Before starting a statistical experimental design, it is helpful to collect and compile all possible influencing variables. This procedure not only helps to identify the important factors, but also ensures that knowledge will be secured in the team. Furthermore, the influence of possibly underestimated factors is clarified and evaluated. Ideally, only after such an assessment are priorities methodically derived. Depending on the challenge, it is appropriate to use different methods and approaches. In this collection of slides, I have compiled my favourite methods and tools from the DFSS context.

1. By info@stefan-moser.com
How do I prioritize with the
team the Faktors for
Design of Experiments / DoE?
The number of factors determines the scope of the
experiments.

2. By info@stefan-moser.com
In the intuitive discussion, the
@-Experts feel a priori addressed to
give their opinion to the best.
Methodical selection procedures, however, allow all
participants to have their say, supplement points of
view and clarify the factor wording. This helps to
share and increase common knowledge.

3. By info@stefan-moser.com
In detail, it is worth clarifying the
wording, does each participant
understand the same under the
chosen factor names?
It is possible that there are several designations or
different interpretations for the same factor.
Grouping the factors and supplementing cluster
headings can help here.

4. By info@stefan-moser.com
Focus can also be on the links of symptoms
to target variables and the factors
involved to promote prioritization.
Not all factors collected in the workshop are
detectable/relevant to all symptoms and to the
measurable outcomes.

5. By info@stefan-moser.com
A next goal-oriented step is the
classification of the factors
according to their adjustability.
Of primary importance are only the controllable
factors! However, all other factors should be
documented in the best possible way for further
consideration / analysis.
Uncontrolled
/Noise
constant
derived,
dependent or
regulated
controllable

6. By info@stefan-moser.com
In addition to selecting the right factors, their
range of variation is also decisive. Design of
experiments serves to determine the effect of a
pragmatic, targeted factor variation.
Too large ranges of variation are not expedient, as
this requires many experiments (support points). Even
too small factor variations are less effective, since
sometimes no measurable effect can be derived from
too small / cautious variation.
6
USL
LSL
Baseline
X
cubic effect
No effect
quadratic
effect
Linear
effect
Y

7. By info@stefan-moser.com
Only independent factors can be
considered in the experimental design,
which may allow reformulation while
reducing the number of factors.
Factors whose adjustmanet depends on other factors
cannot be analyzed independently.
X1: A:B; A/B(const.)
X2: C:(A+B)
A
B
A
B Reformulation
%A
A:B(konst.)
A/B
Reformulation
A
B
C
A
B
C
X1:
A, B, C, …, F, G, H
Reformulation
X0: Start-Point
∆Y / step
X1:
∆Y
∆𝑋
examples

8. By info@stefan-moser.com
After clarifying the significance of the
factors, "multi-voting" is a goal-oriented
method of prioritization.
For "multi-voting", already made Ishikawa's or
mind- maps can be reused.
Whether with points or strokes each participant has
the same number. Evaluations should be carried out
as silently and independently as possible.

9. By info@stefan-moser.com
Methodologically, factors can be
systematically prioritized with the
pairwise comparison or the analytical
hierarchical process.
Factor 1
Factor 2
Factor 3
Factor 4
Factor 5
2 2
2
2
2
2
0
1
0
0
0
0
0
0
1
1
1 4
1
8
2
0
2
4
3
Factor
2
Factor
1
Factor
4
Factor
4
Factor
4
Pareto
During the query, it is useful not to discuss the
factors again, but to establish the weighting with an
independent moderator by a simple show of hands
and in silence.
Usually, only a part of the matrix is queried.

10. By info@stefan-moser.com
The "Vester Paper Computer" proceeds in
a similar way. However, here partly
recursive questioning is added.
The interpretation in the Vester diagram of passive
and active sum indicates not only the general
weighting of the factor but also conclusions about
their behavior / effect.
Factor 1
Factor 2
Factor 3
Factor 4
Factor 5
2 2
1
2
2
2
0
1
0
0
0
0
0
0
1
0
1 4
1
7
0
0
2
2
2
3 0 5 3 5
reactive
uncritical
active critical
Passive sum
Active
Sum

11. By info@stefan-moser.com
If too many sub-systems with significance
are detected in the boundary diagram,
Shainin's component analysis helps to
derive priorities
Through structured, recombination, the decisive sub-
systems can be identified within a few experiments in
order to investigate them more intensively..

12. By info@stefan-moser.com
Visualizations such as the Eisenhower
Matrix also provide a way to classify the
findings from the Boundary Diagram.
This method allows the subsystems to be
sorted according to the importance and
urgency of the examination.
Do it later
Dump it
Delegate Do it first
- importance +
-
urgency
+

13. By info@stefan-moser.com
Risk assessments are also very popular
tools for weighting and prioritizing
factors.
--
0
++
-- 0 ++
xxx
xxx
xxx
xxx
xxx
xxx
xxx
Whether risk, importance, urgency, fame and glory, or
the like, often these weightings are very one-sided
considerations and correspondingly less sustainable in
achieving holistic goals.

14. By info@stefan-moser.com
According to Pareto, 20% of the factors are
responsible for 80% of the variation in results.
Emergent processing can help to place the "right"
tests in the "right" space.
The converse conclusion suggests that unless these
20% or A factors have been varied in the correct
range, the effects of the A factors are too dominant to
describe the influence of B,C factors.
A-Factors B-Factors C-factors
phase
variation
Medium
setting
Medium
setting
A
Fine-tuning
Or freezing
variation
Medium
setting
B
Fine-tuning
Or freezing
Fine-tuning
Or freezing
variation
C
Fine-tuning
Or freezing
Fine-tuning
Or freezing
Fine-tuning
Or freezing
D
disturbances
document
document
document
Examine
Variation

15. By info@stefan-moser.com
Finally, Ockham's razor blade should be mentioned,
where simple hypotheses and explanations,
complicated and complex attempts at explanation
are preferred.
Of course, there is always the threat of over-
simplification in the sense of black-and-white
thinking or focusing on avoidably simple solutions, but
the method has charm to sorting out vague and less
well-founded factors.

16. By info@stefan-moser.com
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