Periodic Reporting for period 2 - RaRe2 (Human-centred Rapid Reconfiguration of Production and Value Chain in Fast Changing Scenarios)
Reporting period: 2024-06-01 to 2025-05-31
Recent years have seen several digital advancements in manufacturing aimed at improving system flexibility through better planning and processes. This has typically focused on enhancing productivity and adapting to midterm customer demands. However, slow adaptation to rapid market changes, crises, and natural disasters is no longer acceptable. The RaRe2 project seeks to introduce smart, sustainable, digitized solutions for rapid, reconfigurable, and crisis-resilient manufacturing systems that can quickly ramp up to full production for new products. It aims to establish a flexible and resilient Holistic Ecosystem Platform by fostering collaboration among European organizations for rapid process chain reconfiguration, enhancing the sector's resilience to market fluctuations, disruptions, legal changes, and crises, including climate-related ones. RaRe2's objectives focus on reconfigurable manufacturing systems, innovative digital solutions, and early-stage certification.
RaRe2 is based on three pillars:
1. Early detection of reconfiguration needs via AI-analyzed data.
2. Smart integration of physical and logical elements to adapt products, processes, and supply chains.
3. Empowering and up-skilling humans for rapid decision-making and workforce adaptation.
Environmental Sustainability is prioritized by incorporating green elements into decision simulations, promoting recycled materials, and focusing on inclusivity and diversity. The project aims to establish a European network capable of rapid reconfiguration, considering social, market, legal, sustainability, and economic factors. Collaboration with a Japanese partner will explore future exploitation beyond Europe, aligning with digital potentials and local standards.
RaRe2 is based on three pillars:
1. Early detection of reconfiguration needs via AI-analyzed data.
2. Smart integration of physical and logical elements to adapt products, processes, and supply chains.
3. Empowering and up-skilling humans for rapid decision-making and workforce adaptation.
Environmental Sustainability is prioritized by incorporating green elements into decision simulations, promoting recycled materials, and focusing on inclusivity and diversity. The project aims to establish a European network capable of rapid reconfiguration, considering social, market, legal, sustainability, and economic factors. Collaboration with a Japanese partner will explore future exploitation beyond Europe, aligning with digital potentials and local standards.
The first phase of the project successfully established a collaborative network, disseminated research findings, created communication channels, conducted an in-depth analysis of existing systems, developed advanced technologies and identified commercialisation paths. This set a solid foundation for subsequent phases and ensured readiness to achieve the project's long-term goals.
In the second phase of the project, technical solutions to support the rapid repurposing, adaptation and scaling up of production lines were investigated and developed. Specific digital technologies, AI methods and cyber-physical systems (CPSs) were employed to improve the adaptability of the systems under consideration. A key achievement was the development of early detection software, which can detect and analyse reconfiguration needs for specific sectors and support decision-makers by providing them with information on the probability of occurrence and impact on their manufacturing system. The developed methods and findings were translated into modern assistance technologies for end users, based on two pillars. For decision-makers, a decision support tool was developed to help them understand the information provided by the system and make optimal choices. Conversely, workers on the shop floor are now supported by a system that identifies possible skill gaps and defines effective training methodologies. In the short term, it provides intelligent assistance based on their skills, right where it is needed most: at the workplace. The current project stage involves developing a digital platform extended by a holistic methodology framework (HMF) to support different use case partners, their processes and human skills while maintaining a high level of cybersecurity. This has involved designing the architecture, developing the core services, interfaces and connectors, integrating the modules, and verifying the first prototypes.
In the second phase of the project, technical solutions to support the rapid repurposing, adaptation and scaling up of production lines were investigated and developed. Specific digital technologies, AI methods and cyber-physical systems (CPSs) were employed to improve the adaptability of the systems under consideration. A key achievement was the development of early detection software, which can detect and analyse reconfiguration needs for specific sectors and support decision-makers by providing them with information on the probability of occurrence and impact on their manufacturing system. The developed methods and findings were translated into modern assistance technologies for end users, based on two pillars. For decision-makers, a decision support tool was developed to help them understand the information provided by the system and make optimal choices. Conversely, workers on the shop floor are now supported by a system that identifies possible skill gaps and defines effective training methodologies. In the short term, it provides intelligent assistance based on their skills, right where it is needed most: at the workplace. The current project stage involves developing a digital platform extended by a holistic methodology framework (HMF) to support different use case partners, their processes and human skills while maintaining a high level of cybersecurity. This has involved designing the architecture, developing the core services, interfaces and connectors, integrating the modules, and verifying the first prototypes.
The second phase of the European research project yielded several validated significant results with potential impactful outcomes. One of the key results was the development of an early detection tool and sector-specific monitoring strategies. These innovations enable manufacturers to gain a time advantage in reacting to disturbances, thereby enhancing their ability to maintain operational continuity and mitigate disruptions effectively.
Additionally, the project focused on creating system-level digital twins for supply chains, intra-logistics, and process chains. These digital twins serve as foundational elements for fast reconfiguration, allowing manufacturers to simulate and optimize changes in real-time. This capability is crucial for adapting quickly to new conditions and maintaining efficiency.
Another important achievement was the emphasis on human-centred assistance and training programs.
The early detection tools and monitoring strategies can significantly reduce the downtime and operational losses associated with unexpected disturbances. The system-level digital twins provide a robust framework for rapid reconfiguration, enhancing the flexibility and responsiveness of manufacturing systems. The human-centred assistance and training programs contribute to a more resilient workforce, capable of adapting to new challenges. Lastly, the emphasis on data-driven decisions promotes a culture of transparency and informed decision-making, which can lead to higher efficiency and greater acceptance of necessary changes.
Overall, the second phase of the project has produced valid results in all the areas targeted, offering the industry potential long-term benefits.
Additionally, the project focused on creating system-level digital twins for supply chains, intra-logistics, and process chains. These digital twins serve as foundational elements for fast reconfiguration, allowing manufacturers to simulate and optimize changes in real-time. This capability is crucial for adapting quickly to new conditions and maintaining efficiency.
Another important achievement was the emphasis on human-centred assistance and training programs.
The early detection tools and monitoring strategies can significantly reduce the downtime and operational losses associated with unexpected disturbances. The system-level digital twins provide a robust framework for rapid reconfiguration, enhancing the flexibility and responsiveness of manufacturing systems. The human-centred assistance and training programs contribute to a more resilient workforce, capable of adapting to new challenges. Lastly, the emphasis on data-driven decisions promotes a culture of transparency and informed decision-making, which can lead to higher efficiency and greater acceptance of necessary changes.
Overall, the second phase of the project has produced valid results in all the areas targeted, offering the industry potential long-term benefits.