Six Sigma Versus DFSS

Looking into the Future
of Design for Six Sigma (DFSS)
• Description of past
deployments
• Comparison and observations
• Suggestions for the future
© Statistical Design Institute, LLC. All Rights Reserved.
Jesse Peplinski
January 16, 2012
Six Sigma Versus DFSS
Define the
Design Problem
Capture the Voice
of the Customer
Identify Critical
Requirements
• You can use a flexible
Improve or
Create New
Design
approach to let each design
problem dictate which
process is followed
• Use DFSS as a rigorous
method for creating a design
to satisfy multiple
requirements
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Create Design
Concept
Measure the
Requirements
Build Math
Models
Analyze the
Root Causes
Optimize
the Design
Improve the
Design
Validate
the Design
Control the
Root Causes
MAIC
data-driven method for
design improvements
DFSS
• Use Six Sigma (MAIC) as a
Select
Approach
Improve
Existing
Design
Page 2
What is a “Deployment”?
• A company-specific attempt to inject Six Sigma
and/or DFSS into its culture and daily activities
• Typically a customized mixture of:
– Training classes with tailored content
– Structure for projects and “belt” certification
– Supporting software tools
– Strategic communication by management and leadership
• Scope of implementation can vary widely
– All employees vs. targeted teams
– Local vs. global
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Page 3
Past DFSS Deployments
Company
Description
Status
Automotive 1
• Global deployment
• Mandatory training for all
engineers
• Projects and certifications
Low level of
activity
Automotive 2
• Local deployment
• Training and tools for selected
experts based on role or skills
Continued
success
Defense 1
• Emphasis on black belts and
projects
• DFSS as an afterthought to six
sigma
Low level of
activity
Defense 2
• Leadership evolved a design
process intertwined with DFSS
tools
Continuing
activity
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Page 4
Past DFSS Deployments
Company
Description
Status
Electronics 1
• Global deployment, mandatory
training
• Projects and certifications
Low level of
activity
Electronics 2
• Local deployment for product
teams
• DFSS tools folded into an
internal process excellence
program
Steady
continuing
activity
Healthcare 1
• Global deployment with
projects and certification
• Significant backlash and years
of inactivity
Quiet
resurgence
through design
reviews
Healthcare 2
• DFSS integrated into
development process
• Emphasis on providing DFSS
tools
Continued
activity
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Page 5
Observations
• Pendulum swing
– Larger, top-down deployments often end up with lower
levels of long-term practice.
• Backlash against projects and certification
– Long-term health of deployment correlated with selective,
low-key implementation
• Challenge of demonstrating DFSS savings
– Heroes get visibility for fixing mistakes; cost avoidance is
difficult to recognize.
• Tools stand the test of time
– Six Sigma: Gage R&R, SOP’s, DOE, process control
– DFSS: QFD, Pugh Matrix, Monte Carlo, Optimization
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Page 6
Suggestions for the Future
• Design for Six Sigma:
– DFSS tools fit naturally within a systems engineering
group. (If you don’t have a systems engineering group,
consider starting one.)
– In addition, DFSS tools should be leveraged by your key
participants in design reviews. (Principals, architects, etc.)
– DFSS success hinges on modeling and simulation
capability. Be prepared for resistance.
• Six Sigma:
– Let DMAIC flow naturally from leadership asking questions
and demanding answers with data
• Let plans for training and employee reward be
driven by the forces above. (Not vice-versa.)
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Page 7
How does DFSS fit within Systems
Engineering?
Product
Development
Process
Best Practice
SE/DFSS Enablers & Tools
Voice of the Customer
Quality Function Deployment
Exploration
S
E
&
D
F
S
S
Conceptual
Design
TRIZ & Design Selection
Identify Critical
Requirements
Failure Modes & Effects Analysis
Physics and First Principles
Create Design Concept
• First – use the Tools
Build Models to
Detail
Design
Design
Verification
Initial
Production
Final
Production
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DOE and Regression
Statistical Allocation
and Monte Carlo
support Sensitivity
the Analysis
Cost
and Reliability Analysis
Process
Optimize the Design
• Allocate Variability
• Analyze Variability
• Optimize Variability
Validate the Design
Multi-Objective Optimization
FMEA & Fault Tree Analysis
Test Effectiveness Analysis
Design that best meets
all requirements
SE/DFSS Process
Scorecards
Page 8
Modeling and Analysis within DFSS
Require that this be done everywhere, and if it isn’t, explain why not!
Understanding
Requirements,
Specifications,
& Capabilities
Applying
Models &
Analyses
Non-Compliance refers to any
condition that results in Defects
or Off-Spec conditions
A
B
C
D
E
Product Model
(equation,
simulation,
workbook,
hardware, etc.)
Predicting
Probability of
Non-Compliance
Y
PNC
“Noncompliant”
LL
“Compliant”
T
“Noncompliant”
UL
The fundamental metric is the Probability of Non-Compliance (PNC)
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Page 9
Modeling: Easier than It May Appear
Key Design
Parameters
(X’s)
Gather Design
Parameter
Information
Can equations
be developed?
Yes
Fast, Accurate
Math Model
No
Yes
Critical
Requirements
(Y’s)
Identify
Existing
Models
A simulation of
sufficient
accuracy exists?
Yes
No
Simulation
computes
very quickly?
No
Best Design
Alternative(s)
Historical
data exists?
Yes
Perform
Regression
Analysis
No
Create New
Models
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No
Prototypes
exist?
Yes
Perform a
Design of
Experiments
Page 10
Six Sigma Examples
• What can we do to
improve our process yield?
Our goal is to get
solid answers:
~
~
It starts with hard
problems:
• How can we reduce
• How can we increase the
throughput of our call
center?
will reduce operating
temperatures by 11 °C.
~
~
increase sales volume?
supplier B will improve
yields by 8%.
• This power supply redesign
operating temperatures
and fix our thermal issues?
• What can we do to
• Switching from supplier A to
• A $50 rebate would increase
sales by 15%.
• Adding two more operators
will increase throughput by
100 calls per day.
How do we bridge the gap with high levels
of confidence based on solid evidence?
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Page 11
Guiding Questions
Answer these questions to bridge the gap:
1. What is our current state?
– Product or process performance in
measurable terms (Y’s)
If we can’t measure it, we
don’t know where we are.
2. What is our desired state?
– How much improvement is needed
in our measurable Y’s?
If we can’t measure it, we can
never know if we get there.
3. How good are our measurement systems?
– If we measure the same thing twice, do we get the same answer?
– If we made a process improvement, could we detect it?
4. What data do we need to collect?
– Responses (Y’s) and Parameters (potential X’s)
– How much data? Time period? Shifts?
– Existing data? Or new data collection effort?
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Page 12
Guiding Questions
Continued
5. If the Y is plotted versus the X’s, is there evidence of
correlation (patterns) for some of the X’s? Which ones?
– May begin to indicate the significant drivers for improvement
6. Is there statistical evidence that the Y changes when some
X’s change? Which ones?
– Type of analysis used (t-Test, F-Test, ANOVA, etc.)
– Confidence level
7. What changes in the X’s are needed to achieve the desired
state?
Implement Six Sigma as a process for
answering these questions.
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Page 13
Thank you…
Questions?
Contact: jpeplinski@stat-design.com
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Page 14