Statistical Methods for Quality Improvement
Steven Wachs Overview:
This webinar introduces important statistical concepts and methods for making objective decisions to ensure and improve product quality.
The methods have many applications including:
- Determining how well my process/product meets requirements
- Knowing when a process or system is behaving consistently or differently than before
- Uncovering which key inputs to my process affect product performance or customer satisfaction
- Ensuring that I can effectively measure what I need to
- Comparing groups of data when random (natural) variation is present?
- Predicting future outcomes using a predictive model
The methods introduced include: Statistical Process Control, Process Capability Assessment, Regression Modeling, Design of Experiments, Hypothesis Testing, and Measurement Systems Assessment.
Why you should Attend:
This webinar provides a solid introduction to important statistical concepts and methods that are essential for making objective decisions related to product quality.
Following the webinar, the participants will possess an understanding of the purpose and benefits of critical methods including:
- Statistical Process Control
- Process Capability Assessment
- Regression Modeling
- Design of Experiments
- Hypothesis Testing
- Measurement Systems Assessment.
Areas Covered in the Session:
- Variation & Quality
- Process Stability/Statistical Process Control
- Process Capability Assessment
- Predictive Models (Regression & Design of Experiments)
- Hypothesis Testing for Decision Making
- Measurement Systems Assessment
- Examples & Applications
Who Will Benefit:
- Quality 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
Steve Wachs has 30 years of wide-ranging industry experience in both technical and management positions. Steve 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.
Steve 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. Steve consults and provides workshops in industrial statistical methods worldwide. He also supports Integral Concepts’ Litigation / Expert Witness practice with data analysis.
Steve possesses an M.A. in Applied Statistics from University of Michigan (Ann Arbor), an M.B.A. from the Katz Graduate School of Business, University of Pittsburgh, and a B.S. in Mechanical Engineering from the University of Michigan (Ann Arbor).