A Predictive Quality Assurance Model Using Lean Six Sigma: Integrating FMEA, SPC, and Root Cause Analysis for Zero-Defect Production Systems

Achieving zero-defect production has become a critical objective in modern manufacturing, necessitating the integration of robust quality assurance strategies. This study proposes a predictive quality assurance model that synergistically combines Lean Six Sigma methodologies with Failure Modes and Effects Analysis (FMEA), Statistical Process Control (SPC), and Root Cause Analysis (RCA). The model is designed to proactively identify, monitor, and mitigate quality deviations before they evolve into defects, aligning with the core principles of continuous improvement and waste reduction. The framework begins with FMEA to systematically prioritize potential failure modes based on severity, occurrence, and detectability. This prioritization informs targeted quality control measures and risk mitigation strategies. SPC is then employed to monitor critical process parameters in real-time using control charts, enabling early detection of process variations that may lead to product nonconformance. RCA tools such as the 5 Whys and Fishbone Diagram are incorporated to trace identified deviations back to their fundamental causes, facilitating corrective and preventive actions. The integration of these tools under the Lean Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) methodology enhances process capability, reduces variability, and fosters a culture of zero-defect mindset across production lines. A predictive analytics component is embedded into the model, leveraging historical quality data and machine learning algorithms to forecast potential defects and trigger timely interventions. A case study in an automotive parts manufacturing plant demonstrated the model’s effectiveness, resulting in a 35% reduction in defect rates and significant improvements in first-pass yield and customer satisfaction. The results validate the model's capacity to drive data-informed decisions, enhance product quality, and reduce costs associated with rework and warranty claims. This model serves as a scalable and adaptable quality assurance solution for various manufacturing environments seeking to achieve operational excellence and competitiveness. Future research can explore the integration of Industry 4.0 technologies, such as IoT and digital twins, to further optimize real-time monitoring and decision-making.