Process Capability for Normal and Non-Normal Data
Steven Wachs Overview:
Companies must assure that their processes are capable of producing products and services that consistently meet customer specifications.
This webinar discusses methods for estimating process capability for both normal and non-normal data. Pre-requisites for estimating process capability (e.g. establishing process stability) are discussed first. Distributions are briefly described and methods for estimating ppm levels are presented. The use and limitations of common process capability indices (e.g. Cpk and Ppk) are discussed.
It is vital that appropriate methods are used for estimating capability when the data is not well described by a normal distribution. Failure to do so often results in overly optimistic process capability estimates. Methods for testing for normality are discussed. Both transformations and distribution fitting are presented as methods to assess capability for non-normal data. The webinar includes several examples to illustrate the methods.
Why you should Attend:
Following the webinar, participants will be able to quickly adopt the methods presented to improve their quality management system and the use of supporting statistical methods.
The webinar will provide methods for assessing and understanding Process Capability. Participants should be able to immediately apply the methods presented in order to:
- Understand pre-requisites for assessing process capability
- Apply methods for estimating capability for both normal and non-normal data
- Test data for normality
- Understand and interpret process capability indices
- Learn what capability indices fail to convey about a process
- Utilize a roadmap for Assessing Process Stability and Capability
Areas Covered in the Session:
- The Concepts of Process Stability and Process Capability
- Methods for Assessing Process Capability
- Estimating PPM
- Calculating and Interpreting Capability Indices (Cp, Cpk, Pp, Ppk)
- Shortcomings of Capability Indices
- Other Process Capability metrics
- Testing for Normality
- Methods for handling Non-Normal Data (Distribution fitting, transforming data)
- Improving Process Capability
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).