At a glance
A digital twin is the digital representation of products, materials, services, processes or devices in a virtual environment. One tries to reproduce the real conditions in a company so faithfully in a digital form. This virtual representation can then be used to carry out analytical and planning operations digitally before they are translated into reality. A digital twin is particularly important for Industry 4.0 in connection with the Internet of Things.
Big Data for Digital Twins
Industry 4.0 and the Internet of Things provide an abundance of sensors and measuring points that, viewed holistically, can provide a detailed picture of the entire value chain. Big Data is therefore an important tool for the detailed analysis of processes from storage to production and delivery.
The use of digital twins offers many different advantages, such as
- cost savings in planning and development
- smoother processes through detailed, digital simulations
- more efficient process optimization
- detailed insight into processes through analysis and simulation
- holistic view of the entire value chain in real-time
- simplified communication with customers and suppliers through accurate insight into production
Application examples for digital twins
The digital representation of reality lends itself to a wide variety of tasks.
Thanks to Big Data and Digital Twins, companies gain a very detailed insight into their processes. And this information helps to continuously improve these processes in order to achieve the best possible efficiency with the available means and possibilities. This not only applies to manufacturing itself, but can also benefit maintenance and logistics within the company.
If the quality of the finished products is not right, the production processes have to be changed. With a digital twin of the production plant, it is easy to simulate which changes in the production process will lead to the desired result or which consequences a variation in product design could have. In this way, it is possible to determine cost-effectively which measures could actually have an effect, before changes are introduced to the real production process.
In addition, the digital representation of the production environment almost offers a kind of sandbox in which engineers can freely try out new ideas and unusual approaches without any consequences in reality. By filling the system with real data from ongoing operations, many possible "what if...?" scenarios with realistic results can be tried out very conveniently - and with low risk.