- Basics of a simple designed experiment; the control versus the experiment.
- Bottom line economic value of DOE as shown by a comparison of a 19th century machining experiment without it (industrial statistics had yet to be invented) and a 20th century experiment to assess the effectiveness of a medical diagnostic test.
- Elements of DOE
- The response variable (what we are trying to model or optimize, such as a critical to quality characteristic)
- Factors (considerations, often from the categories of a cause and effect diagram, that affect the response)
- Levels (choices within each factor such as low versus high, or material A, B, or C)
- Interactions; synergistic or antagonistic relationships between factors that make the whole greater or less than the sum of its parts
- Experimental planning and hypothesis testing; this is the foundation of SPC and acceptance sampling as well.
Design of Experiments plays a central role in continual improvement, as well as root cause analysis when it is used to test hypothesis about the underlying cause of a quality-related problem. Its foundation of hypothesis testing is meanwhile applicable to statistical process control (SPC) and hypothesis testing as well as DOE. A comprehensive hands-on understanding of DOE requires one or more college-level courses; the deliverable of this webinar is an understanding of the basics that will equip attendees to explain the business benefits of DOE and work effectively with subject matter experts like Six Sigma Green and Black Belts. It will include an overview of the fundamentals of DOE, including hypothesis testing, factors, interactions, randomization, blocking, and evaluation of experimental results.
William A. Levinson, P.E., is the principal of Levinson Productivity Systems, P.C. He is an ASQ Fellow, Certified Quality Engineer, Quality Auditor, Quality Manager, Reliability Engineer, and Six Sigma Black Belt. He is also the author of numerous books on quality, productivity, and management.
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