Application of Design of experiments in various industries

1. DOE IN INDUSTRIES
PRESENTED BY GAUTAM MAKESHBABU

2. OVERVIEW
Why DOE?-The basic goal
How can DOE help industries?
Case studies
• Problems
• Factors and response
• Experimental run
• Conclusions

3. WHY DOE?
Quality and Reliability
Early stage implementation
Wide use
Not limited to manufacturing industries

4. DOE IN INDUSTRIES
Comparisons
Variable screenings
Transfer function exploration
System optimization
System robustness

5. CASE STUDY
EXPERIMENTAL DESIGN IN A PET
FOOD MANUFACTURING COMPANY

6. PROCESS OF
MANUFACTURE
DECIDE THE
FORMULA
MIXING AND
CONDITIONING
EXTRUDING
AND CUTTINGs
COOLING AND
DRYING
1.1 Decide the
ingredients.
1.2 Proportion
of each
ingredient
1.3 Value of PQF
(glue material)
2.1 Weight
2.2 Mix
2.3 Set
temperature of
water steam
2.4 Add water
steam to
mixture
3.1 Extrude the
mixture
through a
metallic die
3.2 Cut the
extruded
material in
small cylinders
4.1 Small
cylinders of
rabbit chow on
conveyor belt
are cooled and
dried with air
4.2 Packaging

7. PROBLEMS FACED
During the cooling and drying of the rabbit food
cylinders, a loss of product in the form of powder
was taking place.
During manipulation and transportation, the
cylinders eroded and formed fine powder. This
created loss of product and also digestion problems
in rabbits.

8. IN THE PAST
When quality deteriorated, the following steps were taken in order
to maintain the quality of the yield.
• Reduce flow of mixture in extrusion. low yield
• Raise conditioning temp in mixture. High energy consumption
• Change compression temp in die. Time consuming
• Last resort- change formula by adding glue material.

9. FACTORS AND LEVELS
LEVELS
FACTOR
- +
FORMULA (PQF) 10 20
CONDITIONING
TEMPERATURE (T)
80% of T T (max)
FLOW (F) 80% of F F (max)
COMPRESSION
ZONE IN DIE
2” 2 ½”
CONSTRAINTS:
All experiments should be performed in a single day.
All activities must be done inside the plant.
Factor 4(compression zone in die) cant be changed easily.

10. EXPERIMENTAL DESIGN
Y1- Powder in product Y3- Yield
Y2- Powder in process Y4- Energy consumption
RANDOM
ORDER
RUN NUMBER FACTORS
X1 X2 X3 X4
RESPONSES
Y1 Y2 Y3 Y4
12 1 - + - + 0.916 1.92 7.50 222.5
9 2 + - - - 1.178 2.07 8.70 238.0
3 3 - + + - 1.216 1.85 10.2 250.4
4 4 + - + + 1.119 2.03 6.20 250.4
1 5 - - - - 1.315 1.66 8.3 235.0
10 6 + + - + 0.911 2.08 7.20 222.0
11 7 - - + + 1.07 1.96 7.95 267.5
2 8 + + + - 1.273 2.13 9.6 248.2
8 9 - + - - 1.071 1.62 8.50 224.0
6 10 + - - + 1.025 1.73 5.90 233.3
5 11 - + + + 1.04 1.64 7.3 248.5
7 12 + - + - 1.174 1.93 9.95 255.0

11. 0.20.10.0-0.1-0.2
99
95
90
80
70
60
50
40
30
20
10
5
1
Effect
Percent
A x1
B x2
C x3
D x4
Factor Name
Not Significant
Significant
Effect Type
Normal Plot of the Effects
(response is y1, Alpha = 0.05)
Lenth's PSE = 0.093
0.20.10.0-0.1-0.2
99
95
90
80
70
60
50
40
30
20
10
5
1
Effect
Percent
A x1
B x2
C x3
D x4
Factor Name
Not Significant
Significant
Effect Type
ACD
AB
A
Normal Plot of the Effects
(response is y2, Alpha = 0.05)
Lenth's PSE = 0.06375
210-1-2-3
99
95
90
80
70
60
50
40
30
20
10
5
1
Effect
Percent
A x1
B x2
C x3
D x4
Factor Name
Not Significant
Significant
Effect Type
D
Normal Plot of the Effects
(response is y3, Alpha = 0.05)
Lenth's PSE = 0.76875
2520151050-5-10
99
95
90
80
70
60
50
40
30
20
10
5
1
Effect
Percent
A x1
B x2
C x3
D x4
Factor Name
Not Significant
Significant
Effect Type
C
Normal Plot of the Effects
(response is y4, Alpha = 0.05)
Lenth's PSE = 4.6875

12. 1-1
1.20
1.15
1.10
1.05
1.00
1-1
1-1
1.20
1.15
1.10
1.05
1.00
1-1
x1
Mean
x2
x3 x4
Main Effects Plot for y1
Data Means
1-1
2.00
1.95
1.90
1.85
1.80
1-1
1-1
2.00
1.95
1.90
1.85
1.80
1-1
x1
Mean
x2
x3 x4
Main Effects Plot for y2
Data Means
1-1
9.0
8.5
8.0
7.5
7.0
1-1
1-1
9.0
8.5
8.0
7.5
7.0
1-1
x1
Mean
x2
x3 x4
Main Effects Plot for y3
Data Means
1-1
250
245
240
235
230
1-1
1-1
250
245
240
235
230
1-1
x1
Mean
x2
x3 x4
Main Effects Plot for y4
Data Means

13. Results
The compression in die(X4), when set at its high
level, produced higher quality but lower yield.
Suggestion was made to reduce the viscosity of the
extruded material in order to increase productivity
and compensate for the compression zone high
setting.
Internal losses (Y2) could be reduced by using low
levels of glue material(X1).
Conditioning temperature(X2) and Flow(X3) were
changed in order to save energy.

14. Another Example
Goal:
Find the factors that affect the gain in a earphone.
Factors:
• Width of lines(W)
• Resistance(R)
• Capacitance(C)
Response:
Gain(dB)

15. W R C Gain
0 0 0 13.88
-1 1 -1 14.52
1 -1 -1 13.01
1 -1 1 13.09
-1 1 1 14.61
-1 -1 1 12.93
-1 -1 -1 12.85
1 1 1 14.81
1 1 -1 14.71
Factor Low High
W(um) 9.5o 10.50
R(ohm) 30.4 33.6
C(pF) 19 21
Factors and levels
W= 10±0.5 um
R= 32±1.6 ohm
C= 20±1 pF

16. 1.81.61.41.21.00.80.60.40.20.0
99
95
90
80
70
60
50
40
30
20
10
5
1
Effect
Percent
A Width of lines
B Resistance
C Capacitance
Factor Name
Not Significant
Significant
Effect Type
C
B
A
Normal Plot of the Effects
(response is Gain, Alpha = 0.05)
Lenth's PSE = 0.0075

17. Main Effects
1-1
14.5
14.0
13.5
13.0
1-1
1-1
14.5
14.0
13.5
13.0
Width of lines
Mean
Resistance
Capacitance
Main Effects Plot for Gain
Data Means

18. Factorial Fit: Gain versus Width of lines, Resistance,
Capacitance
Term Effect Coef
Constant 13.8163
Width of lines 0.1775 0.0888
Resistance 1.6925 0.8463
Capacitance 0.0875 0.0437
Width of lines*Resistance 0.0175 0.0088
Width of lines*Capacitance 0.0025 0.0012
Resistance*Capacitance 0.0075 0.0037
Width of lines*Resistance* 0.0025 0.0012
Capacitance
Gain= 13.81+ 0.088W+ 0.8463R+ 0.8463C+
0.0088W*R+ 0.0012W*C+ 0.0037R*C+ 0.0012W*R*C

19. Management &
Service Industries
Analysis to find the factors that affect the acceptance of presentations.
Done by an ISRU(In-site Resource utilization)
Question: How can we improve the showcase in order to make it more
resourceful to the members.
Methodology: Questionnaires and ranking.
Factors:
Presentation Content (General or specific)
Number of speakers(Single or Multiple)
Timing of showcase(morning or afternoon)
Response:
Rank(average rank given by members)

20. FACTORS LEVELS
PRESENTATION
CONTENT
Specific case studies General Overview
NUMBER OF
SPEAKERS
One speaker Multiple speakers
TIMING OF THE
SHOWCASE
Morning Afternoon
Option Presentation Content Number of Speakers Timing of the showcase Rank
1 Specific Case Studies Multiple Speakers Afternoon 5.1
2 General Overview One Speaker Afternoon 4.9
3 Specific Case Studies One Speaker Morning 5.0
4 General Overview Multiple Speakers Afternoon 4.5
5 Specific Case Studies One Speaker Afternoon 6.1
6 General Overview Multiple Speakers Morning 4.3
7 General Overview One Speaker Morning 3.3
8 Specific Case Studies Multiple Speakers Morning 2.8

21. timingspeakerscontent
lunch
morning
multiple
single
cases
general
5.2
4.9
4.6
4.3
4.0
mean
Main Effects Plot (data means) for mean

22. Estimated Effects and Coefficients for rank (coded units)
Term Effect Coef
Constant 4.5000
presentation content -0.7500 -0.3750
no of speakers -1.2000 -0.6000
timing -0.7500 -0.3750
presentation content*no of speakers 0.2500 0.1250
presentation content*timing -0.4000 -0.2000
no of speakers*timing -0.9500 -0.4750
presentation content*no of speakers* 0.4000 0.2000
timing
general
cases
multiplemultiplesinglesingle
5.5
5.0
4.5
4.0
3.5
3.0
speakers
content
Mean
Interaction Plot (data means) for mean

23. Conclusions made
• The audience prefer to have multiple speakers overall as the mean rank
scores are higher for multiple than for single speakers.
• They feel more strongly that they prefer multiple speakers if the content is
general, but are not so bothered if the content is case studies.
• The audience preferred an afternoon presentation as a whole but
specifically wanted afternoon for case studies.
1-1
5.25
5.00
4.75
4.50
4.25
4.00
3.75
3.50
timing
Mean
-1
1
content
presentation
Interaction Plot for rank
Data Means

24. Factors Levels
Application type Loan Lease
Region Midwest Northeast
Description Current Enhanced
Example Current Enhanced
Neg. Example Yes None
DOE in Financial
Operations
Problem: 60% reprocessing rate of applications.
Main reason: Incomplete information provided by customer

25. Experimental Design

26. 403020100
99
95
90
80
70
60
50
40
30
20
10
5
1
Effect
Percent
A Application type
B Region
C Description
D Example
E Neg example
Factor Name
Not Significant
Significant
Effect Type
D
C
Normal Plot of the Effects
(response is Avg % completed, Alpha = 0.05)
Lenth's PSE = 1.8

27. LeaseLoan
90
80
70
60
50
NortheastMidwest EnhancedCurrent
EnhancedCurrent
90
80
70
60
50
NoneYes
Application type
Mean
Region Description
Example Neg example
Main Effects Plot for Avg % completed
Data Means

28. NortheastMidwest EnhancedC urrent EnhancedC urrent NoneYes
100
75
50
100
75
50
100
75
50
100
75
50
A pplication type
Region
Description
Example
Neg example
Loan
Lease
type
Application
Midwest
Northeast
Region
Current
Enhanced
Description
Current
Enhanced
Example
Interaction Plot for Avg % completed
Data Means

29. NoneYes
74
73
72
71
70
69
68
Neg example
Mean
Midwest
Northeast
Region
Interaction Plot for Avg % completed
Data Means
Conclusions made:
• Provide enhanced
descriptions and
examples.
• Neg. examples were
stopped in the Northeast
region.
Results:
• Reprocessing rates reduced to 5%.
• Reduced cycle time for application processing.
• Increase in profit without increasing customers.

30. Reference
• http://en.wikipedia.org/wiki/Design_of_experiments
• http://www.ese.wustl.edu/~psm/405quality-control-
article.pdf
• http://www.obgyn.cam.ac.uk/cam-
only/statsbook/stexdes.html
• http://www.iaeng.org/publication/WCE2007/WCE2007_pp
1108-1112.pdf

31. Thank You