Digital twin strategy

A digital twin strategy is the plan for which physical assets or processes a business will mirror in software, why it will mirror them, and which decisions the model has to improve. Its subject is physical, which sets it clearly apart.

A twin is not a dashboard, and not a simulation you run once. It stays connected to the real thing, takes live readings, and shows the states that thing moves through over time.

The strategy part matters because twins are expensive to keep accurate. A digital maturity model would grade your capability to build one, and a digital operating model would say who owns it. This document says what to mirror and why.

Key takeaways

• A digital twin strategy decides which physical assets and processes are worth mirroring in software.
• A twin stays connected to the real asset and tracks the states it moves through.
• Scope it by the decision it improves, because accuracy costs money every month.
• Security belongs in the strategy, since a twin concentrates sensitive operating data in one model.

How it works

Start from a decision that is currently made badly, then ask whether a live model of the asset would make it better. If yes, work out what sensors, data rights and refresh rate that model needs, and price all three.

Standards bodies have caught up with the security side, and their work is worth reading before you connect anything to a plant network.

A February 2025 report from the National Institute of Standards and Technology (NIST) describes twins as electronic representations of real world entities whose state transitions can be viewed, and lists the trust questions that follow.

Practitioner communities are a cheaper starting point than consultants. The UK’s Digital Twin Hub has grown past 7,000 members sharing digital twin projects and publishes the Gemini Principles that underpin good twin work.

A twin is a model, not an actor. Robotic process automation (RPA) and intelligent automation execute steps — the twin only reports what the real asset is doing.

Artificial intelligence usually sits on top, forecasting failures from the twin’s history. Keep the two apart in the business case, because a wrong forecast and a wrong mirror need very different fixes.

Price the running cost before the build cost. Sensors fail, networks change and models drift, so a twin needs a maintenance line from the first year — not from the year it stops matching reality.

Decide who may command the twin, not just who may read it. A model that can write back to the physical asset is a control system, and it needs the controls that go with one.

Examples

Twins earn their place where the asset is expensive, hard to inspect, or dangerous to test on. Three sectors have gone furthest, and in each the strategy question was which asset to mirror first rather than whether to start.

Manufacturing went first, which is why the NIST report tags it as a sector. Firms that use manufacturing outsourcing often ask the contract plant to run the twin, then argue later about who owns the readings.

Healthcare twins start with the patient, not the building. Remote patient monitoring feeds live readings into a model of one person’s condition, and the question is which conditions justify the monitoring cost.

UK infrastructure bodies share twin case studies through the Digital Twin Hub rather than building in isolation. The habit worth copying is publishing what failed, not only what worked.

Utilities model distribution networks rather than single assets, because the useful question is where load will fail next. That shifts the strategy from one twin to a connected set, and data rights get harder with every added owner.

Related terms

These five sit next to a twin programme without being one. Two are automation methods, two are sourcing arrangements, and one is a measure — the twin itself is only a live model of something real.

• Supply chain outsourcing: the sourcing arrangement a network twin is often built to oversee.
• Process cycle efficiency: the measure a process twin is usually built to move.
• Intelligent automation: the layer that acts on what a twin reveals, where the twin only observes.
• Manufacturing outsourcing: the contract manufacturing model that raises the question of who owns twin data.
• Artificial Intelligence: the forecasting layer commonly trained on a twin’s accumulated history.

FAQ

What is the difference between a digital twin and a simulation?

A simulation models a scenario you choose, then stops. A twin stays wired to the real asset, updates from live readings, and shows the states it moves through. That live connection is the distinction — the maths behind both is often similar.

Which assets are worth twinning first?

Pick the asset where a bad decision is expensive and current information is poor. Rotating machinery, large vehicles and distribution networks usually clear that test well before office processes do. Office processes can be twinned, but the payback is usually slower.

Is a digital twin strategy only for manufacturers?

No. Healthcare, utilities, logistics and construction all run twins, even though manufacturing is the sector most often tagged in the standards work. The strategy questions stay the same everywhere: what to mirror, how accurate it must be, and who acts on it.

What does a twin cost to keep running?

The build is rarely the expensive part. Sensors, data pipelines and the work of keeping the model honest are annual costs, so budget them before the first build. Treat the maintenance line as the real commitment.

Who should own the twin inside the business?

Give it to whoever owns the asset and the decision it supports. A model owned by the team that built it drifts out of use within a year.

See how providers support asset data and automation programmes across Outsource Accelerator’s outsourcing research.