Transcript Six Sigma Operational Green Belt Workshop Preparation

Six Sigma at
Boston Scientific
Tuesday 12 September 2006
Steve Czarniak
BSC Six Sigma: ASQ Meeting – 12 September 2006
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Session Objectives
•Describe the Boston Scientific Six Sigma Model
•Describe the Boston Scientific Six Sigma Roadmaps
•Identify which Minitab graphs to use to assess
measurement system performance
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Six Sigma at BSC is...
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Improvement Challenges
•Solution Known
•Change in Performance
•Operational Defect / Variation Reduction
•Flow
•Design
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BSC Six Sigma
Problem Solving Roadmap
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BSC Six Sigma Operational
Process Improvement Roadmap
Yield
Control
Process
Improvement
Process
Improve
Control
x’s
Analyze Optimize
x’s
Measure
Define
Identify
Key x’s
(Inputs)
y = f(x)
Identify y’s
(Outputs)
Identify
Opportunity
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Time
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DMAIC
Improvement Process
Define
 Identify
Opportunity
 Define Project
Goal
 Define Process
 Establish
Boundaries
 Determine
Customer
Requirements
 Define Key Y
Variables
Measure
 Evaluate
Measurement
Analyze
 Identify Potential
x’s
System
 Determine Process
 Identify Key x’s
Capability
 Determine the
Improvement
Approach
 Develop
Measures (y’s)
BSC Six Sigma: ASQ Meeting – 12 September 2006
 Determine Stability
& Capability of Key
x’s
 Establish
Relationships
between y’s & x’s
 Analyze x’s
Stability
 Determine Process
Improve
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Control
 Control Key x’s
 Validate Process
 Verify Long Term
Capability
 Establish Targets &
Tolerances for Key
x’s
 Monitor y’s
 Implement Mistake
Proofing
 Finalize the Control
System
 Develop, Select &
Verify Process
Improvements
 Finalize Project
Charter
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Balloon Scrap Reduction
Define: Reduce balloon scrap for major scrap code by 80%
Scrap % by Reason Code
Scaled Scrap Trend
70%
U C L=2.406
60%
1
_
X=0.271
0
-1
LC L=-1.864
-2
1
4
7
10
13
16
O bser vation
19
22
25
28
50%
40%
Stable!
Scrap
Individual V alue
2
30%
3
20%
M oving Range
U C L=2.623
2
10%
1
__
M R=0.803
0
LC L=0
0%
Cause 1
Cause 2
Cause 3
Cause 4
Other
Scrap Code
1
4
7
10
13
16
O bser vation
19
22
25
28
Gage name:
Date of study:
Reported by:
Tolerance:
Misc:
Gage R&R (ANOVA) for Min Measure
Measure: Length
Components of Variation
By Part
1.5
Percent
100
%Contribution
%Study Var
%Process
%Tolerance
50
1.0
0.5
0.0
0
Gage R&R
Repeat
Reprod
Part
Part-to-Part
2
3
5
R Chart by Operator
Sample Range
0.2
1
By Operator
1.0
0.1
UCL=0.08354
0.5
R=0.02557
LCL=0
0.0
0.0
Operator
0
1
2
Xbar Chart by Operator
Sample Mean
Operator*Part Interaction
1.0
UCL=0.7200
Mean=0.6719
LCL=0.6238
0.5
0.0
Operator
1.5
2
Average
1
1.5
1
2
1.0
0.5
0.0
0
BSC Six Sigma: ASQ Meeting – 12 September 2006
12 14 16 21 27 30 32 33
1.5
2
8
Part
2
3
5
12
14
16
21
27 30
32
33
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Balloon Scrap Reduction
Analyze: Designed Experiment
Normal Probability Plot of the Effects
(response is Scaled Length, Alpha = .05)
99
A
95
90
F actor
A
B
C
D
E
80
Percent
Effect Ty pe
Not Significant
Significant
70
60
50
40
N ame
A dhesiv e Ty pe
B
C
D
E
30
20
10
5
1
-1.0
-0.5
0.0
0.5
Effect
1.0
1.5
2.0
Lenth's PSE = 0.289116
Improve / Control:
Mistake Proofing – only use preferred adhesive type!
60% scrap reduction!
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Business Process Improvement
Roadmap - DMAIC
Define
Measure
Analyze
What are you
trying to
accomplish?
How will you
know the project
has been
successful?
What elements in
your process can
be leveraged for
improvement?
Improve
What is your
improvement?
Control
What is your
plan to
implement and
maintain the
improvement?
Lean and Six Sigma both use the
DMAIC roadmap as a common
approach for process improvement
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Business Process Improvement
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Price Approval Process – Reduce
Time, Increase Consistency
Define
Defined goal,
talked to the
customers and
started to
understand
process
complexity
Measure
Collected data on
time and logistics
for price
approvals
Analyze
Developed detailed
process maps,
identified waste
and non-value
added steps,
identified gaps
between ideal and
current state
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Improve
Developed
target state,
piloted tools for
standardizing
price approvals
and automating
repetitive tasks
Control
Developed
control plan,
implemented
and monitored
new process
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Price Approval - Results
•
•
•
•
•
75% reduction in response time to customer
62% reduction in process steps
88% reduction in decision steps
Standardized processes: consistency, accuracy
Customer driven solution
“With BPI, our main focus was on our Customer and the
requirements that they had. Without their feedback and
keeping them our main focus, we would have probably
come up with a totally different solution for the process of
requesting and receipt of approvals” – Team Leader
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Does this product meet spec?
Lower Spec
Upper Spec
A: yes
B: no
C: maybe
D: not sure - phone a friend
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Measurement System Analysis:
Gage R&R
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The Basic Model
The total observed variation is equal to the real
process variation plus the variation due to the
measurement system.

2
observed

2
process
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
2
measuremen t
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Effect of Measurement
Variation
LSL
Actual process variation
- No measurement variation
USL
Frequency
15
10
5
0
30
40
50
60
70
80
90
100
110
Process
Total observed variation
USL
10
Frequency
- With measurement variation
LSL
15
5
0
30
40
50
60
70
80
90
100
110
Observ ed
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Gage R & R
• Means of assessing the repeatability and
reproducibility of a measurement system.
• Evaluates how much total observed variation is due
to the measurement device and measurement
methods
LSL
15
USL
Measurement
Variation vs.
Actual Process
Variation
Frequency
10
5
?
0
30
40
50
60
70
80
90
100
110
Observ ed
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Gage R&R Example:
Graphical Output
Gage R&R (ANOVA) for Measurement
Reported by :
Tolerance:
M isc:
G age name:
Date of study :
Measurement by Part
Components of Variation
100
% Contribution
1.00
Percent
% Study Var
0.75
50
0.50
0
Gage R&R
Repeat
Sample Range
2
3
Measurement by Operator
UCL=0.1252
0.10
1.00
0.75
0.05
_
R=0.0383
0.00
LCL=0
0.50
2
Operator * Part Interaction
3
1.00
_
_
UCL=0.8796
X=0.8075
LCL=0.7354
Operator
1.00
Average
0.75
1
2
3
0.75
0.50
0.50
1
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2
Operator
1
Xbar Chart by Operator
1
Sample Mean
10
9
8
7
6
5
Part
R Chart by Operator
1
4
3
2
1
Part-to-Part
Reprod
19
2
3
4
6
5
Part
7
8
9
10
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Measurement System Terms
•Stability
•Accuracy
•Precision
•Resolution
•Bias
•Reproducibility
•Linearity
•Discrimination
•Repeatability
•Calibration
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Gage R&R Example:
Graphical Output
At each table, identify ONE graphic that
best describes each term.
Gage R&R (ANOVA) for Measurement
Reported by :
Tolerance:
M isc:
G age name:
Date of study :
Components of Variation
Measurement by Part
100
1.00
% Contribution
1
Percent
% Study Var
0.75
50
4
0.50
0
Gage R&R
Repeat
Reprod
1
Part-to-Part
2
3
Sample Range
2
2
3
7
8
9
10
1.00
0.10
0.75
0.05
_
R=0.0383
0.00
LCL=0
1
5
0.50
1
2
2
Operator
0.75
Operator
1.00
_
UCL=0.8796
_
X=0.8075
LCL=0.7354
1
2
3
0.75
0.50
0.50
1
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Operator * Part Interaction
3
1.00
Average
Sample Mean
6
Measurement by Operator
UCL=0.1252
Xbar Chart by Operator
3
5
Part
R Chart by Operator
1
4
21
2
3
4
5
6
Part
7
8
9
6
10
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Destructive Gage R&R
Reference:
De Mast, Jeroen; and Trip, Albert (2005). “Gauge
R&R Studies for Destructive Measurement”. Journal
of Quality Technology 37 (1), pp. 40-49.
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