There is a way of thinking about factory design that the best manufacturing teams have adopted over the last decade. Build the factory twice. Once in software, where mistakes are cheap and iterations are fast. Then once in the real world, where you already know it works.
For Indian EV startups, which are racing to production timelines while managing tight capital, this approach is not a luxury. It is one of the few ways to absorb the complexity of designing a new vehicle and a new factory at the same time without blowing the launch schedule.
This is the story of how one such company, an electric two-wheeler startup based out of Pune, used robotic simulation to rethink their battery pack assembly line before a single robot was installed and ended up with a 35% reduction in cycle time compared to their original layout design.
The company had secured Series B funding and was under pressure to hit a production ramp of 5,000 units per month within 18 months. Their vehicle design was largely frozen. Their greenfield facility in the MIDC area was under construction. And they had just received delivery commitments from three fleet customers that made the timeline non-negotiable.
The battery pack assembly was the bottleneck they were most worried about. Lithium-ion cell handling, module stacking, busbar welding, and final pack testing involved a mix of collaborative robots, fixed automation, and manual workstations. Getting the sequence, the robot reach envelopes, and the material feeding right on paper is genuinely difficult. Getting it wrong and discovering that after installation means rework costs and delays that can run into crores.
Robotic simulation using a tool like Siemens Process Simulate lets engineers build a virtual replica of the assembly environment. The robot models are accurate kinematic representations of the actual hardware they plan to install. The cell jigs, conveyor systems, and workpiece geometry are modeled from the actual CAD data. Then the engineers program the robots virtually, define the work sequences, and run the simulation.
The output is not just a video. It is data. Cycle times for each station, robot utilization percentages, collision detection reports, reach feasibility analysis, and bottleneck identification. You can see where a robot is waiting for the upstream station to complete, where a reach problem would require a redesign of the fixture, and where adding a second collaborative robot arm cuts 8 seconds off a critical path cycle.
Three things stood out from their simulation runs that significantly changed the final factory design.
The first was a reach conflict on the busbar welding station. The original layout had two robots working from the same side of the assembly fixture. In simulation, it became immediately clear that their reach envelopes overlapped at a specific point in the sequence, which would have required complex coordination logic to avoid collisions. Shifting one robot to the opposite side of the fixture, a change that cost nothing in simulation, resolved the conflict entirely.
The second finding was a bottleneck at the cell insertion station. The simulation showed that the manual cell feeding operation upstream could not keep pace with the robot, so the robot was idle for an average of 11 seconds per cycle waiting for cells. Adding a buffer magazine between the cell feeder and the robot station brought robot utilization from 61% to 84% on that station.
The third finding was about the overall line sequence. The original design had quality inspection happening at the end of the line, after all assembly steps were complete. Simulation of the overall flow showed that moving a mid-process voltage check to after module stacking, rather than at final pack level, would catch reject conditions four stations earlier and reduce rework travel distance significantly.
When the actual factory came up and the line was commissioned, the cycle time performance was within 8% of the simulation prediction from day one. The 35% improvement over the original layout design was realized. More importantly, the commissioning period, which is often where Indian factories lose weeks of production ramp time while engineers debug robot programs and fixture issues, was cut by roughly half because the programs had already been validated in the virtual environment.
The total cost of the simulation exercise, including software and engineering time, was recovered in less than two months of production at target volumes.
India’s EV manufacturing sector is at a point where the companies that invest in front-loaded simulation will have a production ramp advantage over those that rely on physical trial and error. With government PLI incentives tied to production volume targets, getting to volume faster has direct financial value beyond just the customer delivery commitments.
If you are currently in the factory planning phase for an EV or battery manufacturing facility, simulation is the right investment to make before you finalize the layout and place equipment orders.
