Digital Twins
Digital twins are digital replicas of physical objects or systems used for real-time monitoring, analysis, and optimization.
Classification
- ComplexityMedium
- Impact areaTechnical
- Decision typeArchitectural
- Organizational maturityIntermediate
Technical context
Principles & goals
Use cases & scenarios
Compromises
- Technological Stagnation
- Data Integrity Issues
- Security Risks
- Ensure data integrity.
- Regularly update the model.
- Engagement of all stakeholders.
I/O & resources
- Real-time Sensor Data
- Historical Data Analyses
- Customer Feedback
- Optimized Processes
- Predictive Outcomes
- Reports on Improvements
Description
Digital twins enable the simulation and analysis of physical systems in a digital environment. They assist companies in optimizing processes, generating predictions, and improving decision-making. By utilizing real-time data, they provide deeper insights and help reduce costs.
✔Benefits
- Improved Efficiency
- Cost Reduction
- Better Decision Making
✖Limitations
- High Implementation Costs
- Data Quality Requirements
- Maintenance Needs
Trade-offs
Metrics
- ROI (Return on Investment)
Measures the profitability of investments in digital twins.
- Revenue Increase
Captures revenue increases achieved through digital twins.
- Number of Maintenance Actions
Counts the number of maintenance actions performed based on digital twins.
Examples & implementations
Automotive Industry - Production Optimization
A leading automotive manufacturer uses digital twins for real-time monitoring of their production facilities.
Aerospace - Maintenance Strategies
An aerospace company utilizes digital twins for predictive maintenance of its aircraft.
Manufacturing - Product Testing
In manufacturing, digital twins are used to make product testing more efficient.
Implementation steps
Identifying relevant processes.
Collecting necessary data.
Developing the digital twin.
⚠️ Technical debt & bottlenecks
Technical debt
- Outdated technology.
- Lack of documentation.
- Insufficient system integrations.
Known bottlenecks
Misuse examples
- Overly simplifying the data.
- Reliance on insufficient data sources.
- Misusing the model without real use cases.
Typical traps
- Insufficient validation of data.
- Lack of regular updates.
- Unclear objectives.
Required skills
Architectural drivers
Constraints
- • Compliance with personal data and privacy regulations
- • Technological infrastructure requirements
- • Budget constraints