Modeling and Optimizing Process Behavior using Design of Experiments
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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.
Design of Experiments has numerous applications, including:
Why should you Attend:
- 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
Areas Covered in the Session:
- 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
Who Will Benefit:
- 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
- Operations / Production Managers
- Quality Assurance Managers
- Process or Manufacturing Engineers or Managers
- Product Design Personnel
- Research & Development personnel
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.