introduction-to-design-of-experiments

Introduction to Design of Experiments

Live Webinar

  • 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.

Join this session by expert speaker William Levinson, where he will provide an introduction to Design of Experiments (DOE) that will equip attendees to recognize the practical and bottom line value of DOE, and understand the principles sufficiently well to collaborate with subject matter experts.

Session Highlights:

  • Recognize the value of DOE in the language of money and also time. DOE can realize useful and actionable results quickly, and with a minimum of expenditure.

  • Know the basic elements of DOE

  • Response variable; often the critical to quality (CTQ) characteristic we seek to optimize

  • Factors: considerations (such as choice of material, machine, or operating conditions) that affect the response

  • Levels: choices within factors such as material A, B, or C, or low versus high machine speed or temperature

  • Interactions: synergies or antagonisms that make the whole greater or less than the sum of its parts (main factor effects)

  • Understand hypothesis testing—the foundation of everything we do with industrial statistics

  • The null hypothesis is the starting assumption, e.g. there is no difference between the experiment and the control

  • The alternate hypothesis must be proven beyond a quantifiable reasonable doubt.

  • There are quantifiable risks of wrongly rejecting the null hypothesis (e.g. concluding that the experiment works when it doesn't) or failing to reject it when we should (e.g. accepting a production lot at its rejectable quality level). The first risk is "the boy who cried wolf" and the second is "the boy didn't see the wolf."

  • Recognize that experiments generate test statistics (such as the t statistic) that quantify evidence against the null hypothesis (e.g. that the experiment is really better than the control).

  • Recognize key considerations in planning experiments to exclude extraneous variation sources. Methods include randomization and blocking.

  • Recognize how factorial designs can screen large numbers of factors with minimal experimentation, thus saving time and money. Screening out unimportant factors then allows attention to be focused on the important ones.

Why You Should Attend:

Design of Experiments (DOE) plays a key role in root cause analysis as well as continual improvement. While comprehensive training in DOE requires a couple of college-level courses, this one-hour webinar will cover all the basic principles to enable attendees to recognize the bottom line benefits of scientific experimental design, also know the basic requirements for a successful experiment and work effectively with subject matter experts such as industrial statisticians and Six Sigma Black Belts.

Who Should Attend:

  • Manufacturing professionals

  • Manufacturing engineers

  • Production

  • Quality

  • Engineering

  • Product Management

  • Project Management

  • Technician


*You may ask your Question directly to our expert during the Q&A session.

** You can buy On-Demand and view it as per your convenience.

William Levinson

William Levinson

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 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, TOC World 2004, and other national conferences on productivity and quality.

Levinson is also the author of several books on quality, productivity, and management. Henry Ford's Lean Vision is a comprehensive overview of the lean manufacturing and organizational management methods that Ford employed to achieve unprecedented bottom line results, and Beyond the Theory of Constraints describes how Ford's elimination of variation from material transfer and processing times allowed him to come close to running a balanced factory at full capacity. Statistical Process Control for Real-World Applications shows what to do when the process doesn't conform to the traditional bell curve assumption.

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