SCALABLE SOLUTIONS FOR DETECTING STATISTICAL DRIFT IN MANUFACTURINGPIPELINES

Abstract

In modern manufacturing environments, maintaining product quality and operational efficiency is paramount. Statistical drift in manufacturing pipelines poses significant challenges, potentially leading to increased defects and reduced yield. This study explores scalable solutions for detecting statistical drift, leveraging advanced analytics and machine learning techniques. By implementing robust monitoring frameworks, manufacturers can identify deviations from expected patterns in real-time, enabling prompt corrective actions. The research discusses the integration of statistical process control (SPC) with machine learning algorithms to enhance predictive capabilities. Key methodologies, such as control charts and anomaly detection models, are examined for their effectiveness in identifying shifts in process behavior. The findings highlight the importance of real-time data collection and analysis, suggesting that a proactive approach to drift detection not only mitigates risks but also contributes to overall productivity and cost-effectiveness. Ultimately, this study provides a comprehensive overview of scalable solutions that empower manufacturers to adapt to dynamic operational conditions, ensuring consistent product quality and operational excellence.