In a reservoir-restoration project in Quebec, Tetra Tech used Prevu3D's 3D scanning and modelling platform to digitally capture a degraded rock wall and produce an accurate “as-is” 3D mesh suitable for structural analysis and reinforcement planning. The automated workflow reduced modelling time by 97%, converting what would have been 60 hours of manual Revit work into a 2-hour mesh export. The method delivered high-fidelity geometry and CAD/BIM-ready assets, enabling efficient assessment and decision-making for the restoration project.

Tetra Tech faced a challenging structural assessment: a rock wall — part of a reservoir site — exhibited irregular geometry and visible degradation, with no reliable architectural or structural plans available. Manual reconstruction in CAD/BIM was estimated to take 60 hours given the complexity and required detail.

Using Prevu3D’s reality-capture solution, the team scanned the existing structure to produce point-cloud data. This data was processed in Prevu3D’s RealityPlatform and RealityPlan to generate a high-resolution mesh, which was then imported into Autodesk Revit via the RealityConnect plugin — as native, editable components.

The result: an accurate, fully editable 3D model of the degraded wall — geometry, texture and all — recreated in a fraction of the time. The mesh served as a reliable base for structural analysis, reinforcement planning, and design coordination. The case study reports a 97% reduction in modelling time, from 60 hours to 2 hours.

Because this model is based on scanned reality and not dynamically linked with real-time data from the physical asset (no sensors, no live updates, no active simulation), it fits the definition of a Digital Model (or Visual Twin / As-Built Model) rather than a full Digital Twin.

In infrastructure and brownfield projects — where documentation is missing and geometry is complex — such high-fidelity as-built models provide strong value: faster engineering, fewer site visits, alignment across disciplines, and a reliable baseline for renovation or reinforcement.

<p>https://www.prevu3d.com/wp-content/uploads/2025/06/TetraTech-Case-Study-Streamlining-Structural-As-Built-Modeling-with-Prevu3D-1.pdf</p>

Konecranes digitized their crane fleet with a system-wide digital twin using the Process Genius platform. By integrating real-time operational data, maintenance logs, and structural configurations into a virtual model, they gained enhanced visibility on usage patterns, load cycles, and maintenance needs. This improved transparency enables better service scheduling, reduces downtime risks, and offers clients data-driven insights into crane performance and lifespan.

Konecranes — a global leader in lifting equipment and crane manufacturing — collaborated with Process Genius to implement a digital twin across its crane installations. The twin captures data from sensors, maintenance records, load histories, and structural layouts, forming a comprehensive virtual representation of each crane and its usage lifecycle.

Through the digital twin, operators and maintenance teams can monitor real-time and historical performance metrics, track load cycles, and assess wear and tear. This supports proactive maintenance planning, early detection of potential failures, and optimized scheduling. The platform’s dashboards provide insights into usage patterns and equipment health without interrupting operations.

For clients, the twin offers transparency into crane utilization, load statistics, and maintenance history — a proposition valuable for asset management, lifecycle costing, and compliance. The system helps to extend crane lifespan, prevent unplanned downtime, and reduce safety risks by ensuring maintenance is performed before problems occur.

By unifying data from multiple sources into a single digital model, Konecranes leverages the twin for both operational efficiency and enhanced customer service. The implementation demonstrates how heavy-equipment manufacturers can modernize asset management and maintenance through tailored digital twins.

<p>https://processgenius.eu/references-en/konecranes/</p>

Stora Enso has implemented a factory-wide digital twin platform from Process Genius to visualize production data, ERP information, safety data, and maintenance status in a unified 3D environment. The digital twin improves operational transparency, speeds up decision-making, and supports safety and maintenance processes. After its initial success at one mill, the solution has been scaled to multiple Stora Enso factories in Finland and abroad.

Stora Enso, a global provider of renewable packaging, biomaterials, and wooden construction solutions, partnered with Process Genius to digitalize factory operations using a 3D, cloud-based digital twin platform. The solution creates a detailed virtual model of production facilities and links it to real-time and ERP-driven operational data. This provides an instant, visual overview of equipment status, production flows, and key performance indicators across the entire site.

Once a factory is connected to the platform, new features and updates become available automatically, allowing Stora Enso to manage several sites in parallel. This makes the twin an effective management tool not only for single assets, but also for cross-site planning, monitoring, and benchmarking.

The digital twin delivers several benefits:

• Real-time visibility into production status and factory workflows

• Faster identification of bottlenecks or deviations from planned operations

• Support for preventive maintenance and equipment lifecycle management

• Improved safety through detailed visualization of emergency routes and safety-critical areas

• Simplified operational reporting through graphical dashboards replacing large spreadsheets

Following its successful deployment at one production facility, Stora Enso expanded the solution to additional factories in Finland and internationally. According to Process Genius, the digital twin enables operators to access hundreds of data points through a single visual interface, strengthening situational awareness and decision quality on the factory floor.

<p><a href="https://processgenius.eu/references-en/stora-enso/" target="_blank">https://processgenius.eu/references-en/stora-enso/</a></p><p><a href="https://www.storaenso.com/en/inspiration-centre/renewable-future-blog/2022/12/meet-the-twins-who-turn-data-into-insights" target="_blank">https://www.storaenso.com/en/inspiration-centre/renewable-future-blog/2022/12/meet-the-twins-who-turn-data-into-insights</a></p><p>https://puunvuoro.fi/en/alalla-tapahtuu/mill-digital-twins-collaboration/</p>

EFM Group implemented Process Genius’s 3D Digital Twin platform to gain real-time visibility into machine utilization, production capacity, and maintenance needs across its CNC-machining operations. The twin enables transparent resource use, optimizes utilization, and triggers preventive maintenance—replacing guesswork with data-driven management.

EFM Group Oy — comprising several engineering workshops such as CNC-Machining Oy — partnered with Process Genius to digitize its production environment. All machines were mapped in 3D, and connected to a central digital twin platform. From any device and location, management and operators can view real-time data: machine uptime, load utilization, shift-by-shift comparisons, and maintenance status.

Using dashboards, EFM tracks how much of the machines’ actual capacity is used, and monitors when maintenance is due. This transparency supports lean manufacturing: the company bills only for finished parts and avoids overcapacity. The twin also helps prevent premature failures and unnecessary downtime — saving maintenance costs and improving overall efficiency. Quality documents, safety instructions, and production records are managed in the same platform, simplifying documentation and compliance. EFM reports significant gains in resource utilization and more reliable production scheduling.

This project shows how a factory-level digital twin can transform a traditional workshop into a data-driven operation. By combining 3D modeling, real-time data feeds and maintenance management in one platform, EFM Group moves from reactive to proactive operations management, paving the way for continuous improvement and scalability.

<p><a href="https://processgenius.eu/references-en/efm-group/" target="_blank">https://processgenius.eu/references-en/efm-group/</a></p><p>images: <br><a href="https://efmgroup.fi/efm-group/joensuun-cnc-machining-oy-n-tuotantotilojen-ja-koneiden-investointi" target="_blank">https://efmgroup.fi/efm-group/joensuun-cnc-machining-oy-n-tuotantotilojen-ja-koneiden-investointi</a><br>https://efmgroup.fi/en/stories/investoimalla-tehokkaampaa-tuotantoa-ja-parempaa-palvelua</p>

An agricultural operator partnered with Toobler to reduce rising downtime and maintenance costs across critical irrigation and farming equipment. By integrating IoT sensors with a real-time digital twin, the company gained continuous visibility into machine health and shifted from reactive repairs to predictive maintenance. The solution decreased downtime by 20% and cut maintenance costs in half.

Agricultural machinery failures can disrupt irrigation cycles and impact crop productivity. Toobler implemented a digital-twin platform for a client facing recurring equipment breakdowns and high service costs. The project equipped machines with IoT sensors that captured vibration, pressure, temperature, and operational-cycle data. This information fed directly into a cloud-based digital twin, providing a live view of asset health.

The twin continuously analyzed incoming data to identify anomalies and warn of potential failures. Maintenance teams received alerts through a mobile interface, enabling them to intervene before breakdowns occurred. This shift to predictive insights reduced unplanned downtime by 20% and lowered maintenance spend by 50%, while improving equipment availability during critical farming periods.

Beyond failure prevention, the system unified performance data across all deployed machines. Operators gained a single dashboard to monitor usage, health trends, and maintenance priorities. The digital twin also supported optimization of irrigation and operational schedules based on real-time machine behavior.

The implementation shows how digital twins can modernize agricultural operations. With end-to-end monitoring, predictive analytics, and actionable alerts, equipment reliability improves and costs decrease. Toobler’s approach demonstrates the practical value of adopting digital-twin technologies in industrial agriculture.

<p>https://www.toobler.com/casestudy/reduce-downtime-predictive-maintenance</p>