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- Ongoing calibration is neglected post-launch, causing model drift and outdated predictions.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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