Innovative Modelling and Assessment Capabilities Through Maas for Manufacturing Ecosystem Resiliency
DMaaST is developing an innovative Smart Manufacturing Platform to strengthen manufacturing ecosystem resilience through decentralised data interoperability, Cognitive Digital Twins, distributed decision support, and sustainability assessment.
12
PARTNERS
10
COUNTRIES
2
DEMO CASES
€5.86M
BUDGET
What is DMaaST?
DMaaST aims to reinforce manufacturing value networks’ resilience and support the transition towards Manufacturing as a Service. The project enables cross-organisation data integration, anticipates unforeseen disruptions and supports better industrial decision-making across complex value chains.
Core technology pillars
The DMaaST Project aims to tackle various challenges hindering the evolution of the manufacturing industry into a more flexible and adaptable ecosystem
Human Centered Interfaces
A human-centred approach enables stakeholders to optimise, monitor performance and receive alerts through a user-friendly platform.
Circularity & Sustainability
DMaaST includes a module that enhances sustainability, circularity and traceability, aligned with the Digital Product Passport.
Multi-Objective Distributed Decision Support System
A self-adaptable MO-DDSS enables industries to respond to threats while optimising production and maintaining resilient performance.
Cognitive Digital Twins
We use CDT to model disrupted manufacturing ecosystems, improving reliability and helping industries anticipate and mitigate unforeseen events.
Decentralised Knowledge Graph
DMaaST uses DKG and ontologies to enable interoperable, secure and real-time data sharing across organisations.
Ensuring the Path to TRL6
DMaaST will enable a seamless and efficient transition from Technology Readiness Level (TRL) 3 to TRL6, within 4 years, using a 4-step process:
- Consolidate TRL3. Proof of concept consolidation, team alignment, data scrapping, specifications definition, technical planning.
- Reach and consolidate TRL4. Main development of the innovations pillars.
- Reach and consolidate TRL5. Adapt the technologies to the use-cases scenarios and validation.
- Reach and consolidate TRL6. Technolgies Demonstration on real scenarios.
Real-world industrial use cases
Aerospace – JPB Système
Leading French aerospace manufacturer specialised in innovative locking solutions for critical aircraft applications, supplying major OEMs and Tier-1 companies worldwide.
Electronics – Kamstrup
Global technology company specialised in intelligent metering solutions, with advanced manufacturing capabilities and a strong focus on digitalisation and sustainability.
DMaaST will revolutionise the manufacturing industry by enabling reliable cross-organisation communication, enhancing the value chain’s responsiveness to unforeseen events and optimising production planning.
Given current geopolitical tensions, climate change, and COVID-19, it is increasingly apparent that manufacturers need a dynamic system to understand the responsiveness of their manufacturing ecosystems.
I believe this project is very important for the advancement of sustainable and efficient manufacturing by introducing innovative technology and enhancing industry resilience responsibly.
DMaaST aims to enhance manufacturing ecosystem resilience and adaptability by utilising OriginTrail Decentralised Knowledge Graph and Knowledge Assets to encapsulate vital product, process, facility, and expertise data.
A lack of reliable communication within the manufacturing sector impedes both incident prevention and responsiveness to unexpected events. Using OriginTrail DKG, holds the promise of establishing a trusted knowledge foundation.
At JPB Système, we lack an overall production vision, which hinders our ability to react quickly to external conditions. DMaaST will help us better understand our global system and make informed decisions to reduce stock.
Both internally and externally, DMaaST is all about communication and improving the value chain’s integration and making sure all partners and potential stakeholders can benefits from the results of the project.
To improve decision support and efficiency, we develop methodologies and algorithms as well as create cognitive Digital Twins that incorporate a double level perspective including manufacturing services and value chain stages.
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