Design Validation should ensure that product performance, quality, and reliability requirements are met.
In order to have high confidence that products will perform as intended, enough data must be collected and analyzed using various statistical methods.
Selecting appropriate sample sizes often vexes many practitioners. Testing only a few units does not provide a high level of confidence that performance requirements will be consistently met. Testing too many units may be unnecessarily expensive and can lead to misleading conclusions.
Statistical Methods are typically used to ensure that product performance, quality, and reliability requirements are met during the Design Validation phase of product development.
This webinar discusses common elements of sample size determination and several specific sample size applications for various design validation activities including Reliability Demonstration/Estimation, Acceptance Sampling, and Hypothesis Testing. Numerous examples are provided to illustrate the key concepts and applications.
Why you should Attend: Sample sizes have a significant impact on the uncertainty in estimates of key process performance characteristics. To have high confidence in results, sufficient sample sizes must be used.
Potential problems should be uncovered during Design Validation, prior to launching a product. Failure to do so may result in customer dissatisfaction, excessive warranty, costly recalls, or litigation.
Participants in the webinar will be able to understand the impact of sample sizes on the results from various statistical analysis methods commonly used during Design Validation.
Areas Covered in the Session:
Populations, Samples, Data Types, and Basic Statistics
Common Elements of Sample Size Determination
Design Validation Applications
Sample Sizes for Reliability Demonstration (Pass/Fail Outcomes)
Sample Sizes for Reliability Estimation
Sample Sizes for Estimating Proportion Failing (Pass/Fail Test Outcomes)
Sample Sizes for Acceptance Sampling / Lot Disposition
Other Common Sample Size Applications (Hypothesis Testing, Equivalence Testing)
Who Will Benefit:
Product Design/Development personnel
Operations / Production Managers
Supplier Quality personnel
Quality Assurance Managers, Engineers
Process or Manufacturing Engineers or Managers
Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. He has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control. Mr. Wachs is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty. Mr. Wachs regularly speaks at industry conferences and provides workshops in industrial statistical methods worldwide.
He has an M.A. in Applied Statistics from the University of Michigan, an M.B.A, Katz Graduate School of Business from the University of Pittsburgh, 1992, and a B.S., Mechanical Engineering from the University of Michigan.
Netzealous LLC, DBA -Compliance4all
Email: [email protected]