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Batch-Level Signal Detection: Early-Warning Analytics for Quality Drifts

Author(s) Pawankumar Suresh
Country India
Abstract The capacity to detect early warning signs of quality variations at the batch level is paramount to both operational excellence, lower costs, and compliance. Conventional Statistical Process Control (SPC) solutions offer useful trend control but are vulnerable to complex, multivariate and non-linear outliers which may result in out-of-specification (OOS) or out-of-trend (OOT) events. This paper suggests an advanced and comprehensive early-warning analytics platform design integrating SPC with modern anomaly detection algorithms, such as machine learning and signal processing algorithms, to improve predictive monitoring of batch data. The framework allows proactive detection of quality drifts using both structured process parameters as well as high-frequency surrogate signals. The combination of SPC with anomaly detection has been utilized in real-world applications in healthcare, manufacturing, environmental monitoring, and pipeline safety to provide probabilistic early-warning cues to support preventive interventions. Findings indicate that this type of hybrid system not only minimizes batch rework and resource wastage, but enhances compliance, reliability, and decision-making within industries. In the paper, we can point out the growing importance of early-warning AI analytics to predictive quality management and provide a path to the powerful, real-time monitoring system that can boost efficiency and safety.
Keywords Signal detection at the batch level, early-warning analytics, Statistical Process Control (SPC), anomaly detection, out-of-specification (OOS), out-of-trend (OOT), predictive quality management, machine learning, process monitoring, reduction of batch rework.
Field Medical / Pharmacy
Published In Volume 4, Issue 11, November 2023
Published On 2023-11-08
Cite This Batch-Level Signal Detection: Early-Warning Analytics for Quality Drifts - Pawankumar Suresh - IJLRP Volume 4, Issue 11, November 2023. DOI 10.5281/zenodo.17205399
DOI https://doi.org/10.5281/zenodo.17205399
Short DOI https://doi.org/g943n7
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