A Metric-based approach to Concept Design

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

[email protected].

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

[email protected].

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


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



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


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


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


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


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 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




Merit value (mv)

Correlation




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

A Metric-based approach to Concept Design

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