Why and How to use Design of Experiments

Duration: 60 Minutes
Design of Experiments (DOE) is a key tool for quality management and continual improvement, as well as part of the Six Sigma body of knowledge. It allows the scientific determination, beyond a quantifiable reasonable doubt, of whether there is a difference between two or more treatments (such as a control or experiment), or treatment combinations. This in turn allows us to determine whether a proposed improvement worked, and also to pinpoint the source of poor quality when a cause and effect diagram suggests multiple sources.
Design of Experiments
Product ID: 504045
Objectives of the Presentation
  • Know the benefits of DOE, including its ability to save time and resources
  • Understand hypothesis testing and the risks of incorrect decisions
    • Understand how the same concepts apply to SPC and acceptance sampling
  • Understand factors, levels, and interactions
  • Understand how to avoid the effects of extraneous variation sources when planning an experiment
  • Know how to interpret experimental results in terms of significance levels and P values
Why Should you Attend
Design of Experiments is a vital tool in root cause analysis (as might take place during corrective or preventive action), and also process improvement. This webinar will cover the basic principles, including the vital concept of hypothesis testing. Hypothesis testing applies to everything we do with statistics, including statistical process control and acceptance sampling as well as design of experiments.

Areas Covered
  • A case study of Frederick Winslow Taylor's attempt to optimize machining operations prior to the invention of DOE underscores the ability of DOE to save enormous amounts of time and money, and deliver meaningful results
  • Hypothesis testing applies to everything we do with statistics
    • The null hypothesis is the starting assumption that there is no difference between a control and an experiment, a process is in control (SPC), and a production lot is acceptable (acceptance sampling)
    • The alternate hypothesis is that the experiment differs from the control, the process is out of control (SPC), or the production lot is not acceptable. We must prove the alternate hypothesis beyond a reasonable doubt before we accept it
    • The significance level or Type I risk is the quantifiable reasonable doubt in question. It is also known as the producer's risk in acceptance sampling because it is the risk of rejecting a lot at the acceptable quality level (AQL)
    • The Type II risk is the risk of not accepting the alternate hypothesis when we should. The consumer's risk in acceptance sampling is the risk of accepting a lot whose quality is at the rejectable quality level (RQL)
  • Factors, levels, and interactions
    • Factors are potential influences on the response variable (often the critical to quality characteristic, i.e. what we measure to determine whether the experiment worked or whether the factor affects quality). Choice of material, a factor from the cause and effect diagram, is an example
    • Levels are the choices associated with the factor, such as Material A or Material B
    • Interactions are synergies or antagonisms between factors. The whole is greater or less than the sum of its parts
  • Techniques to exclude extraneous variation from the experiment and to collect adequate data
    • Randomization of the specimens
    • Blocking
    • Replication (taking enough measurements to get meaningful results)
While mastery of DOE requires a couple of college-level courses, this webinar will equip the attendee to understand the vital considerations involved as well as the language used by statisticians and Six Sigma Black Belts in the planning and assessment of experiments.

Who will Benefit
Manufacturing engineers, technicians, and managers, and quality professionals

Topic Background
Design of experiments evolved during the 20th century to apply statistical methods to scientific experiments to improve (1) their efficiency in terms of resource utilization and (2) assurance of meaningful results. Experiments that might have once taken years and used millions of dollars in today's money could be performed in months or weeks, and at far lower cost. DOE also made it possible to study interactions between factors, that is, identification of situations in which the whole might be more or less than the sum of its parts.
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Instructor Profile:
William Levinson 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 holds degrees in chemistry and chemical engineering from Penn State and Cornell Universities, and night school degrees in business administration and applied statistics from Union College, and he has given presentations at the ASQ World Conference, ISO/Lean Six Sigma World Conference, and others.


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