Genetic Cost Modelling

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Progress in Aerospace Sciences 40 (2004) 487–534

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Review of aerospace engineering cost modelling:The genetic causal approach

R. Curran�, S. Raghunathan, M. Price

Centre of Excellence for Integrated Aircraft Technologies, School of Aeronautical Engineering, Queens University Belfast,

David Keir Building, Stranhillis Road, Belfast BT9 5AG, United Kingdom

Abstract

The primary intention of this paper is to review the current state of the art in engineering cost modelling as applied to

aerospace. This is a topic of current interest and in addressing the literature, the presented work also sets out some of

the recognised definitions of cost that relate to the engineering domain. The paper does not attempt to address the

higher-level financial sector but rather focuses on the costing issues directly relevant to the engineering process,

primarily those of design and manufacture. This is of more contemporary interest as there is now a shift towards the

analysis of the influence of cost, as defined in more engineering related terms; in an attempt to link into integrated

product and process development (IPPD) within a concurrent engineering environment. Consequently, the cost

definitions are reviewed in the context of the nature of cost as applicable to the engineering process stages: from bidding

through to design, to manufacture, to procurement and ultimately, to operation. The linkage and integration of design

and manufacture is addressed in some detail. This leads naturally to the concept of engineers influencing and controlling

cost within their own domain rather than trusting this to financers who have little control over the cause of cost. In

terms of influence, the engineer creates the potential for cost and in a concurrent environment this requires models that

integrate cost into the decision making process.

r 2004 Published by Elsevier Ltd.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489

1.1. Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489

2. The nature of cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491

2.1. Production. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491

2.1.1. Customer requirement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491

2.1.2. Manufacturing practice. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 492

2.1.3. Integrated design and manufacture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493

2.2. Cost definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495

2.2.1. Non-recurring and recurring costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495

e front matter r 2004 Published by Elsevier Ltd.

erosci.2004.10.001

ing author. Tel.: +44 28 90 274190/335; fax: +44 28 90 382701.

ess: [email protected] (R. Curran).

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ARTICLE IN PRESS

Nomenclature

E energy (kJ)

ABC activity-based costing

AC acquisition cost

Al artificial intelligence

BOM bill of material

CAD computer-aided design

CAM computer-aided modelling

CEM cost estimating model

CER cost estimating relationships

CFD computational fluid dynamics

COTS commercial off the shelf

DFA design for assembly

DFC design for cost

DFM design for manufacture

DFMA design for manufacture and assembly

DFSS design for Six Sigma

DOC direct operating cost

DoD US department of defence

DTC design to cost

ESDU engineering and science data unit

FB fuel burn

FEA finite element analysis

ICT information and communication technology

IPPD integrated product process development

KBS knowledge-based systems

LCC life cycle cost

MCR material conversion route

MDO multidisciplinary design optimisation

MFC manufacturing cost

ROM rough order of magnitude

SFC specific fuel consumption

WBS work breakdown structure

b learning curve slope

C cost

CP historical first unit cost

DC cost differential due to f Geom; f Manuf and f Spec

DC0 cost differential from the baseline character-

istic

clframes frame labour coefficient

cm2024 material cost coefficient for 2024 T3 alumi-

nium ($/g)

Dfan engine fan diameter

FC factor representing complexity

FM factor representing miniaturization

FP factor representing productivity

f c factor representing cost incurred

f Geom factor representing geometric complexity

f Manuf factor representing manufacturing complex-

ity

f p factor representing performance level

achieved

f Spec factor representing specification complexity

f t factor representing time elapsed in reaching

market

hf frame height

lf frame flange length

mdata slope of the characteristic

nframes number of frames

p probability of an event occurring

r costing ratios, according to f Geom; f Manuf and

f Spec

R the regression coefficient R2 representing

goodness of fit for a data population

Rr production rate at r production rate curve

slope

rlframes the frame labour cost per hour ($/h)

tf frame thickness

U unit number

V f volume of a ‘C’ shape frame

zdata constant of the characteristic

r material density

n factor relating to overhead or mark-up from

manufacturing cost to unit cost

Superscript

l labour

m material

R. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534488

2.2.2. Fixed and variable costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495

2.2.3. Direct and indirect costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496

2.2.4. Life cycle cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496

2.3. Cost allocation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496

3. Controlling cost. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 498

3.1. Cost engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 498

3.2. Cost estimating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499

3.3. Cost-integrated design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 500

3.4. Supply chain cost control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502

3.5. Knowledge-based systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504

4. State-of-the-art: cost estimating. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504

4.1. Classic estimating techniques. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504

ARTICLE IN PRESSR. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534 489

4.1.1. Analogous . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504

4.1.2. Parametric. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507

4.1.3. Bottom-up. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 510

4.2. Advanced estimating techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511

4.2.1. Feature-based modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511

4.2.2. Fuzzy logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513

4.2.3. Neural networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514

4.2.4. Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515

4.2.5. Data mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517

5. State of the science: genetic causal cost theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517

5.1. State-of-the-art. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517

5.2. Causation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519

5.3. Genetic nature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 520

5.4. Relevancy of genetic causal cost modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 520

5.5. Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 521

5.6. Genetic causal case study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 522

5.6.1. Measured costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523

5.6.2. Cost prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523

5.6.3. Direct operating cost optimisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525

6. Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527

1. Introduction

The paper also reviews the more traditional and

advanced methods of cost estimating as the functional

techniques that are currently available. The final section

reviews the literature in terms of the modelling

methodology. Cost modelling is a particularly difficult

field to assess in terms of scientific theory as it is not

normally addressed as a scientific field but rather as an

attribute of either design and manufacturing decisions

or indeed a product; the latter being further confused

with price (as cost plus profit). However, one of the main

aims of the paper is to consider the basis of the science in

some detail in an attempt to establish a consolidating

basis for costing methodologies. As there is little

literature that addresses the fundamental nature of cost

but rather focuses on establishing, at best, a rationale for

applied relationships and models, this concerns the

genetic and causal requirements that are a fundamental

requirement of any scientific theory. The resultant

theoretical basis of the cost modelling is termed the

genetic causal approach and underpins the need for an

analytical foundation that is a platform for applied

models that can be adjudged to be appropriate relative

to both theoretical correctness and application.

Ultimately, the cost modelling domain is reviewed (1)

according to its genetic nature: relative to its general

applicability to engineering products and the concept of

cost being inherited from certain design attributes and

manufacturing processes; and (2) its causal nature:

relative to the effect of design definition and the

manufacturing processes employed in its causation. A

case study is presented in detail to illustrate the

applicability of the approach. However, the intention

is not to present a definitive modelling technique but to

underwrite the value of having fundamental theoretical

principles to the modelling solution adopted. This is

especially relevant to cost modelling where application

has dominated theory, and where there is a major

influence from environmental factors. There is a lot of

current development in the area of systems engineering

that is adopting a behavioural approach to integrated

technical product development in an attempt to more

accurately model the performance within the wider

customer context, including cost.

1.1. Context

The UK aerospace industry is one of the most

successful manufacturing sectors with a turnover of

around £20 billion and producing about 10% of UK

manufactured exports, with a consistent trade surplus

since 1980 [1]. The industry, both civil and military,

employs more than 150,000 people and is second only in

size to the US, with a world market share of 13%. Major

companies in the UK aerospace industry include BAE

Systems and Airbus UK, Rolls Royce, TRW Lucas and

Smiths industries, while Shorts of Belfast are now part

of the Bombardier Aerospace group.

ARTICLE IN PRESSR. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534490

Already in the late 1980s, the customer was increas-

ingly being considered more explicitly in the commercial

aircraft design process through their demand for

reduced operating cost and lead-time, whereas technol-

ogy had been the dominant driver in the past [2]. This is

in the context of the continuing rise in labour rates and

the higher non-recurring costs associated with reduced

labour processes. The price of a Boeing 737 is now

approximately 6 times that of 3 decades ago, a rise of

6.5% per year. Naturally, there have been advances in

the design and operational capabilities, with both the

Airbus 380 and Boeing 7E7 being reported to have the

lowest direct operating costs (DOC) in the large carrier

class. With reference to the oil crisis of the mid-1970s,

fuel prices have also fluctuated and air travel is now very

cost sensitive [3]. Typical aircraft DOC breakdowns

show that the aircraft cost contribution to DOC is two

to four times higher than the contribution made by fuel

cost [4]. That is reflected in the message from the airlines

that the paradigm of ‘Better, Quicker to market, and

Cheaper’ is replacing the old mantra of ‘Higher, Faster,

Farther’ [5]. Aircraft producers now realise that this

demand to reduce cost and lead-time needs to be tackled

at the conceptual engineering design phase. Typically,

Burt and Doyle [6] report that 70–80% of the total

avoidable cost is controllable at the design stage and

indeed many authors agree that conceptual design

wields the greatest cost influence and is often irreversible

[7]. Consequently, this results in: (1) a more critical

assessment of technology suitability and maturity; (2) a

reassessment of the processes and the establishment of

best practises; and (3) a more rigorous approach to the

issue of cost.

The importance of engineering costing within aircraft

design [8] should have a more directly influential role,

for example as part of an integrated process that is

embedded within multidisciplinary systems modelling

architecture. Differential product evaluation with re-

gards to cost, technology, reliability and maintainability,

along with risk analysis, are all important considerations

in the current aerospace industry. Cost modelling also

assists in preliminary planning for procurement and

partnership sourcing. Ultimately, the goal is that aircraft

acquisition is driven by the balanced trade-off between

cost and performance [9]: leading to affordability and

sustainability for operators over the product life cycle.

The challenge for the industry is to look into all of the

aspects of ownership cost and to link these into the design

decision making process at the conceptual stage on.

The recognised need for cost evaluation at the design

stage is also intrinsically linked to aircraft production.

This is why the principle of Design for Manufacture is so

important, addressed in detail in Section 2.2. Chisholm

[10] has pointed out that manufacture is a series of

interrelated activities and operations that involve design,

materials selection, planning, production and quality

assurance. As production is the result of engineering

effort, it can be defined by the activities of design,

process planning and production planning while the

associated decision making process is typically driven by

technical definition and constraints; although cost is

being increasingly recognised as an important design

criterion within the definition process. However, cost is

not known in advance of production and therefore a

cost estimation system is required. Ten Brink [11] has

pointed out that this will rely on the available product

information at whatever stage of the product develop-

ment cycle and relevant information maturity. There is

also the possibility of using such a design-oriented

capability to implement product changes that reduce

cost. For example, concurrent engineering can be used in

the simultaneous integration of engineering tasks during

the product development cycle but requires the inte-

grated support of a cost estimation capability.

The cost of manufacturing to produce an output is a

function of resource utilization; including physical

entities such as: manpower, equipment, facilities, supply,

etc. [12]. The costs are then representative of the

resources consumed, such as: machine tools and fixtures,

operators and materials, etc. Therefore, it is the

engineering effort that gives rise to cost as decisions

are made. It is often reported but perhaps not well

heeded that conceptual engineering decisions signifi-

cantly influence the costs caused by engineering deci-

sions later in the engineering cycle, within a reduced

design space. However, although design itself is typically

quoted at contributing less than 10% of the product

costs while fixing around 70%, this may be misleading as

product specification has been noted to already commit

a significant level of cost. Wierda [13] has noted that

design may be responsible for 20–30% of total product

cost, relative to the production environment. This

unfortunately leads to the cost estimating paradox of

the design process: that product information is not yet

available in detail and consequently, there are varying

needs and difficulties in making accurate estimates

throughout the duration of the process [14]; leading to

the further paradox of confidence being higher after

design completion and therefore leading to reengineer-

ing and modification.

The aim of Concurrent Engineering and integrated

product process development (IPPD) is to impose the

simultaneous sharing of task information that originates

within the individual engineering functions, in order to

facilitate and control cooperative decision-making [15].

This is best facilitated with a modular system that has

generic elements, for the enhanced integration of multi-

disciplinary analysis and diverse models, along with the

flexibility of extension and system maintenance [16].

This is the hard end of concurrent cost engineering and

addresses the sharing of information in a holistic

manner through integrating data systems, rather than


the fragmented and dated approach of estimating

isolated costs with diverse models at disparate levels of

the product breakdown structure. However, the analysis

architecture needs to be unified and the linkage between

engineering functions needs to be established, in order to

enable communication and data/knowledge sharing.

This is highly dependant on both the availability and

accessibility of coherent information [17] and conse-

quently, engineering databases and systems architectures

do play a key role in the continued automation of the

product development cycle. Billo [18] has pointed out

that such engineering databases may be populated with

geometric, physical, technological or other influential

engineering properties, for example, in an object

oriented framework that also relates to the hierarchical

nature of the objects and their attributes being modelled.

2. The nature of cost

2.1. Production

2.1.1. Customer requirement

In aerospace engineering there has always been a wide

variety of manufacturing alternatives, whether pro-

cesses, methodologies, or technologies. There are even

more materials now available. Data management

systems are continually evolving, and computational

modelling of behaviour is being pursued on all fronts,

although especially in computational fluid dynamics

(CFD) and finite element modelling (FEM) for aero-

space applications. However, there is still a basic need

for tools that help and support engineers in making

reasonable and measured design decisions that are cost-

effective and ultimately, more competitive [23]. As

mentioned, aircraft engineering is adapting concurrent

engineering principles but it is not yet integrated in

nature as the inter-linkage between key variables and

parameters has not yet been built into a structured

modelling environment. In addition, there is now a

heightened strategic need between industry and acade-

mia for mutually beneficial research, now much more

formalised than in times when industry invested more

heavily in its’ own research and development. The

relevance is that cost is now viewed as a metric that can

facilitate an integrated approach, as engineering variables

and parameters can be explicitly linked to cost, whilst also

providing guidelines that directly relate to the quantifiable

measure of value and competitive advantage.

Slack [20] has proposed that value is a measure of

worth for a specific product or service by the customer,

and is a function of the following aspects:

�
the product’s usefulness in satisfying customer need,
�
the relative importance of the need being satisfied,
�
the availability of the product relative to when
needed, and

�
the cost of ownership to the customer.
The first two aspects are associated with the perceived

performance; the third relates to the timing of the

product availability in response to demand; and the

fourth relates to cost and robustness. Each aspect can be

related to the ‘‘Better, Faster, Cheaper’’ paradigm,

where Murman [19] has proposed that some measure

of Value can be defined with the following functional

relationship:

Value ¼f p

f c � f t

,

where f p represents performance level achieved, f c

represents cost incurred, and f t represents time elapsed

in reaching market. This formulation highlights the need

for aircraft manufacturing companies to be meeting the

‘‘Better, Faster, Cheaper’’ requirement by re-assessing

their practises, and improving the existing methods and

work processes with quantitative tools and integration

methodologies. This is a key step towards the new

environment within aviation: the concurrent integrated

design of aircraft for the production of a highly

synthesised product that fulfils customer requirement,

whether in terms of performance, cost or availability.

However, in the shorter term, one can already look

towards more competitive aircraft for producers, ulti-

mately maximising their profit. Affordability can be

formalised as relating to a product with a selling price

that has proportional functional worth and which is

priced within the customer’s range. In addressing

affordability, cost can be readily employed as an

evaluation criterion at the conceptual design stage in

two ways: (1) design for cost (DFC) and (2) design to

cost (DTC). DFC can be viewed as a feed-forward

engineering process that makes conscious use of

engineering process information during design and is

directly aligned with concurrent engineering [24]. Alter-

natively, DTC is a more management driven process

that aims to provide a design that satisfies specification

requirements for a given cost target [25]. In both cases,

however, cost is used to link design and manufacturing.

Consequently, affordability becomes a major design

driver that can be measured with cost as the dependent

variable. Ultimately, a more general Cost Integrated

Design approach is advocated, which is less specific

than DFC/DTC but encompasses the industrial need

for various levels of cost evaluation, for various

purposes. This should allow customer defined cost

targets to flow down into the design process and to be

addressed with other engineering requirements and

specifications.


2.1.2. Manufacturing practice

Concurrent engineering is an important framework

within which advanced engineering tools and techniques

can be deployed; with a focus on improving product

definition and development by concentrating life cycle

issues on the early design process [26]. Such tools should

strengthen the multidisciplinary approach at all phases

of the design process, thus ensuring that the technical

expertise of the participants can be optimally used or at

least, successfully utilised to improve the design solu-

tion. Management strategies such as Six Sigma Meth-

odology (a probabilistic approach to process capability

and improvements), Agile Manufacturing (with a focus

on flexibility and response), Lean Manufacture (a value

mapping and efficiency approach), and effective human

resource management also need to be taken into

consideration if improvements are to be met in the

areas of manufacture and assembly system profitability.

In the Design for Six Sigma [21,27] context, the

product design team works with other cross-functional

members from marketing, sales, quality, manufacturing,

procurement and customers. Design for Six Sigma

espouses an integrated approach to design, so that the

product is manufacturable at the highest quality and

lowest cost, and satisfies all of the customer require-

ments. Six Sigma methodology helps identify wastage by

taking a routine approach to issues that are causing

problems. Typically, one key issue addressed within

aerospace is the statistical reduction of opportunities for

defects, scrap and rework.

The concept of Agile Manufacturing [28] is driven by

the need to quickly respond to changing customer and

market requirements. Agile manufacturing requires that

a manufacturing system is able to efficiently produce a

large variety of products and that it can be reconfigured

to accommodate changes in both product mix and

product design. This requires a simple manufacturing

system that is flexible while design for agile assembly is

accomplished by considering operational issues of

assembly systems at the early product design stage.

Lean manufacture [29] focuses heavily on the concept

of ensuring that value is always added to products and

that wasteful practice and processes can therefore be

identified and eradicated. The approach has been

developed through the Lean Aerospace Initiative (LAI)

[30] that was born out of the need for affordability as

defence procurement budgets were reduced in the US due

to increasing costs and military industrial overcapacity

[20]. There is also a UK Lean Aerospace Initiative (UK-

LAI) and a Lean Aircraft Research Program (LARP)

based at Linkoping University in Sweden.

Aircraft manufacturing companies are now beginning

to consider commercial software that facilitates assem-

bly process simulation for the planning and verification

of their operations. Such software can aid the manu-

facturing engineer to validate the feasibility of the

process plan, determine cycle time and potential bottle-

necks, and to estimate the product and capital costs.

However, specific effort or facilitating software could

help capture the knowledge of the manufacturing

engineer and facilitate the setting of accurate and

consistent time standards through automated graphical

user interfaces [31]. The need for ease of assembly plays a

dominant role in aircraft production due to the high part

count of an aircraft. Assembly is even more important in

today’s climate as so much of part manufacture is

increasingly being subcontracted to smaller more compe-

titive suppliers. The four main goals of design for

assembly (DFA) are, as defined by Andreasen [32]:

�
assembly efficiency,
�
product quality,
�
assembly system profitability, and
�
improved working environment within the assembly
system.

With reference to the functional relationship of value

previously described, the first three aspects can be seen

to impact on cost and time, while the fourth influences

performance in meeting the challenges of improvements

in the overall product value.

Several DFA methodologies exist which concentrate

the designers interest on ease of assembly during the

design concept stage, including: the design for manu-

facture and assembly (DFMA) procedure suggested by

Boothroyd and Dewhurst [33], the Lucas DFA techni-

que [34] and the Hitachi Assemblability Evaluation

Method (AEM) [35]. The Boothroyd–Dewhurst DFMA

methodology suggests that the best way to achieve

assembly cost reduction is to first reduce the number of

components; standardise where possible; and then

ensure that the remaining components are easy to

assemble. The Lucas DFA technique arose from the

concept of a knowledge-based approach used in

conjunction with a CAD system. This technique uses

the Boothroyd–Dewhurst principles of reducing compo-

nent numbers and analysing the assembly processes. An

important feature is an emphasis on establishing the

requirements of all customers in the supply chain and

not limiting the assessment to the immediate business

customer. Hitachi AEM facilitates design improvement

at the concept stage by identifying weak points in the

design using two key indices:

�
an assemblability evaluation score that is used to
assess design quality and the difficulty of assembly

operations and

�
an assemblability cost ratio that is used to generate
the projected assembly cost.

It will be shown in the following section that the

application of the Boothroyd–Dewhurst methodology


can also result in reductions in non-recurring cost.

However, tooling engineers are also making a direct

contribution to reducing non-recurring costs through

approaches such as the jig-less assembly approach to

manufacturing. For example, a case study has been

presented which looks at the redesign of the Airbus

A320 fixed-leading-edge conducted by BAE Systems

[36]. Jig-less assembly aims to reduce cost and to

increase the flexibility of tooling systems for aircraft

manufacture through the minimisation of product-

specific jigs, fixtures and tooling. During the develop-

ment phase, tooling costs are quoted at over a third of

the overall cost in the civil sector and nearly a quarter

for the military. Consequently, savings in this aspect of

aircraft manufacture are significant and they also impact

on the lead time from concept to market. Jig-less

assembly does not mean tool-less assembly, rather, the

eradication or at least reduction of jigs. Simple fixtures

may still be needed to hold the parts during particular

operations but other methods are being found to

correctly locate parts relative to one another, the most

advanced systems using lasers for datums. Assembly

techniques can be simplified by using precision posi-

tioned holes in panels and other parts of the structure to

‘‘self-locate’’ the panels. This process, known as

determinant assembly, uses part-to-part indexing, rather

than the conventional part-to-tool systems used in the

past.

Within aerospace industry, it is generally recorded

that approximately 10% of the overall manufacturing

cost of each airframe can be attributed to the

manufacture and maintenance of assembly jigs and

fixtures. A traditional ‘‘hard tooling’’ philosophy

dictates that the desired quality of the finished structure

is built into the tooling. The tooling must therefore be

regularly calibrated to ensure build-quality through

tolerancing. The alternative philosophy of ‘‘Flyaway

Tooling’’ has been conceived with the purpose of

reducing tooling costs and improving build quality

[37]. This approach envisages that future airframe

components will be designed with integral location

features and that they will incorporate positional

datum’s that transfer into the assembly. This enables

Fig. 1. Flight control pressure box: baseli

in-process measurement and aids in-service repair

operations. It may also be possible to design an

aerospace structure that has sufficient inherent stiffness

for the assembly tooling to be reduced to simple,

reusable and re-configurable (from program to pro-

gram) supporting structure.

2.1.3. Integrated design and manufacture

There is a substantial amount of general information

and case studies available in the realms of DFM and

DFA, or the more general Design For ‘X’ [38]. However,

the relative importance and roles of DFM and DFA are

not well distinguished, nor is it clear how organised and

systematic the general approach needs to be to reach its

full potential, or ultimately, what the quantifiable

benefits are likely to be relative to the change in design

metrics. In saying this, there is not a conflict of interest

between DFM and DFA, as essentially, both work in

complement to deliver simplified designs, as part of a

concurrent DFMA approach. However, the distinction

is correct in terms of either part manufacture or

assembly respectively, and helps simplify the identifica-

tion of associated cost drivers and the formulation of

rules and guiding metrics.

A case study [22] has been presented which illustrates

the use of the machining process to reduce the number

of operations in an assembly, where the baseline design

involved sheet metal fabrication with fasteners. The

assembly in question is a Pressure Box that functions as

one of two cavities located between the floor beams in

the pressurised mid-fuselage section of a regional jet

aircraft, where the wing passes through the belly of the

fuselage. The boxes seal the floor for pressurisation at a

location where there are two indents that allow flight

control components to extend beyond the floor-line. The

baseline and redesign are shown in Fig. 1 while the

process improvement results are presented in Table 1.

With regards to the tooling cost, it should be noted that

this non-recurring element was not already spent on the

contract under consideration but relates to the fabri-

cated design solution being used on the older aircraft

variant. Therefore, the reduced amount refers to the

ne design and redesign, respectively.

ARTICLE IN PRESS

Table 1

DFA results for pressure box

Before After Reduced %

Number of parts 29 1 28 96

Number of fasteners 346 124 222 64

Assembly man-hours 20 3.3 16.7 83

Recurring mfg cost (£) 770 459 311 40

Tooling cost (£) 3863 2847 1016 26

Fig. 2. Tailcone forward firewall.

Fig. 3. Initial design of ‘‘I’’ beams (upper) and simpler DFA

redesign (lower).

Table 2

DFA results for fire bulkhead

Before After Delta %

Raw material (kg) 143 96 47 32

Machine time (h) 138 90 48 34

Weight (kg) 10.6 9.9 0.7 6

Recurring mfg cost (£) 17,827 8413 9414 52


relative savings to be made upfront at the conceptual

design stage.

A quite different example of DFM implementation is

on the firewall bulkhead of a tail-cone on a Lear 45

business jet as shown in Fig. 2. The purpose of the

Firewall is to resist the excessive heat that may emanate

from a malfunctioning of the auxiliary power unit

(APU) and in extreme conditions, to withstand a fire

situation. Typically, for such thermal applications, a

stiffened Titanium structure is used consisting of sheet

metal sub-assemblies and a number of ‘‘I’’ shaped

beams, as shown in Fig. 3. A cross-functional engineer-

ing team was established which promptly identified the

five ‘‘I’’ beams in the baseline as a major manufacturing

cost driver, and focused on these as part of the DFM

process. The redesign then focused on the minimisation

of material usage and reduced machining time, achieving

a cost reduction of around 50% and a weight reduction

of 6%, as shown in Table 2.

Commercially, an aircraft’s specifications list is

drafted when considering the market niche and asso-

ciated requirements. From that point, the design concept

is established and at this juncture the manufacturability

of the aircraft already needs to be integrated into the

early design process in a concurrent engineering context.

Fig. 4 refers to an aircraft as composed of a number of

interrelated multidisciplinary systems [39,40]. The key

design parameters that characterise these systems must

be optimally, or at least satisfactorily, integrated to

reduce the negative manufacturing implications that

compromise competitive advantage and value: reducing

quality and timeliness while inflating cost. Therefore,

cost becomes an integral part of the design process and

an explicit design driver [41,42].

A methodology for integrating competitive manufac-

turability is also highlighted in Fig. 4, with specific

reference to the three underlying design principles that

underwrite the genetic causal approach later advocated

in the paper; namely, DFM, DFA, and DFC. An

example of this is to first simplify the assembly concept

through DFA application; then to match the materials

and process through the combined use of DFA and

DFM; and finally, to simplify the part design through

DFM. Cost-integrated design is applied at all stages in

order to provide quantitative information regarding the

cost impact of the decisions being made [43]. Supporting

methodologies such as statistical process control (SPC)

can also be exploited concurrently with cost-integrated

design so that as a consequence, the competitive

manufacturability of the product can be maximised in

measures of cost, quality and time. This can all be

carried out in the wider context of Six Sigma for

example, following the principle of: Definition; Mea-

surement; Analysis; Improvement and Control [27]. The

Six Sigma methodology is not restrictive and advocates

the use of whatever supporting tools that can be used to

improve processes and products, such as DFA, tolerance

design, robust design, pareto analysis, etc.

ARTICLE IN PRESS

Integration of disciplines within each

system

Large Number

Of Interdependent

Systems

New Advanced

Technologies

Output = performance

cost

Integration of Systems

Integrated Aircraft

Fig. 4. Aircraft systems design integration.

R. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534 495

Ultimately, it is necessary to couple all of the key

design parameters at the early definition stage so that the

aircraft can be integrated as a whole system [44].

Methodologies need to be formulated into models that

can provide the linkage between performance models

and production realities. In this context, DFM, DFA

and cost-integrated design are not only approaches that

support the principle of designing with a view towards

the implications on manufacture, assembly and cost,

respectively. Rather, these should be embodied into

models that drive the process by providing a quantita-

tive predicted outcome that can then be analytically

linked into an integrated engineering system that may

include more traditional engineering models such as

computational fluid dynamics (CFD) and finite element

analysis (FEA).

2.2. Cost definitions

This section includes a brief explanation of the

various cost categories recognised as being incurred by

an aircraft producer. The following categorisations are

well documented in the literature [45,46] and are

included primarily for clarity and fullness. A product’s

costs can be arranged into a cost breakdown structure,

such as presented by Fabrycky and Blanchard [47] or

Liebers [48]. This cost breakdown structure is driven by

the design of the particular product and must include all

costs only once. Some useful classifications that facilitate

this process are: (1) non-recurring or recurring; (2) direct

or indirect costs, and (3) variable/fixed costs. Another

distinction sometimes made is to relevant and irrelevant

costs [52] where relevant costs are treated as those that

are in one of several design alternatives but absent in

other alternatives, and therefore can be treated as

differential costs. These costs play a specific role in the

decision-making processes whereas all other costs are

then termed irrelevant [48].

2.2.1. Non-recurring and recurring costs

A non-recurring cost refers typically to a capital

expenditure that is incurred prior to the first unit of

production and is an element of the development and

investment costs that generally occurs only once in the

life cycle of a work activity or work output [12]. It may

be broadly defined as a one-time cost per programme or

narrowly as per contract. Typically, this would include:

initial engineering effort in design; jigs and tooling

acquisition and/or upgrade; system testing and certifica-

tion; pre-production manufacturing costs such as plan-

ning, etc. On the other hand, capital expenditures

allocated to prepaid materials, supplies and parts used

to produce a unit of output are designated as recurring

costs. Recurring costs are ongoing costs that are

proportionally incurred from the production of the first

unit of output then on but are also required in order to

maintain and update the manufacturing set-up as a

whole. These costs occur throughout a programme’s life

and arise due to the repetitive nature of: commercial

procurement costs; production overhead costs; materials

procurement costs; technical upgrade costs; labour and

personnel costs; consumables; utility costs, etc. These

are similar to variable costs, explained in the following

section, as they vary as a function of quantity acquired.

It is important to note that both non-recurring and

recurring costs are important when modelling learning

and improvement curves, especially as the recurring

estimates should decrease over the production run.

2.2.2. Fixed and variable costs

Recurring and non-recurring costs can be incorrectly

confused with variable and fixed costs respectively. The


terms variable cost and fixed cost are often associated

with higher-level financial studies and with break-even

analysis over investment decisions. Typical examples

would be the cost of telecommunication, executive

board salaries, leasing, etc. Consequently, when a

company is being assessed financially the fixed costs

are often investigated in order to see whether the

company’s profits are superior, that being a sign of

general economic health. Schiller [49] defines fixed costs

as the ‘‘costs of production that do not change when the

rate of output is altered’’. Therefore, the association

with non-recurring is clear. However, recurring over-

heads could be fixed while non-recurring costs could be

programme or contract specific. In general, fixed costs

remain unchanged on the global level and are indepen-

dent of the enterprise performance. They are therefore

treated as a general production cost incurred in keeping

the company operational. Conversely, variable costs are

costs of production that change when the rate of output

is altered. Typical examples include many recurring

elements such as labour and material costs, machining

expenditure, etc. Stewart [46] has defined variable costs

as those which change with the rate of production or the

performance of services, whereas fixed costs are those

that do not vary with the volume of business. Company

financiers like to have a good understanding of the

general variable cost expenditure so that they can put a

case forward for reducing it as a way of increasing

profit. However, this can be counter-productive as

variable costs must be incurred in the production of

good quality products that satisfy customer expecta-

tions; quality as well as quantity. In addition, semi-

variable costs can be considered as varying in relation to

volume although the percentage change is not equal to

that of the volume change [52]. Finally, stepped fixed

costs can be considered to be fixed costs that alter as the

activity level moves from one level to another [52].

2.2.3. Direct and indirect costs

A direct cost is an expenditure that can be broken

down and allocated to specific items or causes. Conse-

quently, they are more easily identified and associated

with an end result such as a product, service, pro-

gramme, function, or project. These costs are typically

charged directly to a given contract in the way that

procured items can be easily associated with the bill of

material (BOM) for a particular aircraft unit. On the

other hand indirect costs cannot be identified specifically

and consistently with an end objective [52]. Conse-

quently, indirect costs are the opposite of direct, and

where direct costs can be allocated directly as the

allocation base is known, the allocation base for indirect

cost has to be defined [51]. These costs may be difficult

either to identify in the first instance or to be associated

with a given operation or outcome. This is accommo-

dated by labelling such costs as overheads or a burden

that is summed and then spread over the enterprise

as a whole, typically being added as a portion of

the direct labour cost. This may typically include the

cost of electrical power, cleaning, building works,

pilfering, etc.

2.2.4. Life cycle cost

It is worthwhile to introduce the concept of life cycle

cost (LCC). This is a customer driven cost assessment

that is concerned with the overall LCC of the product,

facility, system, service, or other. This is of interest when

making acquisition decisions but aircraft producers are

also using it increasingly to assess the competitiveness of

their product’s design. For instance, an LCC analysis

might be useful when the estimate is to be used in a

performance trade-off study of a process or activity

within a company or enterprise. LCC is typically

associated with the estimation of total acquisition cost,

from ‘womb to tomb’ or ‘cradle to grave’. LCC

components can be defined in many different ways

but, nevertheless, all classifications tend to start with

either product development or acquisition, and continue

through to product disposal or retirement. Asiedu and

Gu [47] divide the total product cost or life cycle cost

into four distinctive phases: (1) research and develop-

ment costs; (2) production and construction costs; (3)

operations and maintenance costs; and (4) retirement

and disposal costs (as illustrated in Fig. 5).

Notwithstanding the LCC breakdown shown in Fig.

5, commercial airlines tend to focus in on several aspects

of this and in particular DOC. This is addressed later in

the paper and will be presented through Fig. 30, which

shows a DOC breakdown for a regional transport jet

and incorporates the key cost elements incurred by the

company. In particular, there is the cost of: ownership,

which is a function of price and borrowing; and of

operation, which is a function of fuel burn and the cost

of aviation fuel and maintenance, the latter being a

function of quality, complexity and spares. One might

consider the fact that the cost of maintenance for the

airline industry is some $36billion in comparison with

the industry’s fuel cost at only $8 billion, while the cost

of food on flights is $12 billion.

2.3. Cost allocation

Cost allocation refers to the interpretation of cost and

its categorisation in order to arrive at a reasonable

distribution of those costs [12]. As mentioned in Section

2.2.3, direct costs can be readily allocated according to

their nature whereas indirect costs need to have their

allocation base pre-defined. The definition can be based

on historic information or from prognoses or a

combination of both. The traditional approach is to

ARTICLE IN PRESS

Total Product Costs

Research and Development

Cost

Production andConstruction

Costs

Retirement and Disposal Costs

Operations and maintenance

Costs

Documentation

Product Management

ProductPlanning

ProductResearch

Design Documentation

ProductSoftware

Product Test and Evaluation

Manufacturing Management

Industrial Engineering and operations

Analysis

Manufacturing

Construction

Quality Control

Initial LogisticSupport

Operation Management

Product Operation

ProductDistribution

Design Maintenance

Inventory

Operator and Maintenance

Training

Technical Data

Product Modification

Disposal of Non-repairable

ProductRetirement

Fig. 5. Cost breakdown structure.


allocate the overhead using volume-based allocation

bases such as labour hours and machine hours.

However, this can lead to incorrect conclusions if the

allocation base is chosen incorrectly. This is evident

when indirect costs are calculated with the direct cost

burden rate, which incorrectly implies that every

product with high direct costs also has high indirect

costs.

It has been noted that the actual ratio between direct

and indirect costs has significantly changed due to the

increased use of automation [50]. Half a century ago, the

indirect cost was a small fraction of the total product

cost in comparison to direct labour. Consequently, it

was not important to have extremely accurate estimates

of the indirect costs and the traditional estimating

method was appropriate for overheads. That paradigm

has changed significantly and now overheads constitute

the major share of total product cost, with direct labour

costs being only a small component and material costs

remaining relatively unchanged. Therefore, there is now

a need to accurately calculate overheads by some other

allocation base that is more realistic.

A more detailed method that meets this requirement is

activity-based costing (ABC), which assumes [51,52] that

costs are caused by activities and that products consume

those activities. The implementation procedure is as follows:

�
Determine the activity centres that relate to certain
cost aspects of the product development cycle, as

monitored individually by management. These ad-

ministrative units are basic units of control in cost

accounting with managerial responsibility.

�
Determine the activity pools that relate to sets of
activities which are carried out by the functions.

�
Determine the allocation base per activity pool as the
cost driver that is a measure directly related to the

amount of an activity used.

�
Determine the overhead costs per activity pool, which
are typically based on the adjusted overhead costs

from the previous year.

�
Calculate the overhead costs per cost driver (rate),
which are divided by the budgeted quantity for the

allocation base.


3. Controlling cost

3.1. Cost engineering

Humphreys [53] has defined cost engineering as ‘‘the

application of scientific and engineering principles and

techniques to problems of cost estimation, cost control,

business planning and management science’’. In the

wider sense this includes aspects of profitability analysis,

project management, and the planning scheduling of

major engineering projects, in general, ensuring that

technically feasible engineering projects are economic-

ally attractive [54]. Firstly, the function of cost control

includes the detection of cost values and the causes of

those costs in order to keep cost within a pre-determined

range or to identify opportunities for cost reduction.

Secondly, cost control must be able to compare and

contrast cost estimates with actual values in order to

feed findings back into the process and improve under-

standing. This is facilitated for complex systems, such as

in manufacturing, by the decomposition of the system

into a reference model. Such models represent the

system as an structured organisation of relatively

independent, interacting components, and their globally

defined tasks [55]. Many reference models for the

manufacturing system have been developed [17]. One

possible representation of decomposition is by an

architecture that defines the functions within a given

framework, each function’s input and output being

required to perform the task of the system [56].

The reference model of Liebers [48] was developed to

clarify the relation between cost control and manufac-

turing so that when the position of cost control in the

manufacturing system is known, the cost control

component can also be decomposed. Ten Brink [11]

explains that the hierarchical model consists of three

main components in planning, execution and control,

which are then sub-divided into sub-components. The

four planning and control levels are the strategic,

tactical, operational and production levels. The cost

control component can be decomposed into four

functions: (1) cost estimation; (2) production monitor-

ing; (3) cost calculation and evaluation; and (4) cost

modelling [48]. The cost estimation function generates

cost estimates that are based on the specification of a

solution by a decision maker, in conjunction with a cost

model with defined cost rates. The production monitor-

ing function provides the relevant information and data

from the execution of the production plan, to the cost

calculation, evaluation and accounting. The manufac-

turing input data is used to generate the actual costs,

which are then compared with the cost estimates and

their underlying assumptions. This then becomes the

basis of the cost modelling that learns, while the cost

accounting generates the cost rates based on the

manufacturing data. There are four feedback loops that

can be distinguished in the architecture: (1) engineering

and planning; (2) order acceptance; (3) production; and

(4) accounting. The engineering and planning loop

provides decision makers with both qualitative and

quantitative cost information for the various design

alternatives. The order acceptance loop provides cost

information to the decision maker about the total cost

consequences and needs to be of a quantitative nature.

In the production loop, information from the actual

production of a product is fed back in order to compare

the cost estimate with the actuals, again facilitating

improvement of the cost models. Finally the accounting

loop feeds back information from production over a

given period of time in terms of the comparison of

estimates with the actuals during that period, in order to

improve the rates.

The modelling of cost as a means of enhancing cost

control can be traced back to some very specific

equations that were formulated to estimate the cost of

aircraft over long production runs; later to become

known as learning curve theory [57]. This early work

was later developed into the parametric cost modelling

technique that was fully established in the 1950s by the

Rand Corporation. The Rand Corporation is credited

with the development of cost estimating relations

(CERs) for different classes of aircraft and various

operational parameters, developed at that time to help

the department of defence (DOD) estimate the cost of

new military aircraft [58].

There are three well-recognised methods that are

currently employed in evaluating cost in aerospace

engineering. The bottom-up method is associated with

collecting all of the product cost values that are

available; the analogous method is associated with

comparative costing according to the similarity and

differentiation of like products; and the parametric

method is associated with the use of probabilistic

relations between appropriate product features and cost

(the CERs). Rush [59] has pointed out that cost

modelling is knowledge intensive and that it requires

the skills and knowledge capture from a number of

disparate disciplines. It relies on an accurate under-

standing of the company’s and supplier’s product

development capabilities, which ensures that the models

are provided with the appropriate data. Hammaker [60]

has noted that the reasoning and logic that an estimator

develops, is not readily evident because the knowledge

required is complex while its sources are varied, as

depicted in Fig. 6.

Cost estimators are required to apply a combination

of logic, common sense, skill, experience, and judge-

ment, in order to generate a final estimate that is timely,

relevant, and meaningful [61]. Normally, engineers are

more required to do this when interpreting predictions

and modelling results, not within the actual modelling

process itself. They interpret and manipulate data from

ARTICLE IN PRESS

Fig. 6. Skills and knowledge of cost estimating.


all of the functions that contribute throughout the

product development cycle in order to provide the

platform on which cost estimating and project planning

may be built [62]. This view is confirmed by the Society

of Cost Estimating Analysts (SCES) that defines a cost

model as: ‘‘a compilation of cost estimating logic that

aggregates cost estimating details into a total cost

estimate... an ordered arrangement of data, assump-

tions, and equations that permits translation of physical

resources or characteristics into costs’’. In general, a cost

model can be said to consist of a set of equations, logic,

programs and input formats that specify the problem.

Some formulation or framework of these can be

supplied with input program information of a descrip-

tive nature in order to produce an output format. This

also highlights the fact that the origin of cost modelling

is always with data analysis or data mining (see Section

4.2.5), which serves as the basis for the development of

analytical models [63].

3.2. Cost estimating

Cost estimating is the process of predicting or

forecasting the cost of a work activity or output [64]

by interpreting historical data. Rush [65] has noted that

traditionally there are two main estimates: (1) a first-

sight estimate early on in the design process; and (2) a

detailed estimate that is associated with precision

costing. First-sight estimates are useful for what is often

referred to as a rough order magnitude (ROM) estimate

[66] and provide useful information at an early stage of

product definition but are not suitable for decisions

regarding product detail. On the other hand, detailed or

bottom-up cost estimates are based on specific recorded

cost details, such as the number of operations, time per

operation, labour cost, material cost and overhead costs,

etc. However, Boehm [67] offers a more detailed

classification of estimating methods that includes the

following:

�
Parametric: using cost drivers that represent and
model certain characteristics of the target system and

the implementation environment.

�
Expert judgement: the advice of knowledgeable staff
is solicited.

�
Analogy: a similar, completed, project is identified
and its recorded costs are used as a basis.

�
Parkinson: the premise that work expands to fill the
time available and uses the available resource level to

drive the estimate.

�
Price to win: a figure that is sufficiently low to win the
contract.

�
Top down: an overall estimate of effort for the whole
project that can be broken down into the effort

required for individual component tasks.

�
Bottom-up: component tasks are identified and sized
and the individual estimates are aggregated to

produce an overall estimate.

Boehm [67] refers to all seven entries in his list as

‘software cost estimation techniques’, although Hughes

[68] correctly points out that the ‘Parkinson’ method is

not an effort prediction method but a way of setting the

scope of a project. Similarly, ‘Price to win’ is a pricing

tactic and not a prediction method, although both are

recognised management techniques. However, Boehm’s

list can be further distilled [58] to leave the three

most basic and inclusive classifications of bottom-up,


analogy, and parametric. These three are addressed in

Section 4.1 while some of the more advanced techniques

that are currently being developed are presented in

Section 4.2. Bode [14] uses a similar logic to define two

basic approaches for cost estimation: generative cost

estimation and variant-based cost estimation. Genera-

tive cost estimation is seen as the composition of costs

from the key constituents while variant-based cost

estimation uses similar products that have been manu-

factured in the past. The various techniques for cost

estimating are presented in detail in Section 4.

3.3. Cost-integrated design

It is well documented in the literature that cost is an

important attribute of any product and highly relevant

to the engineering design process [69,70]. Sheldon [71]

has stated that customer affordability, product quality

and market timeliness are the three key elements of

competitiveness. He also points out that there are two

fundamental engineering approaches to controlling cost:

namely, (1) designing for cost and (2) costing for design.

Within aerospace, Dean [72] is well known for promot-

ing such considerations within NASA. Although Shel-

don defines the DFC methodology as being driven by

management imposed cost targets, this is usually

referred to specifically as DTC [73]; implying that a

cost target has to be met and adhered to. DFC is

generally taken to mean that the design process is

mindful of cost. Many authors now believe that

imposing strict target costing on engineering design, as

for DTC, is not effective as it tends to result in inferior

design that still overshoots the poor cost estimates used

as the initial guidance [73] . Rather, it seems to be more

important to give designers supportive costing tools that

facilitate the product definition process by linking design

decisions to estimated cost impact.

Fig. 7 shows a typical generic model of a cost

estimating tool which can be used within the design

domain [74]. However, most of these DFC/DTC tools

are application specific and highly customised within the

aerospace industry [75–77]. For example, Geider and

COMMERCIAL FACTOR

Cost Algorithms

INPUTS Part features / geometry

Feature attribute

Planned process

Material/BO details

Design rules Producibility rules

Production standards Material costs

Fig. 7. Typical generic model of a

Dilts [78] have presented an automated design-to-cost

tool which can be linked to a CAD package in order to

provide the estimated cost of machined parts from a

particular material. Within aerospace, this would be

most relevant to the detailed design process for a range

of parts from smaller complex machinings but could be

extended to larger fuselage frames of machined-finish

aluminium forging for example. The cost tool interprets

the machined part using Feature-Based Modelling [79]

and classes it accordingly using Group Technology [80].

Various costing modules then plan and cost the

machining process using a mixture of activity-based

costing [81,82] and analogous costing in a comparative

manner. Taylor [83] has also advocated a feature-based

approach to aerospace cost estimating and this is often

used in traditional aircraft cost estimating, although in a

less formalised manner.

Analogous costing is also a traditional costing

technique that uses the cost of a similar product to gain

a first baseline estimate. Deviations in the design or

manufacture of the new product are then used to

account for alterations in the initial cost estimate [83].

Apart from the analogous, ABC and feature-based

techniques, there are a range of other methods for

generating the actual cost estimates from input data and

constraints [85] including: regression-oriented para-

metrics [86], bottom-up costing, fuzzy logic [87] and

neural networks [88]. It is the level of input data and the

range of constraints, as well as the technique itself,

which tend to differentiate these techniques and to make

them more or less suitable to a given application,

especially according to the level of product and process

definition available. The parametric estimating techni-

que [89,90] is widespread within aerospace and varies

greatly from being based on purely statistical signifi-

cance, to being more causal in nature; being either

linear, exponential (logarithmic linearity) or polynomial

in form.

It is also well documented in the literature that the

impact of cost needs to be introduced upfront at the

concept design stage. Pugh [91] has advised that a top-

down cost estimation should carried out even before the

aircraft development process begins. Thurston [92]

S

OUTPUTS

Process decision models

Cost by part, assembly, material, etc.

Design guidance

Inputs to risk

Producibility guidance

Labour rates

design-oriented cost model.


advocates a holistic approach to the design process that

is appropriate at the concept stage where a product is

defined in terms of a measure of its utility value to the

customer. This includes cost in a multi-attribute analysis

[93] of the design that can then be mathematically

optimised [94]. Another form of this design methodol-

ogy has also been applied by Collopy [95] to satisfy the

more holistic design requirements of an unmanned arial

vehicle (UAV). A high-level objective function that

reflected the wider design requirements of both cost and

performance is at the core of the method, providing a

trade-off mechanism that through maximisation pro-

motes the optimal choice of design parameters within

stipulated ranges of constraint.

This type of approach can be traced back to much of

the classic research within the aerospace industry into

parametric optimisation [96]: the identification of key

design parameters that drive performance and which can

be optimised when combined in mathematical formula.

Much of the current mainstream research is focused on

multi-disciplinary optimisation (MDO), whether at a

high level or a lower level that links discrete computa-

tional models [97]. Marx and Mavris [98] have linked

MDO to Life Cycle Analysis by defining high level

objective functions that encompass the life cycle needs of

aircraft, supported by necessary disciplinary models

which facilitate the optimisation process through a

linkage that is defined by an objective function. In

aerospace, life cycle analysis tends to be associated with

military applications while the commercial sector

focuses on DOC; the latter being more associated with

the cost of transporting a person a number of air-miles

at as cheap a cost as possible. There are various DOC

models available, which tend to be of a parametric

nature [99,100], which allow the trade-off of design

parameters and which can be linked to manufacturing

models to couple the impact on production [73,101].

It has been shown from the literature that aerospace

design is a key fundamental driver of the overall cost of

aircraft, whether considering high-level cost control

methodologies such as DFC/DTC or cost integrated

design methodologies; for higher level concept stages or

at the lower level preliminary scheming and detailed

stages. The impact of the work of Boothroyd and

Dewhurst [101] in highlighting the need for a methodol-

ogy that links the impact of design decisions on

manufacture is well referenced. The major contribution

in addition to firmly establishing the DFMA principle

was in providing an analytical technique that introduced

quantitative analysis when comparing a given design

with a theoretical baseline in terms of design complexity;

classically with regard to part count and fastener count.

Stoll [102] has also addressed many of the organisational

and implementation aspects of DFMA while other

authors were also reporting the important linkage

between DFMA and LCC [103].

The basic principle of relevance to LCC is still as

prevalent today as shown by Murman et al. [104] who

defines better–faster–cheaper life cycle needs in terms of

value-oriented cost, performance and time functions.

The process technology aspects are addressed by

considering ‘Lean’ practises for design, engineering

and manufacturing. Marx et al. [98] have presented a

parametric solution for linking life cycle needs back to

design. They use the case study of a high-speed

commercial transporter to investigate the best structural

layout for the wing in terms of life cycle requirements;

including chord-wise stiffened, span-wise stiffened and

bi-axially stiffened structural layouts. On the other

hand, a much more detailed analysis platform for

manufacturing cost drivers has been developed by

Rais-Rohani [105], where he incorporates many of the

relevant manufacturing issues in terms of parametrically

defined complexity factors; including; compatibility,

complexity, quality, efficiency and coupling. Rais-

Rohani’s work is integrated into the aircraft design

process using a three-tier MDO methodology [106]. For

example, with respect to the three alternate structural

designs of a wing box (thin heavily stiffened skin; thick

lightly stiffened skin; multispar), the authors advocate

firstly setting out the structural design configuration, as

well as defining materials, part manufacture and

assembly method. Secondly, a single or multiple

optimisation procedure is carried out according to some

objective function with a multidisciplinary set of design

and manufacturing constraints. Thirdly, the design is

validated and the cost estimates improved to allow for

trade-off, sensitivity studies and optimisation of the

airframe structures.

With regards to the aircraft fuselage panel case study

considered later in this paper, the need to understand the

linkage between material and process selection, structur-

al design needs and LCC was driven by industrial need;

in the face of ever-tighter competition and demanding

passenger requirements. Sandoz [107], a chief engineer

on the Boeing 747, was projecting a value-oriented

approach to the integration of these needs for aircraft

structures already in the early 1970s. Other authors have

continued to address the impact on manufacturing by

characterising the various manufacturing processes for

fuselage panel parts [108], along with the associated

assembly processes [109]; with respect to key design

drivers and cost. Much of the work has again been

industrial-oriented and focuses on assessing the trade-off

between technologies or materials [110]. However, there

has been very limited published work carried out in the

linkage and simulation of accurate cost estimation and

detailed structural requirements.

Consequently, this paper sets out a methodology in

Section 5 for the integration of cost into the airframe

design process, at the performance analysis stage so that

a proper trade-off of design solutions can be carried out

ARTICLE IN PRESS

Fig. 8. Make/buy flow chart.


through the explicit optimisation procedures, involving

both structural performance and manufacturing cost in

this case.

3.4. Supply chain cost control

Within the procurement and logistics function, accurate

costing information is required to drive market strategy,

design and manufacture, and ultimately, to ensure

competitive advantage. The procurement function tends

to be characterised as exploiting the aerospace supply

chain in order to develop opportunities for increased

profitability. It has been noted [111] that this is envisaged

through manipulating the areas that directly affect asset

and resource utilisation, and profit margins, including:

production decisions, supplier relationships, outsourcing

verses in-house management, and inventory turnover.

Humphreys [112] states that organisations traditionally

buy on the basis of lowest price, only sometimes taking

other factors into account such as quality and delivery.

Other authors have also noted this very limited apprecia-

tion of the wider issues of delivery reliability, technical

capability, cost capability and financial stability [113].

Williamson’s [114] theory of Transaction Cost Ana-

lysis provides a conceptual basis for the make/buy

decision making process. The analysis can be likened to

Activity Based Modelling (see Section 4) as it considers

the transfer of goods and services across technologically

separate units as they move from one stage of distinct

activity to another. The transactions, rather than the

commodity, are the basis of the analysis, which focuses

attention on the cost of planning, adapting and

monitoring activities under alternative governance

structures. Williamson [115] has further noted the need

to understand and control the factors that make

transactions simple or difficult to mediate, and especially

to establish monitoring and governance structures that

can be matched to the transactions. He also combines

economic theory with management theory in order to

lay the foundations for a purchasing discipline that

respects both internal and external boundaries in both

the short and long term [112], whereas design and

manufacture is traditionally ineffective in even appre-

ciating their in-house cost base.

Fig. 8 [116] shows the most important ‘exit points’ in

the process at which a company can opt to buy rather

than make, including several stages of product design

and process design, rather than just basing outsourcing

decisions on the reduction of immediate overheads [117].

However, many of the generic aspects in Fig. 8 are

shared in a more collaborative relationship. Alterna-

tively, Probert [118] has proposed a 4-stage process

characterised by the following methodological steps:

�
preliminary business and strategic appraisal based on
the company’s, competitors’ and supplier’s data;

�
internal and external analysis for major part families,
manufacturing process categories, cost allocations

and the alignment of parts and technologies within a

competitiveness/importance matrix;

�
strategic evaluation of sourcing options now identi-
fied in conjunction with the business data;

�
final selection based on current and future projections
through application of financial decision support

models.

Typically, it is recommended to formalise best practise

procedures for all of the activities that describe the

procurement function. For example, Fig. 9 [116] high-

lights the degree of risk associated with the degree to

which an item is interrelated to other items or activities.

The best practise principles that have been identified as

procedurally correct need to be supported by facilitating

tools that provide quantitative measures of cost, time,

risk, quality, etc. In particular cost-modelling tools can

be easily related to the following procurement needs as

described in the literature [119,120]:

�
eliciting active support from top management,
�
integrating and modelling the supply chain,
�
understanding cost drivers in appropriate detail,
�
measuring the performance of suppliers, systems, and
employees,

�
developing cooperative supplier relations,
�
delivering and establishing a culture of continuous
improvement,

�
facilitating a cross-functional approach linked
through cost,

�
managing and reducing cost across the whole
business structure,

�
developing integrated data management systems and

ARTICLE IN PRESS

Fig. 9. Matrix of dependency and decomposability.


�
justifying investment in procurement/supply tooling
and management.

Supply chain management is driven by the need for

aerospace companies to reduce cost, shorten product

development time and manage risk, all in an effort to

maximise value added [121]. The transactions between

companies in supply chains or the extended enterprise

can be conceptualised by the adding of value up through

the chain and consequent payment in return back down

the chain. This is the integration of key business

processes from the end user through to the original

supplier, in providing products, services, and informa-

tion that add value. On the other hand, it has been noted

that lack of cohesion destroys value in the supply chain

[122], and therefore collaboration is the process that

results in the opportunity to create value. Lockamy and

Smith [123] have characterised the supply chain with

three common components: suppliers, producers and

customers. The components must interact in a coordi-

nated manner in order to ensure the efficient delivery of

goods and services, rather than the more typical

management of each as a separate independent entity

with localised objectives [122].

The changing nature of purchasing towards supply

chain management has been investigated by Giunipero

and Brand [124]. They define four levels of development

for the purchasing function:

(1)
traditional: vendor selection for the lowest possible
price;

(2)
partnership-relational: close supplier relations for
reduced total cost and risk in an atmosphere of trust;

(3)
operational (material logistics management): coor-
dinating material and information flows to improve

quality, inventory levels, and overall cost;

(4)
strategic (integrated value added): flexible business
processes for speed, flexibility and advantage in the

market place.

Narasimhan [126] has noted that the key concept that

distinguishes a supply chain from its constituent entities

is the integration of operations across the extended

enterprise. The management of the supply chain goes

beyond the simple interface coordination which sees

firms optimise local objectives. It explicitly recognises

interdependencies and the wider need for adequate

supply within the global market, while protecting profit

margins under such global competition [125]. With the

rise of global opportunities, the outsourcing of manu-

facturing activities has been followed by the outsourcing

of design and development work. To an increasing


extent, suppliers are contributing to the technical

development of the end products and are therefore

increasing the importance of supply chain management

and intelligence as part of their strategic approach [127].

3.5. Knowledge-based systems

It has been pointed out by Rush and Roy [7] that

Knowledge-Based Systems (KBS) help to formalise

specialised knowledge so that it can be reused. This

overcomes many of the human fallibilities that have

negative impact, such as poor memories, bias, incon-

sistency, retirement, job change, and illness, etc. There-

fore, this aspect of KBS is intrinsic to the approach of

capturing human expertise in order to be able to make it

available when required [128], However, such expert

knowledge needs to be captured and formalised in a

meaningful way so that it can be reused, although the

capture and embedding of knowledge is not easy and has

been viewed as a key weakness of KBS design [129].

These difficulties are exacerbated when trying to identify

representative experts and then interpreting their multi-

ple views [130].

Kingsman and de Souza [130] have presented a

methodology in support of a knowledge-based decision

support system for made-to-order companies. The

method included identifying when most judgments were

made and then examining both the cost estimating and

pricing processes. The identified judgments are then

taken to represent the expert knowledge capture and are

formalised through the use of ‘‘If (Condition)...Then

(Action)’’ rules. The research method included the use of

expert interviews to facilitate the capture and develop-

ment of the rules. It is reported that managers found the

end result to be useful as an aid to their decision-making

but it was also noted that one of the limitations of the

approach is that it is more suited to companies that have

a similar project base as KBS tends to be domain

specific.

4. State-of-the-art: cost estimating

4.1. Classic estimating techniques

4.1.1. Analogous

Analogous costing is one of the best-established and

applied methods of costing [131–139]. In industry, it is

still deployed in an ad hoc and expert oriented manner

but the term is also synonymous with case-based

reasoning tools [140–142]. Typically, a CBR tool will

store and organise past projects with a view to later

retrieving these projects in order to help identify a costed

solution for a new project. Consequently, the develop-

ment entails capturing the knowledge from domain

experts in order to formalise that into similarity

functions and analogy rules [143,144]. However, such a

formalised knowledge-based tool can be complex and

always entails the use of subjectivity to some degree.

Therefore, its development, underlying rules and

assumptions, and its repeatable utilisation are difficult

and subject to the expertise and understanding of the

user [145,146].

The analogous costing methodology is characterised

by adjusting the cost of a similar product relative to

differences between it and the target product. As stated,

the principle is widely used within aerospace costing and

there is a similarly wide range of implementation

techniques, ranging from subjective expert opinion

[146,147] to objective use of calculated differentials [83]

according to percentage of unit cost or even from

bottom-up variations in the BOM. The effectiveness of

this method depends heavily upon the ability to identify

correctly the differences between the two cases [47].

Analogous estimates can utilise a single historical data

point as the basis for the estimate or a programme cost

estimate may use a number of analogous estimates

relative to a number of cost elements that make up the

programme. There is an obvious risk in basing a single

point estimate on one historical instance and in addition,

the technique usually involves a high degree of expert

judgment. However, it is a reasonable approach for

estimating the unit cost of a new product that does not

incorporate very different design features or utilise new

processes for that company. The FAA Life Cycle Cost

Estimating Handbook [148] recommends its use for a

new product or system that is primarily a combination

of existing sub-systems, equipment or components for

which recent and complete historical cost data is

available. They also point out that analogy methods

are less likely to overlook the impact of rapid technology

changes, whereas it may be less obvious that a

parametric cost model database is no longer valid and

needs updating. The recommended practice for generat-

ing analogous estimates is lengthy but the standardisa-

tion helps to ensure that the process is as rigorous as

possible, as presented in Fig. 10:

(1) The first stage is one of definition. This includes

the general features of the estimate, including its type

and accuracy, and the assumptions made in terms of

inflation, quantities, scheduling, etc. The product must

also be defined in terms of its physical design

parameters; performance characteristics such as relia-

bility and maintainability; training and operational/

support issues; test and certification requirements;

technology maturity levels, etc. This then allows the

estimate breakdown structure to be identified in terms of

the hardware and activity components whose estimates

are to be incorporated in a cumulative estimate.

(2) The second stage is one of practical preparation in

assessing the availability of data downstream in the

ARTICLE IN PRESS

Fig. 10. Best practices for generating analogous cost estimates.


process. This includes data relating to the quantity,

design and performance characteristics of the product

components for both the historical and new cases, and

the cost data for the historical case. The components for

the new case also need to be described in relative terms

to the most comparable historical cases that are most

likely to reflect the cost differentials.

(3) The third stage is actual data collection, which

includes both quantitative and qualitative data for as

many historical cases as possible that are current and

comparable with the new specification. The historical

cost data should be as well defined as possible and

distinguish between prototype, full-scale development

and production costs, and between non-recurring and

recurring costs. All historical data also needs to be

normalised relative to time and a baseline year, as well

as ascertaining the first unit recurring costs and the

improvement slopes. This then provides the necessary

factors, etc. based on historic costs, including those from

the extrapolation of historic cost elements to the new

case or adopting existing factors that have been

reconciled for any major differences. These ratios,

factors and improvement curve should then be reviewed

with input also from technical specialists who are

familiar with the historical and new design cases.

(4) The fourth step is to generate a range of factors

that characterise the product in terms of design features,

etc. that influence cost and manufacturing capabilities.

Complexity factors are recommended by the technical

specialists relative to cost. There is an assumption that

these relative factors map across to the cost ratios

through their performance and design ratios, and are

not influenced by productivity improvement differences

between the cases. Miniaturisation factors are also used

as typically in aerospace the smaller the subsystem is for

a given level of performance; the more costly it is likely

to be to produce. These factors may relate to weight or

space constraints and again are assessed initially by

technical specialists. Productivity improvement factors

are used to map the cost reduction expected from

significant productivity improvements between the

historic and new cases, being anticipated from improved

design for manufacturability, more effective manufac-

turing technology and reduced material costs.

(5) The fifth step is the generation of the actual cost

estimates. It is recommended to initially estimate the

first-unit cost from the historic cost CP for first-unit

value in conjunction with the three ratios generated for

complexity FC; miniaturisation FM and productivity FP:Therefore, the analogous cost estimate is calculated

according to CN ¼ CPFCFMFP: Typically, these factors

are estimated by expert opinion within the companies

but could be more rigorously defined on an analytical

basis from historical data, e.g. miniaturisation being

modelled according to the recorded impact of reduced

part size on cost for components with a like functional

value. Following on from that, the first unit values

estimated are combined with the cost improvement

curve slope values developed to generate the total

recurring costs for each component. The non-recurring

ARTICLE IN PRESS

R2 = 0.8971

R2 = 0.8716

R2 = 0.955

R2 = 0.8625

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 2 4 6 8 10 12

Normalised cost driver

No

rmal

ised

man

ufa

ctu

rin

g c

ost

Fan Diameter

Weight

Airwash Area

Thrust

Fig. 11. Plot showing high-level design cost drivers.

Table 3

Attributes used to characterise cost drivers

Specification Manufacturability Geometry

Functionality Part count Cylindricity

Certification Process capability Circularity

Aerodynamic

smoothness

Assembly

philosophy

Concentricity

Structural efficiency Manufacturing

tolerances

Curvature


costs are developed similarly or are based on recurring

to non-recurring ratios. The relevant full-scale develop-

ment estimates must be aggregated separately for the

specified production amount, unless one is to be

developed based on the other. In addition, other costs

can be generated using determined factors; for systems

engineering, programme management, spares, support

equipment, training and IT.

(6) The final step is the development of the total

programme cost estimates. This includes the addition of

agreed profit levels according to market research and

company policy and any additional factors such as,

mission support, testing facilities (if external), contrac-

tors costs, etc. Ultimately, the final estimate is to be

reviewed in terms of the results and the balance of

complexity value judgements. It is important that the

documentation should not only list the total costs but

also the main complexity judgments applied, the

historical cases used, and the qualification of the

technical specialists who set the criterion.

An example of analogous costing taken from the

literature [83] details one methodology that was devel-

oped for the costing of nose-cowls on engine nacelles.

The cost of a nose-cowl is driven fundamentally by the

various design requirements that meet aerodynamic,

thermodynamic, and structural needs, with some addi-

tional functionality such as thermal anti-icing and Full

Authority Digital Electronic Control (FADEC) systems,

and engine integration. However, with regard to

production, this assembled component is relatively

generic in form and nature, the key function being to

direct airflow cleanly into the engine fan. Consequently,

this commonality reduces the complexity of the costing

as it is less likely that there will be major design

differences that make analogous costing more difficult in

terms of accuracy. The clear symbols in Fig. 11 show the

unit recurring costs of a number of nose-cowls plotted

against component size or engine fan diameter. It can be

seen from the trend line that the characteristic is linear

and there is a statistical significance of R2 ¼ 0:9; where

approximately 90% of the scatter in the points is being

modelled by the linear regression trending. In terms of

analogous costing, there is an assumption that there is a

linear baseline relationship between the two variables

and that it is the complexity factors which give rise to the

cost differentials from that baseline characteristic or cost

floor.

Three categories of cost driver were identified as

relevant to characterising the cost variance and are

typified in Table 3 as: geometric complexity factor

f Geom; manufacturing complexity factor f Manuf ; and

specification complexity factor f Spec: However, there are

also other higher-level cost drivers that can be used to

develop a rough order of magnitude (ROM) for the

baseline prediction. For example, many commercially

available cost estimating packages [150] use weight as

the baseline cost driver and then generate measures of

differential cost driver to refine the cost estimate, such as

those listed in Table 3. The presented model was based

on the premise that recurring manufacturing cost is a

function of four parameters, including size (rather than

weight) for the baseline relation and three specific design

and manufacturing drivers. The component size is given

by the engine fan diameter Dfan being also linked to

design specification through engine fan size.

Having determined the key cost drivers; the next step

was to gather data that would quantify these. These

were largely determined through knowledge capture

based on expert opinion. For example, a rating of ‘1’

was assigned to a baseline level and a rating of ‘4’ to the

most extreme deviation from that baseline. This

qualitative approach can be easily replaced by a more

quantitative approach, which should be developed

relative to the product definition available in terms of

pre-concept bid, preliminary design or detailed design

for example. The approach presented identified two

Nacelles with one of the complexity ratings ( f Geom;f Manuf or f Spec) being equal and the third with a different

value in order to ‘calibrate’ the cost differential. In a

principle similar to the solution of simultaneous

equations, the difference in the dependant variable, i.e.

the cost differential between the Nacelles, was equal to

ARTICLE IN PRESS

R2 = 0.8971R2 = 0.9372

Fan diameter

Man

ufa

ctu

rin

g c

ost

Original data

Baseline prediction

Fig. 12. Evidence of the baseline concept used in analogous

costing.

Fan diameter

Man

ufa

ctu

rin

g c

ost

Prediction

Original data

Fig. 13. Comparison of analogous cost estimates.


the rating differential for that complexity factor.

However, this was calculated as a ratio of fan diameter

Dfan in order to allow for the baseline influence of

component size. This procedure yields three costing

ratios r of the form described in Eq. (1) for geometry:

rGeom ¼ðC2=Dfan2

Þ � ðC1=Dfan1Þ

ð f Geom2� f Geom1

Þ. (1)

Consequently, the total cost impact of each of the

complexity ratings could be calculated, relative to some

baseline cost that is purely a function of size. For

example, Eq. (2) shows the form cost differential

associated with each of the geometric complexity factor:

DCGeom ¼ rGeomDfanð f Geom � 1Þ. (2)

To establish the linear baseline equation as a function of

size, the trend identified for the original data points is

shifted vertically downwards by the cost differential DC0

between it and the baseline Nacelle. This gives an

equation of the form described in Eq. (3), where z is the

linear constant from the original data:

C ¼ mdataDfan þ zdata � DC0. (3)

Subsequently, the predicted cost CPred of any new

Nacelle with a given engine fan diameter Dfan and

complexity factors of f Geom; f Manuf and f Spec is

calculated as shown below:

CPred ¼ mdataDfan þ zdata � DC0 þ DCComplexity. (4)

The diamond symbols in Fig. 12 denote the original cost

of each Nacelle while the circles represent that actual

cost minus the predicted cost differential arising from

the complexity. The latter should represent the baseline

cost or cost floor that is a function of size only and is

important in suggesting whether the methodology is

improving the regularity and predictability of the cost.

In support of this, it can be seen that the linearity is

further improved to R2 ¼ 0:94: Finally, Fig. 13 plots the

original data against the predicted values, with reason-

ably good correlation. It is interesting to note that the

trend line for the original regression analysis of the data,

shown in Fig. 10, had an average absolute error of 14%

in predicting the cost of each nose-cowl while the

proposed complexity method delivered a reduced

average error of 10%.

Yet another technique within analogous cost model-

ing is the pair-wise comparison approach. It has been

used for various estimating tasks such as software sizing

and manufacturing design effort [132–134,150]. Given a

number of reference projects, the comparative analysis is

carried out by quantitatively rating how similar in size

or attribute the various projects are [151]. This

quantitative approach utilises statistical analysis to

normalise and order the ratings and provides a more

formalised process to cost knowledge capture and

utilisation. The results of pair-wise comparisons have

been recorded to outperformed experts who do not use a

structured approach [152]. However, the user requires

knowledge or data relating to both the functional and

the technological aspects of the product [134] and

therefore, there is an implicit requirement for a

structured technique for capturing such input data in

order to provide better results.

4.1.2. Parametric

According to the Parametric Cost Estimating Hand-

book of the Department of Defence [90]: ‘‘A parametric

cost estimate is one that uses CERs and associated

mathematical algorithms (or logic) to establish cost

estimates’’. This is a commonly used technique within

aerospace which typically utilises linear regression for

CER development [153–155]. The CER is developed by


establishing a relationship between one or more para-

meters that are observed to change as cost changes.

These parameters are typically referred to as cost

drivers, as they are known to be highly influential in

effecting a change in cost or at least, to vary similarly

with cost. Using historical data, a correlation between

cost as the dependant variable and the cost driving

parameters as independent variables, establishes the

statistical accuracy of the relationship. An example of a

simple CER would be the relationship or correlation

between the number of design drawings and the cost of

the design process for a large aircraft assembly. The

rationale behind the choice of drawing number (as the

cost driver) is that one would expect the number of

drawings to increase with the complexity and part count

of the assembly, which is linked therefore to design

effort and time, and ultimately to the design cost [93],

The latter part of the DOD definition quoted above

refers to the way in which the CERs are used to arrive at

a cost estimate for a product. In a sense this is driven by

the perceived costing architecture that is used to describe

all the relevant costs and how they are combined to

account for the product’s total cost. Typically, this is

referred to as a Cost Estimating Model (CEM) and for

the above example of an aircraft sub-assembly might

include additional CERs that are required to generate an

estimate of unit cost. For example, in addition to the

design cost, this might include CERs for estimating the

cost of: materials and treatments, fabrication and

assembly, support and inspection, overheads, contin-

gency, etc. [45–47]. From these, the estimator is able to

generate a cost estimate for a similar product that

accounts for all of the perceived costs, with the accuracy

being dependant on the combined correlation accuracies

of all the individual CERs. The resulting parametric

models can be used easily and speedily by engineers of

varying experience and at a very early stage in the design

process when there is little product definition.

The birth of parametric cost estimating is often traced

back to the work of Wright when he first proposed the

learning curve [57]. That early work was a forerunner of

parametric techniques to come as it specifically con-

sidered the relation between the unit cost of aircraft as a

function of the number of aircraft produced, i.e. linked

cost to an observed cost driver. His theory was used

extensively during World War II when there was an

exponential increase in the production of military

aircraft but little knowledge of how the unit cost would

decrease with the benefits of production scale and

learning. A typical learning curve in its class, for

example for the high production C47 aircraft, would

record the unit cost after 10,000 aircraft have been made

decreasing to approximately a quarter of that of the first

aircraft. However, the major point of interest is that the

unit cost was already close to that level after some 3000

units. Wright’s work was validated in the post-war

period by Stanford Research Institute, establishing the

relationship as a function of the cost of the first set and

the total unit number to be investigated. In the

formulation, there is typically an exponential term that

determines the slope of the characteristic and which is

associated with several influencing factors. Most im-

portantly, the learning exponent would be function of

the efficiency of the company’s processes in general, the

use of new technology and the design complexity of the

aircraft. It should be noted that in the time domain

analysis, such cost data needs to be normalised

according to financial rates and inflation index so that

the analysis is fair and true. This is especially true and

relevant for unstable periods in history when rates

fluctuate more widely [156].

Typically, learning is factored into the estimating

process through some deviant of the following formula-

tion:

Hours=unit ¼ UbRr

or

ðFixed_year_costÞ=unit ¼ ðFirst_unit_costÞUbRr,

where U is unit number, b is learning curve slope, R is

production rate, r is production rate curve slope. The

slope of the curve can be estimated or derived from

historical data from particular programs but then would

have a specific range of application, similar to a CER.

The slope should be determined while holding the

learning curve constant as the rate effect can vary

considerably with changes in plant facilities, manpower

and redeployment, and overtime.

The main period of fast development for parametric

methods started in the 1950s with the establishment of

the Rand Corporation [151] by the military, which was

to be an independent civil forum for discussion and

analysis. The main concern of the DoD and the United

States Air Force in particular, was to have the capability

to analyse future scenarios in terms of technology and

cost. In terms of current technology utilisation there was

no established methodology for estimating the first unit

cost, also being the required input value for the learning

curve formulation. In addition, although the learning

curve addressed recurring cost, there were no methods of

estimating the early non-recurring costs such as

research, development, testing and evaluation. During

the 1950s the Rand Corporation established parametric

ways of both estimating first unit cost and the non-

recurring costs [151]. It has been noted that even then

these techniques were being utilised for all phases of

aircraft systems during the 1960s.

Due to the potential for fast and easy estimating

capabilities based on company practise, the world of

parametric costing has grown and spread into other

fields and the civil sector. In the same way that certain

drivers can be chosen to relate to aircraft cost or weight

ARTICLE IN PRESS

Fig. 14. Methodology for developing parametric models.


[151], any dependant cost or performance variable has

the propensity to be related statistically to a product’s

attributes. One of the strongest deployment areas for

parametric technology is within the construction

industry [159]. Typically, they relate costs to size,

assuming that statistically this provides a reasonable

estimate based on historical data, regardless of unfore-

seen wastage, build problems or other variations in cost.

In aeronautics, it is substantially used at the bidding and

cost-targeting stage. However, manufacturing also use

parametric relations as an experience-based guide when

facilitating ultimate Estimated At Completion (EACs)

cost estimates; although with the advent of design for

manufacture (DFM), there is a growing recognition of

the additional potential as a DFM enabler. The growth

in this method has been a commensurate with the

appearance of supporting organisations such as Inter-

national Society of Parametric Analyst (ISPA) in 1978,

the Society of Cost Estimating and Analysis (SCEA),

and the Space Systems Cost Analysis Group (SSCAG)

in 1977.

The basic methodology for developing parametric

estimating models was developed in the 1950s by the

Rand Corporation, illustrated in Fig. 14, who are

accredited with the following key developments [90]:

�
Developing the most basic tool of the cost estimating
discipline, the Cost Estimating Relationship (CER).

�
Merging the CER with the learning curve to form the
foundation of parametric aerospace estimating.

�
Deriving CERs for aircraft cost as a function of such
variables as speed, range, and altitude.

�
Observing acceptable statistical correlations in check-
ing the CERs.

�
Developing families of curves data segregated by
aircraft types, e.g., fighters, bombers, cargo aircraft,

etc.


�
Developing curves corresponding to different levels
of product or program complexity.

The three categories of parameters central to the

development of parametric relationships as defined

within RAND [158] are:

�
Performance and physical parameters are measures of
technical capability and may be further divided into

parameters that are scale dependent and independent.

�
Technical risk and design maturity parameters
measure or quantify the relative difficulty of devel-

oping and producing a particular system.

�
Programmatic parameters address issues related to
the way in which programs are operated.

Ideally, parameters from all three groups would be

included when developing parametric relationships

however there are some limitations to including all

parameters. Parameters should be selected based upon

the availability of appropriate information while a

rationale must exist as to why a particular parameter

correlates with the dependent variable, i.e. a causal link.

It is well documented that parametric relations are

extremely sensitive to range of use due to their inability

to estimate for differences in the product definition not

evident in the historical data. The other fundamental

aspect is the choice of data, its gathering and manipula-

tion. In this respect, one must first determine the input

variables to be related. The independent variables are

the cost drivers that are (thought to be) related to a

change in cost while the dependant variable is the actual

cost data. Some form of regression analysis can then be

formed on the two sets of data, e.g. linear, multiple

linear, or curvilinear. However, it is very important that

the various cost data is well understood in terms of

auditing and is of a similar makeup. This ensures that

the data points are comparative in terms of what they

represent and how they arose in the first place. To this

end, normalisation is often necessary to account for

variations in the inflation rate. Other factors include the

learning curve already mentioned and also the produc-

tion rate. It is recognised that the production rate is

related to the speed at which learning [161] can be

established, with faster production rates leading to a

steeper gradient in the learning curve.

In a similar way to factoring the basic CER with

production information that adjusts the cost, the CERs

can also be calibrated to give an improved estimate at

current expectations. Calibration is also important to

commercial CEMs that use more universal data and

therefore, require tailoring to a given company database

[150]. Furthermore, this brings in validation and the

comparison of estimates with actuals for any parametric

model. The validation process and the estimating

accuracy of the model is subject to the relevancy of

application. The Parametric Handbook [90] notes the

following pitfalls to avoid:

�
using the parametric model outside the database
range,

�
using a parametric model not researched or validated,
�
using a parametric model without adjustment when
new system requirements are not reflected in the

database,

�
using a parametric model without access to realistic
estimates of the independent variables and

�
requesting impossible or impractical point estimates
for independent variable values over a required range.

Beltramo [162] has stated that with parametric model-

ling development there is often a poor correlation

between the data analysis and the actual product

breakdowns and therefore the modelers need to carefully

document their assumptions in order to help the users to

put the models to appropriate use [163–166]. For

example, Kitchenham [165] has reported that in the

case of the COCOMO parametric cost model, many of

the underlying assumptions were not valid while

Shepperd and Cartwright [167] reported that much of

the cost input data was inaccurate and captured from

people with a poor recollection of projects that were

completed a long time ago. Ultimately, this is a highly

speculative process and is subject to both technology

and organisational process changes. Nonetheless, poor

quality data is often all that is available and therefore

requires extensive use of expert judgment [168] in

formulating models that do aid in providing a formal

method of generating cost estimates [166,169]. Pengelly

[170] agrees that subjective measures and assumptions,

which are often embodied in ratings within the models,

are a necessary requirement during the analysis and

input of data. This raises another question of the quality

and adequacy of the data collection [171,172]. This is

exacerbated by the inability of models to predict the cost

of a technology that is not a part of the underlying

database [162,90]. Within aerospace, the design of new

aircraft often entails a step increment in the technology

exploited on previous products, which necessitates

expert judgment and knowledge in adjusting costs

relative to these changes. This judgment must guide in

whether a particular parametric CER can be used and

whether this is feasible [173], and whether the result

reflects the cost of new technologies and if the outputs

are relevant.

4.1.3. Bottom-up

As the name suggests the bottom-up or engineering

build-up method [174] identifies and sizes the component

parts and tasks, and then estimates these to be

ARTICLE IN PRESS

Table 4

Matrix of comparative assessment for tradition methods

Approach Advantages Disadvantages

Bottom-up Cause and effect understood Difficult to develop and implement

Substantial, detailed expert data are required

Very detailed estimate Requires expert knowledge

Estimate by analogy Cause and effect understood Appropriate baseline must exist

Substantial, detailed data are required

More easily applied than the bottom-up method Requires expert knowledge

Parametric Easiest to implement Can be difficult to develop

Non-technical experts can apply method Factors might be associative but not

causative (i.e. lack of direct cause-and-effect

relationships)

Uncertainty of the forecast is generated Extrapolation of existing data to forecast the

future, which might include radical

Allows scope for quantifying risk technological changes, might not be properly

forecast


aggregated in order to produce the overall estimate. The

bottom-up approach relies on detailed engineering

analysis and calculation to determine an estimate. To

apply this approach to any system manufacture, the

analyst would need the detailed design and configura-

tion information for the various system components and

accounting information for all material, equipment, and

labour [175]. Within the software industry, the bottom-

up approach is also used [170]. The result of either is a

detailed estimate and breakdown of costs.

Some of the characteristics of the method are as

follows:

�
It is performed at a detailed level within the Work
Breakdown Structure (WBS).

�
Cost is estimated for basic tasks such as engineering
design, tooling, fabrication of parts, manufacturing

engineering, and quality control.

�
The cost of materials is estimated or obtained from
the supplier.

�
The approach requires detailed and accurate data and
should be undertaken by an experienced engineer.

Consequently, it can be seen that relative to the bottom-

up method, the parametric method can be used at the

early stage of a program when limited data and technical

definition is available. Similarly, the analogous method

also does not require highly detailed definition as it uses

the actual cost from a comparable program although the

adjustments to cost require information regarding

differences in the program’s complexity as well as the

technical and physical differences to the baseline chosen

as comparable.

In addition, Table 4 summarises the advantages and

disadvantages associated with each of the three approaches

[175]. It appears that the bottom-up method is strong in

detail and causation but difficult to implement while

inversely the parametric method is too associative in

generating relationships but is easy to implement. The

analogous method is somewhere between the two and

perhaps is seen as the compromise. However, apart from

finding a comparable program, it is very difficult generally

within aerospace to gain access to well documented and

understood costing data. In addition, all three methods

rely heavily on that historic data and relate well to new

materials, technology or design features.

4.2. Advanced estimating techniques

4.2.1. Feature-based modelling

Design features are often used as relational drivers of

cost for two reasons as set out by Wierda [176]: (1) cost

functions can be derived for classes of similar objects

that serve as key drivers of global cost estimation and

are linked to the engineering domain; and (2) the

designer wants to know the causes of cost so that when

linked to design features, they are able to influence

committed cost directly.

Wierda [176] has also identified three components of

cost that relate to design features and which are valid for

any class of similar objects to which the costs are related

[177,178]. The difference between the allocation of direct

and indirect costs is also illustrated through:

�
Costs assigned directly to individual design features:
at a feature or assembly level,


�
Costs incurred for a collection of design features: at a
component or batch level and

�
Costs assigned to design features: at an order or
facility level.

Fig. 15. Design feature-based definitions.

Table 5

Definition of feature drivers

Feature

type

Examples

Geometric Length, width, depth, perimeter, volume, area

Attribute Tolerance, finish, density, mass, material,

composition

Physical Hole, pocket, skin, core, PC board, cable, spar,

wing

Process Drill, lay, weld, machine, form, chemi-mill, SPF

Assembly Interconnect, insert, align, engage, attach

Activity Design engineering, structural analysis, quality

assurance

In simple implementation, feature interrelationships can

be ignored so that resource selection is allocated

according to each separate feature. For example,

production times and resultant costs can be calculated

from a simple time formulation for each of the standard

design features [179], which may include feature para-

meters and machining rates. Kiritsis [180] has assigned

machining operations to surfaces in calculating cost

while Schaal [181] has used only a rough process plan for

each of the features as a gauge of manufacturability and

cost. These plans include some information relating to

the next level of aggregation in the hierarchy, which can

be used in conjunction with manufacturing rules. A

rough process plan is sufficient at the early design stage

when the cost estimate does not need to be so accurate.

As more detailed production information becomes

available, the complexity of the cost estimation can be

increased as necessary relative to accuracy.

Typically, material costs can be related directly to the

material blank with some additional design features

being incorporated if they further influence material

costs. Wierda [176] presents one approach to this

procedure: (1) material costs can be directly assigned

directly to a feature if it implies a positive volume; and

(2) negative material costs (waste revenue) can be

directly assigned if a feature implies a negative volume.

For the latter, however, a negative volume may also

be created directly by casting or injection moulding,

which does not entail material removal. Further

complication arises when both positive and negative

volumes of different features overlap, or when parts of a

blank lie outside the final product envelope and are not

described by any design feature. An inherent anomaly

with feature-based cost attribution is that most opera-

tions are carried out for groups of inter-related features

[182] making allocation difficult, however, the approach

demands that the costs involved with the operations

must be assigned to the number of features identified.

This can be confusing when cost does not fall as a

feature is removed, due to the fact that the cost is

incurred regardless, and actually results in an increase in

cost for the other features still included in the opera-

tions. In terms of the ultimate usefulness of feature-

based costing, another fundamental difficulty is that the

preoccupation with the costs of individual features mat

not lead to the global reduction in cost. For this reason,

Wierda [176] has suggested the use of high-level features

which include both the component and assembly levels

at which the costs occur. However, there is a clear

problem with the cost allocation, as the assembly, batch

and order level costs are associated with certain product

levels within the Work Breakdown Structure but costs

associated at the facility and other product levels not

being evident.

It has been noted by Rush and Roy [65] that the

growth of CAD/CAM technology and 3D modelling has

probably played a significant part in the development of

feature-based costing. Most manufacturers do have a

good supply of historical geometric data (if not direct

cost) that can be related to features and therefore can be

linked to technical specification through functionality

and performance, and manufacturing capability. Con-

sequently, many researchers are using the feature-based

approach in costing studies looking at the integration of

design, process planning and manufacturing [183–185].

This is driven by the ability of a feature-based

methodology to describe the product as a number of

associated features that the designer and manufacturer

both relate to, i.e. to holes, faces, edges, folds, etc. (see

Fig. 15). A key observation is that typically, the more

features a product has, the more designing, manufactur-

ing, planning it will require [186]; leading to an increase

in committed cost downstream in the life cycle.

With respect to this problem, companies are faced

with producing their own feature definitions. Table 5

shows an example of how one cost engineering group


categorised features for the purpose of costing [187]. It is

evident that on one level of feature definition however

there are several levels of feature’s definitions. For

example, a feature of an aircraft could be a wing, yet this

wing contains many parts, each of which consists of

many lower level features. Therefore companies are

also left to decide on how to cope with the changing

product definition and the application of an appropriate

feature-based CER. The feature-based costing approach

is not yet well established and its application is not yet

fully understood although companies do seem to

appreciate the concept, features apparently being one

way in which engineers decompose or define a design

concept.

Fig. 16. Membership functions for (a) crisps and (b) fuzzy sets.

4.2.2. Fuzzy logic

Ting [188] has stated that most traditional cost

modelling tools are crisp, deterministic, and precise in

character. However, in the actual industrial aerospace

environment there are many parameters that are

uncertain in nature. Fuzzy logic addresses this char-

acteristic and is a mathematical discipline that was

originally created to bridge the gap between the binary

world of digital computing and that of continuous

intervals, as displayed in nature [189]. Fuzzy theory was

first introduced in 1965 by Lotfi Zadeh to deal

quantitatively with imprecision and uncertainty

[190,191]. The literature agrees that the major contribu-

tion of fuzzy set theory is in its inherent capability of

representing vague and imprecise knowledge, as applied

to classification, modelling and control [192]. Cross [193]

states that since its inception, fuzzy set theory has been

advocated as a formal and quantitative method of

specifying vagueness in human knowledge. Typically,

the fuzzy approach provides a methodology in which

algorithms for the prediction or control of a system are

arrived at through qualitative expressions that link

linguistic variables [194].

It is of special interest to cost modelling to consider

that the theory states that fuzzy sets are the basis of the

logic, this being the collective name given to the set of

conditions that a fuzzy variable can belong to. A fuzzy

set F is defined as a set of ordered pairs ðx; mðxÞÞ: The

membership function f establishes the relationship:

f : x ! mðxÞ; where x is the value of an element in the

domain of function f (mðxÞ being the value of f at x) and

mðxÞ has values in the interval [0,1]. For a given value

x; mðxÞ ¼ 0 denotes x with null membership within F

while mðxÞ ¼ 1 denotes x having full membership.

Therefore, the membership function mðxÞ consists of

real numbers within the interval [0,1] and represents the

degree of membership that an object exhibits within a

fuzzy set. Kishk [190] points out that the fuzzy set

introduces vagueness by eliminating the sharp boundary

dividing members of the set from non-members, the

transition from member to non-member being gradual;

as illustrated in Fig. 16.

Fig. 16 highlights that the membership function is

described by a characteristic that defines how each

instance within the design space is mapped to a degree of

membership between 0 and 1 [195]. However, the key

contribution of the fuzzy methodology is that these

membership functions can be of any characteristic

shape, within the known boundaries assigned to 0 and

1. The characteristic is dependent on the relationship

being modelled and is usually described by the simplest

function that represents the relational behaviour.

Typically, these include the: piece-wise linear function,

Gaussian distribution, sigmoid curve, and quadratic or

cubic polynomial curves [196]. These are often described

by straight line characteristics to give the triangular or

trapezoidal functions illustrated in Fig. 17. Fuzzy

modelling is a formulaic representation of a knowl-

edge-based approach that consists of a collection of n

rules of the form: If V1 is Li1 and V2 is Li2 and . . .Vp is

Lip then U is Mi; where Lij and Mi are linguistic values

associated with the corresponding variables. As such,

the linguistic variables are controlling rules within a

fuzzy inference mechanism, as distinguished by the

appropriate use of inputs and outputs. Ultimately, this is

ARTICLE IN PRESS

Fig. 17. Characteristic relationship membership functions

(distributions).


used to map an input space to an output space through

the use of a set of rules that take the form of a series of

‘if-then’ statements. As such, antecedent clauses im-

mediately follow ‘if’ statements but precede ‘then’

statements of the rule and contribute to the logic

process by implementing evaluation measures that

control progress through fuzzified rules [194]. The

consequence of the process defines the action to be

taken as the antecedent is satisfied, relative to the degree

of membership of the input to the antecedent. The three

main procedures within fuzzy logic are as follows

[189,195,194]:

1.
Recognise one or more assigned physical conditions
that require analysis or control.

2.
Process these as inputs according to fuzzy ‘if-then’
rules that are expressed linguistically.

3.
Average and weight the outputs from all of the
individual rules into a single defuzzified output that

results in the decisions and/or actions required of the

system.

Kishk [190] proposes that fuzzy logic is appropriate in

two kinds of situations: firstly, very complex models

where understanding is limited or judgmental, and

secondly, processes where human reasoning, human

perception, or human decision making are inextricably

linked. This results in a number of key advantages in the

costing sphere: (1) the simplicity and transparency of the

mathematical concepts utilised; (2) the ability to match

any set of input–output behavioural data; and (3) the

integration with traditional techniques and experiences.

In terms of knowledge utilization, the fuzzy logic

approach can be viewed as a form of Artificial

Intelligence (Al) that formulates the human thought

process [197], similarly as for neural networks. Conse-

quently, it is appropriate to be developed and applied to

the realm of aerospace cost estimating [198–200]. A

number of authors have explored the use of captured

and coded fuzzy logic within cost estimating [201–203].

However, the technique is not well established and it can

be said that these ‘models only know what the expert has

told the model builder’ [204]. As a consequence, fuzzy

cost estimating is subject to the domain rule of being

limited through limited scope and application.

4.2.3. Neural networks

In a similar way to fuzzy logic, neural networks have

also been developed with a view to simulating the

human thought process, and as a method of linking

historic costing information with design stimuli [205]. As

such, this can be viewed as a form of artificial

intelligence that can be used to develop links between

cost as the effect and certain cost drivers as the cause

[206–209]. The method is based on the concept of a

system that learns to predict the effect on cost when

presented with a range of product-related attributes.

This in turn is derived from the analogy of a number and

hierarchy of neurons as logic gates being able to

simulate various procedural permutations and combina-

tions as it trains itself in being able to repeatedly arrive

at a logical conclusion, given input data available from

historic case studies. Once trained, the attribute values

can be supplied to the network of neurons in order for it

to apply the approximated functional steps in comput-

ing an expected resultant cost. The technique does not

simplify any of the analysis but does transfer much of

the logic and rules to the coded neural network process.

However, the analyst must still define the problem

domain and apparent cost drivers, and also must supply

the relevant cost data perceived to be important.

Bode [210] states that under certain conditions, neural

networks can produce better-cost predictions than more

conventional parametric regression costing methods.

However, it is also made clear that in certain cases there

are disadvantages in terms of accuracy, variability,

model creation and model examination [211]. Notwith-

standing, one of the key advantages is that a neural

network can detect obscure relationships within the

database. These would not be evident if the user had to

provide the complete input assumptions [212]. One of

the defining aspects of neural networks is that they

require a large historic data bank from which to learn

and that the data base needs to be comprised of similar


information and form to the new products that are to be

analysed. Consequently, their prediction accuracy is as

poor as the quality, quantity and relevancy of the input

learning data is. Neural networks are not applicable to

novel or innovative product developments that deviate

significantly from the historic precedent or where the

environmental aspects have changed [213]. Furthermore,

one must trust to the ‘‘black box’’ nature of the process

whereas the regression approach assumed with para-

metric analysis does have a more transparent audit trail

for the estimating procedure. It has been said that the

neural network solution often does not appear to be

logical [65], even if one were to extract it by examining

the weights, architecture, and neuron functions that

were adopted by the final trained model. Consequently,

the ‘‘black box’’ nature of the costing relationship is less

appropriate CER for users that need a transparent audit

of the reasons and assumptions behind the cost estimate,

which also impacts on the use of additional analysis

tools such as risk and uncertainty. Naturally, this is a

fundament requirement of the designer who wants to be

able to learn from the estimating procedure in order to

be able to influence the design process in arriving at a

more optimal solution [214].

4.2.4. Uncertainty

The aerospace industry poses substantial difficulties

for the financiers and directors who are trying to develop

sustainable products with established in-house capabil-

ities and a stable extended supplier base. Changeable

markets and global issues through shifts in emphasis

regarding development, politics, commerce and military

action exacerbate this. There is also the continued need

for product differentiation, cost rationalisation and

increased competitiveness, with regard to lead-time, cost

and customer defined quality. This is embodied in the

European Vision 2020, which sets out cost and efficiency

goals such as a 20–50% reduction of aircraft operating

costs in the short to long term, respectively, and 20–50%

reduction of aircraft development costs in the short to

long term, respectively; along with substantial reduc-

tions in lead time. That is set against technological

progress and policy, such as reduced impact on the

environment through quantitative reductions in emis-

sions and noise, and the requirement for improved

safety margins and air transport network flexibility and

service. This drives the industry into higher risk areas of

research and development, forcing them to manage and

mitigate that risk accordingly. In addition, the aerospace

industry is characterised often as having lengthy project

time scales and extremely high initial investment up

front.

This section looks predominately at some of the key

costing issues to be addressed during the early stages of

product development and definition, where potential

risk is highest. Rather than focusing on the actual

management of risk, the focus is more on combining

statistical analysis with cost estimation in order to

predict the cost estimation uncertainty to be attributed.

It is more realistic to have a range of cost estimates

rather than a discrete value, and this is more likely to be

accurate in modelling the effect of cost variance, which

is a reality for any product. At a more detailed level,

such an analysis facilitates the mitigation of risk in

reducing uncertainty through avoidance, adjustment

and contingency. At a higher level, risk analysis

facilitates go/no-go decisions that need to be made

regarding exit criteria when moving from each stage

within the Integrated Product Process Development life

cycle. It can also be used to rate all of the potential

design solutions between the range of scenarios envi-

sioned at the concept stage: when the majority of the

aircraft’s life cycle costs are committed. This shows

which variables and parameters have the most impact on

the design and therefore, highlight where most of the

effort should be targeted in making decisions that

influence the cost and viability of the product. In terms

of the benefits of risk management, Edmonds [215] has

noted that the use of risk analysis provides under-

standing with regards to the consequences of risks to

programme cost and scheduling. However, risk analysis

needs to be first employed during the commercial

bidding and planning stages when a programme’s price

and duration are being estimated, a range of probability

level being attributed to each cost estimate required of

the project definition process.

In context, the majority of research carried out into

risk analysis has been concerned with the combined

effect of an accumulation of uncertainties associated

with the estimates required to estimate a product’s cost.

This provides a better understanding of the potential

correlation between itemised cost variations and the

combined effect on the overall distribution [216,217]. As

a consequence, risk analysis is being used to alter the

normal cost/price estimate at an early stage in order to

raise awareness of the sensitivity of the product cost to

the cost breakdown. This contingency range of values is

quantified and rated relative to uncertainty and can be

used to guide bidding and planning and ultimately, the

product development process. There are a number of

statistical methods that are suited to performing this

function and software tailored towards risk assessment

is now more readily available. However, much of the

actual risk assessment within a company is more of a

procedural exercise that is qualitative and not bench-

marked.

One form of a risk model is described by the

Stochastic Aggregation Model (SAM) that is based on

a Monte Carlo analysis [218]. The model is essentially a

simulation program that quantifies the uncertainty

associated with parametric cost estimates and it

ARTICLE IN PRESS

Fig. 18. Risk management process.

Minimum Most Likely Maximum

Fig. 19. Triangular relation adopted.


specifically addresses: (1) uncertainty related to the

magnitude of the independent variable used as a cost

driver; (2) uncertainty in rating the complexity factor

used to increase the accuracy and (3) the actual

statistical uncertainty of the relationship developed.

The model has generic application but requires a

formalised equation relating inputs that are statistically

relevant to the output cost estimate. Another example is

given by the RiTo (Risk Tool) model has been

developed by Crossland et al. [219], which was especially

developed to deal with the uncertainty experienced

during the early stages of design. It was based on an

object-oriented approach and again incorporated a

number of features to model and assesses various risks

associated with the estimations. The model was oriented

towards decision tooling that could be used by designers

in evaluating the cost impact of conceptual and detailed

definitions; relative to the design space and constraints

that drives the product’s likely cost base and price range.

Turner [220] has noted five key steps as part of a

methodology for managing and mitigating risk. These

are concerned with controlling risk so that the final

product is as it was envisaged and at a realistic cost and

schedule that were targeted. The procedure is illustrated

in Fig. 18. Roy et al. [216] has used the core of Turner’s

model in suggesting a number of ways in which each step

can be facilitated to produce a number of model types:

Identification: Risk is driven by the uncertainty

introduced by the inclusion of the independent variables

selected through the statistical analysis. For example, a

parametric CER could relate cost as a function of both

weight and surface area, as a result of the statistical

analysis:

Y ¼ C0 þ C1 ðMassÞ þ C2 ðsurface areaÞ, (1)

where Y is the estimate of the dependent variable; C0 is

a constant; C1 is a coefficient associated with mass; and

C2 is a coefficient associated with surface area. The two

independent variables can be assumed to be potential

sources of uncertainty due to their highly influential

relation with that estimate. Both are likely to change as

the product definition develops and the statistical

analysis infers that this will have a significant influence

on the accuracy of the initial cost estimate generated at

the bidding or concept stage: the more likely the change,

the less accurate the cost estimate.

Assessment: The relationship between the likelihood

of a change in the products cost drivers (the perceived

risk) and the impact on cost needs to be formalised and

quantified. Consequently, the risk, or more accurately

the cost-impact of risk, is quantified by calculating the

probability of an event occurring p (a change in a cost

driver) and the impact that will have on cost c; as

described in Eq. (2). It is important to note that this

incorporates both the probability of the risk occurring

and its impact on cost:

Risk ¼ p � c. (2)

Fig. 19 shows the likelihood of variance, on the y-axis,

characterised by a triangular probability distribution to

give the range from minimum variation, to most likely

variation, to maximum variation. Consequently, the

likelihood of the design variable being within a

designated range needs to also be provided. The

magnitude of the variation in cost being represented

along the x-axis is given by multiplying by the coefficient

associated with that independent variable (as in Eq. (1)),

while Eq. (2) can be used to calculate the actual risk by

multiplying this cost variation with the value from the

risk assessment. Consequently, the final value for risk

grows with variation in cost but also occurrence.

Analysis: With reference to the previous section, if

there is a 50% likelihood of a mass increase (see Eq. (1))

then Fig. 19 can be used to give the cumulative

likelihood of a variation of a given magnitude not

occurring from 50% to 100%. This is shown typically in

Fig. 20. This can be completed using a Monte Carlo or

Latin Hypercube simulation. This type of a risk analysis

provides a range of costs and probabilities rather than a

single value as normal. Therefore, one can assume with

some level of confidence that the cost will not exceed a

specific value, typically a threshold of 85% probability

being used.

The above methodology was also extended to the risk

analysis for the prediction of the range of the actual

CER. This is particularly relevant when there is limited

data available for the statistical analysis of new high-risk

product developments. The aim is to provide a predic-

tion of the maximum value of the cost estimate with its

associated probability occurrence. The initial two stages

ARTICLE IN PRESS

Fig. 20. Cumulative likelihood of occurrence.


are implemented as before using the data that was used

for the development of the CER, as shown in Fig. 20.

4.2.5. Data mining

All cost modelling is ultimately based on the mining

and analysis of data [63] which is a form of Knowledge

Management. Liao [221] has classified other forms of

knowledge management technology as: knowledge

management frameworks, knowledge-based systems

(KBS), information and communication technology

(ICT), artificial intelligence (AI), expert systems, data-

base technology, and modelling. Data mining is an

interdisciplinary field that Chen [222] describes as a

process of non-trivial extraction from databases of

implicit, previously unknown and potentially useful

information, such as rules, constraints, and regularities.

This therefore can be used to facilitate decision-making,

problem solving, analysis, planning, diagnosis, detec-

tion, integration, prevention, learning, and innovation.

Liao [221] notes that quantitative methods for exploring

the issues of knowledge discovery, knowledge classifica-

tion, knowledge acquisition, learning, pattern recogni-

tion, artificial intelligence algorithms, and decision

support are the modelling technology of knowledge

management.

In conducting effective data mining, Chen [222] has

highlighted the need to first examine what kind of

features an applied knowledge discovery system is

expected to have and what kind of challenges one may

face at the development of data mining techniques. This

includes: the handling of different types of data; the

efficiency and scalability of data mining algorithms; the

usefulness, certainty and expressiveness of data mining

results; the expression of different kinds of data mining

results; the interactive mining knowledge at various

levels of abstraction; the mining of information from

different sources of data; and the protection of privacy

and data security. The term data mining is increasingly

being used to describe the process of extracting

probabilistic characteristics from a mass of data held

in some pre-determined databank. He points out that

some of these requirements may be conflicting where, for

example, data security issues can often conflict with

interactive mining of multiple-level knowledge from

different angles. A methodology for the mining of cost

data can be defined as follows [221–223]:

1.
Data cleaning: to manage noisy, erroneous, missing
or irrelevant entries.

2.
Data integration: for the integration of multiple,
heterogeneous data sources.

3.
Data selection: to retrieve data that is relevant to the
analysis task.

4.
Data transformation: for consolidation through
summing or aggregation.

5.
Data mining: where intelligent methods are applied to
extract data patterns.

6.
Data pattern evaluation: to identify the significant
patterns that constitute knowledge.

7.
Knowledge presentation: visualisation and represen-
tation for the user.

According to Sorensen [223], two general types of data

mining approaches exist: (1) knowledge and (2) predic-

tion discovery. Prediction discovery identifies causal

relationships between certain fields (parameters) in the

database. These relationships are established by finding

predictor variables that model the variation of other

independent variables. If a causal relationship has been

established, action can be undertaken to reach a specific

goal such as cost reduction. Knowledge discovery

problems are usually associated with the stage prior to

prediction, where information is insufficient for predic-

tion. Sorensen [223] states that data mining techniques

can be characterised according to the kind of knowledge

to be mined, which for costing includes: association

rules, characteristic rules, classification rules, discrimi-

nate rules, clustering, evolution, and deviation analysis.

In particular, data classification is a process that finds

the common properties among a set of objects in a

database and then classifies them into different classes,

also referred to as clustering [222], whether for the

grouping of physical or abstract objects into classes of

similarity. This was a technique that was already being

advocated in the 1950s by the Rand Corporation when

they grouped aircraft into clusters of a similar type in

order to increase the predictability of the CERs [58].

5. State of the science: genetic causal cost theory

5.1. State-of-the-art

From the assessment of cost modelling techniques in

Section 4 it is evident that there is no consolidating


theory on which the models are based and indeed there

seems to be many different types of model. Anthologies

often include many recognised but yet inconsistent

classifications. For current purposes, it is helpful to first

separate costing methodologies into two specific func-

tional classifications: (1) compilational costing: aggre-

gating various identified costs; and (2) relational costing:

comparative relation of product defining parameters.

The first category represents the compilational method

of modelling cost within a designated cost breakdown

structure and includes:

�
Activity-based costing (ABC): assigning costs to each
activity performed.

�
Absorption costing: assigning cost according to
resources utilised.

�
Bottom-up costing: accumulating cost from the BOM
and Work Breakdown Structure (WBS).

�
LCC: attributing costs to all stages of the life cycle
from ‘womb to tomb’

Scenario-based reasoning: a subset projecting and

forecasting future product scenarios, inclusive of

market.

�
Feature-based costing: attributing cost to geometric
part features.

The second category represents the relational method of

linking cost to one or more attributes to form discrete

associations and includes:

�
Physical process modelling: focusing on the time
required to carry out work.

�
Parametrics: stochastic relations within product
classes.

�
Neural nets: learnt mapping of attributes to cost.
�
Analogous costing: using precedent at product level.
Case-based reasoning: a subset using precedent at

detailed level.

�
Fuzzy logic: interpolating along established cost
functions.

�
Financial modelling: using mathematical series for
cost variance.

The above provides a categorisation that is based on the

basic nature of the method and as such, the distinction is

more technological and relates to their industrial use.

However, science is concerned with the causal founda-

tions for each. The importance of the scientific basis of

the modelling method will be expanded in the following

section because of its role as a key differentiator in

assessing the engineering understanding on which each

method relies. Understanding greatly increases the

flexibility, usefulness, robustness, and accuracy of any

engineering model.

It is clear that most of the compilational costing

methods do have a strong causal basis. In each case it

has been observed and recorded that the cost architec-

ture, or cost breakdown structure, can be organised

according to factors that give rise to cost whether due to:

activity performed, resources utilised, parts assembly,

product life cycle stages, or part design features.

However, these are all types of compilation methods

and each framework requires additional techniques to

supply the actual cost estimates they refer to. This is true

also of Feature-Based Modelling, which is more often

associated with the second category yet requires some

functional technique that enables it with the capability

to estimate the actual costs it requires for each feature.

On the other hand, scenario-based costing is more

ambiguous and undefined in terms of which aspect of

the life cycle is being considered, and to what aspect it

refers. Notwithstanding, all those listed have factual

relevancy and address costs that arise due to some

element of causation relevant to the application. They are

all functional driven and have a technological nature.

The distinction between causal and non-causal

foundation becomes much more acute when applied to

the second category: relational costing techniques. The

only relational method that is intrinsically founded on a

causal basis is physical process modelling. An example

of this would be a cost model for a machining process

that is based on cutter speed, feed rate, etc., and

therefore, may be based on the modelled usage of

material and time. However, the other methods listed do

have varying degrees of causality, although in all cases it

must be explicitly enforced. For example, parametric

models do not intrinsically require that causal cost

drivers (as independent variables) be used but that

explicit distinction could be used as a desirable attribute

when identifying the cost drivers for the parametric cost

estimating relations. Neural network models seem to be

the least causal as the technique operates to a large

degree as black box, the neurons learning how to map

cost to independent variables given a databank of

historical data, in order to replicate the result. The

network can be designed to a degree while the

independent variables can be chosen for their causal

relation to cost, even although it is likely that there will

be very little insight that can be used to facilitate the

choice-dilemma of engineering decision making.

Looking at the current state of the art in cost

modelling in general the following observations can be

made:

�
The major effort is directed towards estimating costs
rather than first developing a causal understanding

that is a basis for that modelling:

function over foundation!

�
Modelling is directed towards a particular element of
cost but is not mindful of the holistic cost architecture:

micro over macro!


�
Modelling is directed towards a particular stage in the
cost life cycle and is not mindful of the holistic cost

structure:

inhibiting over inheriting!

�
Methodologies are based primarily on a mechanistic
approach and not a causal truth:

casual over causal!

�
Modelling is product specific rather than generic:
generative over genetic!

�
Costing is experience based rather than scientific:
experience over experiment.

Many of the above points highlight the negative

scientific basis that surrounds engineering cost model-

ling and the lack of a consolidating theory that can

establish its fundamental basis. The discipline is made

more difficult to define because it has such a breadth of

relevancy and application and has both qualitative and

quantitative aspects. Notwithstanding, the basis of all

scientific thought and theory is built on the principle of

understanding and the modelling of cause and effect

relations. Consequently, the following section will look

at the importance of causality in this respect.

5.2. Causation

It is easier to first begin with non-causal modelling

and to say that good examples of such models can be

extremely useful in estimating the likely behaviour of

cost as the dependant variable. These models should

conform to the covering-law of Hempel and Oppenheim

[224], which gives validation to the explanation of a

phenomenon if that phenomenon is subsumed under

some general formulation of regularity [225]. The ideal

gas law is an example of this, where pressure, volume,

temperature and quantity of matter are all incorporated

into an expression of repeatable consistency. Non-causal

models highlight general trends at a higher level with

little thought to abstraction and therefore, are suited to

an appreciation of the likely systems’ behaviour, or in

this instance, the cost of complex products. Such models

tend to be of simple formulation and are therefore easy

and quick to deploy and maintain, thereby facilitating

the immediate engineering task at hand of estimating

cost. A potent example is the infamous relation within

aerospace of product cost as a function of weight. The

weight and unit cost relation do show a remarkable

degree of statistical significance and indeed there is a

partial truth in the proposition that heavier things tend

to be larger in size and in turn cost more. However, the

aerospace industry has always been striving at great cost

and effort to reduce weight in order to reduce the area

required of lifting surfaces and ultimately, the fuel burn.

The scientific proposition is disproved although it has a

range of limited usefulness that needs to be well

understood and bounded. Unfortunately, that under-

standing is the very quality that is often suppressed in

following a stochastic technique that expresses a casual

relationship rather than a causal one. Non-causal

relations can be used out of context with unclear

boundary limits being set and there can be little

appreciation for their total inability to deal with

anything that has not been instrumental in their

formulation.

This issue of application and relevancy is the main

functional limitation of non-causal models. A second

more fundamental limitation is the near total lack, or at

best incidental inclusion, of understanding regarding the

reason for cost behaviour. Consequently, such relations

are severely limited and cannot be used readily in

making decisions regarding product definition and

development; remembering that the weight-cost relation

would encourage the designer to always choose the

lightest option. This states that such an approach will

always result in the lowest cost, regardless of the certain

direct costs, perhaps having to remove more of a

material that is likely to be more expensive in its raw

form due to its higher structural efficiency. Although

Bertrand Russell once stated that in terms of the

philosophy of science, ‘‘[the] law of causality... is a relic

of a bygone age’’ [226], the physical world and its

relation to cost can only be understood truly in terms of

causal understanding [227,228].

The need for a causal approach to modelling is

founded on a few basic intentions that are summarised

according to Cowan and Rizzo [229]: ‘those that render

the overall explanatory structure complete, and those

that make it more nearly correct’. Primarily, complete-

ness helps show that which drives outcomes and

secondly, it also helps formulate guiding principles and

useful rules. These are linked in providing a more full

explanation that can be developed into a predictive

model for engineering purposes. On the other hand

correctness is also a necessary attribute that will provide

greater insight and detail. This will lead to more robust

modelling that is based on the correct causal relations

and which gives a more useful understanding of the

influence certain parameters wield. Correctness will

distinguish between a coincidence (possibly statistical)

and result (causal). A more thorough understanding of

causation will be based on completeness and correctness

and will therefore result in an improved predictive

capacity.

Cowan and Rizzo [229] have also noted that the

existence of causation is also highlighted by: (1)

purposeful endeavour; and (2) the time span between

cause and effect. The purposefulness is an obvious but

important aspect as it points to doing something to

instigate change and produce something new. With

purpose is associated worth and therefore, this has given

rise to the monetary value attributed to such products.


The second aspect, of time, is also of fundamental

significance as it introduces the concept of a process that

sees something converted from material state A to

material state B. Menger noted this already back in

1871: ‘‘The idea of causality... is inseparable from the

idea of time. A process of change involves a beginning

and a becoming, and these are only conceivable as

processes in time’’ [230]. The consequence of this is that

time is a fundamental characteristic of a cause and a

process and therefore, anything that has a time-span

associated with it has a causal nature. This is especially

relevant to labour costs and it can be concluded that cost

can indeed have a scientific basis.

To summarise there is evidence of: the shortcomings

of non-causal models; a fundamental scientific nature to

costing that causal models should exhibit; the need for

causal models that encompass our current experience

and understanding; the need for recognition of the key

attributes of completeness and correctness. All these

form the basic tenements on which the genetic causal

approach to cost modelling is based, as suggested in this

paper.

5.3. Genetic nature

It has been noted that there is evidence of a genetic

nature within economics, which is described as the

tendency for economic processes to be unidirectional:

the outcome of which is the effect [231]. The importance

of the descriptor ‘genetic’ relates also to the causal

nature and the observation that there is origination, the

process being unidirectional from some start-point.

Genetic nature would be more appropriately defined as

the evidence of the same recurring prime drivers that can

be assigned as the causal advent of cost. This is a

powerful proposition that is underscores the concept of

a Cost Gene; like the analogous genealogy within the

natural world. This implies that product cost is a

function of certain building blocks that determine the

resultant cost make-up. These can be viewed as universal

cost drivers that have some absolute nature that does

not change. There are a number of observations that one

can make regarding the analogy between natural

genetics and the cause of cost:

(a)
Cost is an attribute of a product.
(b)
Cost has physical causes.
(c)
Cost is not fixed but is influenced by the economic
scenario.

(d)
Cost can be broken down into a number of distinct
categories.

(e)
There is a small number of discrete primary cost
drivers that are building blocks for all the higher

level cost groupings.

(f)
The sequencing of these quantities gives rise to cost.
(g)
Cost will be inherited by a derivative version of
parent product.

5.4. Relevancy of genetic causal cost modelling

It has been established that there is no recognised

scientific method of cost modelling and little common-

ality between the various models. Technological cost

models are based on a wide range of principles and

methodologies and have been devised for a wide range

of applications. The review of current modelling

techniques raised the issue of causality. It was proposed

that this is fundamental in terms of establishing a

scientific understanding that is both more complete and

more correct. This will then provide a better basis for

engineering models that are more robust and accurate.

The additional concept of adopting a genetic scientific

basis was then addressed. This is especially relevant to

modelling as it provides a potential scientific basis and

generic framework for developing any analysis. Specifi-

cally, and with respect to the previous points, it identifies

that:

�
Cost should be primarily viewed as a design attribute
of a product, i.e. it is a variable or parameter that is

designed into a product.

�
Cost originates primarily within the product defini-
tion and therefore is primarily determined at the

design stage.

�
Cost is also influenced by secondary environmental
factors such as economics (supply and demand) and

technology.

�
Cost can be broken down into hierarchical groupings
that have their own distinct influence or nature.

�
Fundamentally, cost is caused by a small number of
base cost drivers: materials, time, and energy.

�
It is the manner in which these base cost drivers are
formulated which dictates the cost categorizations.

�
The genetic nature of cost gives rise to the concept of
inheritance, where cost can be passed down to

derivative versions (or derivative features) of the

parent product.

Materials are converted by human endeavour from a

raw state to a manufactured form through the use of

devised processes. This ability is facilitated by techno-

logical know-how that results in a primary cost that is

determined by the product definition. This primary cost

then becomes some marketed product that is influenced

by its environmental. However, underlying all of these

are the fundamental base cost drivers of material

availability, labour time and energy utilisation, although

it is equally important to establish the hierarchical

structure of cost in order to structure this theory into

a useful framework that can be used as a scientific

basis for cost modelling. Production costs are often


categorised according to the procurement, labour, and

capital costs, and investment associated with producing

engineering designs. However, in the development of a

science of cost, the causal and genetic principles of

origination can be used to formulate some basic rules

that underwrite the subject matter. Cost has been

explicitly linked to product definition and therefore,

design-oriented rules might include:

1.
The required material characteristics affect produc-
ability: part cost increases as the amount of design

information increases, for constant process capabil-

ity.

2.
Assembly cost increases as part complexity and part
number increase, for constant process capability.

3.
Production cost increases as tolerance is tightened.
4.
The design process results in a non-recurring cost.
Secondly, complimentary rules would be more oriented

towards the production of the designs and could include:

5.
Materials cause cost through labour, capital equip-
ment and market ‘supply and demand’.

6.
Part forming processes cause cost through labour,
capital equipment and wastage.

7.
Assembly processes cause cost through labour,
materials (gigs and tools), capital equipment and

wastage.

8.
Unit production cost depends on both recurring and
non-recurring costs.

9.
Unit production cost decreases with number of
units, learning and ‘economies of scale’.

Finally, the overriding law of economics applies:

10.
All costs are adjusted by environmental equilibrium
through the law of ‘supply and demand’.

In summary, there is a hierarchical framework:

�
The basic resources of materials, labour time and
energy are the fundamental building blocks of cost.

�
The product definition is the primary cost driver and
imbues cost into a design.

�
The production process is the subsequent cost driver
and actualises that propensity to have cost.

�
The environmental market scenario will drive design
effort towards an equilibrium that is dictated by

supply and demand.

5.5. Application

Although there is not an established theory to the

scientific modelling of manufacturing cost within en-

gineering design, there are two key aspects that are seen

to consistently relate to cost: form (or geometric

definition) and the relation of production processes to

materials. It is also evident that there are a number of

ways in which to quantitatively formulate relations but

that statistical significance is a fitting manner in which to

formulate relations that are sensitive to environmental

noise but yet characterised by certain generic aspects,

typically relating to design information. The genetic-

causal approach is proposed as a valid scientific

approach to the modelling of manufacturing cost, as

arising from the work done in converting a raw material,

through a number of stages, into a part that may then be

assembled into a product.

It is proposed that manufacturing cost is modelled

using a new methodology referred to as the genetic-

causal method. This is achieved by

1.
Classifying the generic cost elements that are linked
to particular genetic indicators, according to product,

life cycle phase or process.

2.
Developing parametric relations that link the manu-
facturing cost to design attributes within each of the

identified genetic families.

This is illustrated conceptually in Fig. 21. In proceeding

with a hierarchical design-oriented classification there

are three key aspects that can be considered as genetic,

cost being a result of design definition. The relevant

information from these three aspects can be thought of

as bits of genetic information that are coded into the

design and which give rise to cost. The actual cost

however, is only fixed if all things remain equal.

Otherwise, environmental factors such as rates, interest

and technology vary while process cycle indexes will

vary relative to Company efficiency. Therefore, any

scientific cost prediction really is truly termed an

estimate as the prediction is the most likely potential

cost given (1) the nature of the pure design and (2) the

environmental factors that could influence in the

production domain.

The aerospace application presented in the following

section is for stringer-skin panels that make up the

aircraft fuselage. With this application in mind, the

genetic-causal method utilises the following drivers and

hierarchy:

1. Form—the required shape: the classification accord-

ing to form or geometric similarity is crucial for linking

manufacturing cost into the design definition process.

This may also include additional form definition in

terms of identified features or increased fidelity ratings

relating to detailed design information; such as through

complexity factors. It will be seen in the case study

presented in the following section that a first-order

classification is imposed to identify: skin, stringer,

ARTICLE IN PRESS

Part count

Weight

Size Features

Fastenercount

Geometricshape

DesignDesignattributeattribute

Cost family 1Cost family 1

Cost families1-n =>• Product• Phase• Process

Part count

Weight

Size Features

Fastenercount

Geometricshape



Part count

Weight

Size Features

Fastenercount

Geometricshape



Cost families1-n =>• Product• Phase• Process

Fig. 21. Conceptual illustration of the genetic causal cost modelling approach.


frame, cleat and rivet as forms within the skin-stringer

application, while a second-order classification of light-

ening-hole is used in conjunction with the Frame Form

to improve the resolution of design information.

2. Material—relative to the required behaviour: The

choice of material is associated with the required

behaviour of the parts but is strongly coupled to process

selection. Producers may preference a process and then

work to satisfy material requirements; for example,

developing stringer alloys that can be welded; although

it is recognised that the material categorisation con-

tributes to both the raw material and treatments costs.

This is a function of the material quantities required by

the design Form but it is also coupled to the process type

in terms of material addition or material removal. A

further complication with materials procurement is the

degree of pre-processing, such as rolling, forming or

the extrusion of the stringer lengths. This need not affect

the costing accuracy significantly but does impact on the

practical implementation of the trade studies, within the

context of the design process. However, the addition of

bought-out and subcontracted items does require a

procurement factor.

3. Process—the available material conversion route

(MCR): the classification of physical form can then be

matched to potential available processes that can

achieve the Form identified. There are two aspects to

this: (1) understanding the various process stages, (2)

understanding each of those processes. The significant

stages in the production cycle are identified through the

definition of a material conversion route (MCR), after

which individual process models can be assigned to each

stage. At this stage, cycle time factors and established

rates need to be introduced to characterise the processes

relative to influential geometric information. For exam-

ple, it will be seen that the form: stringer and feature: T-

shape is first used to classify the stringer riveting, after

which the cost is predicted using the design length of

stringers in conjunction with a process performance rate

and its cost rate.

It can be seen from the above three aspects that design

information is absolutely fundamental to the under-

standing of manufacturing cost, according to the genetic

causal cost coding imposed by the designer through the

impact of their decisions on form, process and material.

The impact of environmental noise has also been

included in tempering the casual impact of form, process

and material. This justifies these causal relations being

modelled using statistical significance with appropriate

normalisation for the environmental factors. This results

in scientifically based relations that formally link cost to

their causal sources embedded in the design definition.

Apart from being a highly generic cost modelling

technique, the genetic-casual technique is also inherently

suited to use within an integrated design platform as

changes to the design for performance benefit are

mapped to cost. Such interactions can now be directly

traded off relative to some global objective function, as

exemplified in the following section with a case study.

5.6. Genetic causal case study

A preliminary case study of the application of the

genetic causal cost modelling approach has been carried

out [232], the study being based on an empirical case

carried out in conjunction with Bombardier Aerospace

Shorts. The main aim was to provide a manufacturing

cost model based on the theory, and then to link this to a

structural analysis in order to show that detailed

engineering design can be driven by such a modelling

technique to minimise the Direct Operating Cost to the

customer. Therefore, it explicitly links customer require-

ment and affordability to the design process. The

application focused on the design of a traditional

metallic fuselage panel but could be applied to more

advanced processes such as laser welding of stringers or

friction stir welding of panels, or to different materials

such as carbon composites or metal fibre laminates such

as GLARE. A semi-empirical numerical analysis using

ARTICLE IN PRESS

Fabrication37%

Material28%

Assembly35%

Fig. 22. Total cost breakdown.

Rivets3%

Drilling and setup

49%

Manual riveting4%

Automatic riveting

13%

Lay-offoperations

7%

Final riveting12%

Frames sub-assembly

12%

Fig. 23. Actual assembly cost.


ESDU reference data [233,234] was coupled to model

the structural integrity of these thin-walled metal

structures with regard to material failure and buckling:

the latter including skin buckling, stringer buckling,

flexural buckling and inter-rivet buckling. The optimisa-

tion process focuses on the minimisation of DOC as the

objective, being a function of acquisition cost and fuel

burn.

5.6.1. Measured costs

The genetic causal cost modelling methodology

imposes a breakdown of the cost into a number of cost

elements, including material cost, fabrication cost and

assembly cost; so that cost can be formulated into semi-

empirical equations to be linked to the same design

variables as considered in the structural analysis. The

generic product families used on a typical stringer-skin

panel are: the panel, which forms the skin of the aircraft;

the stringers and the frames that support it in the

longitudinal and lateral directions respectively; the cleats

that are present at every stringer-frame junction; and the

rivets that fasten the assembly together. The overall

breakdown in the manufacturing cost analysis is

summarised through Eq. (1), expressed in term of the

identified product families (skin, stringers, frames, cleats

and rivets):

CPanel ¼X5

i¼1

Ci ¼ CSkin þ CStringers þ CFrames

þ CCleats þ CRivets, ð1Þ

where CPanel is the total cost of the panel and Ci the total

cost for the family i:According to the empirical data provided for the

stringer-skin panel, the repartition of material costs,

fabrication costs and assembly costs is shown in Fig. 22.

It is worth noting that the fabrication costs only include

the in-house labour costs. This means that for several

parts the material costs also include fabrication costs

while the rivets are part of the material cost. The total

cost breakdown illustrated in Fig. 22 shows that the

repartition of the three cost elements are almost

equivalent. The assembly or riveting cost has been

further divided into various causal processes as shown in

Fig. 23. The major contribution is from the drilling cost,

which also includes the cost linked to the set-up and

preparation of the parts. It is interesting to note that the

cost of the rivets is insignificant relative to the later

assembly cost associated with them, thereby highlighting

the need for a causal breakdown rather than using

higher-level parametrics. The remaining costs account

for the sub-assembly of the frames, the manual and

automatic riveting, the final riveting and the lay-off

operations such as cleaning and inspection. Additional

parts such as antennas, lighting or electrical provisions

(totalling 8% of the all-up cost) have not been included

as they are not part of the structural configuration but

are added at the end of the estimation process as a fixed

cost for accuracy. The cost of such supply and

commercial off-the-shelf (COTS) items is a function of

different cost drivers and would require a different

implementation of the genetic causal methodology, for

example, relative to manufacturing quality of supply,

quantity ordered and performance specification. The

part family cost breakdown is given in Fig. 24, showing

a rivet (33%), then skin (30%), then stringer (18%)

hierarchy.

5.6.2. Cost prediction

For each part family identified in Eq. (1) there are two

causal cost components that are modelled as genetic

contributors: the material cost Cmi and the labour cost

Cli; the latter being subdivided into either fabrication or

assembly, where assembly is all the remaining costs after

fabrication repartition:

Ci ¼ Cmi þ Cl

i, (2)

where superscripts m and l denote material and labour,

respectively. The associated cost coefficients were

determined empirically from the data supplied from

the industrial partner. Each coefficient is computed, for

each family part and cost element, as an average of the

ARTICLE IN PRESS

Skin30%

Stringers18%

Frames9%

Cleats2%

Rivets33%

Additional parts8%

Fig. 24. Panel cost breakdown. Fig. 25. Section of the panel.

Fig. 26. Frame design.

0.00

0.05

0.10

0.15

0.20

0.25

Skin Stringers Frames Cleats Rivets

No

rm

ali

sed

co

ntr

ibu

tio

n

Data

Estimates

Fig. 27. Comparison of material costs.


actual cost data found in the WBS spreadsheets. Three

types of coefficients are employed in the equations: the

material coefficient cmi ($/[unit]) and two labour

coefficients for the time factor cli(h/[unit]), which

includes learning, etc., and the wage rate per hour rli($/

h). The drawing in Fig. 25 illustrates a section of the

panel from which the geometrical data are issued,

including: panel length, width, and thickness; frame

pitch, rivet pitch, and cross-section dimensions; stringer

pitch, rivet pitch and cross-section dimensions.

It is useful to illustrate the modelling implementation,

for example, to the frames exemplified in Fig. 26. The

frames were manufactured from 2024 T3 aluminium

alloy and investigations showed that the material cost

for the frames should be computed as a function of the

volume. For tf being the frame thickness, hf the frame

height, lf ; the frame flange length, the volume V f of one

‘C’ shape frame is given by

V f ¼ ðð2lf þ hf Þtf � 2ðtf Þ2ÞW . (7)

Given nframes as the number of frames, r the material

density and cm2024 ($/g), the material cost coefficient for

the 2024 T3 aluminium, the material cost for the frames

is computed as

Cmframes ¼ nframesV frcm

2024. (8)

The frame labour coefficient clframes (h/hole) was found

to be a function of the number of lightening holes in the

frames nholes: For rlframes as the frame labour cost per

hour ($/h), the total frames labour holes cost was

calculated as

Clframes ¼ nframesnholesr

lframesc

lframes. (9)

Using all of the derived cost relations, the comparison of

the actual and predicted costs for the complete skin-

stringer panel is shown in Figs. 27–29. The cost data and

estimates have been normalised for proprietary reasons

relative to the total actual cost. Fig. 27 shows the

breakdown of material costs and highlights that the

panel is the most significant expenditure. Fig. 28 shows

the breakdown of labour costs for the various product

families that constitute the stringer-skin panel. It can be

seen that the labour cost associated with the rivets is

now significant, as for the stringers. Finally, the overall

breakdown of the total manufacturing costs is shown in

Fig. 29 being the aggregate of Figs. 27 and 28. It is

evident that the greatest expenditure is caused by the

riveting process, the assembly process and the skin being

almost 35% of the total cost, while the stringers

contribute 20%.

ARTICLE IN PRESS

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40


No

rmal

ised

co

ntr

ibu

tio

n

DataEstimates

Fig. 28. Comparison of labour costs.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40


No

rm

alis

ed

co

ntr

ibu

tio

n

Data

Estimates

Fig. 29. Comparison of total costs.

Crew

13% Fuel

15%

Landing fee

2%

Insurance

3%

Maintenance

13%

Ownership

54%

Fig. 30. Life cycle cost breakdown for regional jets.


5.6.3. Direct operating cost optimisation

The study was ultimately concerned with linking and

trading off structural efficiency with manufacturing cost.

Structural efficiency is already a trade-off between

maximising material strength utilisation and reducing

weight [235], while manufacturing cost is a trade-off

between specified design requirements (within tolerance)

and process capability. Eq. (29) highlights that the trade-

off can be achieved through the minimisation of DOC:

DOC ¼ fn ðacquisition; fuel burn; maintenance,

crew and navigation; ground servicesÞ. ð29Þ

However, for the purposes of the structural design trade-

off, all DOC drivers can be said to be fixed apart from

the acquisition cost and fuel burn. The neglected

elements can be said to be of much less importance to

the structural airframe designer where for example even

airframe maintenance has been estimated by Russell

[236] to be of the order of only 6%; relative to

subsystems and the power plant. Acquisition cost is

driven by the cost of financing the acquisition cost of the

aircraft, plus a 15% profit margin for example, and can

again be simplified and stripped of overheads, con-

tingency, etc. to be a function of the cost of manufacture

for design trade-off purposes. Fuel burn is a function of

the specific fuel consumption (SFC) and the cost of fuel

and therefore can be said to be a function of weight in

the current context.

For the purposes of structural optimisation relative to

DOC, it is simple to use some estimate of the cost of

transporting each unit weight of structure over the life

span of the aircraft: effectively being a cost per unit

mass-distance with units of either d/kg km or $/lb m for

example. With respect to the isolation of manufacturing

cost and structural weight being the key DOC drivers, it

can be seen that manufacturing cost has a direct relation

to the magnitude of DOC/unit mass-distance while

weight is its multiplier. Therefore, one cannot assume to

use a fixed figure for the DOC estimate within the

optimisation process but a more correct weighted

formula that includes the direct relation of manufactur-

ing cost as well as the more obvious one of weight.

Essentially, to optimise according to an objective

function that only includes a fixed DOC/unit weight-

distance would lead to the improper assessment of the

minimum manufacturing cost condition; as occurring at

that point at which the weight corresponds to minimum

DOC rather than the minimum manufacturing cost

being the decider. This is consistent with literature that

has stated that minimum manufacturing cost does not

necessarily correspond to minimum weight [237]. There-

fore, a change in manufacturing cost must be linked

through the impact on both acquisition cost (AC) and

fuel burn (FB) (at that associated weight) while a change

in weight is linked through fuel burn alone. The pie

chart shown in Fig. 30 shows that a 50% weighting for

acquisition cost and 15% weighting for fuel burn is

reasonable for the DOC split for an aircraft of the

regional type; in keeping also with the panel sizing used

in the paper. It was found from this basic correlation

that the manufacturing cost (MFC) needs to be multi-

plied by a weighting factor n that truly reflects the cost

penalty. This is relative to the factory and company

overheads, etc. and would be typically from 2 to 4 times

ARTICLE IN PRESS

0

500

1000

1500

2000

2500

3000

3500

4000

minW

minmat

minmfc

mindoc

savi

ng in

dir

ect o

pera

ting

cos

t US

$ / m

2

Fig. 31. Saving in direct operating cost.

Table 6

Savings according to the choice of objective

Panel optimised for Saving in

W MAT MFC DOC

Minimum W 1.60 �11 807 2898

Minimum MAT 0.99 36 680 2335

Minimum MFC �2.29 �108 1186 2872

Minimum DOC 0.58 �26 1122 3539

All cost savings are in US $ per m2 of panel, weight in kg per m2

(values in italics indicate an increase). W ¼ total weight;MAT ¼ bare material cost; MFC ¼ total manufacturing cost;DOC ¼ direct operating cost:


the basic value:

DOC ¼ FB þ AC ¼ FB þ n MFC. (30)

The above approach to DOC optimisation has been

investigated more rigorously for the optimal trade-off

between aerodynamic tolerances and manufacturing

cost [4,8,96,85,238–242]. In that study, DOC was

calculated using industry PIANO software while the

manufacturing cost was stochastically modelled as a

function of tolerance allocation and process capability.

Incidentally, it was found that the introduction of

manufacturing cost into the objective design function

changed the design definition, and that is again reflected

in the following results.

For cost-weight optimisation of the panel, a marginal

saving in the direct operating cost of the aircraft (i.e.

saving directly attributable to the design of the panel) is

assumed to be made up of a saving in manufacturing

cost offset against a fixed cost penalty for any increase in

structure weight. The fixed cost penalty is a function of

the fuel burn with normal utilisation over the useful life

of the aircraft, expressed in terms of its all-up weight. A

reduction in manufacturing cost through design itera-

tion of the panel will result in weight increase and

implies an increase in the cost of fuel consumed.

Minimisation of this total cost (i.e. manufacturing

cost + fixed cost penalty) is the basis of the optimisation

performed. It should be noted that additional fuel costs

are paid for over the life of the aircraft, whereas

manufacturing costs are met at the outset. A fixed cost

penalty (often referred to as the economic value of

weight saving) of 300 US $/kg was been adopted, this

figure having been adjusted to reflect interest on the

initial investment.

In the optimisation process the structural analysis

simply ensures that the panel continues to withstand the

applied loads. Due to the explicit nature of both

the genetic causal manufacturing cost model and the

structural modelling, it was possible to employ the

simple ‘Solver’ optimisation routine within MS Excel,

which uses a generalised reduced gradient method. The

formulations for the various modes of failure from the

structural modelling act as constraints in the cost

optimisation, together with constraints arising from

the limits of validity for the buckling data and further

constraints imposed to reflect practical limits of spacing,

etc. The weight of the panel, its bare material cost, the

total manufacturing cost (i.e. including material cost)

and the marginal saving in direct operating cost were

assessed by the objective function. The active design

variables, which also are genetic links to cost, were

chosen to be: stringer pitch b; stringer height h; skin

thickness t; stringer thickness ts and rivet pitch rp: The

last was chosen primarily as it is causal in being a major

contributor to assembly cost of manufacture.

The panel was loaded in compression-shear, not

introducing tensile loading and crack propagation, at a

structural index value p=LF ¼ 0:5 N=mm2: This will

result in a relatively low stress level that is appropriate to

the design of panel studied. The panel was first

optimised for maximum theoretical efficiency Z; which

is equivalent to minimising the cross-sectional area of

the skin and stringers to give a maximum efficiency Z ¼

0:693: The cost and the total weight of this optimised

panel was used as the reference datum for decrease or

increase in cost or weight in the subsequent optimisation

approaches. The optimisation was then repeated for

minimum total weight, minimum material cost, mini-

mum total manufacturing cost and minimum direct

operating cost. The marginal change in direct operating

cost with different choice of objective function is

illustrated in the bar chart in Fig. 31 and detailed in

Table 3; the first column showing the quantity mini-

mised, and the other columns the relative change.

Positive values denote reductions relative to the refer-

ence panel while negative values indicating increase.

It is evident from Table 6 that substantial reductions

in both weight and direct operating cost are obtained

when the panel is optimised for minimum total weight,

ARTICLE IN PRESS

Table 7

Panel dimensions after optimisation

Panel optimised for H b h t ts rp

Efficiency Z 0.693 42.8 27.6 0.85 1.61 31.1

Minimum W 0.632 71.5 31.0 1.60 1.60 61.6

Minimum MAT 0.628 65.5 27.0 1.09 2.53 41.9

Minimum MFC 0.383 192.3 38.64 2.52 6.07 124.7

Minimum DOC 0.517 125.1 28.2 1.97 3.73 83.7

All dimensions in mm (values in italics indicate that the limits of

validity of the local buckling data have been reached).


rather than for maximum theoretical efficiency. This

only emphasises the causal importance of including the

weight of connections and similar items in the optimisa-

tion. Minimisation of material cost and total manufac-

turing cost both show improvements with regard to

direct operating cost, even though they induce a weight

penalty. Optimisation for minimum direct operating

cost rather than for minimal weight shows a further

improvement of 10% for total DOC. This is a significant

result as much structural optimization is performed

according to a minimum weight goal, it being implicitly

assumed that this also reduces cost. When optimised for

minimum direct operating cost, it was found that the

ratio of acquisition cost to fuel burn was typically 4:3,

although a different impact might be expected for other

panel cases. Finally, it can be seen in Table 7 that the

various criteria for optimisation lead to widely differing

panel dimensions. Minimisation of direct operating cost

leads to a stringer pitch almost triple that of the

theoretical optimum, at the same time more than

doubling the skin and stringer thickness and the rivet

pitch. Increased stringer pitch implies a reduced number

of connecting cleats, and this together with increased

rivet pitch leads to substantial cost savings in assembly.

Again, this is significant in the cost modelling proving to

be a very influential factor in the design process. The

genetic nature of the costing method is of relevance to

the general applicability while the causal nature ensures

that design relevance is inherent and quantitatively

linked for use in decision making facilitated with more

recognised modelling.

6. Conclusions

The paper has presented the more established

techniques presented in the modelling of aerospace

costing, while also presented the recognised definition of

these costs. It has been established that there is no

consolidating theoretical approach to the domain and

consequently, this has been proposed using the so-called

genetic causal approach. Through this proposition, the

inherent genetic and causal nature of aerospace costing

has been illustrated. Furthermore, this has been shown

to be an appropriate basis for the assessment of the

scientific relevance of the methods presented in the

literature. Therefore, although no different from the

proper basis that would be adopted on a scientific basis,

the genetic causal theory of cost modelling can now be

referenced in assessing the balance of modelling applic-

ability and fundamental basis.

Finally, it is concluded that engineering can be

scientifically modelled and that a consequence of this

is that it can be integrated into the engineering design

process and promoted to the status of a key design

variable. This is a contentious issue for many design

purists who still adhere to the performance and technical

specification paradigm but who will be increasingly

marginalised by the age old need for the engineering

profession to be called on to apply science in meeting the

perceived market need. That now most definitely

includes both value and initial cost, as demonstrated

through the culminating case study presented.

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Documents

Genetic Cost Modelling