Cyber-Physical Production Systems (CPPS) integrate software, hardware, and communication networks into highly interconnected manufacturing environments. Their engineering challenges extend beyond traditional automation, as evolving requirements, heterogeneous platforms, and frequent reconfigurations demand systems to remain reliable under change. Increasingly, data-driven methodologies complement classical design flows such as Model-Based Systems Engineering (MBSE), supporting the development process from early requirement analysis to run-time adaptation. These methods include both modeling and reasoning techniques – from control-theoretic to AI-assisted approaches – that enhance specification, design, and verification across the CPPS lifecycle. However, ensuring traceability and consistency between models and implementations across abstraction levels remains difficult due to fragmented toolchains and ad-hoc verification practices. At the same time, robustness and reliability cannot be compromised, as failures in CPPS can propagate through supply chains, leading to downtime or safety-critical incidents.

This GI-Dagstuhl Seminar will explore how data-driven methodologies, traceability mechanisms, and verification techniques can be combined to enable trustworthy, evolvable, and resilient CPPS. By connecting experts from automation, software engineering, and data-driven modelling, the seminar aims to identify the necessary trade-offs and establish foundational principles for reliable systems engineering from design to operation.

Creative Commons BY 4.0

Friederike Bruns, Alexander Diedrich, and Sandra Greiner