Emerging Tech · 5 min
Digital twin: what it is and what it is for
A digital twin is a virtual replica of a real object, system or process, fed by data that reflect its state and behaviour. It allows you to simulate scenarios, monitor conditions and optimise performance without physical intervention, reducing the risks and costs of experimentation.
Key points
- A digital twin is a virtual replica of a real object or process.
- It is fed by data (often IoT) that reflect its state.
- It serves to simulate, monitor and optimise without physical risk.
- It is worth it on complex assets and critical processes with sufficient data.
How it works
The digital twin receives data from its real counterpart — often through IoT sensors — and reconstructs its state in real time or near real time. On this replica you can try out changes, predict behaviours and analyse situations that would be costly or risky to test in reality.
Where it creates value
Digital twins are used above all in industry, but the concept extends to buildings, processes and products.
- Predictive maintenance: anticipating machinery failures.
- Optimisation of processes and plants.
- Simulating scenarios before applying them in reality.
- Designing and testing products in a virtual environment.
When it makes sense to adopt it
A digital twin is worth it when physical intervention is costly, slow or risky, and when sufficient data exist to feed it. It is more relevant for those managing complex assets or critical processes than for the average company. As always, value comes from a concrete use case, not from the technology in itself.
FAQ
Are digital twins only for industry? +
They are most widespread in industry, but the concept also applies to buildings, logistics, products and processes. The value depends on the complexity and the cost of experimenting in reality.
What is the relationship between digital twins and IoT? +
A close one: IoT sensors provide the data that feed the digital twin, keeping it aligned with the real state of the object or system.
Is it a technology accessible to SMEs? +
It depends on the use case. Simple, focused versions are accessible; complex projects require more data and expertise. It is best to start from a concrete objective.
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