Abstract
Identifying bottlenecks is a crucial challenge for manufacturing firms in their quest to increase capacity. While many approaches have been proposed to address this issue, existing solutions still have limitations in dealing with highly dynamic, stochastic systems in modern smart manufacturing. Hence, identifying bottlenecks remains a complex task. To address these shortcomings, we propose a novel data-enabled mathematical model for evaluating the performance of assembly lines with limited sensor information. A dynamic bottleneck detection method is then developed based on this proposed model. Furthermore, the production loss caused by the detected bottleneck stations, referred to as the dynamic production loss (DPL), can be evaluated and attributed. Numerical case studies are presented to validate the accuracy of the data-enabled model and demonstrate the effectiveness of the bottleneck detection and DPL evaluation methods.
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