RAASCEMAN aims at developing and testing a decentralized capability-based Manufacturing-as-a-Service-network (MaaS-network) creating resilience for European supply chains while enabling the human to make informed decisions in case of medium- and shortterm disruptions. The technology developed in RAASCEMAN will be implemented in two challenging Industrial Use-Cases from the domains of automotive and bike manufacturing validating technical and economic viability of MaaS. Together with partners, the focus on the following objectives:

• Actionable propositions for adaptation—quantify probability and impact of unforeseen events and propose mitigation alternatives.
• Actionable propositions for adaptation—quantify probability and impact of unforeseen events and propose mitigation alternatives.
• Trust in MaaS networks—supplier auditing and plausibility checks using digital models (e.g., Digital Twins) to increase reliability and transparency.
• Dynamic factory‑level planning & scheduling—AI‑driven planning/scheduling with Human‑in‑the‑Loop for fast adaptation during execution.
• Dynamic assembly/disassembly at field level—machine‑level reaction using methods such as Model Predictive Control and human‑centred instructions.
• Digital twins & standards‑based data exchange—create infrastructure for intra‑/inter‑factory exchange aligned with European values and standards.
• Validation of technical and economic viability of MaaS—including the advantage of circularity via remanufactured goods as local procurement.

Recent disruptions have exposed the fragility of rigid supply chains. RAASCEMAN targets short‑ and medium‑term unforeseen events by equipping manufacturers with tools to: (i) replan production from supply‑chain data, (ii) switch suppliers within a capability‑based MaaS network, and (iii) leverage remanufacturing as a circular, local procurement option. The overarching aim is to improve resilience while enabling human decision‑makers to make informed, explainable choices.

WP 1: Requirements and Architecture (Lead: INTRA):
Capture stakeholder requirements across supply‑chain and shop‑floor levels; describe demonstrators and KPIs; define software and data architecture aligned with relevant European standards.

WP 2: Data models & data exchange infrastructure (Lead: CEA):
Define capability/service/skill models and Digital Twins; implement Asset Administration Shell (AAS)‑based information bus, OPC UA interfaces and trusted connectors for cross‑company exchange; prepare Digital Product Passport integration.

WP 3: Supply‑Chain‑Level Support Tools (Lead: LMS):
Using FL methods, a specialized form of machine learning, to gather quality data and develop a global model, ensuring data confidentiality through decentralized architecture.

WP 4: Factory‑Level Support Tools (Lead: DFKI):
Deliver planning & scheduling with orchestration to handle short‑term disruptions; integrate Digital Product Passport for monitoring; implement offer generation based on feasibility, time, cost, and energy; provide a standardized skill interface for dynamic execution at field level (Task 4.3 led by RPTU).

WP 5: Use‑Cases (Lead: Flanders Make):
T5.1 Bike (ÅSKA): MaaS and remanufacturing for medium‑term resilience. T5.2 Automotive (Continental Brandýs): dynamic replanning and circularity for short‑term events. T5.3 Interconnected Pilot Line (DFKI‑led, with FM, CTU, RPTU): EU‑wide pilot line network for integration/validation. T5.4 Evaluation (CTU): KPI‑based assessment and onboarding guide for MaaS participation.

WP 6: Exploitation, Dissemination & Communication (Lead: CTU):
T6.1 Communication & dissemination plan and activities; T6.2 community‑based actions; T6.3 exploitation strategy, IP management and business models (INTRA); T6.4 standardization efforts (DFKI).

WP 7: Project Management (Lead: DFKI):
Overall coordination, quality/risk management, timely delivery of milestones and deliverables..