Why a Digital Twin Makes Your Business More Efficient

• Digital Twin vs. BIM Modell
• Added Value for Real Estate Valuation
• Added Value for Building Operations
• Added Value in Production

• Creating a Digital Twin

   

Many companies have building portfolios that have grown over many years. It is often unclear where individual buildings are located, which components have been installed, or what condition the technical systems are in. Plans are incomplete, data is not centralized or is difficult to access. This makes investment decisions more challenging, prolongs planning processes, increases cost uncertainty, and raises risks.

A digital twin creates transparency and provides a reliable basis for decision-making. With structured and up-to-date building data, companies can make processes more efficient, reduce costs, and implement innovations faster. This gives companies a genuine competitive advantage.

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What a Digital Twin Is and How It Differs from a BIM Model

 

A digital twin is a digital representation of a real building containing all relevant information about its components, materials, technical systems, condition, and use. It makes data centrally accessible and enables evaluations, analyses, and simulations.

The BIM model provides the structural foundation for this. It describes the geometry and properties of the building components. A digital twin is created when this model is enhanced with additional data and can be used throughout the building’s operational lifecycle.

In short: The BIM model provides the foundation, while the digital twin delivers added value throughout the building’s use.

 

Added Value for Real Estate Valuation and Portfolio Strategy

A digital twin enables a comprehensive assessment of a building. Building components, materials, and technical systems are documented and evaluated. In addition, the data can be used to create simulations that realistically model different renovation and modernization scenarios. This makes it possible to assess renovation requirements with significantly greater reliability.

A concrete example:

Digital data capture reveals that an older roof area has insufficient insulation and is causing significant heat loss. Based on the stored information on surface areas, material layers, and technical specifications, different renovation options can be calculated and compared directly. The system not only shows the building’s current energy performance, but also simulates how different insulation thicknesses would affect energy consumption, cost-effectiveness, and future operating costs. This makes decision-making data-driven, dynamic, and significantly more informed than a purely static assessment.

For portfolio strategy, this creates a clear overview of buildings with a high need for modernization and sites where investments are particularly worthwhile. Companies can prioritize measures and make well-founded decisions about future development.

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Added Value for Building Operations

 

In building operations, the digital twin primarily supports maintenance and energy management. Equipment conditions and operating times can be analyzed, making it possible to identify early signs of an impending malfunction. This enables predictive maintenance based on data patterns, helping to reduce unplanned downtime and lower maintenance costs.

At the same time, energy consumption can be analyzed and different scenarios tested. Optimizing operating hours or temperature settings typically results in energy savings of around 5% to 10%.

A practical example:

The digital model reveals that a central ventilation system is showing unusually high power consumption and extended start-up times at certain times of the day. By linking operational data with technical specifications, it is possible to simulate whether the cause is a clogged filter, an incorrect damper position, or a declining fan efficiency. At the same time, different control strategies can be tested to determine their impact on energy consumption. This makes it possible to identify which measure both reduces energy consumption and helps protect the system.

 

Added Value for Production and Logistics Processes

 

When machines or logistics systems are integrated into the digital twin, setup times, utilization rates, and disruptions can be analyzed in a targeted manner. Many companies achieve improvements of around 10% to 15% in these areas.

An additional advantage is greater transparency across entire process chains. Once it becomes clear where bottlenecks occur, processes can be adjusted accordingly, maintenance windows can be planned more effectively, and operating costs can be reduced.

How to Create Your Digital Twin

 

1. Define Your Objectives

The first step is to define the purpose and requirements. The objectives determine which information is needed and how detailed the model needs to be.

2. Collect the Data

The next step is to compile existing data and supplement any missing information. A 3D scan provides the necessary foundation for ensuring complete and accurate data capture.

3. Create the Model

Based on this data, a BIM model is created. It accurately represents the building, including its components, materials, and technical systems, and provides the structured foundation for the digital twin.

4. Integrate the Software

To use the model in building operations, it must be connected to suitable systems. Maintenance, energy, and process data require additional software, such as CAFM systems, energy management systems, or IoT platforms. Only this integration enables the digital twin to be used effectively in practice.

5. Use and Analyze the Data

Finally, the model can be used to support maintenance and modernization planning. It simplifies maintenance, reduces downtime, and provides a transparent overview that facilitates cost and energy optimization as well as strategic decision-making.

Find out more in our whitepaper “Digital Twin”

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