Data-driven bottleneck detection in manufacturing systems: A statistical approach

Data-driven bottleneck detection has received an increasing interest during the recent years. This approach locates the throughput bottleneck of manufacturing systems based on indicators derived from measured machine performance metrics. However, the variability in manufacturing systems may affect the quality of bottleneck indicators, leading to possible inaccurate detection results. This paper presents a statistical framework to decrease the data-driven detection inaccuracy caused by system variability. The proposed statistical framework is numerically verified to be spectacularly effective in decreasing the wrong bottleneck identifications in production lines.