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AI USE CASE

Digital Twin Simulation Platform

Mirror physical assets in software to simulate, predict, and optimise operations before changes happen.

Typical budget
€150K–€600K
Time to value
20 weeks
Effort
24–72 weeks
Monthly ongoing
€8K–€40K
Minimum data maturity
advanced
Technical prerequisite
ml team
Industries
Manufacturing, Logistics, Cross-industry
AI type
optimization

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  • Ongoing calibration is neglected post-launch, causing model drift and outdated predictions.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

This use case is part of a larger Data & AI catalog built from 50+ enterprise transformation programs. Take the free diagnostic to see how it ranks against your specific context.