11
Section
WARWICK MANUFACTURING GROUP
Product Excellence using 6 Sigma (PEUSS)
DFSS
Scorecards
Warwick Manufacturing Group
DFSS SCORECARDS
Contents
1
Introduction
1
2
Example of scorecard
2
3
Lessons from performance scorecard example
6
Copyright © 2007 University of Warwick
Warwick Manufacturing Group
DFSS scorecards
Page 1
DFSS SCORECARDS
1 Introduction
The DFSS Product Scorecard is an approach for collecting, displaying, and analyzing the facts
around a design in order to predict future performance and to improve upon the initial design.
In a scorecard, a comparison is made between the voice of the customer and the voice of the
process. The purpose of the scorecard is to help find design solutions to any problems, not
culprits! A DFSS product scorecard facilitates bridging customer requirements with product
and process performance at all stages of the design process.
It is a living document and revised accordingly.
Uses of a DFSS Product Scorecard include:
•
Focus on customer CTQs
•
Bridge customer requirements and product performance
•
Predict performance with a statistical model
•
Optimize design
•
Recognize missing key issues
•
Locate areas of improvement
•
Communicate with all stakeholders
•
Record design progress
•
Store learning process
Useful questions for using the scorecard include:
•
What are the customer expectations?
•
What are the capabilities of the parts, process and product?
•
What is the current Voice of the Process?
•
What is the design entitlement?
•
Have we included all parties and processes?
•
How can we create a robust design?
•
Are there any gaps between reality and prediction?
•
What are the (un)intended consequences?
•
Can this success can be replicated?
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Page 2
A product scorecard contains a top level scorecard that is a combination of scores from 4
elements:
•
The performance sigma scorecard
o Contains all important product performance parameters
o Lists all customer CTQs
o Ensures that the product meets critical customer requirements
•
The part sigma scorecard
o Sigma scores for parts and sub-assemblies used in the product
o Contains parts list with defect data in it
o Uses it to choose high quality suppliers using DFM principles
•
The process sigma scorecard
o Contains Sigma scores for all processes that are used to build sub and final
assemblies
o Uses detailed process map
o Identifies process capability and improvement opportunities
•
The software sigma scorecard
o Contains all the major steps of the software development phases
o Tracks defects in each phase
o Computes efficiency in each phase to detect and eliminate defects
2 Example of scorecard
Example: Treadmill
•
Product Specifications:
•
Gross Weight 31 kg
•
Dimensions
Treadmill: 49 X 20"
Track: 43 X 13"
Flywheel: 6" diameter
Roller: 2.5" diameter
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Page 3
Handlebars: 1.5" diameter
•
Weight Limit 250 lbs
•
Cushioned Track
•
Motor Rating 13.5 Amp
•
Peak Power
•
Speed Range 0-10 mph
•
Speed Control Rheostat, Safety pull
•
Console Height
34"
•
Console Feature
Speed, Distance, Time, Calories, Memory
•
Preset Programs
3
Non Slip, Variable Incline
Type DC
5.0 hp Continuous
1.75 hp
Increments
8 Major Components
0.10 mph
Console Assembly
Distance
Motor
Housing
Column
Speed
Control
Knob
MAX
Speed
ON|OFF
Handlebars (2)
MIN
Time
Calories
RESET
Motor Assembly (Inside Housing)
Fly wheel
Motor
Front Pulley
Walking Belt
Cross Bar
Walking
Platform
Side Rails (2)
Back Pulley
Frame Assembly
All customer requirements should be listed and prioritized before a scorecard is developed for
the key customer CTQs. In the treadmill example, we are assuming that this step has already
been performed.
Since design is an iterative process, the scorecard will be revisited throughout the design
process.
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Page 4
It is important to pay close attention to the data and metrics. Use appropriate distributions,
based on research, to reflect likely actual conditions. Use continuous data whenever possible.
However, if continuous data is not available, use discrete data. Remember that all continuous
data is not normal, although the assumption behind sigma calculations is that data is behaving
normally. It is important to verify whether the data is really normal. If it is not normal,
normalize it using transformations or at least state the validity of the assumption.
The data is usually long-term variation, but this should be verified. If a test is performed
under constant controlled conditions, it is more correct to say it is short-term variation.
Assume Zst=Zlt+1.5.
Select all customer CTQs for the treadmill
–
List and characterize all critical parameters
–
Assign metrics and units; no metrics = no improvement
–
If it is variable data, obtain target and specification limits
–
Note that there could be only one spec limit
–
For attribute data obtain target defect level (usually 0)
Parameter
Metric Unit
Data Type
Quietness
sone
Variable
Speed change
miles
Variable
Reliability
miles
Variable
Safety
# problems
Attribute
After collecting the data the following results are calculated:
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Target
USL
LSL
1.2
0.1
0.2
0.05
300
0
Page 5
mean
μ
Std. Dev
σ
Metric Unit
LT/
Data Type ST
Quietness
sone
Variable
ST
0.8
0.15
Speed change
miles
Variable
LT
0.1
0.06
Reliability
miles
Variable
LT
350
30.00
Safety
# problems
Attribute
LT
CTQ Parameter
Parameter
Metric Unit
Data
Type
Quiteness
Speed change
Reliability
Safety
sone
mi
mi
# problems
Cont
Cont
Cont
Attb
0.1
0
1 / 100
LT/
ST
mean
μ
1.2
ST
0.2 0.05 LT
300 LT
LT
0.8
0.1
350
Target USL LSL
Defect Level
Std.
Dev σ
0.15
0.06
30
Z
Z
USL LSL
Total
DPU
Yield
RTY
2.05 N/A 2.01E-02 0.98008
1.67 0.83 2.50E-01 0.77871
N/A 1.67 4.78E-02 0.95333
1.00E-02 0.99005
3.28E-01 0.72034
Total Number of Parameters
Avg Defect per parameter
Avg Yield per parameter
Avg Parameter LT Sigma
Avg Parameter ST Sigma
4
0.0820
0.9213
1.4136
2.9136
Questions to help interpret the scorecard
–
Is our ST sigma value (2.9) competitive?
•
Refer to benchmarking and QFD to compare our performance
–
What are the drivers for this performance?
–
What parameters perform best? Worst?
•
–
Speed Change needs to be improved. Quietness is the best.
Have we reached our entitlement sigma level?
•
We do not know yet. If we can achieve the same level as quietness our
ST sigma would be around 5. But is it realistic?
–
Any design tradeoffs possible to improve it?
–
How critical are they for our customers?
•
May want to shift efforts after checking QFD for CTQ priority
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Page 6
–
How can I make a cost based analysis?
–
A Scorecard facilitates disciplined design iteration
3 Lessons from performance scorecard example
All customer requirements should be listed and prioritized before a scorecard is developed for
the key customer CTQs. In the treadmill example, we are assuming that this step has already
been performed.
Since design is an iterative process, the scorecard will be revisited throughout the design
process.
It is important to pay close attention to the data and metrics. Use appropriate distributions,
based on research, to reflect likely actual conditions. Use continuous data whenever possible.
However, if continuous data is not available, use discrete data. Remember that all continuous
data is not normal, although the assumption behind sigma calculations is that data are
behaving normally. It is important to verify whether the data is really normal. If it is not
normal, normalize it using transformations or at least state the validity of the assumption.
The data is usually long-term variation, but this should be verified. If a test is performed
under constant controlled conditions, it is more correct to say it is short-term variation.
Assume Zst=Zlt+1.5.
For more information on balanced scorecards see Paul Roberts leadership and excellence
module.
Warwick Manufacturing Group