Data-driven analysis of dynamic bottlenecks in order-based value streams

Abstract

Traditional bottleneck approaches often have a static view, neglecting dynamically shifting bottlenecks and logistical goals such as on-time delivery. In industry 4.0 factories, the amount of shop floor data increases dramatically – yet is often underutilized. Existing approaches for detection are based on the measuring machine states, buffer levels, or process times. However, prioritization and cause-based diagnosis of bottlenecks for targeted elimination are ongoing research. This paper proposes a data-driven approach for bottleneck detection, prioritization and diagnosis. For detection, the utilization method and the active period method are applied. For prioritization, the current backlog situation is relevant. For diagnosis, a cause-based machine and buffer perspective is used. This extension and combination of existing approaches in a extended value stream diagram enables data-driven analysis of dynamic bottlenecks and considers additional logistical goals. The practical approach is successfully tested in a steel carrier production.