What is a Digital Twin?

A digital twin is a virtual image of a real existing object, process or system that reflects its condition and behavior in real or near real time. Using sensors, IoT connections and data analysis, the digital twin continuously collects information from the physical (surrounding) world in order to derive simulations, forecasts and optimizations. A digital twin can represent individual components (e.g. a turbine) as well as complex production plants or entire cities. The digital twin can be used to reduce maintenance costs, improve operating processes and test design changes in advance. Thanks to a digital twin and corresponding synthetically generated data sets, companies can make well-founded decisions, even and especially in unknown situations, and optimize processes iteratively before they are implemented in the real environment. Modeling using digital twin technology can therefore be a real game changer for various industries.

In the following, we have outlined five typical application scenarios in which a digital twin can create real added value:

1. Predictive maintenance in production:
A digital twin of production machines can reduce downtimes. Sensor data flows into the Digital Twin, which recognizes wear patterns and provides early maintenance recommendations.

2. Product development & simulation:
Car manufacturers use Digital Twin to virtually test new vehicle models in crash tests or on different surfaces. Design changes are simulated directly in the Digital Twin before prototypes are built. This application is particularly resource-efficient.

3. Smart Building Management:
Real estate operators can integrate a Digital Twin in their respective buildings to monitor and automatically control energy consumption, heating and ventilation in real time. This allows climate comfort and efficiency to be increased in equal measure.

4. Urban planning & infrastructure:
Municipalities have recently started to create digital twins of entire city districts in order to simulate traffic flows, environmental pollution and emergency scenarios. New road layouts or construction projects can thus be tested on the digital twin without interfering with real life.

5 Logistics & Supply Chain Optimization:
Logistics service providers are moving towards modelling warehouses, transport vehicles and routes in the form of a digital twin. Thanks to live data on stocks and traffic, supply chains can be flexibly adapted and bottlenecks proactively avoided.

A digital twin makes it possible to digitally map physical objects and systems in order to monitor operating processes, create forecasts and test scenarios risk-free. The synthetic data generated by the digital twin can reduce costs, increase system availability and sustainably shorten development cycles. Anyone who strategically integrates a digital twin into their processes gains sound insights and relies on a future-oriented, data-driven innovation strategy. However, despite all the euphoria, caution is advised: as with any generative technology, the quality of the training data set is crucial! If it is contaminated, the digital twin model will also show distortions.