Manufacturing Line-Level Root Cause Analysis and Bottleneck Detection Using the Digital Shadow Concept and Cloud Computing

The paper introduces a method for online detection of root-cause machine and bottleneck identification in manufacturing systems, aiming to enhance reliability and productivity. The study addresses the interconnected nature of manufacturing systems, emphasizing the impact of equipment faults on overall performance of the line. Existing fault propagation methods are discussed, highlighting the need for a comprehensive approach considering the entire production process. The proposed root-cause algorithm utilizes a digital shadow concept, analyzing machine states and historical data to identify the primary source of faults. The study focuses on machine workstates, distinguishing between internal and external causes, thus determining the root-cause machine affecting the entire line. A Gantt chart-based approach considers the relative timing of events, enhancing accuracy in root-cause determination. Bottleneck detection methods are presented, including analysis of active periods and an arrow method based on machine blockages and starvations. The study showcases a cloud-based system, LogiX, for real-time data processing and visualization, integrating Industry 4.0 principles. A case study evaluates the proposed methods using an 8-h shift period in a bottle filling&packaging line. Root-cause analysis achieved an 89.23% detection efficiency, demonstrating practical applicability. Bottleneck detection methods, both active period analysis and the arrow method, identified the Labeller as the potential bottleneck. In conclusion, the paper provides a valuable contribution to manufacturing system optimization, offering a systematic on-line approach to root-cause identification and bottleneck detection. The proposed methods exhibit promising results, with potential applications in enhancing overall equipment effectiveness and production efficiency.