Statistical Process Control Applied to Monitoring of Cable Networks

Statistical Process Control and control charts, long-established quality control methodologies in manufacturing and process industries, have recently emerged as powerful tools for monitoring and optimizing cable networks. Building upon decades of successful implementations in sectors ranging from semiconductor fabrication to pharmaceutical production, this paper presents an adaptation of those methodologies to cable network monitoring. Traditional network monitoring approaches relied on expert-informed global thresholds for detection of out-of-control events, limiting the ability to detect device-specific anomalies and performance degradation. Our implementation leverages individualized control charts for subscriber gateways, enabling dynamic threshold computation based on historical device behavior rather than system-wide metrics. By analyzing device-specific patterns and variations, the methodology demonstrates improved anomaly detection precision compared to global threshold approaches. Furthermore, the refined granularity of device-level control charts provides higher quality training data for backend machine learning algorithms, reducing false positives and enhancing predictive maintenance capabilities. This approach demonstrates significant potential for improving network reliability and maintenance efficiency in next-generation cable networks through data-driven, device-specific monitoring strategies.