Lean Design
of Experiments (Lean DOE)
Six Sigma and Lean Design for Six Sigma are initiatives that have
had a huge impact on industry during the last 15 years. Classical
Design of Experiments on the other hand was introduced nearly 100
years ago and has gradually 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 in comparison to classical full and fractional designs.
Mixed levels for factors can be readily accommodated as well as
infeasible experimental regions. More than two or three levels can
be accommodated for factors. Multiple responses can be traded-off
with ease.
With Lean Design for Six Sigma's focus on customer requirements
and functional trees, Lean DOE approaches can be utilized to quickly
optimize functions and assure adequate robustness.
In this session attendees will learn how to apply state-of-the-art
Lean DOE approaches in the product and process development arena.
Our instructors have taught thousands about these approaches and
have lead many successful applications.
Efficient and effective application of experimental design require
a blending of key skills. Knowledge of the relevant technologies,
team communication skills, and some simple mathematical tools all
come into play (in this session we rely upon the computer to crunch
the numbers while we focus on interpretation of key graphics and
output tables).
The following
is an outline of the various topics covered in this 2 day course:
What
is Lean DOE?
How
recent developments in DOE can enhance experimental efficiency
and effectiveness
Linking
Lean DOE techniques with product design and development
Quick
review of the fundamentals
Key
planning issues
Orthogonal
arrays
Obtaining
reliable data
Graphical
and statistical analysis
The
basics of confirmation
What
to do if you cannot pay the price of perfect orthogonality
What
really is an interaction?
Why
you cannot be cavalier about selection of levels
Replication,
repetition, and energy
The
basics of computer generated designs
Advantages
of D-optimal and I-optimal designs
Determination
of the number of unique trials
Specifying
inclusions and exclusions
The
setup of computer generated designs using leading software
What
is the assumed model?
Analysis
of computer generated designs using software
Numerous
student exercises
The
trade-off of multiple responses using co-optimization approaches
Residual
analysis
Linking
experimental objectives to functional trees
Computer
generated designs and robustness objectives
Introduction
to Neural Network analysis of complex datasets
Team
exercises
Getting
started on your applications
Fees:
$1995 includes the text Engineering Today'sDesigned Experiments,
Student Version of DOE Wisdom Software, and Participant Guide.
Lunch and coffee breaks are provided each day.
$2695
includes all of the above plus the full version of DOE Wisdom
Software (a $200 discount).
Using
practical, hands-on applications, learn from the experts in
Experimental Design how to successfully blend the best of
Japanese and Western techniques.
Register
Early - class sizes are limited for optimal interaction and
instruction.
