DOE IN INDUSTRIESPRESENTED BY GAUTAM MAKESHBABU

OVERVIEW

Why DOE?-The basic goal

How can DOE help industries?

Case studies

• Problems

• Factors and response

• Experimental run

• Conclusions

WHY DOE?

Quality and Reliability

Early stage implementation

Wide use

Not limited to manufacturing industries

DOE IN INDUSTRIES

Comparisons

Variable screenings

Transfer function exploration

System optimization

System robustness

CASE STUDY

EXPERIMENTAL DESIGN IN A PET FOOD MANUFACTURING COMPANY

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 ingredient1.3 Value of PQF (glue material)

2.1 Weight2.2 Mix2.3 Set temperature of water steam2.4 Add water steam to mixture

3.1 Extrude the mixture through a metallic die3.2 Cut the extruded material in small cylinders

4.1 Small cylinders of rabbit chow on conveyor belt are cooled and dried with air4.2 Packaging

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.

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.

FACTORS AND LEVELSLEVELS

FACTOR - +FORMULA (PQF) 10 20

CONDITIONINGTEMPERATURE (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.

EXPERIMENTAL DESIGN

Y1- Powder in product Y3- YieldY2- Powder in process Y4- Energy consumption

RANDOM ORDER

RUN NUMBER FACTORSX1 X2 X3 X4

RESPONSESY1 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

0.20.10.0-0.1-0.2

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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

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ACD

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Normal Plot of the Effects(response is y2, Alpha = 0.05)

Lenth's PSE = 0.06375

210-1-2-3

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Normal Plot of the Effects(response is y3, Alpha = 0.05)

Lenth's PSE = 0.76875

2520151050-5-10

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Normal Plot of the Effects(response is y4, Alpha = 0.05)

Lenth's PSE = 4.6875

1-1

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Main Effects Plot for y1Data Means

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Main Effects Plot for y2Data Means

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Main Effects Plot for y3Data Means

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Main Effects Plot for y4Data Means

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.

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)

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 levelsW= 10±0.5 umR= 32±1.6 ohmC= 20±1 pF

1.81.61.41.21.00.80.60.40.20.0

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Factor Name

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Normal Plot of the Effects(response is Gain, Alpha = 0.05)

Lenth's PSE = 0.0075

Main Effects

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Capacitance

Main Effects Plot for GainData Means

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

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)

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

timingspeakerscontent

lunch

morning

multiple

single

c ases

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4.9

4.6

4.3

4.0

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Main Effects Plot (data means) for mean

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

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content

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Interaction Plot (data means) for mean

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

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3.50

timing

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1

content

presentation

Interaction Plot for rankData Means

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

Experimental Design

403020100

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Factor Name

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Normal Plot of the Effects(response is Avg % completed, Alpha = 0.05)

Lenth's PSE = 1.8

LeaseLoan

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EnhancedCurrent

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Example Neg example

Main Effects Plot for Avg % completedData Means

NortheastMidwest EnhancedC urrent EnhancedC urrent NoneYes

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Current

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NoneYes

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Northeast

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Interaction Plot for Avg % completedData 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.

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_pp1108-1112.pdf

Thank You