A Metric-based approach to Concept Design
Keywords
Concept Design, Optimisation, Value Analysis, Systematic Design, Design Methodology, Design
Trade-offs.
James Scanlan
Professor of Design, Southampton University, School of Engineering Sciences,
Highfield, Southampton, SO17 1BJ, Telephone 02380 592369, e-mail
Max Woolley
Research Associate, Faculty of CEMS, University of the West of England, Bristol
Coldharbour Lane, Frenchay,Bristol BS16 1QY, Telephone: 011732 82943, e-mail
Hakki Eres
Research Fellow Southampton University, School of Engineering Sciences, Highfield,
Southampton, SO17 1BJ
A metric-based approach to Concept Design
“It must be remembered that there is nothing more difficult, more doubtful of success, more dangerous to manage than the creation of a new system. For
the initiator has the enmity of all who would profit by the representation of the old institution, and merely lukewarm defenders in those who would
gain by the new one. ” *
Abstract
This paper details work undertaken in the development of a new design method,
termed CODA (COncept Design Analysis), to aid the conceptual design and selection
phase within new product development. The paper discusses how CODA has evolved
from the QFD(Quality Function Deployment) technique, as first proposed by Akao &
Mizuno8, and outlines how the new methodology integrates the quality loss function
approach as described by Taguchi14 and the work into customer preference trends as
described by Kano15, 16.
Some key weaknesses of QFD are identified. A demonstration of how CODA
addresses these limitations is shown. A number of simple design examples are given
which demonstrate how the ‘voice of the customer is deployed within CODA and how
the technique is applied. The paper goes on to discuss the use of optimisation
techniques to find trade-off values for complex designs. Finally the paper briefly
suggests where CODA fits within an overall concept design process and how it should
be applied to develop high value products.
* Machiavelli; 1513.
1 Background and Introduction
In the 1920’s the English Southern Railway board commissioned a new class of
locomotives which were designed by Richard Maunsell1. Figure 1 shows a
photograph of "Sir John Hawkins"; the last of the Lord Nelson class of locomotives.
The design was somewhat ambitious and introduced a number of novel features;
notably the use of four cylinders and the use of a 135 degree crank rather than the
more usual 90 degrees.
Figure 1 "Sir John Hawkins" the last of the Lord Nelson class of locomotives.
Potentially the four cylinder design was significantly heavier than a two cylinder
design and considerable care was put into weight reduction to meet axle loading
limits. High tensile steel was employed for the motion. Parts which would normally
have been left as cast or forged were machined to remove excess metal. Much use was
made of lightening holes in the frames.
On paper this was a very successful design as it was only 1 ton heavier than the
previous King Arthur class of locomotive but achieved an increase in tractive effort of
nearly 33 percent.
Ultimately, however, this class of locomotive was unsuccessful and much of its
failure is attributed to its unpopularity with the footplate crew. One of the interesting
side-effects of the use of 4 cylinders and the 135 degree crank was a substantial
reduction in vibration. This had an unintended effect in that coal in the firebox was
not “shaken-down” towards the front of the long, narrow firebox design. This subtle
effect resulted in a considerable increase in workload for the crew who thus expressed
a strong preference for the previous King Arthur class of design.
The Lord Nelson class developed a poor reputation for steaming and a series of costly
but ultimately unsuccessful modifications were carried out including the reversion to
a more conventional 2 cylinder 90 degree configuration.
This historical reflection illustrates a number of issues concerning the design process:
• Firstly, even though this product was designed nearly 90 years ago it exhibited
such complexity of design trade-offs that it was impossible for a single human
to retain the whole design “landscape” in one’s head. Reduction in vibration
did improve the overall robustness of the design and would generally be
viewed as a positive feature (the Lord Nelson class was notoriously reliable).
However it had the unintended effect of decreasing operating efficiency to an
unacceptable level. This was demonstrably a bad compromise.
• Secondly this study illustrates the importance of explicitly capturing and
understanding stakeholder requirements. Locomotive crews clearly had strong
and deeply held opinions concerning the operating characteristics of a design.
As a very experienced and eminent locomotive designer Maunsell would
probably been well aware of these. However, articulating the opinions of such
stakeholders and ensuring that such issues are understood, quantified and
explicitly addressed in the design process is exceedingly difficult to achieve.
This paper proposes a new methodology that builds on existing design methods and
which seeks to improve that ability to model and find good design trade-offs at the
concept design stage.
2 Systematic Design
The engineering design process has been and continues to be the subject intense
research interest. The classic works of Hubka24, Pahl & Beitz2, Pugh3 and Ullman4
describe a systematic approach to design in great detail. Most large companies are
aware of the benefits of a formal design process and have embraced many of the
principles of systematic design.
A rigorous approach during the early phase of the design process has been shown to
be closely linked to the success of new products. Blanchard25 suggests that 75% of the
lifecycle costs of a product are committed during the conceptual design phase where
product performance characteristics are established.
There is a growing consensus emerging amongst researchers and practitioners as to
the recommended framework of design tools and techniques that need to be deployed
to support the concept design process 5,6,7 and central to this framework is the use of
tools such as Quality Function Deployment (QFD). This technique was originally
developed by Drs. Yoji Akao and Shigeru Mizuno in the early 1960s. The first
published article on QFD was in 1966 by Mr. Oshiumi of the Bridgestone Tyre
company8.
Of all the design techniques that have been developed QFD is arguably the most
widely adopted; particularly in Japan and the US. In a 1986 study by the Japanese
Union of Scientists and Engineers (JUSE)9 it was revealed that 54% of 148 member
companies surveyed were using QFD. The sectors with the highest penetration of
QFD were transportation (86%), construction (82%), electronics (63%), and precision
machinery (66%). Many of the service companies surveyed (32%) were also using
QFD.
Essentially QFD is a hierarchy of matrices, which ensures the “Voice of the
Customer” is deployed throughout the design process by mapping essential customer
requirements to explicit design attributes. This process provides a means to
quantitatively measure customer satisfaction or design quality. For a detailed
understanding of the QFD technique the reader is invited to refer to the excellent
works of Clausing10, or Revelle11.
An example of a top-level QFD matrix is given in Figure 2. which shows all the key
features of the standard “House of Quality”.
Figure 2 QFD Matrix for a simple product
Many studies have reported on the benefits of using QFD which include;
• improved communication and sharing of information within a cross-functional
team
• the identification of 'holes' in the current knowledge of the design team
• the capture and display of a wide variety of important design information in a
single, compact representation.
• support for understanding, consensus, and decision making, especially when
complex relationships and trade-offs are involved
• the creation of a document which is a valuable asset for repeated cycles of
product improvement
Customer Rating
Customer Rating - Us
Customer Rating - Company 1
Customer Rating - Company 2
Planned Level
Improvement Ratio
Importance W
eight
Relative W
eight
2.0
4.0
3.0
5.0
2.0
2.0
3.0
4.0
5.0
1.0
3
1
3
2
1
2
1
4
5
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
4
3
5
5
1
1
1
1
1
0.7
4.0
1.0
2.5
5.0
0.5
1.0
0.3
0.2
1.0
1.3
16.0
3.0
12.5
10.0
1.0
3.0
1.0
1.0
1.0
2.7
32.1
6.0
25.1
20.1
2.0
6.0
2.0
2.0
2.0
Target
Benchmark
Customer Needs Light 1
Easy to set up 2
Clean (no grease) 3
Stable 4
Easy to locate jacking points 5
Will w ork on slight slope (camber) 6
Will w ork on sof t ground 7
Fast to operate 8
Requires little effort 9
reliable 10
Importance Weight Total
Relative Weight Total
No Title
Automotive Jack
RelationshipStrong
Medium
Weak
RelationshipPositive
Negative
Total mass
11
Mechanical Advantage
22
Ground contact area
33
Friction losses
44
no of set-up operations
55
maximum extension
66
no of turns to reach jacking height
77
lubricant viscosity
88
Working life (operations)
99
C of G height
10
10
Input force
11
11
Customer Assessments
1 2 3 4 5
Customer Rating - Us
Customer Rating - Company 1
Customer Rating - Company 2
Planned Level
206
14
73
5
321
22
42
3
333
23
112
8
23
2
56
4
21
1
234
16
24
2
3 QFD Weaknesses
Despite its popularity and overt benefits, QFD has some important weaknesses.
The first of these concerns the requirement/product attribute mapping process. A
standard QFD matrix only allows the user to indicate the strength of such a mapping.
In certain circumstances this is inadequate. Although QFD is able to record
conflicting product attributes it is not able to indicate where customer requirements
are in conflict.
A hypothetical example drawing on the locomotive design example given in the
introduction to this paper is used to illustrate this point.
If we imagine that Maunsell had used a QFD approach and had carefully collected all
the stakeholder requirements an extract from the resulting QFD matrix might look like
that given in Figure 3.
Figure 3 Hypothetical QFD matrix for Steam Locomotive design
Customer Needs Reliability and life of bearings and linkages 1
Ease of Coaling 2
Minimal smoke 3
Easy to maintain 4
Good access to machinery 5
Good driver visibility 6
Fast steam from cold 7
No ash discharge 8
No sparks from chimney 9
Low noise 10
Minimum lubrication points 11
Fast f ill boiler 12
Fast coal loading 13
Tolerant of different coal types 14
Long range 15
Locomotive Design
House of Quality
RelationshipStrong
Medium
Weak Overall level of vibration
1
Tractive effort
2
Mechanical efficiency
3
Boiler pressure
4
Overall empty mass
5
Therm
al efficiency
6
Boiler capacity
7
Grate area
8
Number of cylinders
9
Cylinder area
10
This rightly shows that the QFD matrix would have indicated a strong relationship
between the product attribute “Vibration” and the requirements “Reliability” and
“Ease of Coaling”. This, however, will merely elevate the relative importance of the
vibration product attribute, perhaps misleading the designer into thinking this
important parameter should be reduced as far as possible. In other words the QFD
matrix has failed to give the designer the full picture and gives no indication as to the
overall effect of both the direction and magnitude of this parameter.
In this case a more sophisticated mapping would be more informative because, as
discussed earlier, these two customer requirements are themselves in conflict.
In such a case QFD fails to capture the complexity of the relationship mappings and
thus cannot help to direct the design team towards areas of overall improvement.
4 CODA: The philosophy and methodology of a new hybrid approach
A modified matrix (CODA; COncept Design Analysis) is therefore proposed. This
resembles the standard QFD matrix but provides a more sophisticated mapping
between requirements and key product attributes.
This approach was developed as a result of experience in developing medical
products12,13 as part of an EPSRC-funded research project known as SuPort. Extensive
use was made of a range of formal design techniques within this project, including
QFD26, where the “Voice of the Customer” was deployed throughout the design
phase.
CODA is not intended as a replacement for QFD but is a complementary approach as
discussed later in this paper. CODA draws inspiration from the work of Taguchi14 and
Kano15,16.
4.1 CODA foundation
Dr Genichi Taguchi developed his ideas whilst working at the Japanese
telecommunications company NTT in the 1950s and 1960s. He attempted to use
experimental techniques to achieve both high quality and low-cost design solutions.
Taguchi is widely known for the development of the concept of the “Loss Function”
which establishes a measure of the user dissatisfaction with a product's performance
as it deviates from a target value. Taguchi used statistical methods to enable
identification of the important design factors responsible for degrading product
performance.
The Kano model is a theory of product development researched in the 80's by
Professor Noriaki Kano. This model classifies customer preferences into five
categories: Attractive, One-Dimensional, Must-Be, Indifferent and Reverse. Kano
proposed a set of relationships between these preferences and a customer satisfaction
dimension (shown in Figure 4 17).
Figure 4 The Kano model of quality
The CODA model uses a “Merit Function” to measure the overall “worth” of the
design. This is analogous to the Taguchi Loss Function. The CODA approach also
uses the three characteristic relationships functions that Taguchi first identified as
shown in Figure 5. These characteristic functions allow customer requirements and
product attributes to be described with greater fidelity and accuracy than standard
QFD.
CODA uses the Kano concept of classifying requirements and relating product
characteristics to customer satisfaction.
Figure 5 CODA relationship functions
Both the maximise and minimise functions have a non-linear form to reflect the
benefit trend that is perceived by a customer as a product attribute is either increased
or decreased respectively. An exponential curve is used for two reasons. Firstly such a
curve is a reasonable approximation of customer response to changes in a product
attribute. Secondly, the non-linear relationship facilitates the use of numerical search
techniques as discussed later in this paper.
• Maximise
• Minimise
• Nominal
maximise function
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 5 10 15 20
Parameter value
Customer Satisfaction
maximise function
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 5 10 15 20
Parameter value
Customer Satisfaction
minimise function
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 5 10 15 20
Parameter value
Customer Satisfaction
minimise function
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 5 10 15 20
Parameter value
Customer Satisfaction
optimise function
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
actual value
optimise function
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
actual value
4.2 Illustration of CODA
As an example, we shall consider the concept design of mobile phone. The
relationship between the customer requirement “long time between recharges” and the
product attribute “battery capacity” would be modelled as a “maximise” function.
Figure 6 shows the Kano-like relationship where the y-axis relates to customer
satisfaction measured between 0 (completely unsatisfied) and 1 (perfectly satisfied).
This exponential curve has been calibrated by the use of what is known as a “neutral-
point”.
Figure 6 Maximise function
In this case, the design team have estimated that a battery capacity of 1250 mAh will
result in a life that will gain a “neutral” response from the customer (numerically 0.5
on the customer satisfaction scale). A product attribute exceeding this threshold value
leads to an increasingly positive, but non-linear (asymptotic) customer response.
The minimise function is essentially an inverse of the maximise function and similarly
requires calibration by the use of a neutral point.
Design Parameter; Battery capacity
maximise function
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 5 10 15 20
Parameter value
Customer Satisfaction
maximise function
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 5 10 15 20
Parameter value
Customer Satisfaction
Customer Requirement; Long time between recharges Neutral point
1250 mAh
Figure 7 The CODA optimise function
The optimise function, which is analogous to the Tauguchi “nominal is best” is shown
in Figure 7. This requires two pieces of calibration information; the nominal point and
the tolerance band between the upper and lower neutral points.
The mathematical expressions used for the three characteristic functions are
tolerance
point neutral
valueparameter
=
=
=
τηρwhere
To illustrate the three relationships and to demonstrate how CODA is implemented an
example matrix is shown in Figure 8.
−+
=
−=
−=
2
1
1
2
11
2
11
τηρ
ρη
ηρ
opt
Min
Max
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
0 1 2 3 4 5 6 7 8 9 10 11
Tolerance
Positive
Negative
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
0 1 2 3 4 5 6 7 8 9 10 11
Tolerance
Positive
Negative
Positive
Negative
Neutral point
Figure 8 Tyre Example CODA matrix
The example uses a hypothetical tyre design problem with a single product attribute
namely; “elastomer hardness”. The CODA matrix allows the user to define upper and
lower constraints for each product attribute as shown. Three customer requirements,
“Tyre Life”, “Grip” and “Puncture Resistance” are defined as row entries with the
relative importance of these shown as normalised values in each row very like a QFD
matrix.
The mappings between each requirement and the product attribute have been entered
by selecting an appropriate characteristic function. This somewhat contrived example
shows that;
• as tyre hardness is increased tyre life increases
• as tyre hardness is increased grip decreases
• there is an optimum value of hardness that results in maximum puncture
resistance.
The overall design merit is calculated which is a weighted sum of the three
characteristic functions. The plot in Figure 8 shows the non-monotonic nature of the
design merit (plotted in bold) which suggests a global optimum figure for the product
Design Merit Elastomer hardness (Shore A)
53% Value U constraint L constraint
89.02 120 40
Customer Requirement
Cus
tom
er Im
porta
nce
Wei
ght o
f rel
atio
nshi
p
Type
of r
elat
ions
hip
Neu
tral /
targ
et p
oint
Tole
ranc
e
Mer
it
wei
ghte
d m
erit
How long tyres last 0.2 1 max 50 0.71 0.14Grip 0.6 1 min 50 0.32 0.19
Puncture resistance 0.2 1 opt 90 20 1.00 0.20
Merit profile
0
0.2
0.4
0.6
0.8
1
1.2
0 10 20 30 40 50 60 70 80 90 100 110 120
hardness
Design merit
attribute which can be found using standard numerical (or in simple cases analytical)
methods.
4.3 Mathematical Representation of CODA
Assume there are I customer requirements and P product attributes. The binary
weighting matrix, iiCR , describes the relative importance of customer requirements.
By definition it is an upper triangular and square matrix with size I .
The normalised importance of customer requirements, iN , is calculated as follows:
∑=
++
++=
I
i
ii
iii
YX
YXN
1
1
1,
where iX and iY are the customer requirements scores in x and y directions
respectively and they are calculated as
∑+=
=I
ij
iji CRX1
for 11 −= Ii L , and 0≡IX ,
01 =Y and ∑−
=
−=1
1
)1(i
j
jii CRY for Ii L2= .
Assume there are functional relationships ipF between the merit values ipMV and the
design attributes values pA
)( pipip AFMV = .
And, assume that the correlation factors ipCF are known. We can define the sum of
correlation factors as
∑=
≡P
p
ipi CFSCF1
.
The customer satisfaction related to each customer requirement iCS can be found as
∑=
⋅=P
p
ipip
i
ii CFMV
SCF
NCS
1
.
And, the overall design merit ODM is simply the sum of customer satisfactions
∑=
≡I
i
iCSODM1
.
4.4 Trade-Off optimisation.
A slightly more complex hypothetical design example shows how CODA is applied
across multiple product attributes.
Figure 9 Multiple attribute CODA example
• Figure 9 shows a motorcycle helmet design problem. This has six customer
requirements and four product attributes. Again the normalised relative
importance of the customer requirements have been determined and entered in
the rows next to each customer requirement. This top-level view shows the
level of correlation between the product attribute and the customer
requirement. Conditional formatting has shaded these cells for visual impact
Overall Design Merit 59%Parameter
value
Parameter
value
Parameter
value
Parameter
value
5.01 34.00 4.95 200.00
Customer Requirements
Normalised customer importance
(a1 to am)
CorrelationMerit value
(mv)Correlation
Merit value (mv)
CorrelationMerit value
(mv)Correlation
Merit value (mv)
Overall
Customer
Satisfaction
Light weight 0.10 0.90 0.34 0.30 0.33 FALSE FALSE 0.34
Won't crack 0.29 0.90 0.44 0.90 0.54 0.90 1.00 0.30 0.29 0.57
Good visibility 0.14 FALSE 0.10 0.26 FALSE 0.90 0.75 0.51
Low noise 0.14 0.30 0.96 0.30 0.86 0.30 0.39 0.10 0.29 0.63
Easy to put on/remove 0.14 0.10 0.43 0.10 0.31 FALSE FALSE 0.37
Comfortable 0.19 0.10 1.00 0.10 0.61 0.10 0.34 FALSE 0.65
Visor area (cm2)Shell thickness (mm) Liner thickness (mm) Liner density (Kg/m2)
(dark = strong relationship). Similarly the cells under column heading “Merit
Value” have conditional formatting using a traffic light logic;
• Red indicates a concern where the current product attribute value gives a low
customer satisfaction.
• Amber shows medium satisfaction
• green shows good satisfaction.
Figure 10 shows a more detailed view of a single design parameter and the
relationship editor
Figure 10 Detailed view of relationship matrix
At a glance this provides the design team with an indication of areas for concern.
By clicking on a particular product attribute (Figure 10) a detailed view is provided
which shows that the user has populated the matrix with a variety of characteristic
functions and calibrated them.
Having filled in the matrix the user now has the opportunity to explore the effect of
changing the product attributes.
Figure 11 shows the effect of changing a product attribute(in this case the design
parameter “shell thickness”) on both the “traffic light” indicators and the overall
Design Merit value.
Figure 11 Effect of changing Shell thickness attribute value
This manual exploration is useful in giving the design team an insight into the effect
of design changes where there is a complex mapping. Furthermore the CODA
representation allows the use of automated search techniques to find optimal values
for each product attribute (subject to the declared constraints) by using the overall
Design Merit as an objective function.
4.5 Automated Search
Spreadsheets such as Excel typically employ the simplex, generalized-reduced-
gradient, and branch and bound methods to find an optimal solution The most obvious
limitation associated with this relatively basic approach is that the optimiser
frequently fails to find a global optimum. For large or complex CODA matrices a
more sophisticated search technique may be required. Spreadsheet plug-ins such as
Parameter
value
Upper
Constraint
Lower
Constraint
1.00 8.00 1.00
Correlation
Neutral point
or optimum
point
Relationship
Type
(max min
opt)
Tolerance
(for type
opt)
Merit value
(mv)
0.90 3.00 Min 0.88
0.90 6.00 Max 0.11
4.00 Min FALSE
0.30 6.00 Opt 5.00 0.50
0.10 4.00 Min 0.94
0.10 5.00 Opt 2.00 0.20
Shell thickness (mm)
Overall Design Merit 56%
Parameter
value
Upper
Constraint
Lower
Constraint
8.00 8.00 1.00
CorrelationNeutral point or optimum
point
Relationship Type
(max min opt)
Tolerance (for type
opt)
Merit value (mv)
0.90 3.00 Min 0.23
0.90 6.00 Max 0.60
4.00 Min FALSE
0.30 6.00 Opt 5.00 0.86
0.10 4.00 Min 0.29
0.10 5.00 Opt 2.00 0.31
Shell thickness (mm)
Overall Design Merit 53%
Premium Solver18 are available that use more powerful evolutionary techniques.
Further discussion on the topic of design optimisation is beyond the scope of this
paper but useful references include Keane 19, 20.
In general the use of optimisation techniques combined with the CODA
representation can provide a more rigorous, objective and auditable set of trade-off
values for a given design concept.
4.6 Illustration of Automated Design Search
Figure 12 UAV Fuselage Design
Figure 12 shows an extract from an undergraduate final year project. This project
concerns the design of a fuselage to which a range of different UAV wings can be
attached for a variety of mission profiles.
This CODA matrix has 12 customer requirements and 16 independent product
attributes. The student has used CODA to identify the key relationships between
requirements and attributes as well as the constraints for each attribute.
Dist frm Wing AC to Tail leading edge (mm) Vertical Tail Root Chord (mm) Vertical Tail Height (mm)
Actual parameter
572.06
Constraints (U, L)
900.00
450.00
Actual parameter
99.97
Constraints (U, L)
150.00
50.00
Actual parameter
162.09
Constraints (U, L)
200.00
120.00
Customer Requirement Norm
alised customer importance
Correlation
Neutral point or optimum point
Relationship Type (max_min_opt)
Tolerance (for type opt)
Merit value (mv)
Correlation
Neutral point or optimum point
Relationship Type (max_min_opt)
Tolerance (for type opt)
Merit value (mv)
Correlation
Neutral point or optimum point
Relationship Type (max_min_opt)
Tolerance (for type opt)
Merit value (mv)
Control of throttle, rudder, elevators 10.87% 0.10 450.00 Min 0.42 0.30 100.00 Opt 10.00 1.00 0.90 100.00 Min 0.35
Easily visible from operator on ground 10.43% 0.90 900.00 Max 0.36 0.30 150.00 Max 0.37 0.10 200.00 Max 0.43
Able to take various wing designs 10.00% FALSE FALSE FALSE
Reliable control systems 9.57% FALSE FALSE FALSE
Durability 9.13% 0.30 450.00 Min 0.42 0.30 50.00 Min 0.29 0.90 100.00 Min 0.35
Stiff structure in all planes 8.70% 0.90 450.00 Min 0.42 0.30 50.00 Min 0.29 0.30 100.00 Min 0.35
Lightweight 8.26% 0.10 450.00 Min 0.42 0.10 50.00 Min 0.29 0.10 100.00 Min 0.35
Low cost of Final Product 7.83% 0.10 450.00 Min 0.42 0.10 50.00 Min 0.29 0.10 100.00 Min 0.35
Aerodynamic profile 7.39% 0.30 900.00 Max 0.36 0.90 100.00 Opt 10.00 1.00 0.30 165.00 Opt 25.00 0.99
Ease of access to control systems/ for maintenance 6.52% FALSE FALSE FALSE
Stability in flight/ Flight Performance 6.09% 0.90 580.00 Opt 200.00 1.00 0.90 100.00 Opt 10.00 1.00 0.90 165.00 Opt 25.00 0.99
Protected propellor 5.22% FALSE FALSE FALSE
57.3%
Overall Design Merit
Concept #4
Use was made of the a spreadsheet add-in Direct Optimizer which is a is based on a
modification of the Hooke-Jeeves direct search algorithm 21 22. A search using this
optimiser has found the best set of product attributes that meet the constraints giving a
best Design Merit value of 57.3%.
4.7 CODA as part of a Systematic design process
Figure 13 Context of CODA within the Design Process
Figure 13 illustrates the context of CODA within a systematic design process.
This indicates, a two important points;
• CODA does not necessarily displace QFD. CODA demands considerably
more effort compared with QFD it is proposed that an initial QFD matrix be
used to identify conflicting product attributes only. It is then important to
initially recognise and try and eliminate these conflicts using techniques such
as TRIZ23. If conflicts cannot be eliminated then trade-offs values need to be
Key
Concept Value Analysis
Concept Refinement
Concept Generation
3)
Concept
generation
5)
Concept
Shortlisting
4)
Concept
morph chart
"Cherry-
picking"
Concept
sketches
Stakeholder analysis
6) QFD
matrix
2)
Stakeholder
requirements
analysis
8)
Concept
Design
Analysis
(CODA)
Clear set of
"solution-
neutral"
requirement
descriptors
Relative
importance
of
descriptors
1)
Stakeholder
identification
Sample
Groups and
survey plan
7)
Conflict
analysis and
resolution
(TRIZ)
Conflicting
product
attributes
Residual list
of conflicting
product
attributes
Optimal
product
attribute
"trade-off"
values
10)
Value
analysis
Design
"merit"
value
"Solution-
specific"
product
attributes
Information
Process
9)
Product
Cost
Analysis
Comparative
cost
estimate
Flitered list
of Concepts
Composite
Concepts
Highest
Value
Concept
List of
constraints
essential
features
established using CODA. In effect QFD and TRIZ are used as a filter or pre-
processor prior to CODA.
• The Design Merit is of particular benefit in concept selection and Value
Analysis24. The Design Merit and cost for each concept can be plotted to
indicate the relative value of each concept25. By using a relatively objective
measure of overall “goodness” of a concept this allows true value to be
identified.
5 Conclusions and further work
There are a number of issues associated with the implementation of this research work
in “real” large scale engineering problems.
One issue concerns independence of design attributes. CODA initially treats each
design attribute as an independent variable. In reality it is rare for any design attribute
to be truly independent and certainly when the design progresses past the concept
stage most attributes will be interrelated in some way. One of the implications of this
is that perhaps at the early design stage CODA output should be used to set
aspirations which are later refined and expanded upon. As the aspirations are
modelled and analysed in greater detail, the interrelationships can be recognised by
integrating the CODA matrix with the analysis tools where relationship constraints
are realised. In this context this research could benefit from integration with a DSM
(Design Structure Matrix) approach 26 whereby independence of design attributes
could be identified by using some of the partitioning techniques developed by the
DSM community.
Another issue concerns the scaling up of the technique to address large scale, complex
designs such as aerospace products. This demands a systems engineering approach
whereby the design task is partitioned (again DSM techniques are useful here) and
allocated as sub-tasks to separate parts of the organisation. This has always been a
stumbling block in the past for wide-scale , consistent and coherent use of tools such
as QFD. Inevitably the deployment of these types of design techniques overlaps
strongly with the discipline of Product Data Management (PDM) and associated tools.
Many QFD researchers in the past have alluded to “matrices of matrices” (sometimes
know as the “City of Quality”27) but in reality deploying such integrated systems
engineering approaches required enormous discipline and an excellent IT
infrastructure. Often the intense pressure on design teams to meet milestones and
commercial targets leads to a desire to abandon systematic design techniques to meet
short term goals.
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List of Figures
Figure 1 "Sir John Hawkins" the last of the Lord Nelson class of locomotives.
Figure 2 QFD Matrix for a simple product
Figure 3 Hypothetical QFD matrix for Steam Locomotive design
Figure 4 The Kano model of quality
Figure 5 CODA relationship functions
Figure 6 Maximise function
Figure 7 The CODA optimise function
Figure 8 Tyre Example CODA matrix
Figure 9 Multiple Product attribute CODA example
Figure 10 Detailed view of relationship matrix
Figure 11 Effect of changing Shell thickness product attribute value
Figure 12 UAV Fuselage Design
Figure 13 Context of CODA within the Design Process
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