Digital Twins Are Moving From Industrial Experiment To Operating Necessity

A production manager notices that a critical machine is running slightly hotter than usual. Nothing has failed, the line is still moving and the conventional maintenance schedule says the equipment should remain operational for another three months. Yet a virtual model of the machine suggests that a bearing is deteriorating faster than expected.

The company now has a choice. It can continue operating until the next scheduled inspection, risking an unplanned shutdown, or intervene during a quieter production window before the fault becomes expensive.

This is the practical promise behind digital twins.

The term is often used loosely, sometimes to describe little more than a three-dimensional model or dashboard. A functioning digital twin is more ambitious. It is a digital representation of a physical asset, process or system that is updated with operational data and used to understand how its real-world counterpart is behaving. Depending on its sophistication, it may monitor current conditions, simulate changes, forecast failures or recommend a course of action.

The technology is not new, but its commercial relevance is increasing. Industrial sensors have become more widespread, cloud and edge-computing capacity has expanded, and artificial intelligence can now analyse far more operational data than traditional monitoring systems. Together, these developments are making it possible to create digital models that do more than display what has already happened.

They can help a business decide what to do next.

Beyond The Headline Forecast

Research companies broadly agree that the digital twin market is expanding rapidly, although their estimates differ considerably. That variation reflects a basic problem: there is no single, universally applied definition of what should be included.

Some forecasts count software platforms, simulation tools and data services. Others include consulting, systems integration, industrial Internet of Things infrastructure and sector-specific applications. A digital twin of one aircraft engine is also a very different proposition from a model of an entire factory, electricity network or city.

The exact size of the future market is therefore less informative than the direction of investment. Manufacturers, energy companies, transport operators and infrastructure owners are looking for better ways to manage complex assets whose failure can be costly, disruptive or dangerous.

For these organisations, a digital twin is not valuable because it creates an impressive virtual replica. It is valuable when it improves a decision: when to maintain equipment, how to redesign a process, where capacity is being wasted or how a system may respond to changing conditions.

What Makes A Model A Digital Twin?

A conventional simulation is generally built to answer a defined question. Engineers may use it to test a product before construction or understand how a component behaves under pressure. Once the exercise is complete, the model may no longer change.

A digital twin is intended to maintain a relationship with the physical system it represents. Data from sensors, equipment records, maintenance systems and other operational sources can be used to update the model as conditions change.

The degree of connection varies. Some twins receive periodic updates. Others operate close to real time. A basic version may provide visibility into the condition of a single asset, while a more advanced system may combine physics-based modelling, machine learning and historical data to predict future behaviour.

This distinction matters because not every asset requires the most elaborate possible twin. A company may need a highly detailed model of a safety-critical turbine but only a simpler representation of less important equipment.

The objective should determine the level of detail, not the appeal of the technology.

Designing Before Building

One of the clearest uses for digital twins comes before a physical asset enters operation.

Manufacturers can use virtual models to test products, production lines and machine configurations before committing capital to construction. Engineers can examine how a component may respond to heat, movement, pressure or wear. Factory planners can explore whether a proposed layout creates bottlenecks, safety problems or inefficient material flows.

This changes the economics of experimentation. Altering a virtual production line is usually cheaper than modifying one that has already been installed. A design team can test several scenarios without interrupting a functioning plant or producing repeated physical prototypes.

Virtual commissioning takes the idea further. Control systems and equipment behaviour can be tested in the digital environment before machinery is fully deployed on the factory floor. That can expose programming errors and integration problems earlier, when they are less expensive to correct.

The value is not simply faster product development. It is the ability to make mistakes in a setting where they do not yet stop production.

Predictive Maintenance With Better Context

Predictive maintenance is frequently presented as the flagship digital twin application, and with good reason.

Traditional maintenance generally follows one of two models. Equipment is repaired after it fails, or serviced according to a fixed schedule. The first approach risks costly downtime. The second may replace components that still have useful life or fail to identify a problem developing between inspections.

A digital twin can combine sensor data with information about the asset’s design, operating conditions, maintenance history and expected behaviour. Instead of asking whether a machine has reached a certain age, the company can assess how that particular machine is performing.

A pump working under unusually heavy loads may need earlier intervention than an identical unit in a less demanding environment. A turbine that appears healthy under one operating metric may show a concerning pattern when vibration, heat and output data are considered together.

The twin does not eliminate engineering judgement. It gives engineers a more complete basis on which to exercise it.

The strongest business case arises where downtime is expensive, maintenance access is difficult or equipment failure has significant safety consequences. In these settings, even a modest improvement in fault detection or maintenance planning can justify the investment.

Optimising An Entire Process

The greatest value may not come from modelling individual machines but from understanding how they interact.

A production line can underperform even when each piece of equipment appears to be operating correctly. One process may create queues, another may consume excessive energy and a third may produce small variations that lead to defects further downstream.

A process-level digital twin allows a company to examine the system as a whole. Managers can test the likely effect of changing production speeds, altering shift patterns, introducing a new product or taking equipment offline for maintenance.

This is particularly useful in environments where one decision produces consequences elsewhere. Increasing the output of one machine may simply move the bottleneck to the next stage. Reducing energy use during one part of the process may affect quality or extend production time.

By simulating these trade-offs before making physical changes, companies can avoid improving one metric at the expense of the wider operation.

Energy, Infrastructure And The Built Environment

Digital twins are also moving beyond factory walls.

Energy companies can use them to monitor turbines, power plants and networks. Utilities may model how electricity demand, renewable generation and storage capacity interact. Infrastructure operators can create digital representations of bridges, railways, water systems and buildings to support maintenance and long-term planning.

In the built environment, a twin can follow an asset across its lifecycle. The model used during design and construction may later incorporate information about occupancy, energy consumption, temperature, ventilation and equipment performance.

A building manager could use that information to identify why one part of a property consumes more energy than expected or to test how changes to heating and cooling settings may affect comfort and cost.

The same principle can be applied at a larger scale to transport networks or urban infrastructure. However, complexity rises quickly. A digital twin of a city is not simply a larger version of a digital twin of a machine. It must combine data from different owners, systems and standards while accounting for human behaviour that is far less predictable than mechanical performance.

Sustainability Requires More Than A Digital Model

Digital twins are often promoted as sustainability tools because they can help reduce energy use, material waste and unnecessary maintenance. That potential is real, but it should not be assumed.

A company may use a twin to redesign a product with fewer materials, extend the useful life of equipment or identify an energy-intensive stage in production. A construction business could compare design options before committing concrete and steel. A transport operator might optimise routes or asset utilisation.

These benefits depend on what the system is designed to measure. A digital twin built solely to maximise output will not automatically reduce emissions or resource use. Environmental indicators must be incorporated into the model and included in the decisions it supports.

The technology also has its own footprint. Sensors, connectivity, data storage and computation consume energy and require hardware. A credible sustainability case should therefore compare the resources used by the digital system with the savings or avoided waste it enables.

AI Makes Twins More Capable – And Harder To Govern

Artificial intelligence is expanding what digital twins can do. Machine-learning models can detect patterns across large volumes of sensor data, identify anomalies and estimate how an asset is likely to behave under conditions that have not occurred before.

Generative AI may also make complex systems easier to use. An engineer or manager could potentially ask questions in ordinary language rather than navigating a specialist interface: Why did output fall last week? Which component is most likely to interrupt production? What would happen if demand rose by 15 percent?

Yet AI introduces another layer of uncertainty. A model may identify correlations that do not reflect the physical causes of a problem. It may perform well under normal conditions but become unreliable when equipment or operating environments change.

For industrial decisions, an answer that sounds plausible is not enough. Companies need to know which data informed the result, how the model was validated and when human approval is required.

The greater the authority given to the twin, the more important these controls become.

The Data Problem Comes First

Many digital twin projects encounter difficulty long before the visual model or AI layer is built.

Operational data may be incomplete, inconsistent or stored across equipment from different manufacturers. Older machinery may lack suitable sensors. Maintenance records may use different naming conventions, while design data and live operating data may sit in systems that were never intended to communicate.

A digital twin cannot compensate for poor information indefinitely. If the physical asset is represented inaccurately or sensor data is unreliable, the model may create false confidence rather than better decisions.

This is why successful projects often begin with a narrow use case and a careful assessment of data availability. The company must establish which variables matter, how frequently they need to be updated and what degree of accuracy is required.

It must also decide who owns the information. Digital twins can involve manufacturers, software providers, asset operators and maintenance contractors. Contracts should clarify who may access the data, how it can be used and what happens when a supplier relationship ends.

Cybersecurity Becomes A Physical Risk

The closer a digital twin is connected to operational technology, the more consequential its security becomes.

A compromised marketing database creates serious problems. A compromised industrial system may influence machinery, energy networks or essential infrastructure. Even when a twin does not directly control an asset, manipulated data could cause operators to make the wrong maintenance or safety decision.

Security must therefore cover more than the central platform. Sensors, network connections, cloud services, application interfaces and third-party providers may all become points of vulnerability.

Access should be limited according to role, data integrity monitored and unusual system behaviour investigated. Companies also need a clear distinction between systems that observe, systems that recommend and systems authorised to act.

A digital twin should not become an invisible route around established industrial safety controls.

Start With The Decision, Not The Twin

The most common strategic mistake is to begin with the ambition to “build a digital twin” rather than a defined operational problem.

A more disciplined project begins with a question. Can the company reduce unplanned downtime on a critical asset? Can it shorten commissioning time? Can it understand why energy consumption differs between apparently similar facilities? Can it test a production change without interrupting operations?

The business should then establish a baseline. Without knowing the current cost of downtime, waste, defects or energy use, it will be difficult to judge whether the twin produces an adequate return.

A pilot should focus on an asset or process where the value is measurable and the necessary data can be obtained. The model can then be expanded if it proves reliable. Attempting to replicate an entire enterprise at the outset often produces a large technology programme with an unclear commercial purpose.

Companies should also plan for maintenance of the digital model itself. Physical assets change. Components are replaced, software is updated and operating conditions evolve. A twin that is not kept in step with reality gradually becomes a historical model rather than an operational tool.

From Seeing The System To Testing The Future

The digital twin market will almost certainly continue to expand, supported by industrial digitalisation, more connected equipment and advances in simulation and AI. Yet the technology’s long-term importance will not be determined by the size of a market forecast.

It will be determined on factory floors, in control rooms and during capital-investment decisions.

A useful digital twin helps an organisation understand something it could not see clearly before. A valuable one allows it to act on that understanding: preventing a failure, reducing waste, testing a design or making a complex system work more efficiently.

That is the real shift. Industrial businesses are moving from monitoring physical operations after events occur to modelling how those operations may behave next.

The competitive advantage will not belong to the company with the most elaborate virtual replica. It will belong to the one that knows which real-world decision the replica is there to improve.