Webinar On "Introduction to Design of Experiments"

Event Details

Time: September 27, 2017 from 1pm to 2pm
Location: Online
Street: 9106 Seven Locks Road
City/Town: Bethesda
Website or Map: http://bit.ly/2wO8TWl
Phone: 1-844-216-5230
Event Type: online, webinar
Organized By: Sam Miller
Latest Activity: Sep 18, 2017

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Event Description

Description :

Design of Experiments (DOE) is an important technique for root cause analysis (RCA) and process improvement. As an example, when potential trouble sources are identified from a cause and effect diagram, DOE can be used to determine which of the factors are likely to be important. DOE can also develop quantitative models of the nature y=f(x) (y is a function of x) where y is often a critical to quality characteristic.

Objective of the webinar :

Attendees will learn the fundamentals of DOE, some of which carry over into other industrial statistics applications such as acceptance sampling and statistical process control.

Hypothesis testing is the foundation of almost everything we do with industrial statistics.
DOE can save an enormous amount of time and money, as shown by comparison of an experiment performed in the late 19th century (prior to the development of industrial statistics) and an even more complex one performed roughly 100 years later. This underscores the value of DOE in the language of money, i.e. the language of upper management.
Understand the concepts of factors, levels, and interactions (the whole is greater or less than the sum of its parts). Factors such as machine or material are often identified from a cause and effect diagram during root cause analysis.
Recognize the need to exclude extraneous variation sources from the experiment through randomization and blocking, and also the need to use a sufficiently large sample to get meaningful results (replication).

Areas Covered in the Session :

Value of DOE in the language of time and money, as shown by comparison of an experiment performed by Frederick Winslow Taylor during the late 19th century, and an even more complicated one performed by a pharmaceutical company that sought FDA approval for a diagnostic test
Interactions, or situations in which the whole is greater or less than the sum of its parts. Interactions cannot be detected by one variable at a time experimentation.
Experimental design considerations including randomization, blocking, and replication.

Who Will Benefit:

Manufacturing professionals and others who wish to use DOE to support process improvements or root cause analysis.

About Speaker:

William A. Levinson, P.E., FASQ, CFPIM, is the owner of Levinson Productivity Systems PC. He holds professional certifications from the American Society for Quality, APICS, and Society of Manufacturing Engineers.