Introduction
Success in expansion isn't quantified by square inches; it's quantified by performance. One of India's largest and most well-respected auto manufacturers was at a juncture as it contemplated increasing its Paint Shop capacity 1.5 times. With an expanding product portfolio and aggressive production targets, the stakes were high. But increasing the paint shop isn't as easy as installing new booths and ovens. In such a tightly coordinated system, even slight process inefficiencies can propagate down the line and restrict total throughput.
To validate that the expansion would yield the desired outcomes, the client hired Production Modeling India (PMI) to perform an extensive simulation study based on Tecnomatix Plant Simulation and AutoMOD tools. The mission: to ascertain whether the planned alterations would truly realize increased capacity, and how to make sure that each added resource would be translated into quantifiable efficiency.
Client's Challenge
• Verify if the planned expansion could realize the targeted capacity rise
• Identify hidden bottlenecks that could hamper performance even after expansion
• Understand the impact of key operational variables like repair rates, model mixes, and storage constraints
Given the dynamic nature of paint shop operations, where drying times, repair cycles, and vehicle routing all play critical roles, simulation offered the only way to predict performance in a controlled, data-driven environment.
Simulation of an Automobile Plant for a Leading Vehicle Manufacturer
PMI’s Simulation Strategy
PMI implemented a phased strategy to model and simulate the paint shop system in depth:
PMI created a high-fidelity digital twin of the paint shop with Tecnomatix Plant Simulation, recreating each significant step from pre-treatment to final inspection. The model included actual cycle times, process rules, and vehicle routing logic.
Key performance metrics like system throughput, time-in-state, and number of vehicles per station were monitored. The model was then run through several "what-if" situations by changing:
• Repair rates following polishing
• Storage buffer sizes
• Model mix proportions for four vehicle types
• Downtime distribution
PMI examined how responsive the system output was to the parameters changed, and which factors contributed most to performance.
Key Findings and Recommendations
Some key findings resulted from the simulation:
A constrictive rule of operation close to the end of the oven was the main throughput bottleneck. Vehicles were spending more time than required for the cooling process to complete, holding up downstream stations.
• Recommendation: Enhance cooling zones so they can process the vehicle more quickly. This one adjustment yielded a 15% boost in throughput and enabled the reduction in equipment needs.
The rate of post-polishing repairs was above optimum, resulting in blocking and tardiness.
• Recommendation: Either improve production quality or introduce additional storage buffers to improve variability. Simulation indicated that either action would improve the capacity towards the target.
Not every vehicle variant took the same duration at every station. Some mixes produced imbalanced queues.
• Recommendation: Keep a balanced model mix ratio in the four variants to prevent peak-hour jamming and achieve smooth flow.
Conclusion
The simulation study did not simply validate expansion plans; it revealed essential inefficiencies and delivered realistic solutions. By simulating real-world complexity in a virtual context, PMI enabled the client to make better decisions prior to undertaking physical changes.
Having a clearer grasp of their process flow and limits of performance, the client is now in a position to not only increase capacity but also perform with greater agility and fewer shocks.