Design of Experiments (DOE) for Product / Process Optimization
(ADVANCE LEVEL – Course)
- 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
This 5 Day Online (Advanced DOE 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.
This course agenda builds on the fundamental DOE concepts and experimental designs learned in the first Design of Experiments course. This course teaches participants how to efficiently build upon results obtained from screening (e.g. fractional factorial) experiments. More advanced experimental designs necessary for optimizing processes are covered in detail.
These 4 hours/per day highly interactive course 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.
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.
- Develop advanced knowledge of the concepts and methods behind statistically based experiments
- Utilize software to simplify analysis without compromising understanding of key concepts
- Design experiments efficiently and effectively
- Use phased experimentation approach to optimize products and processes
- Build predictive models that may be used to jointly optimize multiple responses
- Communicate results to customers, suppliers, management, and other stakeholders
- Apply experimental techniques correctly (e.g., randomization, replication, repetition, blocking, etc.)
Who Should Attend
- 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
+1 780 851 7197 (Canada)