Practical guidance on CGMP production controls using SPC, Six Sigma, and statistical methods — for device, pharmaceutical, and other CGMP manufacturers, and the contract manufacturers who support them.
Includes live access & recording
This webinar provides guidance regarding CGMP production controls using SPC, Six Sigma, and statistical controls for use in device, pharmaceutical, and other CGMP manufacturing industries — and for use by contract manufacturers that support the regulated medical products industries.
Statistical process control (SPC) is a method of quality control which employs statistical methods to monitor and control a process. It is one of the subpart requirements of the US FDA's QMSR, 21 CFR 820, the Medical Device CGMP, as well as other CGMPs. It can be a key tool in meeting the Production and Process Control (P&PC) requirements of those same CGMPs, including 21 CFR 211.
SPC used in P&PC and the required periodic quality system analysis helps to ensure that the process operates efficiently, producing more specification-conforming products with less waste (rework or scrap). SPC provides advantages in any production process where the "conforming product" — product meeting specifications — output can be measured.
This webinar will define what the US FDA's expectations are for the use of statistical techniques for establishing, controlling, and verifying the acceptability of process capability and product characteristics, sampling plan justifications for product acceptance or validation, and other studies.
Increasingly, regulatory agencies require trend analysis for non-conformances, complaints, and CAPAs as part of CGMP compliance. Subject areas considered include:
SPC / Six Sigma / control charts in production, test, and lab equipment; sampling plan justification; verifications and validations; and trending of non-conformances, complaints, and CAPA — and its incorporation into the production process.
Four focus areas that connect SPC and process data to defensible quality, stability, and manufacturing decisions under CGMP.
Apply SPC, Six Sigma, and control charts to monitor and control production, test, and laboratory equipment — and to detect process changes early, before conforming output is affected.
Build statistically justified sampling plans for product acceptance and validation studies that meet FDA expectations for establishing and verifying process capability and product characteristics.
Use the right statistical tools — control charts, GR&R, CpK — to support verifications and validations and to demonstrate acceptability of process capability and product characteristics.
Meet growing regulatory expectations for trend analysis of non-conformances, complaints, and CAPAs — and incorporate that trending back into the production process.
Ten focused topics covering SPC fundamentals, regulatory requirements, and the statistical tools used to support CGMP decisions.
Professionals across quality, manufacturing, engineering, and regulatory functions who use process data to support CGMP production and process control decisions.
Quality Assurance Departments
Quality Control Departments
Research and Development Departments
Regulatory Affairs Departments
Manufacturing Departments
Engineering Departments
Operations Departments
Production Departments
Validation Departments
Marketing Departments
Documentation Departments
Statistical process control is written into the CGMPs. The US FDA's QMSR and 21 CFR 820 for medical devices, along with 21 CFR 211 for pharmaceuticals, require manufacturers to establish and use valid statistical techniques for establishing, controlling, and verifying process capability and product characteristics, and to justify sampling plans used for acceptance activities and validation studies.
Beyond acceptance sampling, regulators increasingly expect trend analysis to be a routine part of the quality system — applied to non-conformances, complaints, and CAPAs — so that emerging process changes are detected early and fed back into production and process control. SPC provides the framework for distinguishing common-cause variation from special-cause signals and for acting on the difference.
This session brings those requirements together with the practical tools — control charts, GR&R, CpK, and related methods — that manufacturers and their contract partners use to keep processes in a state of control, reduce waste, and support quality, stability, and manufacturing decisions with defensible data.
John E. Lincoln, Principal of J.E. Lincoln and Associates LLC, has over 40 years of experience in U.S. FDA-regulated industries, 30 of which are as an independent consultant. John has worked with companies from start-up to Fortune 100, worldwide.
He specializes in quality assurance, regulatory affairs, QMS problem remediation and FDA responses, new / changed product 510(k)s, process / product / equipment including QMS and software validations, ISO 14971 product risk management files / reports, Design Control / Design History Files, and Technical Files. He has held positions in Manufacturing Engineering, QA, QAE, and Regulatory Affairs, up to the level of Director and VP (R&D). In addition, John has prior experience in military, government, electronics, and aerospace.
He has published numerous articles in peer-reviewed journals and chapters in books, including RAPS (six chapters in two textbooks on validations), and has conducted workshops and webinars worldwide. John is a graduate of UCLA.
Join John E. Lincoln for this 90-minute session on using SPC and process data to support quality, stability, and manufacturing decisions under CGMP.