Modeling and Optimizing Process/Product Behavior Using Design of Experiments

This webinar covers experiment design, sequential experiment strategies, common types, applications, techniques like replication, blocking, and randomization, and a case study on optimizing a manufacturing process with multiple responses.
Thursday, June 12, 2025
Time: 10:00 AM PDT | 01:00 PM EDT
Duration: 90 Minutes
IMG Steven Wachs
Id: 90270
Live
Session
$119.00
Single Attendee
$249.00
Group Attendees
Recorded
Session
$159.00
Single Attendee
$359.00
Group Attendees
Combo
Live+Recorded
$249.00
Single Attendee
$549.00
Group Attendees

Overview:

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.

Why you should Attend: 

  • Learn a methodology to perform experiments in an optimal fashion
  • Review the common types of experimental designs and important techniques
  • Develop predictive models to describe the effects that variables have on one or more responses
  • Utilize predictive models to develop optimal solutions

Areas Covered in the Session:

  • 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 Will Benefit:

  • Operations / Production Managers
  • Quality Assurance Managers
  • Process or Manufacturing Engineers or Managers
  • Product Design Personnel
  • Scientists 
  • Research & Development personnel

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).