Overview
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 should you 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
- Quality Personnel
- Product Design/Development personnel
- Manufacturing Personnel
- Operations / Production Managers
- Production Supervisors
- Supplier Quality personnel
- Quality Engineering
- Quality Assurance Managers, Engineers
- Process or Manufacturing Engineers or Managers
Speaker Profile
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.