This Slideshare presentation is a partial preview of the full business document. To view and download the full document, please go here: http://flevy.com/browse/business-document/064introduction-to-design-of-experiments-doe-1165 DESCRIPTION A DOE is a planned set of process runs used to find out how certain input factors affect the output of the process. The DOE uses pre-determined tests that measure the output at two or more settings for each input which will: determine the significance of each factor and combination of factors, allow efficient optimization, direct activity to rapid process improvements and provide a mathematical equation that helps determine the optimal settings for Potential KPIV. The objectives of this module are to understand the different experimental types including Trial and Error, Shot Gun, OFAT, and DOE which includes: Classical, Taguchi, and Shainin. It also looks at the importance of an experimental plan. This material is suitable for independent study or formal classroom training and includes quiz questions.

1. 1
Introduction to Design of
Experiments (DOE)

2. 4
DOE Definitions
Term Known as: Definition
Response Y, Big Y, KPOV the output of the process
Factor X, KPIV the input variables of the process
Factor Level Settings discrete values that an experimental factor
can take
Run Treatment
Combination
the settings for all factors in a given
experimental trial
Replicate identical experimental runs, executed at
different times during the experiment
Inference
Space
Operating range of factors under study
Design
Matrix
a table created by MINITAB (based on user
input of inference space for each factor) that
will list all combination treatments in the
proposed run order. After creation of the
design matrix, the user creates a response
column to record experiment output values
from each run.
This document is a partial preview. Full document download can be found on Flevy:
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3. 7
Assumptions
• DOE uses ANOVA as the test engine and therefore all
of the ANOVA assumptions apply:
The residuals must be:
independent
normally distributed
with equal variance
• Factors are independent
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4. 10
Trial and Error
• Not generally recommended
• Trial and Error consists of trying one process, record
what happens then try something else, record what
happens, etc.
• Trial and Error typically requires many, many
“experiments” to determine an acceptable process. It
is usually very expensive
• Trial and Error does not give you information about
the specific relationship between any of the inputs and
your desired result. Trial and Error cannot be used to
predict an outcome.This document is a partial preview. Full document download can be found on Flevy:
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5. 13
OFAT (One Factor at a Time)
• OFAT typically requires many runs and is therefore costly
and time consuming
• OFAT does not lead to a complete picture of the process
not all possible combinations are tested
Interactions are not taken into consideration
non-linear effects are not tested
Does not create a mathematical prediction equation
(linking Y to X’s)
This document is a partial preview. Full document download can be found on Flevy:
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6. 16
DOE Types
• Different types of DOEs are used to gain different types
of information:
• Screening designs:
2k Factorial Designs
General Full Factorial Designs
Fractional Factorial Designs
Center points
Taguchi
Shainin
• Advanced optimizing designs:
Response Surface Methodology
Central Composite Design
Multiple Response Optimization
This document is a partial preview. Full document download can be found on Flevy:
http://flevy.com/browse/document/064introduction-to-design-of-experiments-doe-1165

7. 19
Exercise
None
This document is a partial preview. Full document download can be found on Flevy:
http://flevy.com/browse/document/064introduction-to-design-of-experiments-doe-1165

8. 1
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