six sigma at boston scientific tuesday 12 september 2006 steve czarniak

1BSC Six Sigma: ASQ Meeting – 12 September 2006 Copyright 2006 Boston Scientific

Six Sigma at Six Sigma at Boston ScientificBoston Scientific

Tuesday 12 September 2006Tuesday 12 September 2006Steve CzarniakSteve Czarniak


Session ObjectivesSession 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


Six Sigma at BSC is...Six Sigma at BSC is...

http://elearning/sixsigma/sixsig.htm


Improvement ChallengesImprovement Challenges

•Solution Known•Change in Performance•Operational Defect / Variation Reduction•Flow•Design


BSC Six Sigma BSC Six Sigma Problem Solving RoadmapProblem Solving Roadmap


BSC Six Sigma Operational BSC Six Sigma Operational Process Improvement RoadmapProcess Improvement Roadmap

Yield

Time

Process Improvement

Process

Define

Identify Opportunity

Identify y’s(Outputs)

Measure

y = f(x)IdentifyKey x’s(Inputs)

Analyze Optimize x’s

Improve Controlx’s

Control


MeasureMeasure AnalyzeAnalyze ImproveImprove ControlControl Control Key x’s

Validate Process

Verify Long Term Capability

Monitor y’s

Finalize the Control System

Finalize Project Charter

DefineDefine Identify

Opportunity

Define Project Goal

Define Process

Establish Boundaries

Determine Customer Requirements

Define Key Y Variables

Develop Measures (y’s)

Evaluate Measurement System

Determine Process Stability

Determine Process Capability

Determine the Improvement Approach

Identify Potential x’s

Analyze x’s

Identify Key x’s

Determine Stability & Capability of Key x’s

Establish Relationships between y’s & x’s

Establish Targets & Tolerances for Key x’s

Implement Mistake Proofing

Develop, Select & Verify Process Improvements

DMAIC DMAIC Improvement ProcessImprovement Process


Balloon Scrap ReductionBalloon Scrap Reduction

Define: Reduce balloon scrap for major scrap code by 80%Scrap % by Reason Code

0%

10%

20%

30%

40%

50%

60%

70%

Cause 1 Cause 2 Cause 3 Cause 4 Other

Scrap Code

Scra

p

Observation

Indi

vidu

al V

alue

28252219161310741

2

1

0

-1

-2

_X=0.271

UCL=2.406

LCL=-1.864

Observation

Mov

ing

Rang

e

28252219161310741

3

2

1

0

__MR=0.803

UCL=2.623

LCL=0

Scaled Scrap Trend

Gage name:Date of study:Reported by:Tolerance:Misc:

0

0.0

0.5

1.0

1.5 1 2

Xbar Chart by Operator

Sam

ple

Mea

n

Mean=0.6719UCL=0.7200LCL=0.6238

0

0.0

0.1

0.2 1 2

R Chart by Operator

Sam

ple

Ran

ge

R=0.02557

UCL=0.08354

LCL=0

2 3 5 12 14 16 21 27 30 32 33

0.0

0.5

1.0

1.5

Part

OperatorOperator*Part Interaction

Aver

age

1 2

1 2

0.0

0.5

1.0

1.5

Operator

By Operator 2 3 5 12 14 16 21 27 30 32 33

0.0

0.5

1.0

1.5

Part

By Part

%Contribution %Study Var %Process %Tolerance

Gage R&R Repeat Reprod Part-to-Part0

50

100

Components of Variation

Per

cent

Gage R&R (ANOVA) for Min Measure

Stable!

Measure: Length


Balloon Scrap ReductionBalloon Scrap Reduction

Analyze: Designed Experiment

Effect

Perc

ent

2.01.51.00.50.0-0.5-1.0

99

9590

80706050403020

105

1

A Adhesive TypeB BC CD DE E

Factor Name

Not SignificantSignificant

Effect Type

A

Normal Probability Plot of the Effects(response is Scaled Length, Alpha = .05)

Lenth's PSE = 0.289116

Improve / Control:Mistake Proofing – only use preferred adhesive type!

60% scrap reduction!


Business Process Improvement Business Process Improvement Roadmap - DMAICRoadmap - DMAIC

DefineDefine

What are you trying to accomplish?

MeasureMeasure

How will you know the project has been successful?

AnalyzeAnalyze

What elements in your process can be leveraged for improvement?

ImproveImprove

What is your improvement?

ControlControl

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


Business Process ImprovementBusiness Process Improvement


DefineDefine

Defined goal, talked to the customers and started to understand process complexity

AnalyzeAnalyze

Developed detailed process maps, identified waste and non-value added steps, identified gaps between ideal and current state

ImproveImprove

Developed target state, piloted tools for standardizing price approvals and automating repetitive tasks

ControlControl

Developed control plan, implemented and monitored new process

MeasureMeasure

Collected data on time and logistics for price approvals

Price Approval Process – Reduce Price Approval Process – Reduce Time, Increase ConsistencyTime, Increase Consistency


Price Approval - ResultsPrice 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


Does this product meet spec?Does this product meet spec?

Lower Spec Upper Spec

A: yesB: noC: maybeD: not sure - phone a friend

Copyright 2006 Boston Scientific

Measurement System Analysis:Measurement System Analysis:Gage R&RGage R&R


The Basic ModelThe Basic Model

The total observed variation is equal to the real process variation plus the variation due to the measurement system.

222tmeasuremenprocessobserved


11010090807060504030

15

10

5

0

Observed

Freq

uenc

y

LSL USL

Actual process variation - No measurement variation

Total observed variation - With measurement variation

11010090807060504030

15

10

5

0

Process

Freq

uenc

y

LSL USL

Effect of Measurement Effect of Measurement VariationVariation


Gage R & RGage 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

11010090807060504030

15

10

5

0

Observed

Freq

uenc

y

LSL USL

Measurement Variation vs. Actual Process Variation ?


Gage R&R Example:Gage R&R Example: Graphical OutputGraphical Output

Perc

ent

Part-to-PartReprodRepeatGage R&R

100

50

0

% Contribution% Study Var

Sam

ple

Rang

e 0.10

0.05

0.00

_R=0.0383

UCL=0.1252

LCL=0

1 2 3

Sam

ple

Mea

n

1.00

0.75

0.50

__X=0.8075UCL=0.8796LCL=0.7354

1 2 3

Part10987654321

1.00

0.75

0.50

Operator321

1.00

0.75

0.50

Part

Aver

age

10 9 8 7 6 5 4 3 2 1

1.00

0.75

0.50

123

Operator

Gage name: Date of study:

Reported by: Tolerance: Misc:


R Chart by Operator


Measurement by Part

Measurement by Operator

Operator * Part Interaction

Gage R&R (ANOVA) for Measurement


Measurement System TermsMeasurement System Terms

•Stability•Accuracy•Precision•Resolution•Bias•Reproducibility•Linearity•Discrimination•Repeatability•Calibration


Gage R&R Example:Gage R&R Example: Graphical OutputGraphical Output

1

3

2

4

5

6

Perc

ent

Part-to-PartReprodRepeatGage R&R

100

50

0

% Contribution% Study Var

Sam

ple

Rang

e 0.10

0.05

0.00

_R=0.0383

UCL=0.1252

LCL=0

1 2 3

Sam

ple

Mea

n

1.00

0.75

0.50

__X=0.8075UCL=0.8796LCL=0.7354

1 2 3

Part10987654321

1.00

0.75

0.50

Operator321

1.00

0.75

0.50

Part

Aver

age

10 9 8 7 6 5 4 3 2 1

1.00

0.75

0.50

123

Operator

Gage name: Date of study:

Reported by: Tolerance: Misc:


R Chart by Operator


Measurement by Part

Measurement by Operator

Operator * Part Interaction

Gage R&R (ANOVA) for Measurement

At each table, identify ONE graphic that best describes each term.


Destructive Gage R&R 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.

six sigma at boston scientific tuesday 12 september 2006 steve czarniak

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