Statistical Methods for Process Validation

  • Wednesday
  • October
  • 27
  • 2021
10:00 AM PDT | 01:00 PM EDT
60 Minutes
Heath Rushing Instructor:
Heath Rushing
Webinar Id:

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$149 Live
$299 Corporate Live
$199 Recorded
$399 Corporate Recorded
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This course will focus on teaching how to efficiently and effectively apply recommended statistical methods and tools to process validation. Using hands-on exercises (complete with realistic process data), participants will learn how to apply these tools, interpret results, and draw meaningful conclusions throughout Installation Qualification (IQ), Operational Qualification (OQ), and Performance Qualification (PQ).

In the application of statistical methods in IQ, the following will be discussed and demonstrated: sample size calculations and power, hypothesis testing and confidence intervals, and measurement systems analysis (MSA). In the application of statistical methods in OQ, the following will be discussed and demonstrated: failure modes and effects analysis (FMEA) and cause-and-effect diagrams, statistical process control (SPC), design of experiments (DOE) as well as response surface methodology (RSM), and process capability. In the application of statistical methods in PQ, the following will be discussed and demonstrations: SPC, process capability, and FMEA.

Although this is not a course on statistical theory, participants will gain an understanding and exposure to the appropriate statistical methods that should be applied through process validation.

Why should you Attend: According to 21 CFR Part 820, medical device manufacturers are required to validate as well as monitor and control parameters for their processes. The guideline on Quality Management Systems does not specify how this is accomplished; only that "a process is established that can consistently conform to requirements" and "studies are conducted demonstrating" this. Thorough process development, optimization, and control using appropriate statistical methods and tools are recommended for demonstrating your process is both stable and capable.

Areas Covered in the Session:

  • Know which statistical methods are recommended by the guidance documents
  • Understand where these statistical methods should be applied
  • Understand different types of data intervals
  • Understand the benefits of a measurement systems analysis (MSA)
  • Be able to analyze a hypothesis test and interpret data intervals
  • Be able to design and analyze an experiment
  • Be able to generate and interpret process control charts and capability indices

Who Will Benefit:
  • Process Engineer
  • Design Engineer
  • Design Controls Engineer
  • Six Sigma Green Belt
  • Six Sigma Black Belt
  • Product Development Engineer
  • Continuous Improvement Manager

Speaker Profile
Heath Rushing is the cofounder of Adsurgo and author of the book Design and Analysis of Experiments by Douglas Montgomery: A Supplement for using JMP. Previously, he was the JMP and Six Sigma training manager at SAS.

He led a team of nine technical professionals designing and delivering applied statistics and quality continuing education courses. He created tailored courses, applications, and long-term training plans in quality and statistics across a variety of industries to include biotech, pharmaceutical, medical device, and chemical processing.

Mr. Rushing has been an invited speaker on applicability of statistics for national and international conferences. As a Quality Engineer at Amgen, he championed statistical principles in every business unit. He designed and delivered a DOE course that immediately became the company standard required at multiple sites.

Additionally, he developed and implemented numerous innovative statistical methods advancing corporate risk management, process capability, and validation acceptance criteria. He won the top teaching award out of 54 instructors in the Air Force Academy math department where he taught several semesters and sections of operations research and statistics.

Additionally, he designs and delivers short courses in statistics, data mining, and simulation modeling for SAS.

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