Digital Twin & Simulation
Test production changes virtually, risk-free.
A digital twin virtually replicates equipment or entire production lines, allowing changes to be simulated before they're implemented for real. Layout changes, new products or disruption scenarios can be tested risk-free, shortening planning time and reducing investment risk.
- Risk-free testing of investment decisions
- Shorter commissioning times for new lines
- Better preparedness for disruptions
planning time for layout changes
commissioning risk
payback period
scenarios tested per project
Estimated from avoided misinvestments and shortened planning and commissioning times.
- 01
Define scope
Select the asset, line or process step for the twin.
- 02
Model construction
Build a physical and data-driven model of reality.
- 03
Live data connection
Feed real-time data from PLC and MES into the twin.
- 04
Scenario simulation
Play through changes and disruptions virtually.
- 05
Operational use
Use the twin for ongoing optimization and training.
Steps
Data sources
Stakeholders
From first data access to production – every step delivers a tangible interim result.
CAD/layout data
Geometric foundation of the facility.
PLC real-time data
Live states to synchronize the twin.
MES process data
Order and throughput data for realistic simulation.
Wear and maintenance data
Reused from predictive maintenance models.
Material flow data
Logistics and buffer information.
Historical disruptions
Basis for realistic disruption scenarios.
Plant management
Makes investment decisions on a validated basis.
Process engineers
Tests changes without disrupting production.
Maintenance manager
Rehearses failure scenarios risk-free in advance.
IT/OT leads
Operates a reusable simulation platform.
Risk-free testing of investment decisions
Shorter commissioning times for new lines
Better preparedness for disruptions
Usable as a training environment for staff
Reusable for continuous process improvement
With a solid data foundation this use case gets faster, cheaper and far more stable.
Real-time synchronization between twin and physical asset
Reusable model components for further lines
Central data foundation for all simulations
Scalable compute power for complex scenarios
Predictive maintenance
Wear models make the simulation more realistic.
Process parameter optimization
Optimized parameters can be virtually pre-validated.
Production scheduling
Plan scenarios are simulated before implementation.