Useful Statistical Methods for Defining Product and Process Specifications - Part I


Instructor: Steven Wachs
Product ID: 706897
Training Level: Intermediate

  • Duration: 75 Min
This webinar covers useful and important statistical methods that assist scientists and engineers in the development of appropriate product and process specifications. Appropriate product specifications are critical to achieving adequate and reliable product performance.
Last Recorded Date: May-2023


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Why Should You Attend:

Scientists, Design Engineers, and Manufacturing/Process Engineers must develop product and process specifications that ensure that products delivered to customers perform their intended functions over time. If specifications are too wide, the risks of inadequate product performance and product failures increase. If specifications are too tight, the costs to ensure conformance increase. Scientific and engineering theory, knowledge, and principles play an important role in developing specifications, but usually this must be combined with testing and data analysis to verify appropriate specifications.

This webinar covers useful and important statistical methods that assist scientists and engineers in the development of appropriate product and process specifications.

Learning Objectives:

The information gained in the webinar will allow you improve your ability to develop appropriate and defensible specifications. This manages the risks of overly liberal specifications and the costs associated with overly conservative specifications.

  • Review Product/Process Specifications and why they are important
  • Learn methods for characterizing existing process data to describe expected variation in the population
  • Using predictive models, identify input parameter specifications that ensure key outcomes will be met with high confidence
  • Further optimize process performance with Monte Carlo Simulation

Areas Covered in the Webinar:


  • What are Specifications?
  • Why Are Specs Important?
  • Risks of Inappropriate Specifications

Characterizing Process Data

  • Normal Distribution
  • Characterizing Process Data
  • Reference Intervals
  • Min - Max Interval
  • Tolerance Intervals
  • Coverage Probability and Confidence Levels

Using Predictive Models to develop specifications

  • Review of Predictive Models (Regression/DOE)
  • Confidence and Prediction Intervals
  • Using Models Examples (Contour Plots)
  • Factor Specifications to Optimize a Response
  • Factor Specifications to Jointly Optimize Multiple Responses
  • Introduction to Monte Carlo Simulation for further Optimization

Who Will Benefit:

The target audience includes personnel involved in setting component, product, and process specifications. The methods also apply to service specifications. Typical job titles would include:

  • Quality Personnel
  • Product Design Engineer
  • Scientists
  • Process Engineer
  • Manufacturing Engineer
  • Product / Program Manager
  • Operations / Production Manager
Instructor Profile:
Steven Wachs

Steven Wachs
Principal Statistician, Integral Concepts, Inc

Steven Wachs has 30 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.

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