Shainin B vs C Webinar - ASQ Automotive Division

Shainin B vs C Webinar
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Ha Dao, Chairman
ASQ Automotive Division
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How to Calculate a Risk of
a Decision: Shainin B vs C
February 15, 2010
7:00 pm – 8:15 pm EST
Richard Shainin, Executive VP
Shainin LLC
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Agenda
Housekeeping Items
About ASQ Automotive Division
Webinar Series
Polls
How to Calculate the Risk of a
Decision: Shainin B vs C
Questions & Answers
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Housekeeping Items
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
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Sandy Cornellier - Shainin LLC
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Your Moderator
About Me
Ha Dao, Chairman
ASQ Automotive Division
ASQ Fellow
Six Sigma Master BB
Shainin Red X Master
15+ Yrs of Experience in
Automotive Industry
Troy, Ohio
[email protected]
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Introducing Dick Shainin
How to Calculate a Risk of
a Decision: Shainin B vs C
February 15, 2010
7:00 pm – 8:15 pm EST
Richard Shainin, Executive VP
Shainin LLC
ASQ Automotive Division
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Interactive Polls
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How to Calculate
the Risk of a
Decision: Shainin
B vs C
•
•
•
•
Richard Shainin
Executive Vice President
Shainin
February 15, 2011
Agenda
1.
2.
3.
4.
5.
6.
15
Change.
Impact of ineffective changes.
Minimizing ineffective changes.
B vs. C Test.
Randomization.
Additional examples.
Global Electronics Company
One Quarter of Data, > 2000 changes approved
900
Number of Changes
800
700
600
500
400
300
200
100
0
Align to Current
Process
Process
Improvement
Product Change
Request
Quality
Improvement
Cost Savings
Change Type
Manufacturing changes for released products across 6 manufacturing sites.
16
Effectiveness?
 Lots of engineers making things „better‟.
 Less than 20% of the changes achieved their desired result.
17
Compressor Broken Reed Case
Automotive HVAC
 Compressors fail in the field after
21,000 miles.
 All vehicle platforms experience
the failure.
 Returned compressors have
extensive damage.
 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
Background Information
Product Function
New
Reed
Failure
Initiation
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
Waste from Ineffective Changes
 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.
21
Marketing cost of ineffective changes
 Loss of brand loyalty.
 Loss of credibility within the supply chain.
 Loss of credibility with senior leadership.
22
Problem Solving
Contrast based
convergent approach
Process/Product Control
Technical
problem
Experience
based
approach
Directional correct
design change
Brainstorming based
approach
23
Understandin
g the physics
Design Change
Contrast Based Convergent Approach
 “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.”
“Keep your eyes open and your
mouth shut. Let the parts guide
you to the answer.”
24
Dorian Shainin (1914-2000)
Investigation
Solution Tree
Decrease
Compressor
Energy
∆M
∆P
Compressor
Strength
25
Rationale:
System
Energy
AC
System
Contrast
Volume
Increase
Compressor
Strength
Platform
Contrasts
Pressure
Temperature
 Strategy choice based on
physics of failure. Lowest
risk and lowest cost along
with the fastest timing.
 B vs C test.
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
When B is not better, the B vs C test results become a
game of chance, controlled by sample size and analysis
method.
Let’s evaluate by putting results in rank
order with the best result having the
highest rank.
29
B Not Better Than C
With 2 samples, 1B and 1C, there are only 2 possible rank order outcomes.
Rank
Possible
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
k
Possible 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
33
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.
B Not Better Than C
With 6 samples, 3Bs and 3Cs, there are only 20 possible rank order outcomes.
Ran
k
Possible Outcomes
1
34
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.
B vs C Table for a One Tailed Test
Consequences of a
Wrong Decision
Desired
B
Samples
C
Samples
0.999
0.001
Super Critical
3
4
5
6
16
10
8
6
0.99
2
3
4
5
13
7
5
4
0.05
1
2
3
19
5
3
0.10
1
2
9
3
Confidence
~ Risk
0.01
Critical
0.95
Important
0.90
Moderate
35
Number of
Randomized
Samples
B better than C, not
just different.
B and C sample
sizes are
interchangeable.
No overlap of
ranks is permitted
for this test.
B vs C Table for a One Tailed Test
Consequences of a
Wrong Decision
Desired
B
Samples
C
Samples
0.999
0.001
Super Critical
3
4
5
6
16
10
8
6
0.99
2
3
4
5
13
7
5
4
0.05
1
2
3
19
5
3
0.10
1
2
9
3
Confidence
~ Risk
0.01
Critical
0.95
Important
0.90
Moderate
36
Number of
Randomized
Samples
B better than C, not
just different.
B and C sample
sizes are
interchangeable.
No overlap of
ranks is permitted
for this test.
6 Pack Test
Our Parts or Systems Vary
37
Spurious Associations
How do variables change over time?
DX
Time
1.
38
Cycle
Spurious Associations
How do variables change over time?
DX
Time
1.
2.
39
Cycle
Trend
Spurious Associations
How do variables change over time?
DX
Time
1.
2.
3.
40
Cycle
Trend
Shift
Spurious Associations
Testing in phase with process variation?
WOW
C
C
C
DX
BOB
B
B
B
Time
41
Spurious Associations
Testing in phase with process variation?
C
C
WOW
C
DX
BOB
B
B
B
Time
42
Randomization
Breaking Phase With Every Other X
WOW
C
C
C
DX
BOB
B
B
B
Time
43
Randomization
Breaking Phase With Every Other X: The Result
WOW
B
C
C
DY
BOB
B
C
B
Time
44
Randomization
Breaking Phase With Every Other X
C
C
C
DX
B
B
B
Time
45
Randomization
Breaking Phase With Every Other X
C
C
C
DX
B
B
B
Time
“Randomization is the engineer’s insurance policy for
breaking phase relationships with time.” – Dorian Shainin
46
Planning Randomization
Some of These Do Not Break Phase With Trends,
Shifts and Cycles
C
C
C
B
B
B
47
B
B
C
B
C
C
B
B
C
C
B
C
B
B
C
C
C
B
B
C
B
B
C
C
B
C
B
C
B
C
B
C
B
C
C
B
B
C
C
B
B
C
B
C
C
B
C
B
B
C
C
C
B
B
C
B
B
B
C
C
C
B
B
C
B
C
C
B
B
C
C
B
C
B
C
B
B
C
C
B
C
B
C
B
C
B
C
C
B
B
C
C
B
B
B
C
C
C
B
B
C
B
C
C
B
C
B
B
B
B
B
C
C
C
Planning Randomization
Ten Useful Random Patterns
B
B
C
B
C
C
B
B
C
C
B
C
B
C
B
B
C
C
B
C
B
C
C
B
B
C
C
B
C
B
C
B
B
C
B
C
C
B
C
B
B
C
C
B
C
C
B
B
C
C
B
B
C
B
C
C
B
C
B
B
1
2
3
4
5
6
7
8
9
10
Select an order among these 10 choices.
48
Broken Reed B vs C Verifies Solution
B = Reduce suction port diameter.
C = Current suction port diameter.
Response = Cycles to failure.
Allowed risk = 5%
Required End Count = 6
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
Reducing the suction diameter will improve reed. There was a 5% risk
that these test results happened by chance.
49
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
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
B or C
Run Order
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
Conclusion: Failed to confirm candidates as Red X
Pressure Test
54
B vs C to Verify Inhibitor as Red X
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
B or C
Run Order
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
The Red X is the Rust Inhibitor!
55
B vs C
Pratt & Whitney JD-9D Turbine Blade Creep
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.
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.
Can‟t afford a 5% risk of being wrong.
56
B vs C Table for a One Tailed Test
Consequences of a
Wrong Decision
Desired
B
Samples
C
Samples
0.999
0.001
Super Critical
3
4
5
6
16
10
8
6
0.99
2
3
4
5
13
7
5
4
0.05
1
2
3
19
5
3
0.10
1
2
9
3
Confidence
~ Risk
0.01
Critical
0.95
Important
0.90
Moderate
57
Number of
Randomized
Samples
B better than C, not
just different.
B and C sample
sizes are
interchangeable.
No overlap of
ranks is permitted
for this test.
Critical
consequences
Elements of a Well Designed B vs C Test
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.
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?
58
Bibliography
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
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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Ha Dao, Chair
www.asq-auto.org
[email protected]
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