Design of Experiments (DOE) for Product / Process Optimization
- Real Time Online Delivery – Live Training Sessions
- Virtual Attendance for 5 Days Training
- Guided Learning Hours – 20 (4 Hrs x 5 Days)
- Comprehensive Learning Kit
Course Overview
This 5 Day Online Course is designed to help scientists and engineers plan and conduct experiments and analyze the data to develop predictive models used to optimize processes and products and solve complex problems.
DOE is an extremely efficient method to understand which variables (and interactions) affect key outcomes and allows the development of mathematical models used to optimize process and product performance. The models also provide an understanding of the impact of variability in controllable and uncontrollable factors on important responses. DOE concepts and methods covered in detail. Case studies will also be presented to illustrate the use of the methods.
These 4 hours/per day highly interactive sessions will allow participants the opportunity to practice applying DOE techniques with various data sets using statistical software. The objective is to provide participants with the key tools and knowledge to be able to apply the methods effectively in their process definition/improvement activities and product development efforts. Participants will learn how to design effective experiments, conduct them, and analyze and interpret the results.
Trainer’s Profile
Our expert trainer has experience of 30+years in the application of statistical methods to optimize product designs and manufacturing processes and to assess product liability risk.
A highly sought-after expert witness in product liability litigation, has enjoyed working with companies of all sizes around the world on a variety of training, consultancy. Our expert regularly consults and serves as a testifying witness for cases involving failure root cause determination, risk assessment, product quality, product reliability, warranty, and process control.
Areas of expertise include designed experimentation, reliability analysis, general
statistical methods, statistical process control, measurement system assessment, and
stochastic optimization. Adjunct professor in the College of Engineering at the University of Michigan.
Key Takeaways
- Avoid common misapplications of DOE in practice
- Utilize software to simplify analysis without compromising understanding of key concepts
- Develop predictive models to explain process/product behavior
- Understand and apply very efficient fractional factorial designs in screening experiments
- Identify and interpret significant factor effects and 2-factor interactions
- Build predictive models that may be used to optimize multiple responses jointly
Who Should Attend
- Scientists
- Product and Process Engineers
- Design Engineers
- Quality Engineers
- Personnel involved in product development and validation
- Laboratory Personnel
- Manufacturing/Operations Personnel
- Process Improvement Personnel
For participation details contact
Mithun Siddartha
+1 780 851 7197 (Canada)
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