RAASCEMAN

Resilient and Adaptive Supply Chains for Capability-based Manufacturing as a Service Networks

About the project

Supply chain disruptions can stop production across entire networks of factories. RAASCEMAN aims to mitigate and prevent these disruptions and to help manufacturers and suppliers adapt quickly to change. It does this within the Manufacturing-as-a-Service (MaaS) paradigm, where factories offer their production capabilities as services.

The project connects factories into networks using digital twins and AI. It is developing:

  • a tool that quantifies the likelihood and impact of unexpected production events;
  • a recommendation engine that generates supply chains dynamically;
  • an audit tool that matches service providers' capabilities to new product requests;
  • a digital platform for exchanging information within factories and across supply chains.

The results are validated in pilots in the automotive and bike industries and on interconnected pilot lines.

Key facts
Programme Horizon Europe (grant agreement 101138782)
Duration 3 years
Budget €4.6 million
Coordinator DFKI, Germany
Consortium 9 partners: universities, research and technology organisations, and industry, including Flanders Make

My contributions

I lead a large part of the technical work in the Flanders Make pilot, from definition and implementation to deployment and testing:

  • Standardised digital twins of production assets using the Asset Administration Shell (AAS), designed to be reused across projects.
  • Deployment and integration of the project’s interoperability middleware in a real production environment.
  • Tools for testing and demonstration that allow the full solution to be validated continuously under realistic production conditions.

Related publication: Accelerating the Adoption of Asset Administration Shells through AI Agents (INTELLI 2026).

Technologies: Asset Administration Shell (IDTA templates), industrial middleware, digital twins, edge deployment.

Camilo Velázquez-Rodríguez
Camilo Velázquez-Rodríguez
Research Engineer

Research Engineer turning industrial data and AI into deployed solutions for manufacturing, with expertise in digital twins (AAS), data engineering and machine learning.