AI USE CASE
Digital Twin Simulation Platform
Mirror physical assets in software to simulate, predict, and optimise operations before changes happen.
What it is
A digital twin platform combines ML models with physics-based simulation to create real-time virtual replicas of machinery, facilities, or supply chains. Operations teams can run what-if scenarios to identify inefficiencies and stress-test changes without risk. Typical outcomes include 15–30% reduction in unplanned downtime, 10–20% improvement in asset utilisation, and faster iteration cycles, from weeks to hours. The platform also enables predictive maintenance by detecting anomalies in sensor data before failures occur.
Data you need
High-frequency sensor telemetry from physical assets, historical operational logs, engineering specifications or CAD models, and ideally real-time IoT data streams.
Required systems
- erp
- data warehouse
Why it works
- Cross-functional ownership between IT, operations engineering, and data science from day one.
- Start with a single high-value asset or process to prove ROI before scaling.
- Establish a continuous data pipeline with automated quality checks before building models.
- Tie simulation outputs directly to operational KPIs that line managers already track.
How this goes wrong
- Poor sensor data quality or gaps in telemetry make the twin diverge from reality, eroding trust in simulations.
- Integration complexity with legacy OT systems stalls deployment for months beyond initial estimates.
- Business stakeholders disengage if the platform is positioned as an IT project rather than an operational decision tool.
- Ongoing calibration is neglected post-launch, causing model drift and outdated predictions.
When NOT to do this
Do not pursue a digital twin platform when the organisation lacks reliable sensor instrumentation on its assets or when operational processes are already poorly documented, the twin will model chaos, not reality.
Vendors to consider
Sources
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