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Lean Sigmas Myth Buster Introduction to 22 Factorial Design of Experiments

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Title: Lean Sigmas Myth Buster Introduction to 22 Factorial Design of Experiments


1
Lean Sigmas Myth Buster Introduction to 22
Factorial Design of Experiments
  • Clay Walden, Ph.D.
  • Conference on High Technology
  • Mississippi Telcom Center, Jackson, MS
  • November 28, 2007

2
Myth Buster Can you distinguish truth from myth?
  • Only two Coca-Cola executives know Cokes secret
    formula each one only knows half.
  • A light bulb in 1901 burns bright to this day.
  • Pull tabs from aluminum cans have special
    redemption value for time on kidney dialysis
    machines
  • Great Wall of China is the only man made object
    visible from the moon.
  • In order to implement Six Sigma you need to hire
    a pointed head statistician.

3
Myth Buster Pretest Can you distinguish truth
from myth?
  • Myth
  • Myth
  • Myth
  • Truth
  • ????
  • Only two Coke-Cola executives know Cokes the
    secret formula each one only knows half.
  • Pull tabs from aluminum cans have special
    redemption value for time on kidney dialysis
    machines
  • Great Wall of China is the only man made object
    visible from the moon.
  • A light bulb in 1901 burns bright to this day.
  • In order to implement Six Sigma you need to hire
    a pointed head statistician

4
Pre-Test Evaluation
  • 0-1 Correct Give Up!
  • 2-3 Correct Recommend enrolling in next 1
    sigma pink belt workshop.
  • 4 Correct Need to find a life outside of
    watching Myth Buster reruns

5
Shop Floor Myths
  • Like popular culture myths and urban legends
    abound on the shop floor.
  • Why?

6
Shop Floor Myths
  • Like popular culture myths and urban legends
    abound on the shop floor.
  • Why?
  • Dynamic and sometimes chaotic environment
  • Lots of possible factors Deming stable
    processes are an achievement and NOT a natural
    state.
  • Inadequate measurement systems
  • Shoot from the hip declare victory approach
    to problem solving.

7
Generic Myths
  • If parts are in spec then problem is NOT in
    manufacturing.
  • If parts are out of spec then we have found the
    root cause of our field failures.
  • We have excellent communications between shifts.
  • Our workforce will always be generally unskilled
    and unmotivated.
  • All tasks and operations are equally important.
  • .
  • .

8
How can we dispel these and other manufacturnig
myths?
EASY BUTTON
9
Multiple Factor Approach
  • Assemble experts and thoroughly discuss the
    candidate factors.
  • Engineering
  • Maintenance
  • Operations
  • Good Opportunity for Cause and Effect diagram
    (Fishbone)
  • Use a group consensus technique like multi-voting
    to find the top few factors, at least from the
    teams perspective.
  • Each person has 100 to spend on the factors in
    order of their importance.
  • Very successful in building group consensus.
  • Always do a sanity check never blindly follow
    the approach.

Good subject matter experts are essential, not
just engineers.
10
Now what do you do ?
  • Most industrial settings, interested in making
    conclusions regarding multiple factors.
  • Trial and Error
  • Typical OFAT Approach
  • very carefully vary one factor at a time so that
    we can isolate the impact of each.
  • Any problems?
  • DOE is a better way Why?

11
Example Problem
  • Problem Gas mileage for car is 20 mpg. Would
    like to get 30 mpg.
  • Factors

12
OFAT Example
13
OFAT Reasonable Approach
14
OFAT (One-Factor-At-A-Time)
15
One Factor _at_ a Time
  • Inefficient use of sample size.
  • Interactions can not be investigated.

16
Power of 22 Factorial Design
Factorial Design each level of one factor is
found in combination with each level of the other
factors. Allowing both Main Effects and
Interactions to be estimated
17
Interactions?
  • Interactions occur when the effect of one factor
    depends upon the level of another factor.
  • Example, Drug A reduces blood pressure when
    used by itself, Drug B reduces anxiety when
    used by itself. If Drug A and B are used together
    may lead to a heart attack or stroke.

18
Interactions
  • Are understanding interactions important to
    improving manufacturing processes?

Yield of a chemical process is impacted by
operating temperature and reaction time. The
impact of changing temperature on yield depends
upon the reaction time.
19
22 Factorial Example Main Effect
Objective two factor experiment focusing on the
impact of hardness and lubrication on process
yield (). Main Effects Effect of Hardness (A)
average change in response (yield) as hardness
goes from a low to high level. Effect of
Lubrication (B) average change in response
(yield) as lubrication goes from a low to high
level.
20
22 Factorial Example
Main Effects Main Effect of A A
(3040)/2 (1020)/2 20 Main Effect of B
B (2040)/2 (1030)/2 10 Interaction AB
AB (4010)/2 (3020)/2 0
Notice the effect of A is the same no matter
what the level of B. This indicates there is no
interaction effect.
21
Plot of Effects Main Effect
Parallel lines indicate the absence of an
interaction effect Effect of hardness on yield is
the same regardless of whether clean or dirty lub
is used.
22
22 DOE Example - Interaction
Changed response from 40, notice impact on
effects calculation and effects plot.
23
Plot of Effects - Interaction
50
40
Yield
Lub low
30
20
10
Lub high
low Hardness
High Hardness
Intersecting lines indicate an interactive
effect. Effect of hardness on yield depends on
whether you are using clean or dirty lub.
24
Myths Small Motor Plant
  • Our new rpm/amps tester provides us with a
    reliable evaluation of product performance.
  • Use a special calibrated rubber hammer to
    reduce shaft TIR.
  • Current Method of aligning commutator, while not
    perfect, is adequate.
  • Reason for the 40 failure rate must be in the
    ancient heat treating process. We cant solve the
    problem, because the company is unwilling to
    invest. Catalogue Engineer (Shigeo Shingo)

25
Myths Busted Small Motor Plant
  • Exposed by
  • Measurement System Analysis
  • Cyclical error found which takes up 50 of
    tolerance
  • Design of Experiment on the Shop Floor 23
    factorial 40 runs (1 day)
  • Rubber Hammer process not capable
  • Tested new alignment method verses old method
  • Resulted in
  • Reducing defect rate from 40 to 0
  • 500K per year in savings

26
Myths Acme Tube Pipe
  • Any combination of plugs and dies from the tool
    crib will work.
  • A high degree of variation in tube eccentricity
    at the press is inherent.
  • We need to better train our operators and
    maintenance personnel to repair breakers
    quicker.

27
Busted Acme Tool Tube
  • Exposed by
  • Design of Experiment on the Shop Floor
    factorial (2 day)
  • Lub ID critical
  • Match correct plug and die
  • Standardized work and 5S in tool crib
  • Resulted in
  • Reducing breaker rate from 12 to 5
  • 2,700,000 per year in savings

28
Catapult Dynamics
Producing our nations next generation of missile
defense systems!
CTQ Range Target 75 Specification /-
1
29
Catapult Factory
  • Use 6 Sigma Problem Solving Approach
  • DMAIC
  • Objective reduce process variation and center on
    target (75 inch).
  • Plant Resources
  • Personnel 3 operators, 1 inspector, 1 Recorder
  • Equipment ping pong balls, tape measure, pad,
    pen.

30
Myth or Truth?Catapult Dynamics
  • Variation caused by repeated use of the rubber
    band is unavoidable.
  • Production task is quite simple and should be
    automated.
  • Poorly skilled and unmotivated workforce
  • If the company really cared about quality they
    would invest in a new highly automated CNC
    catapult.
  • Testing can best be done when only factor is
    changed at a time. .

31
Design of Experiments
  • Select two key factors
  • each factor at 2 levels.
  • Replicate the experiment
  • 8 runs are required which ones?
  • Randomize the trails (why?)
  • Analyze the results (i.e., plot the data)
  • Make recommendations for standard work within the
    process.

32
Plot of Effects - Interaction
50
Factor B high
40
Response
30
20
Factor B low
10
low Factor A
High Factor A
The effect of Factor A on distance depends upon
the level of factor B.
33
Plot of Effects Main Effects
50
40
Response
Factor B low
30
20
Factor B high
10
low Factor A
High Factor A
The effect of Factor A on distance does not
depend upon the level of factor B.
34
Plot of Effects
90
Factor B low
80
Response
70
60
Factor B high
50
Low Factor A
High Factor A
35
Key Take-a-Way
  • Catapult Dynamics

36
Myth You must hire a pointed head statistician
to use Six Sigma
Busted !
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