Data-Driven Process FMEA for Flexible Manufacturing Systems: Framework and Industrial Case Study

Flexible automated assembly lines (FAALs) in Industry 4.0 require robust quality management that integrates operational data with systematic risk analysis. However, Process Failure Mode and Effects Analysis (PFMEA) documents are often developed during the design phase and not systematically updated with actual production data, leading to a gap between formal risk assessment and operational reality. This study addresses this gap by developing and validating an integrated data-driven framework that combines classical quality tools (process flow charts, check sheets, cause-and-effect diagrams, and Pareto analysis) with data-driven PFMEA, creating traceable links from operational logs to risk ratings. While individual quality tools are well-established, the core contribution of this work is a structured data transformation pipeline that creates traceable, auditable linkages from raw operational event logs to calibrated PFMEA ratings with quantified uncertainty—a combination not previously demonstrated for flexible assembly systems. The framework was applied to FMS-200, a modular FAAL for bearing units, consisting of eight stations and a common transfer system. Analysis of 186 failure events across 2743 assembly cycles, including 18 product configurations, identified 40 distinct failure modes with risk priority number (RPN) values ranging from 60 to 378, revealing that approximately 90% of the aggregated risk is associated with pneumatic systems. Monte Carlo uncertainty analysis (10,000 iterations) demonstrated robust rank stability, with the top five failure modes maintaining their relative ordering in over 90% of simulations. The framework provides production and quality managers with a systematic methodology to maintain PFMEA relevance through continuous data integration, enabling evidence-based prioritization of improvement actions.