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
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.
Why Should You 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
Demonstrating Reliability with zero or few failures
Developing Predictive Models
Using Models to Develop Optimal Solutions
Understand where and how DOE should be used
Be able to make immediate improvements in using experimentation for problem solving, product development, process improvement, etc.
Principal Statistician ,
Integral Concepts, Inc
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 estimate and reduce warranty.