Modeling and Optimizing Process/Product Behaviour using Design of Experiments

Steven Wachs

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Experimentation is frequently performed using trial and error approaches which are extremely inefficient and rarely lead to optimal solutions.  Furthermore, when it’s desired to understand the effect of multiple variables on an outcome (response), “one-factor-at-a-time” trials are often performed.  Not only is this approach inefficient, it inhibits the ability to understand and model how multiple variables interact to jointly affect a response.  Statistically based Design of Experiments provides a methodology for optimally developing process understanding via experimentation.   

Design of Experiments has numerous applications, including:

  • Fast and Efficient Problem Solving (root cause determination)
  • Shortening R&D Efforts
  • Optimizing Product Designs
  • Optimizing Manufacturing Processes
  • Developing Product or Process Specifications
  • Improving Quality and/or Reliability

This webinar will review the key concepts behind Design of Experiments.  A strategy for utilizing sequential experiments to most efficiently understand and model a process is presented.  Many common types of experiments and their applications are presented.  These include experiments appropriate for screening, optimization, mixtures/formulations, etc.  Several important techniques in experimental design (such as replication, blocking, and randomization) are introduced.  A Case Study involving optimizing a manufacturing process with multiple responses is presented.

LEARNING OBJECTIVE:

  1. Learn a methodology to perform experiments in an optimal fashion
  2. Understand the common types of experimental designs and important techniques
  3. Use DOE Results to describe the effects that variables have on one or more responses
  4. Utilize predictive models to develop optimal solutions

AGENDA:

  • Motivation for Structured Experimentation (DOE)
  • DOE Approach / Methodology
  • Types of Experimental Designs and their Applications
  • DOE Techniques
  • Developing Predictive Models
  • Using Models to Develop Optimal Solutions
  • Case Study

WHO SHOULD ATTEND:

The target audience includes

  • Product development personnel
  • Quality personnel
  • Manufacturing personnel
  • Lab personnel
  • R&D personnel

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Training CD-DVD

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Recorded video

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Speaker: Steven Wachs,

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.