shainin b vs c webinar
TRANSCRIPT
Everyone is muted. We
will start at 7pm EST.
Shainin B vs C Webinar
Ha Dao, ChairmanASQ Automotive Division
Moderator
Call In: 215-383-1016
Code: 853-908-666
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Thank You for Joining our ASQ Webinar
3ASQ Automotive Division Webinar Series
Richard Shainin, Executive VP
How to Calculate a Risk of
a Decision: Shainin B vs C
Shainin LLC
February 15, 2010
7:00 pm – 8:15 pm EST
ASQ Automotive Division Webinar Series
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Housekeeping Items
About ASQ Automotive Division
Webinar Series
Polls
How to Calculate the Risk of a
Decision: Shainin B vs C
Questions & Answers
Agenda
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Everyone is muted
Session is being recorded
Session will last about 75 minutes
Slides posted at www.asq-auto.org
Participate thru polls, chat & questions
Will answer questions at the end:• Q&A in last 10 minutes
• Please type your questions in the panel box
Housekeeping Items
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Tad Kowalski - Emerson Climate Technologies
Sandy Cornellier - Shainin LLC
Kevin Wu - ASQ China
All International Attendees
Special Recognition
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Ha Dao, ChairmanASQ Automotive Division
ASQ Fellow
Six Sigma Master BB
Shainin Red X Master
15+ Yrs of Experience in
Automotive Industry
Troy, Ohio
About Me
Your Moderator
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Richard Shainin, Executive VP
How to Calculate a Risk of
a Decision: Shainin B vs C
Shainin LLC
February 15, 2010
7:00 pm – 8:15 pm EST
Introducing Dick Shainin
13ASQ Automotive Division Webinar Series
Interactive Polls
How to Calculate
the Risk of a
Decision: Shainin
B vs C
• Richard Shainin
• Executive Vice President
• Shainin
• February 15, 2011
1. Change.
2. Impact of ineffective changes.
3. Minimizing ineffective changes.
4. B vs. C Test.
5. Randomization.
6. Additional examples.
Agenda
15
0
100
200
300
400
500
600
700
800
900
Align to Current Process
Process Improvement
Product Change Request
Quality Improvement
Cost Savings
Change Type
One Quarter of Data, > 2000 changes approved
Nu
mb
er
of
Ch
an
ges
Manufacturing changes for released products across 6 manufacturing sites.
Global Electronics Company
16
Effectiveness?
Lots of engineers making things „better‟.
Less than 20% of the changes achieved their desired result.
17
Compressors fail in the field after
21,000 miles.
All vehicle platforms experience
the failure.
Returned compressors have
extensive damage.
Compressor Broken Reed Case
Automotive HVAC
The majority of warranty claims are in warmer climates.
Warranty claims are highest in the summer months.
Warranty costs have risen to $20 million per year.
18
Product Function
Background Information
Failure
Initiation
New
Reed
Failed
Reed
19
The X Y Approach
Four teams of experts have already „solved‟ this problem.
Nine product changes over seven years.
Five documented processing changes over seven years.
Make a change and wait for the next round of summer
warranty data to see if the problem has been solved.
“The customer test fleet!”
20
Engineering cost of designing the change.
Manufacturing cost to implement the change.
Cost of unnecessary processing.
Cost of extra or more expensive materials.
Cost of containment.
Cost of scrap and rework.
Time from decision to change to implementation.
Time from implementation to accurate assessment of change.
Warranty costs.
Waste from Ineffective Changes
21
Loss of brand loyalty.
Loss of credibility within the supply chain.
Loss of credibility with senior leadership.
Marketing cost of ineffective changes
22
Understandin
g the physics
Experience
based
approach
Directional correct
design change
Technical
problem
Process/Product Control
Design Change
Contrast based
convergent approach
Brainstorming based
approach
Problem Solving
23
“Talking to the engineers produced
a list of all the inputs that could be
causing the problem.”
“Talking to the parts revealed the
true answer.”
Contrast Based Convergent Approach
Dorian Shainin (1914-2000)
“Keep your eyes open and your
mouth shut. Let the parts guide
you to the answer.”
24
Solution Tree
Rationale:
Strategy choice based on
physics of failure. Lowest
risk and lowest cost along
with the fastest timing.
B vs C test.
Investigation
PressureVolume Temperature
System
Energy
CompressorStrength
AC
System
Contrast
PlatformContrasts
Decrease
Compressor
Energy
IncreaseCompressor
Strength∆P ∆ M
25
B vs C 6 Pack
The B vs C Test is used to determine if a design or process
change produces an improved Green Y distribution.
The 6 Pack is a B vs C test that is simple to run and confirms the
Red X with only a 5% risk of being fooled.
26
B better than C
Statistical representation of an effective change.
27
B is not better than C
Statistical representation of an ineffective change.
28
B vs C
Let’s evaluate by putting results in rank
order with the best result having the
highest rank.
When B is not better, the B vs C test results become a
game of chance, controlled by sample size and analysis
method.
29
B Not Better Than C
With 2 samples, 1B and 1C, there are only 2 possible rank order outcomes.
RankPossible
Outcomes
1 2
1 B C
2 C B
There is a 50% chance that we will be fooled into believing B is better.
30
B Not Better Than C
With 3 samples, 1B and 2Cs, there are only 3 possible rank order
outcomes.
Ran
kPossible Outcomes
1 2 3
1 B C C
2 C B C
3 C C B
There is a 33% chance that we will be fooled into believing B is better.
31
B Not Better Than C
With 4 samples, 2Bs and 2Cs, there are only 6 possible rank order
outcomes.
Rank Possible Outcomes
1 2 3 4 5 6
1 B B B C C C
2 B C C B B C
3 C B C B C B
4 C C B C B B
There is a 17% chance that we will be fooled into believing B is better.
32
B Not Better Than C
With 5 samples, 2Bs and 3Cs, there are only 10 possible rank order
outcomes.
Rank Possible Outcomes
1 2 3 4 5 6 7 8 9 10
1 B B B B C C C C C C
2 B C C C B B B C C C
3 C B C C B C C B B C
4 C C B C C B C B C B
5 C C C B C C B C B B
There is a 10% chance that we will be fooled into believing B is better.33
B Not Better Than C
With 6 samples, 3Bs and 3Cs, there are only 20 possible rank order outcomes.
Ran
kPossible Outcomes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 B B B B B B B B B B C C C C C C C C C C
2 B B B B C C C C C C B B B B B B C C C C
3 B C C C B B B C C C B B B W W W B B B C
4 C B C C B C C B B C B C C B B C B B C B
5 C C B C C B C B C B C B C B C B B C B B
6 C C C B C C B C B B C C B C B B C B B B
There is a 5% chance that we will be fooled into believing B is better.34
Consequences of a
Wrong Decision
Number of
Randomized
Samples
Desired
Confidence ~ Risk
B
Samples
C
Samples
0.999 0.001
Super Critical
3
4
5
6
16
10
8
6
0.99 0.01
Critical
2
3
4
5
13
7
5
4
0.95 0.05
Important
1
2
3
19
5
3
0.90 0.10
Moderate
1
2
9
3
No overlap of
ranks is permitted
for this test.
B better than C, not
just different.
B vs C Table for a One Tailed Test
B and C sample
sizes are
interchangeable.
35
Consequences of a
Wrong Decision
Number of
Randomized
Samples
Desired
Confidence ~ Risk
B
Samples
C
Samples
0.999 0.001
Super Critical
3
4
5
6
16
10
8
6
0.99 0.01
Critical
2
3
4
5
13
7
5
4
0.95 0.05
Important
1
2
3
19
5
3
0.90 0.10
Moderate
1
2
9
3
No overlap of
ranks is permitted
for this test.
B better than C, not
just different.
B vs C Table for a One Tailed Test
B and C sample
sizes are
interchangeable.
6 Pack Test
36
Our Parts or Systems Vary
37
Time
DX
1. Cycle
How do variables change over time?
Spurious Associations
38
Time
DX
1. Cycle
2. Trend
How do variables change over time?
Spurious Associations
39
Time
DX
1. Cycle
2. Trend
3. Shift
How do variables change over time?
Spurious Associations
40
Time
DX
B B
C C
B
C
Testing in phase with process variation?
BOB
WOW
Spurious Associations
41
Time
DX
B BB
C C C
Testing in phase with process variation?
BOB
WOW
Spurious Associations
42
Time
DX
B
C
B B
C C
Breaking Phase With Every Other X
BOB
WOW
Randomization
43
Time
DY
BCB
B
Breaking Phase With Every Other X: The Result
C C
BOB
WOW
Randomization
44
Time
DX
B
C
B B
C C
Breaking Phase With Every Other X
Randomization
45
Time
“Randomization is the engineer’s insurance policy for breaking phase relationships with time.” – Dorian Shainin
DX
B
C
B B
C C
Breaking Phase With Every Other X
Randomization
46
B
C
B
C
B
C
B
B
C
C
C
B
C
B
C
B
C
B
C
C
B
C
B
B
C
B
B
C
C
B
C
B
C
C
B
B
B
C
C
B
C
B
C
C
B
B
C
B
C
C
C
B
B
B
B
C
B
C
C
B
B
C
C
C
B
B
B
B
C
B
C
C
C
B
B
B
C
C
C
B
C
B
B
C
B
B
B
C
C
C
B
B
C
C
B
C
C
B
B
C
B
C
B
C
B
B
C
C
B
C
C
B
B
C
C
C
B
B
B
C
Some of These Do Not Break Phase With Trends,
Shifts and Cycles
Planning Randomization
47
Select an order among these 10 choices.
C
B
C
B
C
B
C
B
C
B
B
C
C
B
C
B
B
C
C
B
C
B
B
C
C
B
C
B
B
C
C
B
C
B
B
C
B
B
B
C
C
C
C
B
B
C
B
C
B
C
C
B
B
C
C
C
B
B
B
C
1 32 54 6 87 9 10
Ten Useful Random Patterns
Planning Randomization
48
B = Reduce suction port diameter.
C = Current suction port diameter.
Response = Cycles to failure.
Allowed risk = 5%
Required End Count = 6
Reducing the suction diameter will improve reed. There was a 5% risk
that these test results happened by chance.
Broken Reed B vs C Verifies Solution
49
Run Order
Sample Cycles to Fail
Rank Order
Sample Cycles to Fail
B +7.0M DNF B +7.0M DNF
B +7.0M DNF B +7.0M DNF
C 3.1M FAILED B +7.0M DNF
B +7.0M DNF C 3.6M FAILED
C 3.6M FAILED C 3.1M FAILED
C 2.8M FAILED C 2.8M FAILED
Tolerance Parallelogram
Setting the piston orifice diameter to .146” ensures all
reeds will last for at least 5,000,000 cycles by reducing the
maximum energy the reed experiences.
50
Transmission Failures
51
Snap Ring Picture
52
Failed B vs C
Conclusion: Failed to confirm candidates as Red X
B vs C for Group Comparison Red X Candidates
B = Spring Force = 70 lbs., Free ID = 2.25,” Free End Gap = 2.1 mm
C = Spring Force = 40 lbs., Free ID = 2.27,” Free End Gap = 4.6 mm
Response = Line pressure to fail the snap ring at 4000 RPM in reverse.
Allowed Risk = 5%
Required End Count = 6
Run Order
B or C Pressure to Fail
Rank Order
B or C Pressure to Fail
C 380 C 600
B 580 B 580
B 270 C 380
C 600 B 380
C
B
53
Pressure Test
54
Run Order
B or C Pressure to Fail
Rank Order
B or C Pressure to Fail
B 750 DNF B 750 DNF
B 750 DNF B 750 DNF
C 270 FAILED B 600 DNF
B 600 DNF C 290 FAILED
C 200 FAILED C 270 FAILED
C 290 FAILED C 200 FAILED
B = WOW Time part with sealer (wax) removed.
C = WOW Time part with heavy rust inhibitor (wax) coating.
Response = Line pressure to fail the snap ring at 4000 RPM in reverse.
Allowed risk = 5%
Required End Count = 6
The Red X is the Rust Inhibitor!
B vs C to Verify Inhibitor as Red X
55
1973 New commercial jet engine for Boeing 747. Second
stage turbine blades are discovered to be creeping in service.
Engineers believe the problem is new proprietary coating on
blades. They recommend replacing all second stage blades
with more expensive traditional coating.
Can‟t afford a 5% risk of being wrong.
Cost to replace blades is going to be $40 million.
Time to replace blades will be months.
Customer planes have been grounded until answer is known.
Pratt & Whitney JD-9D Turbine Blade Creep
B vs C
56
Consequences of a
Wrong Decision
Number of
Randomized
Samples
Desired
Confidence ~ Risk
B
Samples
C
Samples
0.999 0.001
Super Critical
3
4
5
6
16
10
8
6
0.99 0.01
Critical
2
3
4
5
13
7
5
4
0.95 0.05
Important
1
2
3
19
5
3
0.90 0.10
Moderate
1
2
9
3
No overlap of
ranks is permitted
for this test.
B better than C, not
just different.
B vs C Table for a One Tailed Test
B and C sample
sizes are
interchangeable.
Critical
consequences
57
Data Presentation: Two tables (usually side by side) where the first
table shows the result in run order and the second table shows the
result in rank order with the resulting end count at the bottom of the
second table.
Conclusion: Did you meet the required end count? If so, what can
you conclude?
The following elements should be documented:
B and C Settings: Defined specifically enough that your work may be
replicated.
Response: Clearly defined so that there is no question regarding how the samples will be measured. (e.g., Hole radius measured 458 from the
front parting line. Not – hole radius.)
Allowed risk and the resulting required end count.
Elements of a Well Designed B vs C Test
58
Title: How To Calculate The Risk Of A Decision
Copyright: 1968, ASQC
Author: Shainin, Dorian
Organization: Rath & Strong Inc.
Subject: Administration, Shainin techniques;
Series: Quality Progress, Vol. 1, No. 8, August 1968, pp. 21-23
Bibliography
Title: A Quick, Compact, Two-Sample Test To Duckworth's
Specifications*
Copyright: 1959, ASQC and the American Statistical Association
Author: Tukey, John W.
Organization: Princeton University
Subject: ;
Series: Technometrics, Vol. 1, No. 1, February 1959, pp. 31-48
59
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