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Design of Experiments for Process and Product Optimization
Description
Learn to plan, run, and analyze experiments that improve products and processes across engineering, manufacturing, and research environments. Content is structured around modern design of experiments (DOE) strategies, including factorial, fractional factorial, response surface, mixture , and nested designs.
Applications are drawn from electronics, automotive, pharmaceuticals, chemical processes, and other data-rich fields. Emphasis is placed on practical implementation using real-world case studies and statistical software.
Upon successful completion, learners will earn a certificate of completion from Arizona State University.
Curriculum Duration
45 Hours
What you’ll learn
By the end of this series, learners will be able to:
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Design and execute structured experiments to evaluate multiple variables simultaneously
- Apply statistical methods such as ANOVA, t-tests, and regression in an experimental context
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Use factorial and fractional factorial designs to identify key process inputs
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Optimize systems using response surface and mixture models
- Build a custom design using optimality criteria for non-standard scenarios
- Address practical constraints such as blocking, nesting, and hard-to-change factors.
Skills you’ll gain
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Experimental design and analysis
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Factorial and fractional factorial methods
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Response surface and mixture modeling
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Random and split-plot model analysis
- JMP and other statistical tools
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Process optimization and robustness
Target audience
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Engineers, scientists, and analysts involved in product development, R&D, or process improvement
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Professionals in manufacturing, quality, pharma, semiconductor, or chemical sectors
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Anyone seeking to develop expertise in running statistically valid experiments
Familiarity with basic statistics is helpful. Key concepts are reviewed throughout the program.
Courses and Descriptions
Factorial and Fractional Factorial Designs - Covers 2^k designs, blocking, confounding, and fractional factorial strategies used in early-stage screening and exploration of key variables.
Factorial and Fractional Factorial Designs - Covers 2^k designs, blocking, confounding, and fractional factorial strategies used in early-stage screening and exploration of key variables.
Response Surfaces, Mixtures, and Model Building - Focuses on system optimization using response surface methodology (RSM), mixture designs, regression modeling, and space-filling designs for computer-based experiments.
Random Models, Nested and Split-plot Designs - Explores designs that include random effects, nesting structures, and split-plot layouts to handle constraints like hard-to-change factors or grouped observations.
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