Digital Twin in Maintenance

Maintenance is the backbone of an industrial plant. Yet it remains one of the most information-wasteful functions: work orders are in one system, equipment drawings in another, service history in a third, and field knowledge exists only in the memory of an experienced technician. The digital twin in maintenance is the answer to this fragmentation, and its adoption is shifting from a privilege of the few to a practical choice even for legacy industrial plants.

In this article, we walk through what a digital twin means in day-to-day maintenance, how it works alongside existing systems, and what implementation practically requires.

Maintenance’s biggest problem: information is scattered

Maintenance efficiency rarely fails due to a lack of technology. It fails because the right information is not in the right place at the right time. Equipment history lives in SAP, P&ID diagrams in an archive, the latest inspection report in an email, and valve locations in a field technician’s tacit knowledge. When an experienced technician retires, part of the plant’s memory leaves with them.

According to SolidComp’s estimate, up to 70% of experts’ time can be spent verifying conditions on-site before the actual work even begins. Field visits are made because information is missing, not because of the work itself. Reactive maintenance, incomplete documentation, and poor visibility into the equipment base are all consequences of the same root cause: information does not flow and cannot be found. For this problem, the digital twin offers a structural solution.

How the digital twin changes day-to-day maintenance

An industrial digital twin combines the plant’s geometric data — such as the point cloud and 3D model generated by laser scanning — with operational information about the asset: service history, documents, and the equipment register. The result is a single, clear view of the entire plant, where every piece of equipment can be found by its physical location.

In practice, this means a technician opens a tablet, navigates in the 3D model to the correct pump, and sees directly on that asset its work orders, manuals, and latest service history. Conditions can be checked remotely before a field visit. A new employee can find a piece of equipment and its documentation without having to ask anyone. Maintenance digitalization does not mean replacing existing processes — it means making them visible and accessible.

From a safety perspective, the twin supports lockout/tagout (LOTO) planning, visualization of hazardous areas, and contractor induction. When the access route and danger zones are visible in the 3D model before work begins, job planning is safer and faster.

Integration with existing systems

A digital twin does not replace a maintenance management system. It makes it more effective by adding the location context that spreadsheets lack.

Reality Twin™ operates as a visual layer on top of existing systems. System integration means in practice that SAP work orders, preventive maintenance data, and spare parts history are displayed at the correct piece of equipment in the 3D view. IBM Maximo asset management data has been validated in the same way. M-Files and SharePoint documents open directly from a 3D object via a deep link.

An important principle is that master data remains in the customer’s own systems. Reality Twin reads data and does not write to those systems without permission. This means that existing processes and responsibilities are preserved, but information becomes visible where it is needed: at the equipment, in the right location. SolidComp and Neomore have jointly developed the integration of SAP maintenance data into the Reality Twin environment.

In a user survey conducted by SolidComp in May 2026, document management integration emerged clearly as the most requested new feature: 9 out of 32 respondents named it as the single most important development area. Users want to open a document related to a piece of equipment directly from the 3D model instead of searching for it separately in other systems. Maintenance system integration was mentioned as the second strongest request. This indicates that the majority of Reality Twin’s potential in maintenance has yet to be realized — and that is precisely why the direction of development is clear.

Reality Twin in support of maintenance: practical examples

At Yara Siilinjärvi’s concentrator plant, Reality Twin covers the facility almost in its entirety. Anssi Pietarinen, Maintenance Manager, describes the benefit as follows: “It streamlines communication both internally and with stakeholders. The accurate and visual 3D model benefits field work planning, for example fault localization and installation planning.” Read more about Yara Siilinjärvi’s experiences.

In a mining environment, documenting valve location data in Reality Twin reduced, as reported by the customer, approximately 2–4 field visits per person per month in that task area. Field visits decreased because the information was available before anyone set out to find it on-site.

MM Kotkamills’ Project Engineer Risto Mikkola says he uses Reality Twin especially in Teams meetings. The service is easy to use and contains up-to-date scan data of the entire plant with purpose-built tools. This illustrates well how the twin works in everyday life: not as a separate system, but as part of daily collaboration.

In the customer survey, the greatest benefits cited were fewer field visits and time savings. The May 2026 user survey confirms this: 28 respondents mentioned a reduction in physical site visits as a concrete benefit, and 22 out of 32 respondents said Reality Twin reduces the need to visit the site physically often or very often. Time savings was the second most commonly mentioned benefit — 25 respondents highlighted it. Among those who were able to estimate the savings, the majority estimated at least 15 minutes per use, and some estimated savings of as much as 1–2 hours. These are not technical achievements — they are results that directly impact business performance.

Implementing a digital twin in a maintenance organization

Implementation does not require massive preparation from the organization. In SolidComp’s process, the initial phase is listening: we find out where equipment information currently resides, who uses it, and where finding information is most difficult. A solution is not built until the need is understood.

Laser scanning measures the plant as it exists today, regardless of whether the original drawings exist or are up to date. Age is not a barrier. The data is then processed and the environment created, without requiring any effort from the customer’s team at this stage. The typical total duration for commissioning a process unit is three to five weeks, and the system can be up and running in as little as two weeks.

It is worth starting the rollout with a defined area or a single critical process. Once the benefit has been verified, expansion is straightforward. On the implementation process page you will find more detailed information about what each phase involves in practice.

Based on the user survey, the value of Reality Twin grows with use: those who use it daily or weekly rated the tool’s usefulness significantly higher than occasional users. Support for the most important use case received an average score of 4.12/5 from active users. This means that implementation should be tied immediately to one concrete work process — not general 3D viewing, but for example shutdown work preparation, equipment location, or contractor induction. When a user succeeds in solving a problem in their own work the first time, continued use is likely to follow.

Predictive maintenance is a goal that many plants strive toward. Reality Twin supports it by bringing maintenance system data into a location-bound 3D view, and in the future the integration of sensor data into the same view is also on the way. Predictive maintenance requires reliable information in the right place, and that is precisely what a digital twin enables. The step toward a proactive operating model begins with information being findable before equipment breaks down.