Definition and overview for the use of digital twins in logistics

A digital twin is the image of a real object or process in virtual/digital space. In this type of modeling, digital twins are formed from data and algorithms. They can be supplied with historical as well as real-time data by means of sensors and thus have a connection to the real world. The real-world object is also called an asset, which may or may not already exist. A digital twin can thus also represent a real future object.

Distinction to simulation

Digital twins can reach a very high degree of complexity and at the same time have intersections that make a clear definition difficult. For example, at first glance, a digital twin may appear to be a 3D model or it may appear to be an ordinary simulation. Yet there are significant differences and demarcations in what a digital twin is capable of achieving.

A digital twin can be fed not only with historical data but also with real-time data. Moreover, it not only receives and processes the data but also transmits it back to the real object, as it is networked with it. Thus, a digital twin represents an extensive virtual environment in which, in turn, individual simulations can be performed. Because of the real-time data and the connection with the real object, a digital twin has a flow of information that travels in both directions: Towards the real object as well as from the real object to the digital twin, resulting in a continuous feedback loop. This allows problems to be examined from multiple perspectives, and in much greater numbers – something that ordinary simulations cannot do.

Why digital twins?

Digital twins are used to represent processes that are used for simulation, production, analysis and development. In Industry 4.0 and digitization in general, they form a foundation, so to speak, by accompanying entire cycles of products or services in development and production. In this way, the respective processes can be optimally planned and adapted.

This requires a real object, a virtual space and various data about the respective conditions of the environment. Descriptive algorithms and the real-time data are used to create the digital twin and map it in a virtual representation space. In this process, a digital twin can also be composed of several other digital twins. For example, the digital twin of a production line consists of the elements of the corresponding production machines and the product itself, the continuous material handling systems for parts supply and the floor conveyors. In cases such as the mapping of an entire production line, the composition is often divided into three types of individual twins. These individual digital twins can have different shapes or focal points and thus differ fundamentally from one another.

In the following, this is briefly explained schematically:

  • The twin of the final product can be, for example, a CAD or 3D model,
  • while the programs, tools, and machines form another kind of digital twin or layer of the final product,
  • just like those digital twins that primarily replicate performance and execution by means of metrics on production and production times, as well as quality and delivery times.

The more complex the form of a digital twin, the more data is generated and processed. Accordingly, Big Data and the respective techniques and applications also appear frequently in the context of digital twins.

Application areas of digital twins

Production engineering is the area where digital twins are primarily used, as it can be used throughout the entire lifecycle of a product and enables optimization throughout. In the design and development phase, it makes the time-consuming and cost-intensive construction of prototypes obsolete; during the manufacturing phase, it assists in improving the quality and efficiency of the respective processes. The digital twin also analyzes and simulates the use phase and the increasingly important recycling phase.

Diagram of the digital twin principle showing an icon for materials handling technology with an icon for a humidity sensor and a temperature sensor
In intralogistics, a digital twin of complex materials handling technology can be used to improve its efficiency in physical space.

In practice, for example, new components from suppliers can be tested using digital twins without having to deliver and assemble real components. This type of testing can be performed in a much shorter time and in much larger quantities with less effort. Through this accelerated analysis of many different components, the optimal component can be found quickly or unsuitable parts can be excluded.

In addition, digital twins are increasingly being used for objects that are either system-relevant or on which human lives depend, such as wind turbines or aircraft engines. Buildings with complex structures and processes, such as hospitals, also often have a digital twin.

Digital twins are also used across companies, although this requires interfaces to be created and a certain degree of agreement regarding the digital models.

What advantages do digital twins bring?

The more complex a digital twin is, the more areas it analyzes and simulates. As a consequence, the amount of specific benefits that the digital twin provides also increases. In general, the following benefits are worth noting:

  • Facilities and processes can be optimized as early as the planning phase, i.e. before the first real product even rolls off the production line. This allows for smooth implementation and error-free business operations right from the start.
  • Development and production benefit from enormous time savings, which results in shorter development and production cycles and thus allows new products to be market-ready more quickly.
  • Real-time data from the real object creates a holistic view of the facilities and the products; in addition, meaningful forecasts are obtained when certain parameters change. In intralogistics, for example, checking the acceptance of a rush order would be such a case, performed by a digital twin. In this way, the digital twin simulates this rush order and its effects on the entire operational behavior together with products, machines and services affected by it, as well as personnel deployment. Such orders, which can disrupt normal operations, can be run through digital twins as often as desired and in a wide variety of constellations. Not only for concrete, current cases but also for hypothetical, future ones.
  • The depth of understanding about the individual processes and their totality increases significantly through the digital twin since the gain in knowledge from the virtual space is immense.
  • Cross-company coordination is faster and easier because the digital twin provides data that originates from the virtual space but is valid in the real environment and for real objects.
  • Within a company, a digital twin also serves as a single source of truth for all departments and people involved. This facilitates both communication and organization.


The manifestations of a digital twin are so diverse that a clear demarcation is not always possible. For example, a digital twin can represent a product, a large piece of machinery, a service, a single process, or even an entire building – or all of the above. The decisive factor is that the digital twin is the virtual representation of a real object in a virtual representation space and is supplied with real-time data from the real object, which is usually done by sensors (keyword IoT). Similarly, however, a digital twin can also draw on historical data. The main difference with a simulation is that a digital twin is connected to the real world and real object.

All this puts the digital twin in a position to identify problems and challenges and, if necessary, solve them (virtually) before they arise. In a production process, this means in concrete terms that individual components can be tested, new product properties can be checked, and changed framework conditions such as new laws and standards can be implemented efficiently. Thus, a digital twin is able to accompany a product throughout its entire life cycle and always facilitates optimal decisions and modifications in a resource-saving manner.

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