Return to Meeting Home Page

Third European DOE User Meeting
Lucerne, Switzerland
May 31st - June 3rd, 2010

 

Workshops

On May 31st, there will be two 1-day workshop tracks to chose between: DOE and RSM Simplified or Formulations Simplified. On June 3rd, there will be one 1/2-day workshop offered on Multivariate Data Analysis in a DOE Context by CQ Consultancy.

May 31st—1-day workshops (Note: Please bring along your laptop so that you can be actively involved during the workshop.)

1. DOE and RSM Simplified—Process Improvement via Design of Experiments for Screening and Response Surface Methods Optimization by Mark Anderson, Stat-Ease, Inc.

This workshop offers an overview of design of experiments (DOE) and response surface methods (RSM). It illustrates an array of practical statistical tools for design and analysis of experiments aimed at breakthrough improvement and process optimization. Although this workshop is not a substitute for hands-on computer-intensive training, it will give participants a foundation for upgrading their DOE/RSM skills to a superior level. 

The morning session introduces basic concepts of DOE and then explores full factorials, interactions, fractional factorials, and aliasing. Several informative case studies will be presented in detail.  The afternoon session progresses into response surface methods (RSM). Once again, the material will be presented in case-study format that translates these statistical tools most effectively. During this session, participants will learn to appreciate all that RSM can do to find their sweet spot—the ideal settings for the critical process factors.

2. Formulations Simplified by Pat Whitcomb, Stat-Ease, Inc.

The Recipe for Success
If you do product formulation, then standard factorial and response surface designs just don't work.  You need to use mixture designs to ensure success.  The one-day Formulations Simplified workshop teaches you how to effectively create mixture designs for a variety of problems.  Develop statistical models of product performance.  Then use response surface methods to identify the "sweet spot" where the optimum tradeoffs among multiple responses occur.

Ingredients for Efficient Experimentation
See the outline below for a list of topics you’ll be introduced to in the Formulations Simplified workshop:
Section 1—Introduction to Mixtures

• What makes a mixture?
• Mixture (Scheffé) polynomials
• Simplex lattice designs
• Synthetic medium case study

Section 2—Constrained Mixtures, Simplex

• Detergent formulation case study
• Coding: Actual, Real, L_Pseudo
• Optimization of multiple responses
• Numerical (desirability function)
• Graphical (overlay plot)

Section 3—Constrained Mixtures, Non-Simplex

• Sizing for precision
• Constrained mixtures, extreme vertices
• Shampoo case study
• Optimal point selection
• Flare case study

Section 4—Advanced Topics

• Multicomponent constraints
• Fruit punch case study
• Mixture amount experiments
• Ibuprofen case study
• DOE support

June 3rd—1/2 day workshop

1. Multivariate Data Analysis in a DOE Context by CQ Consultancy

Multivariate methods such as Principal Component Analysis (PCA) have been developed to deal with correlations between variables.  In this workshop a brief introduction to Principal Component Analysis will be given, with two illustrations of its use and power in a DOE context.

Principal Properties Design
You may need to test different solvents, additives, catalysts, system configurations, ...  If so, you’re dealing with so-called categorical variables.  Since categorical variables with n levels can be considered as n-1 variables, the number of experimental runs needed in a design increases rapidly with the number of levels to be tested, and often we can’t test them all. 

A typical example is the choice of solvents to be used.  For this example a first step in Principal Properties Design is to describe the solvents by a set of quantitative properties that are related to what makes them different (e.g. polarity, mass, di-electric constant, …), which results in a set of ten to twenty highly correlated variables. The next step is then to perform a PCA and select a representative set of solvents based on the principal components that adequately describe the set of descriptors, i.e. the principal properties. 

With Principal Properties Design the problem of multi-level categorical factors can be circumvented, and on top of that, we even get information about items / levels that have not been explicitly tested. 

Multi-Response Problems
Textbook examples of design often consider studies with just one or two responses.  In real life you may have dozens.  Again PCA can reveal the correlation between all those responses which helps identifying outlying values and prove unattainable targets (e.g. low value for response Y1 combined with high values for Y2 when they exhibit strong positive correlation).

Two-Day DOE User Meeting Programme
Click here to see the programme. All talks are given in English.


Speakers

Don't miss this opportunity to learn more about DOE. Register today!


Registration Fees
2-day meeting (including lunches and the conference dinner on Tuesday evening) = 420 €
1-day workshop on May 31st (including lunch) = 300 €
1/2-day workshop on June 3rd = 250 €

Deadline for registrations is May 25th.

Have we piqued your interest? Register online or download our PDF-Registration Form and fax it to Statcon (see below). (Note: Please right-click on the link and choose Save Link As if you are having problems opening it.)

STATCON B. Schäfer
Schulstr. 2, D-37213 Witzenhausen
Germany
E-Mail: vertrieb@statcon.de
phone: +49 (0) 5542 93-300
Fax: +49 (0) 5542 93-3030

Meeting Sponsors:

 

Note: Pictures of Switzerland courtesy of freefoto.com.