Modeling and Optimizing Process Behavior Using Design of Experiments
This webinar will review the key concepts behind Design of Experiments, learn a methodology to perform experiments in an optimal fashion, after presentation you'll be able to make immediate improvements in using experimentation for problem solving, product development, process improvement, A strategy for utilizing sequential experiments to most efficiently understand and model a process is presented.
10:00 AM PST | 01:00 PM EST
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Corporate Recorded: Access recorded version, Any number of participants unlimited viewing for 6 months ( Access information will be emailed 24 hours after the completion of live webinar)
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:
- 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:
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
- Motivation for Structured Experimentation(DOE)
- DOE Approach / Methodology
- Types of Experimental Designs and their Applications
- DOE Techniques
- Demonstrating Reliability with zero or few failures
- Developing Predictive Models
- Using Models to Develop Optimal Solutions
- Case Study
Who Will Benefit:
- 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.
The target audience includes anyone with a vested interest in product quality and reliability
- 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.