The Food and Drug Administration’s 2011 Process Validation Guidance and International Council for Harmonization Quality Guidelines recommend continued process verification (CPV) as a mandatory requirement for pharmaceutical, biopharmaceutical, and other regulated industries. As a part of product life cycle management, after process characterization in stage 1 and process qualification and validation in stage-2, CPV is performed as stage-3 validation during commercial manufacturing. CPV ensures that the process continues to remain within a validated state. CPV requires the collection and analysis of data related to critical quality attributes, critical material attributes, and critical process parameters on a minimum basis. Data is then used to elucidate process control regarding the capability to meet predefined specifications and stability via statistical process control (SPC) tools. In SPC, the control charts and Nelson rules are commonly used throughout the industry to monitor and trend data to ensure that a process remains in control. However, basic control charts are susceptible to false alarms and nuisance alarms. Therefore, it is imperative to understand the assumptions behind control charts and the inherent false alarm rates for different Nelson rules. In this article, the authors have detailed the assumptions behind the usage of control charts, the rate of false alarms for different Nelson rules, the impact of skewness and kurtosis of a data distribution on the false alarm rate, and methods for optimizing control chart design by reducing false alarm rates and nuisance signals.
| Published in | Science Journal of Applied Mathematics and Statistics (Volume 12, Issue 2) |
| DOI | 10.11648/j.sjams.20241202.11 |
| Page(s) | 20-28 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
CPV, Control Chart False Alarm Rate, Control Chart Skewness and Kurtosis, Control Chart Nuisance Signal
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APA Style
Muralidharan, N., Johnson, T., Rose, L. S., Davis, M. (2024). CPV Monitoring - Optimization of Control Chart Design by Reducing the False Alarm Rate and Nuisance Signal. Science Journal of Applied Mathematics and Statistics, 12(2), 20-28. https://doi.org/10.11648/j.sjams.20241202.11
ACS Style
Muralidharan, N.; Johnson, T.; Rose, L. S.; Davis, M. CPV Monitoring - Optimization of Control Chart Design by Reducing the False Alarm Rate and Nuisance Signal. Sci. J. Appl. Math. Stat. 2024, 12(2), 20-28. doi: 10.11648/j.sjams.20241202.11
AMA Style
Muralidharan N, Johnson T, Rose LS, Davis M. CPV Monitoring - Optimization of Control Chart Design by Reducing the False Alarm Rate and Nuisance Signal. Sci J Appl Math Stat. 2024;12(2):20-28. doi: 10.11648/j.sjams.20241202.11
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Download@article{10.11648/j.sjams.20241202.11,
author = {Naveenganesh Muralidharan and Thatsinee Johnson and Leyla Saeednia Rose and Mark Davis},
title = {CPV Monitoring - Optimization of Control Chart Design by Reducing the False Alarm Rate and Nuisance Signal},
journal = {Science Journal of Applied Mathematics and Statistics},
volume = {12},
number = {2},
pages = {20-28},
doi = {10.11648/j.sjams.20241202.11},
url = {https://doi.org/10.11648/j.sjams.20241202.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20241202.11},
abstract = {The Food and Drug Administration’s 2011 Process Validation Guidance and International Council for Harmonization Quality Guidelines recommend continued process verification (CPV) as a mandatory requirement for pharmaceutical, biopharmaceutical, and other regulated industries. As a part of product life cycle management, after process characterization in stage 1 and process qualification and validation in stage-2, CPV is performed as stage-3 validation during commercial manufacturing. CPV ensures that the process continues to remain within a validated state. CPV requires the collection and analysis of data related to critical quality attributes, critical material attributes, and critical process parameters on a minimum basis. Data is then used to elucidate process control regarding the capability to meet predefined specifications and stability via statistical process control (SPC) tools. In SPC, the control charts and Nelson rules are commonly used throughout the industry to monitor and trend data to ensure that a process remains in control. However, basic control charts are susceptible to false alarms and nuisance alarms. Therefore, it is imperative to understand the assumptions behind control charts and the inherent false alarm rates for different Nelson rules. In this article, the authors have detailed the assumptions behind the usage of control charts, the rate of false alarms for different Nelson rules, the impact of skewness and kurtosis of a data distribution on the false alarm rate, and methods for optimizing control chart design by reducing false alarm rates and nuisance signals.},
year = {2024}
}
TY - JOUR T1 - CPV Monitoring - Optimization of Control Chart Design by Reducing the False Alarm Rate and Nuisance Signal AU - Naveenganesh Muralidharan AU - Thatsinee Johnson AU - Leyla Saeednia Rose AU - Mark Davis Y1 - 2024/04/02 PY - 2024 N1 - https://doi.org/10.11648/j.sjams.20241202.11 DO - 10.11648/j.sjams.20241202.11 T2 - Science Journal of Applied Mathematics and Statistics JF - Science Journal of Applied Mathematics and Statistics JO - Science Journal of Applied Mathematics and Statistics SP - 20 EP - 28 PB - Science Publishing Group SN - 2376-9513 UR - https://doi.org/10.11648/j.sjams.20241202.11 AB - The Food and Drug Administration’s 2011 Process Validation Guidance and International Council for Harmonization Quality Guidelines recommend continued process verification (CPV) as a mandatory requirement for pharmaceutical, biopharmaceutical, and other regulated industries. As a part of product life cycle management, after process characterization in stage 1 and process qualification and validation in stage-2, CPV is performed as stage-3 validation during commercial manufacturing. CPV ensures that the process continues to remain within a validated state. CPV requires the collection and analysis of data related to critical quality attributes, critical material attributes, and critical process parameters on a minimum basis. Data is then used to elucidate process control regarding the capability to meet predefined specifications and stability via statistical process control (SPC) tools. In SPC, the control charts and Nelson rules are commonly used throughout the industry to monitor and trend data to ensure that a process remains in control. However, basic control charts are susceptible to false alarms and nuisance alarms. Therefore, it is imperative to understand the assumptions behind control charts and the inherent false alarm rates for different Nelson rules. In this article, the authors have detailed the assumptions behind the usage of control charts, the rate of false alarms for different Nelson rules, the impact of skewness and kurtosis of a data distribution on the false alarm rate, and methods for optimizing control chart design by reducing false alarm rates and nuisance signals. VL - 12 IS - 2 ER -
Manufacturing Science and Technology (MSAT), AGC Biologics, Boulder, USA
Manufacturing Science and Technology (MSAT), AGC Biologics, Boulder, USA
Manufacturing Science and Technology (MSAT), AGC Biologics, Boulder, USA
Manufacturing Science and Technology (MSAT), AGC Biologics, Boulder, USA
