Our Project
MISSION
In an era where data is king, the DMaaST (Innovative modelling and assessment capabilities through MaaS for Manufacturing Ecosystem Resiliency) project stands at the forefront of innovation, aiming to revolutionise the manufacturing sector by enhancing resilience, sustainability, and adaptability.
As global supply chains face unprecedented challenges, DMaaST offers a groundbreaking solution that integrates cutting-edge technologies to enhance the manufacturing ecosystem’s resiliency and capability of self-adaptation in response of external events and ensure manufacturing industries can thrive.
OBJECTIVES
- Anticipate and enhance the understanding of unforeseen events’ impacts in the production
- Boost the transition to MaaS
- Implement a sustainable, circular and human centred approach within assessment
- Enhance value-chains’ responsiveness and resiliency to varying events
- Enable trusted and secure cross-organisation real-time data integration
- Promote the MaaS, and project’s modules and platform within the manufacturing industries
Methodology
Our mission is achieved through a Smart Manufacturing Assessment Platform (SMAP) structured into four distinct layers, each strategically designed to tackle key challenges within the manufacturing ecosystem:
Sustainability Assessment Module (SAM)
In order to achieve an easy-to-use platform, a human-centred approach will be used, aiming at developing interfaces that display information clearly and concisely using diagrams, graphics, and other appropriate visual aids. In addition to displaying information, the interfaces will allow the user to select targets for optimisation, monitor system performance, and receive alerts when necessary.
Multi-Objective Distributed Decision Support System (MO-DDSS)
Our self-adaptable MO-DDSS empowers industries to respond efficiently to potential threats while optimising production across various objectives including logistics, customer satisfaction, and business performance. This layer guarantees resilient outputs even under suboptimal conditions.
Cognitive Digital Twins (CDT)
We employ CDT to model the manufacturing ecosystems of disrupted sectors such as aeronautics and electronics. This innovative concept enhances the reliability of manufacturing processes, allowing industries to better anticipate and mitigate the impacts of unforeseen events.
Decentralised Knowledge Graph (DKG)
DMaaST utilises DKG to facilitate the interoperability, exploitation, and understanding of data across organisations. By implementing standard-based ontologies, we ensure seamless data integration and secure, real-time data sharing via blockchain-based data pipelines.
