Upcoming Events at Hyatt Regency Houston Intercontinental Airport
Thu Feb 7, 10:00 AM - Fri Feb 8, 7:00 PM
425 North Sam Houston Parkway East, Houston, TX 77060
Community: North Houston District
Description
This 2-day seminar provides a 1-day introduction to the statistical tools used to analyze Design Verification data and Process Validation results. The entire 2nd day is spent on Statistical Process Control and Process Capability Indices. The goal of the 1st day is to he
Event Details
This 2-day seminar provides a 1-day introduction to the statistical tools used to analyze Design Verification data and Process Validation results. The entire 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 can also help improve product quality.
All design and/or manufacturing companies perform design verification and/or process validation studies. A clear understanding of relevant statistical principles and statistical methods ensures that such studies 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, can lead to erroneously deciding to not launch a new product. And failure to monitor production processes accurately can lead to a slow decline in product quality.
This seminar provides a thorough, practical introduction to the relevant statistical methods and principles that will help ensure that outputs from R&D, Product Transfer, Manufacturing Engineering, and Production are consistently of high quality.
Areas Covered in the Session :
FDA, ISO 9001/13485, and MDD requirements
Statistically valid rationales for sample sizes
The interpretation of statistical significance and statistical non-significance
The impact of normality and non-normality
Tests of Normality
Transformations to Normality
Concepts of “Confidence” and “Reliability” (a.k.a., %-in-specification)
Concepts of “Quality” and “Variability” and “Process”
Risk management
Who Should Attend :
QA/QC Supervisors
Process Engineers
Manufacturing Engineers
QC/QC Technicians
Manufacturing Technicians
R&D Engineers
All design and/or manufacturing companies perform design verification and/or process validation studies. A clear understanding of relevant statistical principles and statistical methods ensures that such studies 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, can lead to erroneously deciding to not launch a new product. And failure to monitor production processes accurately can lead to a slow decline in product quality.
This seminar provides a thorough, practical introduction to the relevant statistical methods and principles that will help ensure that outputs from R&D, Product Transfer, Manufacturing Engineering, and Production are consistently of high quality.
Areas Covered in the Session :
FDA, ISO 9001/13485, and MDD requirements
Statistically valid rationales for sample sizes
The interpretation of statistical significance and statistical non-significance
The impact of normality and non-normality
Tests of Normality
Transformations to Normality
Concepts of “Confidence” and “Reliability” (a.k.a., %-in-specification)
Concepts of “Quality” and “Variability” and “Process”
Risk management
Who Should Attend :
QA/QC Supervisors
Process Engineers
Manufacturing Engineers
QC/QC Technicians
Manufacturing Technicians
R&D Engineers
