Digital transformation has become more than a buzzword in diagnostics and laboratory automation — it’s a survival strategy. At HSE•AG, we’ve embraced this shift with a modular digital engineering approach including Digital Twin.
But what is a digital twin in this context?
At HSE•AG, we define it as a dynamic virtual representation of a diagnostic or laboratory system that combines simulation, model-based engineering, real-world experimental data and validation, and workflow modeling.
Key Benefits
The key benefits of our digital twin approach are:
The Digital Twin Architecture
Our digital twin builds on five core pillars:
1. Simulation
Simulation forms one behavioral core pillar of the digital twin. In collaboration with CADFEM experts, we use high-fidelity tools to create virtual models of thermal, fluidic, and structural behavior.
→ Value: Enables early-stage design optimization and virtual prototyping with significantly less need for physical hardware and testing. Fast design adaptions and iterations.
2. Modeling (MATLAB, Enterprise Architect)
Complementing simulation, simplified physical models help predict performance, adjust parameters, and define system behavior.
→ Value: Supports outcome prediction, correction strategies, and instrument calibration with minimal resource investment.
3. Model-Based Systems Engineering (MBSE)
MBSE provides a structural and logical backbone of the system architecture. It captures interfaces, functional breakdowns, and cross-discipline dependencies using tools like Enterprise Architect.
→ Value: Delivers traceability, interface management, impact assessment, and systematic risk mitigation across the lifecycle.
4. Experiments & Sensing
Real-world data doesn’t just validate the digital twin — it keeps it alive. Sensors feed performance data back into the model for live calibration and accuracy.
→ Value: Ensures real-time feedback, enables model validation, and improves predictive performance.
5. Workflow Modeling
From sample loading to result generation, every operational step is digitally mapped. Workflow modeling helps us simulate full laboratory processes, detect inefficiencies, and automate intelligently.
→ Value: Drives quality reproducibility, timing optimization, and efficient material flow, which are all critical in regulated diagnostic workflows.
Together, these five pillars provide a flexible, scalable foundation for building robust digital engineering tailored to real-world diagnostic systems. Whether optimizing throughput, verifying designs, or syncing cross-functional teams, each module adds precision and power to the engineering process.
Results From a Real-World Client Project
In one of our recent diagnostic platform developments, we applied this approach to:
Want to know more?
Contact Fabian Eckermann and discover how we can help you innovate faster and smarter.