What a Digital Twin can do – and what not

“Digital Twin” is one of the top buzzwords around digitization in the IoT and IIoT industries. This technology offers extensive application possibilities in Industry 4.0 and is also taking digitization a big step forward. It has, however, become a little quiet recently when it comes to this temporarily hyped approach. Have Digital Twins disappointed expectations in their application? And what can the technology really do?

Digital Twin

Digital Twin: What it is, what it can do

A Digital Twin is a virtual image of an existing process, service or product from the Internet of Things (IoT) that enables realistic simulations. It can be used to analyze and evaluate data from the use of real twins. In the field of manufacturing companies, Digital Twins can map entire plants – and that over their entire life cycle. Simulation models are used to comprehensively analyze and optimize processes right from the start. In addition, incorrect planning in complex projects is less likely and wrong decisions can be avoided. All this together generates clear added value for companies.

The use is also exciting in the area of predictive maintenance. A Digital Twin greatly enhances predictive maintenance by not only collecting data on current operating conditions, but also providing forecasts and early warning options. Cost savings compared to other time-based or damage-dependent maintenance strategies can be significant.

Problem areas: What a Digital Twin cannot do

Caution is advised, however, if too much time and effort is put into the Digital-Twin-approach so that it is never really finished and ready for use. This not only leads to frustration for developers and product owners, but also endangers entire projects or can at least slow down their execution.

It also becomes problematic when different business units with their specific requirements expect too many capabilities from the Digital Twin. For example, different applications for a digital project could also be developed in individual projects or sub-projects. Not only can this lead to frustration if carried out at a different pace depending on the task and the team, but the added value of a Digital Twin is also lost. This is essentially what happens when not every project is started and carried out individually. Sufficient attention should therefore be paid in advance to the shared use of infrastructure by different users and their respective requirements.

Even as middleware, such as an Enterprise Service Bus or a queuing system, a Digital Twin is not suitable. A device can mirror data in the Twin, but the other way around this is limited. According to experts, a Digital Twin should therefore not “bring” data to third-party systems like classic middleware. Instead, it is recommended to allow the Twin to record the presence of new data in an event queue but continue to focus on its core competence.

It should also be noted that a Digital Twin only stores data and history to a certain extent. Just as a device does not store all data for capacity reasons, not all data can be stored in a Digital Twin. A complete archive of all transaction data would, for example, overload a Twin. In order to store complete histories, it is recommended to store them in a Data Lake. In general, you should therefore be aware that this technology generates a very large amount of data and that corresponding database technologies are required.

Another aspect to keep in mind is the security risks that a Digital Twin can pose – even if security issues always play a role in IT and are an integral part of discussions involving cloud technologies. Ultimately, it is always a trade-off between security and efficiency.

Digital Twin and digitization strategy

But how can a Digital Twin be sensibly located within the enterprise I(o)T architecture to support the digitization strategy? Experts recommend that the Digital-Twin-strategy be integrated into the corporate digital strategy. Ideally, a Digital-Twin-infrastructure should be built that can be used jointly by different physical products. A Digital Twin should not be rushed but should be developed over a period of time and also – important! – organizational responsibility should be placed at management level in order to receive the necessary attention and support.

It is also advisable to clearly outline and communicate the step-by-step expansion from a simple Twin to a more complex Twin in a clearly defined roadmap. Beyond that, it is advisable to plan, set up and operate Digital-Twin-applications with collaborative teams of technology and process experts. This will ensure that user-friendly usability is taken into account in developing the Digital Twin.

Most important factors for successful use

A Digital Twin generates added value for companies for many reasons: It makes productions more efficient, makes companies more flexible and can lead to exciting innovations, at the same time reducing wrong decisions and saving costs. It is important that a Digital Twin is declared a top priority and that technology and strategy receive the attention they need.

When setting up or expanding their own Digital-Twin-projects, decision-makers should also consult professional IT service providers, software system houses and/or cloud providers with the appropriate solution competence and project experience. Instead of laboriously and tediously building the required skills within your own company, it is advisable to rely on specialized providers who are also up to date with regard to technology and market trends and are able to respond to individual company needs.

Recommended posts


Process Optimization with Foresight: Digital Product Passport

Why the digital product passport is already a relevant tool today for making data available along th...

The Asset Administration Shell – a universal tool for data exchange in industry

How the Asset Administration Shell implements the concept of the Digital Twin in industrial practice...

From M2M to IoT & AI: 20 years of moving to a digital industry

The journey from M2M communications to IoT and AI-enabled systems. A look back.