Analytics & machine learning that understand how your systems actually work
Go beyond dashboards and black-box AI. Use context-aware analytics and machine learning—built on intelligent digital twins—to detect issues early, predict outcomes, and recommend actions that hold up in the real world.
Definition
What are Analytics & ML in an intelligent digital twin platform?
Analytics & machine learning transform twin-ready operational data into insight, prediction, and decision support. Instead of analyzing isolated signals, models learn how assets, processes, and systems behave together—so anomalies, risks, and opportunities are identified in context and projected forward, not just reported after the fact.
Why generic BI and ML struggle in operations
Most analytics tools were designed for reporting—not for running complex, dynamic systems. Common limitations include:
- Metrics analyzed without asset or process context
- Models trained on averages that ignore variability
- Alerts that fire too late—or too often
- Predictions that can't explain why something will happen
- Insights that don't translate into action
When analytics don't understand system behavior, teams lose trust. Digital-twin-native analytics close that gap.
From context to confident decisions
1. Learn from context
Models are trained on normalized, contextualized data that reflects real operating conditions.
2. Understand normal behavior
Establish baselines that adapt to load, environment, schedules, and interactions—not static thresholds.
3. Detect early deviation
Identify subtle changes that indicate emerging issues before failures or disruptions occur.
4. Predict what happens next
Forecast risk, performance, demand, or outcomes under current and alternative conditions.
5. Recommend action
Surface insights in a form teams can act on—prioritized, explainable, and tied to impact.
Designed for operational intelligence
Anomaly detection
Detect early signs of degradation or abnormal behavior—without alarm overload.
Prediction & forecasting
Estimate failure risk, throughput, energy demand, or congestion before it materializes.
Multivariate & system-level modeling
Understand interactions between assets, processes, and constraints—not just single signals.
Optimization & scenario analysis
Evaluate trade-offs and test "what-if" scenarios safely using the digital twin.
Simulation-aware learning
Combine ML with simulation to validate insights and explore outcomes beyond historical data.
Built into real operational use cases
Predictive maintenance
Detect degradation early and predict failures with context-aware models.
Learn more →Production optimization
Identify bottlenecks, variability, and improvement opportunities across lines and plants.
Learn more →Energy optimization
Forecast demand, detect inefficiencies, and optimize cost and emissions.
Learn more →Supply chain visibility
Predict congestion, delays, and service risk across nodes and routes.
Learn more →Emergency simulation & safety
Understand cascading impacts and response outcomes under stress scenarios.
Learn more →Analytics teams and operators can trust
- Explainable outputs that show drivers and contributing factors
- Continuous monitoring of model performance and drift
- Validation against real outcomes, not just training data
- Human-in-the-loop workflows for review and override
- Clear ownership of models, data, and decisions
Analytics should earn trust through transparency—not demand it.
When analytics understand the system
- Earlier detection of risk and opportunity
- Fewer false alarms and reactive decisions
- More accurate predictions under real conditions
- Better alignment between insight and action
- Faster adoption of AI across operations
The result is not "more AI"—it's better decisions, made earlier.
FAQ: Analytics & machine learning
Turn data into prediction—and prediction into action
Build analytics and machine learning on a foundation that understands how your systems really behave.