More precise digital twins in production

The SAS offering combines UE simulations with SAS Viya optimization routines. UE is one of the most open and advanced tools for real-time 3D visualization. The rendering technology has been put to the test by millions of gamers and is now also being used in the automotive and manufacturing sectors. The realistic simulations provided by the highly detailed and precise Digital Twins make it possible to avoid time-consuming and costly test runs in the actual operating environment. Processes are mapped in a credible digital environment and, together with AI, this leads to precise forecasts.

Pilot project at Georgia Pacific

As a manufacturer of paper and cellulose-based building materials, Georgia Pacific uses UE-supported digital twins at its Savannah River Mill production facility. The technology is optimizing its transportation system with AGVs. SAS uses RealityScan, a mobile application from Epic, to create a photorealistic rendering of the mill and import it into UE. The combination of SAS analytics and Epic's engine helps Georgia Pacific align operations without disrupting production lines, with the goal of reducing costs and increasing product quality.

"With SAS and the Unreal Engine, we can create realistic simulations of manufacturing processes. Autonomous vehicles move through the factory floor, reacting in real time to proximity alarms, obstacles and unpredictable events," says Roshan Shah, Vice President of AI & Products at Georgia Pacific.

Blueprint for further application scenarios

The optimized digital twins make advanced analytics accessible to a broader user group - both in terms of roles and industries. In manufacturing, for example, this means that workers on the production line, mechanics and engineers can handle data and AI more easily. In turn, the healthcare sector could benefit from the optimization of patient flows in clinics, predictive maintenance for medical devices or support for employee training. Similarly, digital models could be used in urban planning to support the development of smart cities and thus create sustainable and liveable conditions.