Quality management (QM) methods focus on individual production steps, leaving room for undetected defects to propagate throughout the manufacturing process. This can lead to considerable resources being used on reinspection, rework, and scrap. The digitalization of manufacturing is offering new opportunities for quality management, allowing for proactive intervention and a better understanding of product quality in multi-stage manufacturing systems. However, current approaches lack a comprehensive QM strategies focussing on the overall manufacturing system and do not adequately leverage data analytics. A new data-driven QM concept has been developed that uses cluster-based product characterization and model-based quality control to improve overall product quality and manufacturing productivity in multi-stage manufacturing system.