# Why Digital Twins Are Becoming Operating Infrastructure for Physical Businesses
Published: 2026-09-01
Category: Technology
Category URL: https://companiesdigest.com/category/technology/
Meta Title: Why Digital Twins Are Becoming Operating Infrastructure
Meta Description: Digital twins are moving beyond design simulation as companies connect virtual models to live operations, maintenance, capacity planning and capital decisions.
URL: https://companiesdigest.com/why-digital-twins-are-becoming-operating-infrastructure-for-physical-businesses/

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For years, the most familiar description of a digital twin was a virtual replica: build a model of a factory, machine or facility, test a scenario and use the result to improve a physical decision. That description is becoming incomplete.

The more consequential shift is from digital twins as occasional simulation tools to digital twins as part of the operating infrastructure of physical businesses. When a model is continuously synchronized with production data, equipment status, material flows or warehouse activity, it can become a persistent decision layer rather than a one-off engineering exercise.

That distinction matters because companies are under pressure to extract more output from existing assets, make capital projects more selective and reduce the cost of operational mistakes. A current [NIST digital-twin programme](https://www.nist.gov/programs-projects/digital-twins-advanced-manufacturing) describes digital twins as synchronized virtual models that can help manufacturers represent, diagnose, predict and optimise operations. In July 2026, NIST published a workshop report that also stressed that interoperability, verification, cybersecurity and workforce readiness remain unresolved barriers to trustworthy scale.

The emerging business case, therefore, is not that every company needs a photorealistic virtual factory. It is that businesses with expensive physical systems increasingly need a reliable way to test decisions against a living model of those systems before making changes in the real world.

## From simulation project to operating layer

A conventional simulation usually has a beginning and an end. Engineers define assumptions, model a process, test alternatives and use the output to inform a decision. A digital twin becomes more operational when the connection with the physical system continues after the original project is complete.

NIST's work on the [essential elements of digital twins](https://www.nist.gov/digital-twins/essential-elements) emphasises that useful twins are dynamic and data-driven, with synchronization supported by sensors, industrial connectivity and analytical models. This persistent connection is what makes a twin capable of moving from design support into monitoring, diagnosis, prediction and optimisation.

That can change the economics of operating a physical network. A production line can be tested virtually before machinery is moved. A warehouse layout can be evaluated against simulated pallet and worker flows. A maintenance team can compare equipment condition with expected behaviour. A plant manager can examine whether a bottleneck comes from nominal capacity or from the way assets are scheduled and connected.

The important point is that the digital representation can become part of the operational feedback loop. Once that happens, the twin is less like a drawing of the asset and more like an analytical interface to it.

## PepsiCo shows why the distinction is becoming practical

A prominent 2026 example comes from PepsiCo. In January, the company announced a multi-year collaboration with Siemens and NVIDIA to apply digital twins and AI to manufacturing and warehouse operations. PepsiCo said the programme was intended to simulate, validate and optimise facility layouts before physical builds or modifications, while creating a real-time view of operational systems.

The company reported that initial deployments had produced a [20% increase in throughput and 10% to 15% reductions in capital expenditure](https://www.pepsico.com/newsroom/press-releases/2025/pepsico-announces-industry-first-ai-and-digital-twin-collaboration-with-siemens-and-nvidia), while identifying up to 90% of potential issues before physical changes. These are company-reported results from early deployments rather than independently verified industry benchmarks, so they should not be generalized automatically.

Even with that caveat, the example is strategically useful. The value does not come simply from having a 3D representation of a factory. It comes from using the model to discover hidden capacity, test redesigns and challenge the need for physical investment before capital is committed.

In other words, the twin becomes part of capital allocation. That is a larger role than simulation alone.

## The strongest use case may be avoiding the wrong investment

Digital-twin discussions often focus on productivity gains, but avoiding a poor capital decision can be more valuable than achieving a small efficiency gain.

Physical infrastructure is expensive to reverse. A conveyor installed in the wrong location, an oversized warehouse expansion or a production cell designed around inaccurate throughput assumptions can lock a company into years of avoidable cost. The larger and more interconnected the facility, the harder it becomes to reason about changes from spreadsheets or isolated engineering models.

A credible twin creates a way to run more experiments before concrete, steel or machinery is moved. This does not eliminate forecasting error, but it can reduce the number of decisions made without a systems-level view.

NIST estimates, in its broader [digital twins research overview](https://www.nist.gov/digital-twins), that widespread adoption could produce substantial aggregate benefits for US manufacturing, while also noting the large losses associated with downtime and defects. Such estimates are model-based rather than guaranteed savings for individual companies, but they illustrate why the economics of predictive and prescriptive operating tools are attracting attention.

## Maintenance turns the model into a lifecycle asset

The second route from simulation to infrastructure is maintenance. Equipment does not lose value only when it breaks; it loses economic value whenever deterioration creates uncertainty around production availability.

A digital twin can combine observed condition with a model of expected behaviour. In principle, that allows maintenance to become more condition-based and less dependent on fixed schedules or unexpected failure. The potential benefit is not merely lower repair spending. It is more reliable production capacity.

This is especially relevant for complex assets where a single failure can affect an entire system: manufacturing lines, turbines, large buildings, utilities, logistics equipment and other capital-intensive infrastructure. When the twin is maintained across the asset's life rather than discarded after commissioning, the same data structure can support design, operation, maintenance and eventual replacement decisions.

That lifecycle value helps explain why standards matter. A twin that cannot exchange information with equipment, software or later-stage systems can become another isolated technology asset.

## Interoperability is the hidden constraint

The technical problem is that industrial environments are rarely clean. A factory may contain equipment from multiple decades, different control systems, proprietary data formats and software supplied by several vendors. Creating a model is one challenge; keeping it connected to the real operating environment is another.

NIST's [July 2026 Digital Twins Workshops Summary Report](https://www.nist.gov/publications/digital-twins-workshops-summary-report) identifies interoperability and standards as persistent barriers, alongside verification, validation and uncertainty quantification. The report is significant because it moves the debate away from whether digital twins are technically possible toward whether they can be trusted and scaled.

ISO 23247 already provides a digital-twin framework for manufacturing, but implementation remains uneven. NIST's standards work notes that many applications are still customised, which can increase development cost and make reuse and integration more difficult.

For companies, this means the strategic asset may not be the visual twin itself. It may be the data architecture, semantic consistency and governance that allow multiple twins to operate across a plant or network without becoming disconnected models.

## AI raises the value of the twin - and the risk

Artificial intelligence can make digital twins more useful because it can search for patterns, propose configurations and analyse more scenarios than human teams can examine manually. But it also raises the cost of weak models.

An AI system optimising against an inaccurate digital twin can produce highly confident recommendations that are wrong in the physical environment. Model fidelity, calibration and uncertainty therefore become governance issues rather than purely technical ones.

Cybersecurity is another concern. A twin connected to operational data can reveal sensitive information about facilities, production patterns or equipment configuration. If the architecture also influences control decisions, the potential consequences of compromise become more serious.

This is why NIST's 2026 workshop findings place cybersecurity and trustworthiness alongside interoperability. The more closely a twin is connected to real operations, the less acceptable it becomes to treat the model as an experimental sandbox without production-grade controls.

## Not every asset needs a digital twin

The counterargument is straightforward: many businesses can improve operations with better sensors, dashboards, process models or conventional simulation without building a full digital-twin environment.

The term itself can also become inflated. A static 3D model, a dashboard and a continuously synchronized decision model are not economically equivalent, even if vendors describe all three as digital twins.

A sensible investment case therefore starts with the operational decision, not the technology label. What uncertainty is the company trying to reduce? Which physical decision is expensive to reverse? What data is available? How often will the model be used? What is the consequence if the model is wrong?

If those questions do not produce a clear answer, a digital twin risks becoming an expensive visualization project.

## The next competitive advantage may be better virtual rehearsal

The larger trend is that physical businesses are learning to rehearse more decisions before executing them. Software companies have long tested code before deployment. Manufacturers, warehouse operators and infrastructure owners are increasingly trying to create a similar discipline for physical systems.

Digital twins will not remove the need for engineering judgement, maintenance expertise or capital investment. Their value is narrower and potentially more powerful: they can give companies a persistent environment in which to observe the asset, test alternatives and understand second-order effects before changing reality.

That is why the technology is moving beyond simulation. When the twin informs operating schedules, maintenance, capacity, facility design and investment decisions continuously, it becomes part of the machinery of management itself.

For physical businesses, the question may soon be less about whether they have a digital replica and more about how many important decisions can be tested safely before the physical asset has to bear the cost.

## References

**1\.** [NIST — Digital Twins for Advanced Manufacturing](https://www.nist.gov/programs-projects/digital-twins-advanced-manufacturing)

**2\.** [NIST — Digital Twins Workshops Summary Report, July 21, 2026](https://www.nist.gov/publications/digital-twins-workshops-summary-report)

**3\.** [NIST — Digital Twins: Research Overview and Economics](https://www.nist.gov/digital-twins)

**4\.** [NIST — Essential Elements of Digital Twins](https://www.nist.gov/digital-twins/essential-elements)

**5\.** [NIST — Digital Twins for Advanced Manufacturing: The Standardized Approach](https://www.nist.gov/publications/digital-twins-advanced-manufacturing-standardized-approach)

**6\.** [PepsiCo — AI and Digital Twin Collaboration with Siemens and NVIDIA, January 6, 2026](https://www.pepsico.com/newsroom/press-releases/2025/pepsico-announces-industry-first-ai-and-digital-twin-collaboration-with-siemens-and-nvidia)


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