Periodic Reporting for period 1 - DMaaST (Innovative modelling and assessment capabilities through MaaS for Manufacturing Ecosystem resiliency)
Berichtszeitraum: 2024-05-01 bis 2025-10-31
DMaaST emerged in response to a global landscape marked by persistent supply chain disruptions, unpredictable market demands, and external constraints that challenge the resilience and efficiency of manufacturing ecosystems. DMaaST aims to bridge these gaps by developing innovative solutions that transform MaaS into an accessible production model for manufacturers. To achieve this, the project focuses on designing a comprehensive MaaS platform structured around four technological layers:
1) Data Layer, DMaaST enhances data interoperability, exploitation, and understanding by “mapping” all available information through a Decentralized Knowledge Graph that leverages standard-based ontologies. This ensures seamless integration and cross-organizations data integration, secured by blockchain.
2) Double-Level Digital Twins (DTs), DMaaST aims to model the manufacturing ecosystem of two use cases from disrupted sectors (aeronautics & electronics), implementing cognitive DT’s innovative concept for increased reliability. These twins improve the ability of industries to anticipate unforeseen events, assess potential risks, and enhance system reliability.
3) Distributed Multi Objective Decision Support System, DMaaST aims to deliver a self-adaptive decision-making framework capable of responding dynamically to threats while optimizing industrial production across multiple objectives, (e.g. production, logistics, customer satisfaction, stock management, and business goals). It will also incorporateresilience to failures, ensuring reliable outputs even under non-optimal conditions.
4) Circularity Assessment Module, DMaaST integrates advanced sustainability and circularity analysis techniques into a dedicated assessment module. This module aligns with EU Digital Product Passport (EU-DPP) standards and incorporates traceability opportunities within the pilot use cases, promoting sustainable and circular production practices.
The project also adopts a human-centered approach, ensuring that the resulting platform is both usable and valuable for workers, thereby enhancing its practical implementation. Through this holistic approach, DMaaST not only addresses the immediate challenges of the manufacturing sector but also contributes to creating a future-ready, resilient, and sustainable production ecosystem.
1) Data Layer, DMaaST enhances data interoperability, exploitation, and understanding by “mapping” all available information through a Decentralized Knowledge Graph that leverages standard-based ontologies. This ensures seamless integration and cross-organizations data integration, secured by blockchain.
2) Double-Level Digital Twins (DTs), DMaaST aims to model the manufacturing ecosystem of two use cases from disrupted sectors (aeronautics & electronics), implementing cognitive DT’s innovative concept for increased reliability. These twins improve the ability of industries to anticipate unforeseen events, assess potential risks, and enhance system reliability.
3) Distributed Multi Objective Decision Support System, DMaaST aims to deliver a self-adaptive decision-making framework capable of responding dynamically to threats while optimizing industrial production across multiple objectives, (e.g. production, logistics, customer satisfaction, stock management, and business goals). It will also incorporateresilience to failures, ensuring reliable outputs even under non-optimal conditions.
4) Circularity Assessment Module, DMaaST integrates advanced sustainability and circularity analysis techniques into a dedicated assessment module. This module aligns with EU Digital Product Passport (EU-DPP) standards and incorporates traceability opportunities within the pilot use cases, promoting sustainable and circular production practices.
The project also adopts a human-centered approach, ensuring that the resulting platform is both usable and valuable for workers, thereby enhancing its practical implementation. Through this holistic approach, DMaaST not only addresses the immediate challenges of the manufacturing sector but also contributes to creating a future-ready, resilient, and sustainable production ecosystem.
During the first 18 months, the DMaaST project established the technical foundations for smart, resilient, and self-adaptive manufacturing networks. Activities focused on the specification, design, and initial implementation of core technologies:
1. During this reporting period, the project established the technical foundations of the DMaaST data and intelligence stack. Progress was made in the definition and implementation of the data layer, including decentralized knowledge graphs and ontologies enabling secure, trusted, and real-time data integration across organisational boundaries. These elements were built and/or selected for providing the interoperability required for the rest of the layers/modules within DMaaST. Work is in progress towards building the data streaming services for these components and the main technologies to be used habe been selected.
2. Initial frameworks were defined to connect manufacturing service–level and value-chain–level DTs. At production-line level, detailed models of machinery, processes, and logistics were developed, capturing operational constraints and interdependencies to support realistic simulation of shop-floor behaviour. In parallel, value-chain digital twins for the aeronautics and electronics sectors were specified, modelling inter-company interactions and disruption propagation. The architectural definition and interfaces between both levels were finalised.
3.Learning-based optimisation approaches were developed for dynamic production scheduling, focusing on the Flexible Job Shop Scheduling Problem under realistic disturbances. A reinforcement learning–based scheduling framework was designed and implemented, demonstrating stable training and superior performance compared to heuristic approaches while remaining suitable for real-time use. In parallel, the foundations of the multi-objective optimisation layer were established, formally defining use-case problems, resources, constraints, and conflicting objectives. A modular MO-DDSS architecture was designed to support Pareto-optimal solutions, human-in-the-loop decision-making, and adaptation to disruptive scenarios.
4. The project advanced a comprehensive sustainability assessment of the industrial value chains, covering environmental, economic, and social dimensions. A tailored life-cycle sustainability methodology was defined, supported by detailed value-chain mapping and clear system boundaries. Structured data-collection templates were developed to capture material, energy, cost, and social data at unit level. Stakeholder categories and social impact indicators were identified and validated, resulting in a robust and aligned assessment framework ready for impact analysis and hotspot identification.
5. Human-centred design was strengthened through a systematic analysis of user needs, digital readiness, and training requirements. A Digital Maturity Questionnaire revealed differences between managerial and shop-floor roles and key adoption barriers. Based on these insights, a tiered training structure was defined, and expected learning outcomes were specified. Close collaboration with industrial partners and technology developers ensured alignment between training content, tool complexity, and real operational workflows.
1. During this reporting period, the project established the technical foundations of the DMaaST data and intelligence stack. Progress was made in the definition and implementation of the data layer, including decentralized knowledge graphs and ontologies enabling secure, trusted, and real-time data integration across organisational boundaries. These elements were built and/or selected for providing the interoperability required for the rest of the layers/modules within DMaaST. Work is in progress towards building the data streaming services for these components and the main technologies to be used habe been selected.
2. Initial frameworks were defined to connect manufacturing service–level and value-chain–level DTs. At production-line level, detailed models of machinery, processes, and logistics were developed, capturing operational constraints and interdependencies to support realistic simulation of shop-floor behaviour. In parallel, value-chain digital twins for the aeronautics and electronics sectors were specified, modelling inter-company interactions and disruption propagation. The architectural definition and interfaces between both levels were finalised.
3.Learning-based optimisation approaches were developed for dynamic production scheduling, focusing on the Flexible Job Shop Scheduling Problem under realistic disturbances. A reinforcement learning–based scheduling framework was designed and implemented, demonstrating stable training and superior performance compared to heuristic approaches while remaining suitable for real-time use. In parallel, the foundations of the multi-objective optimisation layer were established, formally defining use-case problems, resources, constraints, and conflicting objectives. A modular MO-DDSS architecture was designed to support Pareto-optimal solutions, human-in-the-loop decision-making, and adaptation to disruptive scenarios.
4. The project advanced a comprehensive sustainability assessment of the industrial value chains, covering environmental, economic, and social dimensions. A tailored life-cycle sustainability methodology was defined, supported by detailed value-chain mapping and clear system boundaries. Structured data-collection templates were developed to capture material, energy, cost, and social data at unit level. Stakeholder categories and social impact indicators were identified and validated, resulting in a robust and aligned assessment framework ready for impact analysis and hotspot identification.
5. Human-centred design was strengthened through a systematic analysis of user needs, digital readiness, and training requirements. A Digital Maturity Questionnaire revealed differences between managerial and shop-floor roles and key adoption barriers. Based on these insights, a tiered training structure was defined, and expected learning outcomes were specified. Close collaboration with industrial partners and technology developers ensured alignment between training content, tool complexity, and real operational workflows.
At mid-point of the first development stage, the DMaaST project has achieved substantial progress in establishing its core innovation pillars. Key advances include the early implementation of two-level cognitive digital twins at production-line and value-chain levels, enabling advanced modelling, simulation, and disruption analysis across complex manufacturing networks. In parallel, the foundations of a learning-based Multi-Objective Distributed Decision Support System were laid, supported by secure and interoperable data exchange mechanisms. Together with decentralized knowledge graphs, early sustainability, circularity, and traceability modules, and the emerging Smart Manufacturing Assessment Platform architecture, these developments enable real-time interaction, distributed optimisation, and self-adaptive behaviours aligned with the Manufacturing-as-a-Service paradigm.
In parallel, the project strengthened the conditions for future uptake and impact through coordinated dissemination, stakeholder engagement, and exploitation activities. Visibility and positioning within the European manufacturing ecosystem were consolidated through targeted events, clustering actions, and direct interaction with industrial, research, and policy stakeholders. Exploitation pathways were systematically structured by refining Key Exploitable Results, clarifying ownership, maturity, and target markets, and aligning them with evidence-based IPR strategies informed by patent landscape and freedom-to-operate analyses. Early work on replicability and MaaS adoption identified key barriers related to scalability, data governance, organisational readiness, and market awareness, highlighting the need for further demonstration, localisation, regulatory alignment, and business model validation to support successful commercialisation and large-scale adoption
In parallel, the project strengthened the conditions for future uptake and impact through coordinated dissemination, stakeholder engagement, and exploitation activities. Visibility and positioning within the European manufacturing ecosystem were consolidated through targeted events, clustering actions, and direct interaction with industrial, research, and policy stakeholders. Exploitation pathways were systematically structured by refining Key Exploitable Results, clarifying ownership, maturity, and target markets, and aligning them with evidence-based IPR strategies informed by patent landscape and freedom-to-operate analyses. Early work on replicability and MaaS adoption identified key barriers related to scalability, data governance, organisational readiness, and market awareness, highlighting the need for further demonstration, localisation, regulatory alignment, and business model validation to support successful commercialisation and large-scale adoption