What Is a Digital Twin

A digital twin is much more than a virtual copy of physical objects. It is an intelligent, data-driven representation of products, machines or processes and revolutionizes the way companies design their production processes, maintenance strategies and innovation processes. It is not just about simulating assets, but also about bidirectional interaction and linking with other digital systems.

A digital twin is created by combining sensor technology, data integration, simulation, and analysis methods. It is based on the continuous collection of data by sensors that measure physical parameters such as temperature, pressure, or vibrations. A digital twin not only describes an object's current condition but can also act as a "digital nameplate" - a digital business card that provides essential information. A digital twin can also contain location data and can, therefore, be used as a spatial twin.

The recorded data is transferred via industrial communication protocols such as OPC UA or MQTT, which are used specifically in the machine environment. Data rooms are not directly part of a digital twin but enable the comprehensive exchange of information - for example, via the Eclipse Dataspace Connector. Forwarding to a data room is, therefore, an additional function that companies can use for higher-level data utilization. There, processing is carried out by AI-supported algorithms that perform simulations and derive optimization suggestions.

Uniform data models and administration shells, such as the Asset Administration Shell (AAS), facilitate the integration of digital twins into existing IT systems. Standardizing these models makes it possible for different companies to jointly use a digital twin and enrich it with data — for example, automotive manufacturers and their suppliers. This ensures that all parties involved can access a consistent and compatible database.

A digital twin combines physical and digital worlds. These are the central components:

• Sensors and data acquisition: Modern measuring systems continuously record parameters such as temperature, pressure, and vibrations.
• Data transmission and integration: The recorded data must be forwarded reliably and in real-time to central systems. Powerful transmission technologies such as 5G, WLAN or industrial communication protocols such as OPC UA and MQTT are used here. However, transmission is not the only crucial factor - integration into existing IT infrastructures, such as ERP, MES or PLM systems, also plays a key role. Data rooms and standardized interfaces enable secure exchange across company boundaries.
• Simulation and modeling: Virtual models depict real processes and simulate different scenarios.
• Real-time analysis: Cloud and edge computing can be used to process and analyze collected data directly. This allows, for example, anomalies in machines to be detected at an early stage, bottlenecks in production processes to be avoided, or process adjustments to be implemented almost in real-time. Edge computing, in particular, offers advantages here, as the data is processed directly at the point of origin - without delays caused by a central cloud. This allows machines and systems to react autonomously to changes and further optimize production processes.
• Visualization: User-friendly dashboards and visualization tools make complex data transparent and support decisions.

A clear understanding of key concepts is crucial to fully exploiting digital twins' potential. Differentiation from the digital shadow plays a particularly important role, as the two terms are often confused.

While a digital shadow is merely a passive, unidirectional representation of an object and collects existing data, the digital twin goes one step further: it can actively simulate, optimize, and make predictions.

Hybrid models combine both concepts and enable flexible optimization of production and maintenance processes. This allows companies to modernize their existing systems step by step while benefiting from new technologies.

The introduction of a digital twin requires both technological and organizational adjustments. One of the biggest challenges is the integration of heterogeneous IT systems. Many companies use proprietary legacy systems that are not easily compatible with modern cloud platforms. At the same time, cybersecurity and data protection are key aspects, as the exchange of sensitive production data requires high-security standards, especially in the context of the GDPR.

Another obstacle is the cost-benefit analysis. Although digital twins offer considerable increases in efficiency, implementation is associated with investments. Companies, therefore, need to develop a clear business model that ensures a long-term return on investment (ROI). A high level of employee acceptance is also required. Training measures are essential to minimize resistance and exploit the full potential of the technology.

The future of the digital twin is closely linked to advances in artificial intelligence (AI) and data analysis. AI-supported digital twins will not only optimize existing processes but also develop new production methods independently. Technologies such as augmented reality (AR) and virtual reality (VR) create new opportunities for interaction, for example through virtual machine inspections.

Another exciting development concerns blockchain technologies that enable secure and transparent management of digital twins. This will become increasingly important, particularly in globally networked supply chains. In addition, digital twins could be traded as independent economic goods in the future, with companies monetizing digital images of their machines or processes.