achievements in WP4 include:
Distributed Data Processing: Establishment of a distributed architecture facilitating data processing across cloud and edge layers, ensuring real-time data understanding and isolated operation capability.
Database Solutions: Implementation of a database capable of distributing storage and workload across edge architecture tiers, with real-time replication and synchronization across instances.
- Key WP5 achievements include:
Collaborative Intelligence Platform: Definition, specifications, and initial designs of the platform.
AI Pipeline Lifecycle Solution: Development and interoperability with the AIoD platform.
Open Hardware: Initial designs and specifications for open hardware solutions.
- WP6 is the core of experimentation and validation. Achievements include:
Monitoring Methodology: Development of the methodology for monitoring experiments.
Trial Handbook: Completion of Chapter 1 and Chapter 2 by most experiments.
Experiment Deployment: All experiments started on time and are running, with As-IS and To-Be scenarios depicted.
- WP7 focuses on assessing the socio-economic impact and legal/ethical considerations. Achievements include:
Regulatory and Ethical Analysis: Analysis of relevant frameworks and identification of applicable standards and certifications.
Surveys: Design and deployment of surveys to collect AS-IS situation information from project partners.
Exploitation Plan Development: Initial activities for market and competition analysis, SWOT assessment, and strategic positioning.
- WP8 is dedicated to communication, dissemination, and stakeholder engagement; a dedicated section provides all the improvements about the WP.
AI REDGIO 5.0 in collaboration with other initiatives, aims to achieve strategic EU goals:
-It lowers barriers for Industry 5.0 stakeholders to adopt trusted AI, increasing digitalization and automation for sustainable manufacturing.
-The project democratizes AI-based automation, enhancing sustainability and fostering flexible, digital value chains through standardized Data Spaces and digital twins.
-AI REDGIO 5.0 improves job attractiveness and safety by promoting human-AI collaboration and identifying new skills essential for the Industry 5.0 workforce.
-It lowers barriers for Industry 5.0 stakeholders to adopt trusted AI, increasing digitalization and automation for sustainable manufacturing.
-The project democratizes AI-based automation, enhancing sustainability and fostering flexible, digital value chains through standardized Data Spaces and digital twins.
-AI REDGIO 5.0 improves job attractiveness and safety by promoting human-AI collaboration and identifying new skills essential for the Industry 5.0 workforce.