Abstract
This paper illustrates how to improve robotic automotive body shop throughput by feeding back actual plant data for input variables for entities to improve discrete simulation model prediction and validation. The input variables are adjusted based on actual plant data, and the simulation model was run for various scenarios to verify and validate the discrete simulation model. The general tendency in an automotive plant body shop is to buy more equipment to improve throughput. It is very interesting to see how mean time to repair/mean time between failure assumptions affect the throughput. The bottleneck issues are identified for automotive plant to improve throughput.
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