The worldwide auto industry is all about intricate, high-speed manufacturing lines where accuracy and productivity take center stage. In this type of setting, knowing how the equipment, the processes, and downtime interact to keep things operating at peak is paramount.
Production Modeling India (PMI) collaborated with an international auto manufacturer that excelled in body-in-white closures and exhaust systems on a comprehensive analysis of production. The client's end-to-end production system needed a simulation-based solution to analyze throughput, determine bottlenecks, and optimize buffer design. This case study describes how PMI's simulation and engineering capabilities assisted in confirming system capacity and revealing opportunities for enhanced operational efficiency.
The client is a world-leader in the automotive manufacturing industry, with specialties in body-in-white closures, exhaust systems, and closure production equipment. Known for their Full Vertical Approach, they provide integrated turn-key solutions that range from product design and tooling through mass production.
This integrated system enables them to deliver quick vehicle development cycles while maintaining quality standards. In order to confirm the strength of a new production line and confirm its performance prior to implementation, the client hired PMI to perform a simulation-based study. The objective was to determine system throughput, find constraints, and identify optimal buffer strategies throughout the line.
The major objectives of the simulation exercise were to:
The client had several operational problems that affected line throughput and efficiency, such as:
Purges that occurred frequently during regular purge cycles interrupted flow and introduced delays in the sealer area.
These maintenance operations were asynchronous, impacting the overall system timing and productivity.
Part removal for quality inspection caused periodic flow disruption.
Downtime in one zone had cascading effects on adjacent zones due to inadequate isolation or buffering, complicating production stability.
To address the client’s complex production challenges, PMI implemented a structured six-stage approach that combined static analysis with advanced simulation modeling.
PMI started by ensuring all data provided by the clients, i.e., robotic timings, cycle times, and machine settings, were verified. This served to provide a theoretical baseline for system performance, utilization, and output capacity.
The production logic, interdependencies, and downtime behaviors were studied by the team to conceptualize an adaptable and flexible model structure. This process ensured all the important parameters and future change patterns were considered.
PMI created an elaborate computer model of the production system with simulation software. The model was thoroughly validated and cross-checked against static analysis results to ensure correctness.
Various scenarios were simulated with different buffer sizes, machine availability, and maintenance cycles to observe their effect on throughput and efficiency.
Important observations were recorded and possible areas of improvement identified.
Throughout the project, PMI collaborated with client stakeholders in close coordination to maintain alignment and transparency in modeling and recommendations.
PMI’s static analysis phase involved summarizing robotic timing data and translating it into event-based activity cycle times. Machine utilization levels were calculated by factoring in changeovers, maintenance, and forced delays. The analysis revealed that while the facility could achieve the required throughput, certain stations operated near capacity limits.
Particularly, Station 5C-010's material handling robot was found to be a chronic bottleneck, having the highest utilization and lowest availability in Zone 1. The other bottlenecks were 5C-030 and 5C-200, suggesting an upstream clustering of performance limitations. Line efficiency was computed at 93.4%, while machine availability throughout zones varied between 97% and almost 100%. The analysis also validated that the proposed buffers between downtime areas were adequate under existing operational conditions, avoiding cascading delays and preserving continuity of flow.
PMI’s simulation model accurately mirrored the production line’s operational behavior and validated static analysis assumptions. Various scenarios were tested, including variations in downtime frequency, buffer capacities, and cycle time adjustments. The model proved flexible enough to integrate potential future changes, such as new stations or modified shift structures.
Derived key performance indicators such as Gross Jobs Per Hour (JPH) and Net JPH indicated the system could produce as much as 4.5% above target. The simulation identified bottlenecks at 5C-010 and 5C-200 to be the main constraints on throughput gain. With this information, the client was able to prioritize optimization and make strategic upgrade planning with certainty.
The analysis and simulation produced some key results. To begin with, the current production line was found to be capable of delivering over throughput goals by about 4.5%, thus validating its design feasibility. Nevertheless, high-utilization stations, i.e., 5C-010, 5C-030, and 5C-200, were seen to be major bottlenecks limiting further improvement in efficiency. PMI suggested specific interventions at these stations, such as balancing workloads, improving automation, or redistributing tasks.
The buffer configuration in place between downtime areas was proved adequate, preventing the requirement for expensive redesigns. Coordination of maintenance activities such as tip dressing and robot purging was recommended in a bid to minimize production downtime. Second, the facility for continuous performance testing through the simulation model is provided as functional parameters change over time.
PMI’s analytical approach helped the client gain critical visibility into their production system's performance before physical implementation. By validating throughput capabilities and pinpointing areas of constraint, the client could make informed, data-backed decisions to enhance line efficiency and stability. The recommendations supported operational readiness and minimized the risk of post-commissioning disruptions.
Additionally, the flexible simulation model gave the client an invaluable tool for ongoing improvement, allowing them to be able to predict and test process changes ahead of time. The project showed how PMI expertise can lead to more intelligent manufacturing results in high-risk settings.
