Outline

Classical Design of Experiments was introduced nearly 100 years ago and has slowly evolved in the decades that followed. Thanks to powerful software, much has recently changed in this arena. Computer generated designs allow us to conduct incredibly efficient experiments. Mixed levels for factors can be readily accommodated. Multiple responses can be traded-off with ease. We no longer need to assume the model of interest is of a simple linear or quadratic form.  Software allows us to fit more complex models.  Interactions can be more effectively visualized with three-dimensions.  New strategies have also emerged regarding run order and number of replications.

Organizations are now able to learn amazing things about their customers and processes using a host of relatively new techniques referred to as data mining.  Instantaneously determining if a client is a good credit risk, selecting what content to display on a web page, spotting fraud, optimization of complex bio-chemical formulations, and predicting which customers are likely to leave in the next three months are just some of the amazing things companies have accomplish with data mining techniques.  With the advent of inexpensive computing capability and powerful software organizations can now collect massive amounts of operational and customer data.  Collecting data is not a problem, but collecting the right data and then being able to extract latent information about relationships is a huge challenge.  These techniques have the potential of  readily determining  latent variable relationships in complex historical datasets. 

Exciting areas of application have emerged from the combination of these seemingly disparate families of tools.  One involves using data mining tools to screen the vital few key variables from a massive number of dataset variables as well as identification of intriguing ranges for the key variables.  This information can then be loaded into a modeling designed experiment so as to approximate underlying relationships between the key inputs variables and key responses.  Simulation and optimization strategies can then be applied to the resultant models.

Who Should Attend?

Anyone who would like to learn more about how to blend classical Designed Experiments with Data Mining principles.

About the Presenter

Robert Launsby is the President of Launsby Consulting in Colorado Springs, CO. Mr. Launsby is the co-author of numerous books including:

-“Understanding Industrial Designed Experiments”
-“Straight Talk on Designed Experiments”
-“Process Validation for Business Success"
-“Experiment Design for Injection Molding”
-"Engineering Today's Designed Experiments"
-"Design for Six Sigma"

With over 20 years of manufacturing experience, Robert has trained thousands of people in various techniques including Experimental Design, Process Control, FMEA, Concept Selection, Design Control, Process Validation, Data Mining, Market Research, Six Sigma, Design for Six Sigma, and QFD.

Price: $99

How to register:
To register, just simply click on the add to cart button below, or, email us at info@Launsby.com for other ordering methods.

System Requirements:
Attendees will need high speed internet access and audio. The webinar will be run with GoToMeeting software. A quick (approx. 2 min) install will be needed to view the webinar. This may be done during registration or upon webinar log-in. A separate phone line will be required to dial into the audio for the webinar.

Contact:
For further information or questions, please contact Matt Launsby at 1-719-282-1143 or Matt@Launsby.com