3D Simulation‑driven optimisation for smart manufacturing lines
The Swiss Smart Factory use‑case
Author: Pantelis Papachristou
May 26th 2025
The latest demo by CORE Group’s technical team showcases a groundbreaking approach to drive digital transformation of manufacturing lines, using 3D simulation‑driven optimisation.
In collaboration with the Swiss Smart Factory (SSF) in Switzerland and using software developed by Visual Components, our team created a demo that shows how 3D simulations and data analytics can address critical bottlenecks and enhance overall efficiency and productivity in complex manufacturing operations.
3D simulation‑driven optimisation for smarter manufacturing
3D simulation is a powerful approach that uses advanced simulation software to create detailed virtual 3D models of production systems. These realistic virtual representations (3D scenes) accurately mirror the real world, including not only the physical geometry of objects and structures but also their texture, colour, lighting, and other visual properties.
In the context of manufacturing, 3D simulations are used to replicate entire production lines, individual machines, and workflows in a virtual environment. This method enables teams to identify inefficiencies and bottlenecks in the production line, optimise workflows, and test improvements without interrupting real‑world operations and risking downtime or disruption to actual production.
Key Features of the demo
The 3D simulation‑driven demo was developed in collaboration with the SSF, using simulation software developed by Visual Components - partnerships which our team has secured through the Twin4Twin and Modul4r Horizon EU projects. The demo was initially showcased during CORE Innovation Days, Greece’s first Industry 4.0 conference organised and hosted by CORE Group, with a follow‑up demonstration at CORE Group’s Beyond Expo booth.
In our demo, we focused on the SSF’s production line for the F330 drone, which includes several automated and robotic‑enhanced stations, such as a 3D printing farm for the drone’s blades, assembly and packaging stations, and a warehouse. By analysing the flow of components and monitoring machine utilisation, we identified areas for improvement and tested various optimisation strategies to boost overall throughput and efficiency.
3D simulation of the production line: Using the Visual Components simulation software, a virtual model of SSF’s production line was developed, including individual workstations and the transition of components (e.g. through Automated Guided Vehicles). To obtain a clear view of the production line’s behavior and establish a baseline for its performance, virtual sensors were integrated in each station to measure its cycle time, utilisation and throughput over time.
Data‑driven analysis: Leveraging data from each station, provided the foundation for identifying bottlenecks and inefficiencies in SSF’s production line that slowed down production flow. Through a dedicated data analysis, we pinpointed the root causes of these bottlenecks, highlighting the problematic areas which are keen to potential improvements and refining.
Optimisation scenarios: Based on the data analysis results, we tested four targeted optimisation scenarios, to address the observed bottlenecks. For example, we introduced intermediate buffer storage systems, expanded the capacity of particular workstations and added extra stations. Running the 3D simulations on these different scenarios allowed us to compare the optimisation results with the baseline, quantifying the simulated improvements in terms of overall production rate. Interestingly, the results were not always straightforward, as adding extra stations can also create new bottlenecks elsewhere in the line, leading to a decrease in productivity. Such unexpected results underscore the need for simulations in complex manufacturing environments, where interactions between different workstations can have non‑linear and counterintuitive effects.
Final report: Finally, we created a report that juxtaposes the cost for implementing each optimisation scenario with the induced improvement in production rate. This analysis was crucial in understanding the trade‑offs between implementing these optimisation strategies and the tangible benefits they provided. Moreover, this report serves as the starting point for deriving a more detailed ROI analysis. By inserting specific financial metrics, such as production costs and profit margins, manufacturers can calculate the potential financial impact of each optimisation scenario.
The Future of Manufacturing: Smarter, Faster, More Efficient
Our demonstration underscored the potential of 3D simulation‑driven optimisation to revolutionise manufacturing processes. By combining simulations with data analytics and photorealistic 3D models, manufacturers can gain deep insights into their operations, experiment with different optimisation scenarios, and implement improvements without disrupting production.
This approach not only helps identify and solve bottlenecks but also enables manufacturers to make smarter, data‑driven decisions that lead to improved efficiency, reduced downtime, and increased productivity. In this case, manufacturers can test and refine their processes, ensuring that their production lines are always operating at peak performance.
