Process Capability Assessment for Normal and Non-Normal Data

Duration 75 Mins
Level Intermediate
Webinar ID IQW20H0803

  • This program will cover the following topics
  • 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

Overview of the webinar

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.

 

Who should attend?

The target audience includes personnel involved in product/process development and manufacturing

  • Quality Personnel
  • Manufacturing Personnel
  • Operations / Production Managers
  • Production Supervisors
  • Supplier Quality personnel
  • Quality Engineering
  • Quality Assurance Managers, Engineers
  • Process or Manufacturing Engineers or Managers

Why should you attend?

Considerable misunderstanding exists regarding methods for assessing process stability and capability.  As a result many companies incur excessive risks of customer dissatisfaction, warranty, recalls, and litigation.

This webinar covers proper methods (and prerequisites) for estimating process capability. Additionally, the shortcomings of popular process capability indices are exposed.

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

Faculty - Mr. Steven Wachs

Steven Wachs has 25 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 to estimate and reduce warranty. In addition to providing consulting services, Steve regularly conducts workshops in industrial statistical methods for companies worldwide.
 
Education:
M.A., Applied Statistics, University of Michigan, 2002
M.B.A, Katz Graduate School of Business, University of Pittsburgh, 1992
B.S., Mechanical Engineering, University of Michigan, 1986

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