Tire Manufacturer Identifies 25% Capacity-Reducing Bottleneck Before Plant Build with Simulation

A tire manufacturer deployed Digital Twin for Process Optimization in Automotive. As reported by rockwellautomation.com: 25% potential capacity loss prevented.

Maintained by Peter Korpak, Lead EditorHow evidence is checked
25%Potential Capacity Loss Prevented

Source-reported figures — cited source: rockwellautomation.com

What the tire manufacturer was trying to fix

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.

What the tire manufacturer deployed

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

Evidence for the tire manufacturer's Process Optimization deployment

Reported outcome metrics
1 cited below
Last updated

Limitation: The cited source does not identify the company.

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Details

Industry
Automotive
AI Technology
Digital Twin
Company Size
Enterprise
Company
Tire Manufacturer

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