Tire Manufacturer Identifies 25% Capacity-Reducing Bottleneck Before Plant Build with Simulation
A documented Process Optimization in Automotive deployment at Anonymous Tire Manufacturer, with source-attributed results and missing evidence labelled explicitly.
Evidence at a glance
- Evidence status:
- Automated evidence gate passed
- Deployment timeframe:
- Not reported by source
- Reported outcome metrics:
- 1 cited below
- Directory entry published:
The source-link check confirms reachability, not independent re-verification of every claim.
Source-reported figures — cited source: rockwellautomation.com
The Challenge
A major tire manufacturer contracted LGI to model and analyze a final finish modernization plan for one of its plants. The plan involved complex interdependent processes including conveyor networks, x-ray systems, uniformity testing, auto balancers, and gantry palletizers that were difficult to evaluate without simulation.
The Solution
Arena Simulation Software modeled all final finish process operations and material handling systems, including conveyor network control logic, x-ray process, tilt-tray conveyors, uniformity processes, and gantry palletizers to identify bottlenecks and labor requirements.
Results
The simulation identified conveyor bottlenecks in the preliminary system design that would have reduced system capacity by 25% if built as designed. This saved the manufacturer the cost of building a suboptimal system and allowed design corrections before construction.
Key Takeaways
- Tire manufacturing final finish simulation can identify capacity-reducing bottlenecks that would cost millions to correct after facility construction
- Conveyor network control logic simulation is essential for identifying dynamic bottlenecks that static capacity analysis cannot reveal
- Pre-construction identification of a 25% capacity shortfall represents the highest-ROI application of simulation in capital-intensive manufacturing projects
Vendor
Details
- Industry
- Automotive
- Use Case
- Process Optimization
- AI Technology
- Digital Twin
- Company Size
- Enterprise
- Company
- Anonymous Tire Manufacturer
- Evidence status
- Automated evidence gate passed
- Deployment timeframe
- Not reported by source
- Directory entry published
Cited source
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