Disruption data collection in low-volume, complex product assembly
Abstract
Disruptions impair the assembly performance of almost any manufacturer of low-volume, complex products (LVCP). This research combines a literature review and a single case study at a LVCP manufacturer to investigate how disruption data collection methods impact an effective disruption management. At the LVCP manufacturer at stake, LVCP are built in a manual assembly and most disruption data stems from manual, event-based data collection methods. Our findings suggest that the following factors favour effective disruption management in the manual assembly of LVCP: trained disruption detection and disruption interpretation processes for operators, awareness of the importance of correct data entries and an efficient design of the human-machine interaction when gathering data.
