Overview:
This 2-day seminar provides a 1-day introduction to the statistical tools used to analyze Design Verification data and Process Validation results. The 2nd day is spent on Statistical Process Control and Process Capability Indices. The goal of the 1st day is to help the student understand how to choose statistical methods and sample sizes, and to correctly interpret the results. The goal of the 2nd say is to explain how to monitor a validated production process, using tools that will also help improve the product.
Why should you attend:
All design and/or manufacturing companies perform design verification and/or process validation studies. A solid understanding of relevant statistical concepts and methods ensures that such efforts are efficient and accurate. In addition, all validated processes must be monitored to ensure their continued suitability (per the FDA).
The statistical methods used for such activities are easily misused when their fundamental principles are not well understood. Mistakes in usage can lead to new products being launched that should have been kept in R&D; or, conversely, deciding to not launch a new product because of incorrectly calculated product reliability or process capability. Failure to monitor processes accurately can lead to a slow decline in product quality.
This seminar provides a thorough, practical introduction to the relevant statistical methods that will help ensure quality results from R&D, Transfer, and Manufacturing.
Areas Covered in the Session:
FDA, ISO 9001/13485, and MDD requirements
Statistically valid sample sizes
The significance of statistical significance
The impact of normality and non-normality
Impact of risk analysis on choice of validation criteria
Who Will Benefit:
QA/QC Supervisor
Process Engineer
Manufacturing Engineer
QC/QC Technician
Manufacturing Technician
R&D Engineer
Agenda:
Day One
STATISTICAL ANALYSIS OF DESIGN VERIFICATION DATA AND PROCESS VALIDATION RESULTS Lecture 1: Regulatory requirements
Lecture 2: Basic vocabulary and concepts
Lecture 3: How to interpret Linear Regression Correlation coefficients
Lecture 4: How to calculate confidence intervals (for proportions & for measurements)
Lecture 5: How to perform an interpret t-Tests, including consideration of "significance", "p-values", "power" and sample-size considerations, and the concepts of "superiority" and "non-inferiority".
Lecture 6: How to understand the output of an ANOVA calculation
Lecture 7: Calculation of confidence and % in-specification (=reliability) for
attribute data
MTTF & MTBF (Mean time to Failure, and Mean time between Failure)
normally-distributed variables data (including test for normality)
non-normal data after transformation to normality
non-normal data that cannot be transformed to normality
Day Two
STATISTICAL PROCESS CONTROL (SPC) AND PROCESS CAPABILITY INDICESLecture 1: What is Quality ?
Lecture 2: Process Variation
Lecture 3: What is Statistical Process Control ( SPC ) ?
Lecture 4: Basic Types of Control Charts and how to construct them: XbarR, XbarS, XmR, P, and U.
Lecture 5: Control Limits: Calculation & Re-calculation
Lecture 6: Out of Control: How to Detect It, & What to Do if Detect It?
Lecture 7: Sample Issues: Random, Sub-grouping, & Sample Size
Lecture 8: Capability Indices and how to calculation them
Lecture 9: Non-normal Data, and its impact on SPC.
Lecture 10: How to Initiate & Implement a Successful SPC Program
Quick Contact:
NetZealous BDA as GlobalCompliancePanel
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Fax: 302-288-6884
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http://www.globalcompliancepanel.com