Periodic Reporting for period 1 - Tec4MaaSEs (Technologies for Manufacturing as a Service Ecosystems)
Período documentado: 2024-01-01 hasta 2025-06-30
Tec4MaaSEs methodological approach builds upon the Smart Factory Web workflow, adopting 3 steps:
(i) The registration and integration of provided resources, including product assets and capabilities, i.e. what product the assets produce and how they produce, process or transform.
(ii) The encapsulation and selection of the necessary resources to compose the requested service.
(iii) The monitoring, reviewing, and enabling operations management of the composed service (and its selected resources).
The project considers two main actors, Resource Provider and Service Consumer, and the Platform Domain, which is acting as a service broker that mediates a production service between service consumers and service providers.
Steps (i) and (ii) require research and innovation in two areas:
1.Interconnected digital twins supporting reconfigurable value networks for MaaS to address interoperability and standardization challenges and automatically detecting and publishing changes/events.
2. A governance framework for resilient and event-driven MaaS ecosystems, introducing a holistic approach for DT governance in value networks which is addressed from three inter-related views: Sustainability, business governance, data and models governance view
Steps (ii) and (iii) require research in two more areas.
3. Robust, resource-aware optimisation methods, offering a suite for real-time service composition that will not myopically look at the service level but instead will adopt a multi-objective approach, while demonstrating technical robustness of the associated models
4. Explainable analytics for resilient and self-adaptable value networks identify which components of the AI system have weighed in, in terms of optimality for the offered MaaS.
(i) The registration and integration of provided resources, including product assets and capabilities, i.e. what product the assets produce and how they produce, process or transform.
(ii) The encapsulation and selection of the necessary resources to compose the requested service.
(iii) The monitoring, reviewing, and enabling operations management of the composed service (and its selected resources).
The project considers two main actors, Resource Provider and Service Consumer, and the Platform Domain, which is acting as a service broker that mediates a production service between service consumers and service providers.
Steps (i) and (ii) require research and innovation in two areas:
1.Interconnected digital twins supporting reconfigurable value networks for MaaS to address interoperability and standardization challenges and automatically detecting and publishing changes/events.
2. A governance framework for resilient and event-driven MaaS ecosystems, introducing a holistic approach for DT governance in value networks which is addressed from three inter-related views: Sustainability, business governance, data and models governance view
Steps (ii) and (iii) require research in two more areas.
3. Robust, resource-aware optimisation methods, offering a suite for real-time service composition that will not myopically look at the service level but instead will adopt a multi-objective approach, while demonstrating technical robustness of the associated models
4. Explainable analytics for resilient and self-adaptable value networks identify which components of the AI system have weighed in, in terms of optimality for the offered MaaS.
Substantial progress can be reported for O1: establish a guiding framework supporting MaaS, whereas the progress in all other objective is ongoing but not yet consolidated.
Tec4MaaSEs currently tries to
• Clarify data sources and the interplay of factory level with platform level
• Shape a generic yet detailed and replicable architecture
• Devise meaningful and innovative decision support tools (analytics, optimisation)
• Highlight the advances compared to existing MaaS platforms and ongoing MaaS research
• Examine or even develop disruptive business models
Tec4MaaSEs currently tries to
• Clarify data sources and the interplay of factory level with platform level
• Shape a generic yet detailed and replicable architecture
• Devise meaningful and innovative decision support tools (analytics, optimisation)
• Highlight the advances compared to existing MaaS platforms and ongoing MaaS research
• Examine or even develop disruptive business models
There are six areas where the project ambition lies:
A1. Actionable models beyond resilient value networks
A2. Robust, resource-aware optimisation methods
A3. Explainable analytics for resilient and self-adaptable value networks
A4. Interconnected DTs supporting reconfigurable value networks for MaaS
A5. Governance framework for resilient and event-driven MaaS ecosystems
A6. Data sharing and data sovereignty among different industrial partners
In parallel, the following research and innovation gaps that have been identified:
• A neat yet comprehensive set of user and functional requirements [WP2]
• A classification of analytics/AI tools for MaaS and the data required to use them [WP3]
• An elaboratde, fair and a multi-objective resource allocation scheme [WP3]
• Semantics and ontologies supporting all the above [WP2]
• Convincing empirical evidence with replicable/transferrable `lessons learnt’ [WP4], in support of coherent and applicable business models [WP5]
For example, the first challenge is already addressed by the requirements collection phase of T4M and it is well documented in the associated deliverable. At the same time, this work captures our first ambition item as it designates actionable models of operation and resilient collaboration within a MaaS ecosystem.
A1. Actionable models beyond resilient value networks
A2. Robust, resource-aware optimisation methods
A3. Explainable analytics for resilient and self-adaptable value networks
A4. Interconnected DTs supporting reconfigurable value networks for MaaS
A5. Governance framework for resilient and event-driven MaaS ecosystems
A6. Data sharing and data sovereignty among different industrial partners
In parallel, the following research and innovation gaps that have been identified:
• A neat yet comprehensive set of user and functional requirements [WP2]
• A classification of analytics/AI tools for MaaS and the data required to use them [WP3]
• An elaboratde, fair and a multi-objective resource allocation scheme [WP3]
• Semantics and ontologies supporting all the above [WP2]
• Convincing empirical evidence with replicable/transferrable `lessons learnt’ [WP4], in support of coherent and applicable business models [WP5]
For example, the first challenge is already addressed by the requirements collection phase of T4M and it is well documented in the associated deliverable. At the same time, this work captures our first ambition item as it designates actionable models of operation and resilient collaboration within a MaaS ecosystem.