Digitalizing an Old Factory Without 3D Data

Many Finnish industrial facilities share the same situation: the plant has been operating for decades, but its documentation has not kept pace. Drawings date from the 1980s, P&ID diagrams no longer reflect current conditions, and critical knowledge lives in the memories of experienced maintenance staff. Digitizing an old plant feels like an almost impossible task in such a situation — until you realize that the starting point is not an obstacle but precisely the place where a modern approach begins.

Industrial digitalization does not require a finished 3D model or a complete archive. It requires the right methodology — one that measures reality as it exists today. This article walks through what digitizing an old plant actually means in practice, why missing source material does not prevent progress, and how a digital twin can serve as the foundation for information management even in an environment that is decades old.

What happens when plant data is missing or outdated

Missing or outdated plant data is not merely a documentation problem. It is an operational risk that shows up in day-to-day work. When a technician looks for a valve location or an engineer checks a pipe routing, they often have to go on-site to verify information that should be available in the system.

According to SolidComp’s estimate, up to 70% of experts’ time can be spent verifying conditions on-site in situations where up-to-date documentation is lacking. This means field visits that could be avoided and decisions that are delayed because the data cannot be trusted. At the same time, the retirement of experienced workers takes with it tacit knowledge that has never been recorded in any system.

Critical information is typically scattered across multiple locations: SAP, Excel spreadsheets, paper archives, and people’s memories. There is no unified view, and assembling the information on a project-by-project basis consumes resources that should be spent on the actual work.

The unique challenges of digitizing an old industrial environment

A brownfield environment — an operational plant that has been running for decades — places special demands on digitalization that are simply not encountered in new construction. Buildings, pipework, and equipment have changed countless times, but those changes have not always been systematically documented. The design model shows what was supposed to be built, not what is actually in the plant today.

A second key challenge is production continuity. Digitalization cannot shut down the factory. Measurements, surveys, and data collection must be fitted around the production schedule — often at night or during planned shutdowns. In ATEX-classified areas there are additional safety requirements that apply to all equipment and methods used.

A third challenge is data fragmentation. Even when some form of documentation exists, it is often in different formats, different systems, and at different levels of detail. Consolidation without a shared visual frame of reference is slow and error-prone. This is precisely the problem that an as-built plant model solves.

How reality capture data replaces missing source material

Laser scanning measures the plant as it is, regardless of whether existing drawings are available or not. Age is not an obstacle. The method produces a point cloud that captures the plant’s geometry to millimeter-level accuracy and creates the foundation for all subsequent data.

In practice, this means that a plant can be documented from scratch without needing old drawings as source material. Mobile laser scanning covers significantly more square meters per day than traditional static scanning, making it realistically feasible to survey even large facilities within a reasonable timeframe. Laser scanning is always carried out on production’s terms and does not require a shutdown.

If the plant already has previously collected scan data that has gone unused, it can be consolidated and incorporated into the digital twin. A new scanning campaign is not always necessary. Existing data is a valuable starting point, as long as there is a place where it can all come together.

Reality Twin as the foundation for information management in an old plant

Once a reliable as-built foundation exists for the plant, a place is needed where all information comes together and can be found. Reality Twin™ is an industrial digital twin delivered as a continuous service, combining scan data, 3D models, documents, and operational data into a single shared view.

The key difference from a bare point cloud or 3D model is that Reality Twin connects geometric information to functional information. A piece of equipment is not merely a point in a coordinate system — its manuals, maintenance history, P&ID diagrams, and work orders from the maintenance management system are all linked to it. Information is found through location, not through search terms or folder structures.

Reality Twin does not replace the customer’s existing systems such as SAP or IBM Maximo. It reads data from those systems and displays it in a location-bound visual view. Master data stays where it has always been. More about this approach can be found in the comprehensive guide to digital twins.

Based on a user survey conducted by SolidComp in May 2026, Reality Twin delivers the most value when used for concrete industrial workflows: checking current conditions, planning projects and changes, locating assets, and reducing physical site visits. Of the 32 users who responded to the survey, 22 reported that Reality Twin reduces the need to visit a site physically — often or very often. Users do not primarily describe Reality Twin as an abstract digital twin, but as a practical tool that helps them see the current state of a site and speed up preparation.

Practical benefits in maintenance and project work

In day-to-day maintenance, up-to-date plant information means above all that the right information is available before a field visit. A technician can check conditions remotely, plan the work safely, and arrive on-site knowing what to expect. This reduces unnecessary visits and shortens the duration of jobs.

Yara Siilinjärvi‘s maintenance manager Anssi Pietarinen describes his experience directly: “Reality Twin covers the concentrator plant almost entirely. It streamlines communication both internally and with stakeholders. The accurate and visual 3D model benefits the planning of field work — for example, locating faults and planning installations.” In the 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.

The results of the user survey confirm this experience more broadly: 28 respondents cited a reduction in physical site visits as a concrete benefit, and 25 respondents highlighted time savings. Among those users who were able to estimate the savings, the majority estimated at least 15 minutes per use, and some estimated 30–60 minutes or more. In addition, 22 respondents said Reality Twin makes it easier to understand a site or space — a benefit that is particularly pronounced when a designer, maintenance professional, or contractor is not familiar with the site in detail and cannot visit in person before making a decision.

In project work, the benefit is especially visible during the planning phase. When the actual state of the plant is available as a visual model, planning modification work is faster and clash detection can be carried out in advance. Onboarding contractors becomes easier when the site can be presented digitally before anyone goes on-site. Maintenance and production use cases provide more detail on how this plays out in practice.

Based on the user survey, Reality Twin’s current strength is centered on planning and project use: planning projects, investments, layouts, or changes, and reviewing the current state of the plant were clearly the most common use cases. The potential in maintenance and shutdown planning is significant and features strongly in users’ wishes — in particular, maintenance system integration and linking documents to physical assets emerged as the most frequently mentioned development directions in the survey.

Where to start with digitalization

When digitizing an old plant, it is not worth trying to solve everything at once. A better approach is to start with a defined area or a precisely scoped use case, verify the benefit, and then expand.

In SolidComp’s onboarding process, the first step is to listen, not to solve. The goal is to find out where plant data currently resides, who uses it, and at which stage of work finding information is most difficult. Only after this is the scope, coordinate system, and the existing material that can be leveraged defined. Implementation can be carried out without production downtime, and the system can be up and running in as little as two weeks. The phased implementation is designed so that all that is needed from the customer is a contact person, access permits, and any existing source material — nothing more.

The user survey shows that the value of Reality Twin grows with use: regular users rate the tool’s usefulness significantly higher than occasional users. The most important factors in activating new users were a clear role-specific use case, brief usage training, and a concrete example scenario — not a generic product presentation. This means that digitalization starts with the right question: where is information needed and who benefits from it most. Once that question is answered, the technical implementation follows naturally. A plant that is decades old is not an obstacle to digitalization. It is precisely the environment where the value of reliable plant data is greatest.