Users of digital twins are usually focusing on improving the product quality together with optimization of the asset maintenance process. Real time monitoring offers data that enables us to see processes in the entire context. Therefore, the algorithms of machine learning can provide an optimal variant of both production and maintenance.

Early adopters of digital twins appreciate the benefit of this technology primarily in these four key areas:

Greater effectiveness

Digital twins bring new findings about streamlining production processes. For creating a reliable digital strategy, it is necessary that each phase of the production process is tracked in real time. This way, the digital twin is a truly accurate representation of a real physical system and enables testing scenarios focused on improving production effectiveness.

Quality management

Digital twins help to improve quality management and especially reveal the cause of why the anomalies occur in the first place, using thorough production analysis in real time. Based on the findings, enterprises are then able to reduce the error rate, enhance the utilization rate of machinery and improve material management. Digital twins are also more and more used in the phase of designing a new product for simulation of the resulting quality which is evaluated from numerous aspects. Thanks to that, it is possible to detect potential defects before creating a new product.

Maintenance optimization

Digital twins proved to be a very effective tool also in terms of maintenance optimization. The objective of digital twins implementation is gaining understanding of how certain combinations of material components, the utilization rate of machinery and especially predictive maintenance based on detailed knowledge of factors influencing the service life of assets manifest. 

Cooperation across groups

Digital twins allow us to achieve a better overview and cooperation across the whole production process. With the ability to simulate at first and then plan changes in the production process, digital twins contribute to more effective processes of new products development, increase the product quality and increase performance across various production systems.

The effect of digital twins use is currently apparent in development of all next generation products. It participates in the development of supersonic aircrafts, satellites, smart technologies, but also optimizations of many current production systems. However, in order for the digital twin utilization to be truly effective, it must be anchored in the entire business strategy and correspond with the specific purpose.