Genetic Cost Modelling
Transcript of 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).
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
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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.
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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
ARTICLE IN PRESSR. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534 491
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 whenneeded, 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.
ARTICLE IN PRESSR. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534492
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 assemblysystem.
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 toassess design quality and the difficulty of assembly
operations and
�
an assemblability cost ratio that is used to generatethe projected assembly cost.
It will be shown in the following section that the
application of the Boothroyd–Dewhurst methodology
ARTICLE IN PRESSR. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534 493
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
R. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534494
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
ARTICLE IN PRESSR. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534496
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.
R. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534 497
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 certaincost 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 ofactivities which are carried out by the functions.
�
Determine the allocation base per activity pool as thecost driver that is a measure directly related to the
amount of an activity used.
�
Determine the overhead costs per activity pool, whichare 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.
ARTICLE IN PRESSR. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534498
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.
R. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534 499
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 andmodel certain characteristics of the target system and
the implementation environment.
�
Expert judgement: the advice of knowledgeable staffis solicited.
�
Analogy: a similar, completed, project is identifiedand its recorded costs are used as a basis.
�
Parkinson: the premise that work expands to fill thetime available and uses the available resource level to
drive the estimate.
�
Price to win: a figure that is sufficiently low to win thecontract.
�
Top down: an overall estimate of effort for the wholeproject that can be broken down into the effort
required for individual component tasks.
�
Bottom-up: component tasks are identified and sizedand 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,
ARTICLE IN PRESSR. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534500
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.
ARTICLE IN PRESSR. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534 501
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.
R. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534502
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 onthe 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 projectionsthrough 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, andemployees,
�
developing cooperative supplier relations,�
delivering and establishing a culture of continuousimprovement,
�
facilitating a cross-functional approach linkedthrough cost,
�
managing and reducing cost across the wholebusiness structure,
�
developing integrated data management systems andARTICLE IN PRESS
Fig. 9. Matrix of dependency and decomposability.
R. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534 503
�
justifying investment in procurement/supply toolingand 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 possibleprice;
(2)
partnership-relational: close supplier relations forreduced 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 businessprocesses 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
ARTICLE IN PRESSR. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534504
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.
R. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534 505
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
R. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534506
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.
R. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534 507
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
ARTICLE IN PRESSR. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534508
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.
R. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534 509
[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 estimatingdiscipline, the Cost Estimating Relationship (CER).
�
Merging the CER with the learning curve to form thefoundation of parametric aerospace estimating.
�
Deriving CERs for aircraft cost as a function of suchvariables as speed, range, and altitude.
�
Observing acceptable statistical correlations in check-ing the CERs.
�
Developing families of curves data segregated byaircraft types, e.g., fighters, bombers, cargo aircraft,
etc.
ARTICLE IN PRESSR. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534510
�
Developing curves corresponding to different levelsof 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 oftechnical capability and may be further divided into
parameters that are scale dependent and independent.
�
Technical risk and design maturity parametersmeasure or quantify the relative difficulty of devel-
oping and producing a particular system.
�
Programmatic parameters address issues related tothe 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 databaserange,
�
using a parametric model not researched or validated,�
using a parametric model without adjustment whennew system requirements are not reflected in the
database,
�
using a parametric model without access to realisticestimates of the independent variables and
�
requesting impossible or impractical point estimatesfor 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
R. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534 511
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 WorkBreakdown Structure (WBS).
�
Cost is estimated for basic tasks such as engineeringdesign, tooling, fabrication of parts, manufacturing
engineering, and quality control.
�
The cost of materials is estimated or obtained fromthe supplier.
�
The approach requires detailed and accurate data andshould 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,
ARTICLE IN PRESSR. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534512
�
Costs incurred for a collection of design features: at acomponent or batch level and
�
Costs assigned to design features: at an order orfacility 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
ARTICLE IN PRESSR. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534 513
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).
R. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534514
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 conditionsthat 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 theindividual 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
ARTICLE IN PRESSR. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534 515
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.
R. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534516
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
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Fig. 20. Cumulative likelihood of occurrence.
R. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534 517
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, missingor irrelevant entries.
2.
Data integration: for the integration of multiple,heterogeneous data sources.
3.
Data selection: to retrieve data that is relevant to theanalysis task.
4.
Data transformation: for consolidation throughsumming or aggregation.
5.
Data mining: where intelligent methods are applied toextract data patterns.
6.
Data pattern evaluation: to identify the significantpatterns 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
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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 eachactivity performed.
�
Absorption costing: assigning cost according toresources utilised.
�
Bottom-up costing: accumulating cost from the BOMand Work Breakdown Structure (WBS).
�
LCC: attributing costs to all stages of the life cyclefrom ‘womb to tomb’
Scenario-based reasoning: a subset projecting and
forecasting future product scenarios, inclusive of
market.
�
Feature-based costing: attributing cost to geometricpart 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 timerequired to carry out work.
�
Parametrics: stochastic relations within productclasses.
�
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 costfunctions.
�
Financial modelling: using mathematical series forcost 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 costsrather than first developing a causal understanding
that is a basis for that modelling:
function over foundation!
�
Modelling is directed towards a particular element ofcost but is not mindful of the holistic cost architecture:
micro over macro!
ARTICLE IN PRESSR. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534 519
�
Modelling is directed towards a particular stage in thecost life cycle and is not mindful of the holistic cost
structure:
inhibiting over inheriting!
�
Methodologies are based primarily on a mechanisticapproach 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.
ARTICLE IN PRESSR. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534520
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 economicscenario.
(d)
Cost can be broken down into a number of distinctcategories.
(e)
There is a small number of discrete primary costdrivers 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 ofparent 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 attributeof 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 environmentalfactors such as economics (supply and demand) and
technology.
�
Cost can be broken down into hierarchical groupingsthat have their own distinct influence or nature.
�
Fundamentally, cost is caused by a small number ofbase cost drivers: materials, time, and energy.
�
It is the manner in which these base cost drivers areformulated which dictates the cost categorizations.
�
The genetic nature of cost gives rise to the concept ofinheritance, 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
ARTICLE IN PRESSR. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534 521
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 partnumber 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 andnon-recurring costs.
9.
Unit production cost decreases with number ofunits, learning and ‘economies of scale’.
Finally, the overriding law of economics applies:
10.
All costs are adjusted by environmental equilibriumthrough the law of ‘supply and demand’.
In summary, there is a hierarchical framework:
�
The basic resources of materials, labour time andenergy are the fundamental building blocks of cost.
�
The product definition is the primary cost driver andimbues cost into a design.
�
The production process is the subsequent cost driverand actualises that propensity to have cost.
�
The environmental market scenario will drive designeffort 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 linkedto 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
DesignDesignattributeattribute
Cost family 1Cost family 1
Part count
Weight
Size Features
Fastenercount
Geometricshape
DesignDesignattributeattribute
Cost family 1Cost family 1
Cost families1-n =>• Product• Phase• Process
Fig. 21. Conceptual illustration of the genetic causal cost modelling approach.
R. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534522
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.
R. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534 523
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.
R. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534524
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
Skin Stringers Frames Cleats Rivets
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
Skin Stringers Frames Cleats Rivets
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.
R. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534 525
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:
R. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534526
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).
R. Curran et al. / Progress in Aerospace Sciences 40 (2004) 487–534 527
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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