aircraft cost model for preliminary design...

Aircraft Cost Model for Preliminary Design Synthesis

Tim Lammering�, Katharina Franz�, Kristof Risse�,

Ralf Hoernschemeyeryand Eike Stumpfz

Institute of Aeronautics and Astronautics (ILR), RWTH Aachen University, 52062 Aachen, Germany

A new methodology for assessment of cost and bene�t in preliminary aircraft design ispresented. The proposed model allows for estimating aircraft list price, unit costs, as well asnon-recurring and recurring costs for development and production. Further, the targetedaircraft units during life cycle and for break-even are estimated. Focus is put on civil jettransport aircraft and on applicability of the proposed model in early preliminary design.Although the proposed model is tailored for early design, it shows the required sensitivitiesto important design parameters, so that design trade-o�s can be assessed in terms of costs.The di�erent cost items are derived from a combined top-down and bottom-up approach.The model is based on list price data of current transport aircraft in combination with semi-empirical analyses that are published in literature. The proposed methodology is directlyintegrated into the ILR Preliminary Aircraft Design Suite for fast assessment of new aircraftconcepts and was veri�ed against current aircraft cost data. In the scope of this paper,sensitivity studies are presented to show the in uences of di�erent design parameters oncosts. In a case study, the proposed model is applied to an existing aircraft program. Inanother case study, the proposed model is fully integrated into preliminary design synthesisand the resulting in uence of costs on multi-disciplinary design optimization are discussed.

Nomenclature

’25;wing Quarter chord sweep, degb Span, mCA=C Aircraft unit costs, 2010-USDCF Communality factorCP Communality percentageD Discount per unit sold, 2010-USDE Earnings per unit sold, 2010-USDi Relative interest rate per period re-

quired for production of one aircraftIA=C Investment costs, 2010-USDl Length, mLFL Landing �eld length, mmpayload;max Maximum payload, kgMFW Maximum fuel weight, kgMTOW Maximum take-o� weight, kgnengines Number of enginesNPV Net present value, 2010-USDNRC Non-recurring costs, 2010-USD

OWE Operating weight empty, kgP List price function, 2010-USDPA=C Aircraft list price, 2010-USDpA=C Monthly production rate, 1/monthPengine Engine power (turboprop), kWPAX PassengersQBE Aircraft units for break-evenQLC Aircraft units during life cycleR Range, NMR2 Coe�cient of determinationRC Recurring costs per unit, 2010-USDS Reference area, kg=m2

SLST Sea level static thrust (jet), kNSTD Statistical standard deviationTOFL Take-o� �eld length, mv Velocity, ktsVMO Maximum operating speed, ktsWC Component weight, kgwfuselage Fuselage width, m

�Researcher and PhD student, Institute of Aeronautics and Astronautics (ILR), RWTH Aachen University, Wuellnerstrasse 7,52062 Aachen/Germany.yAcademic Counselor, Institute of Aeronautics and Astronautics (ILR), RWTH Aachen University, Wuellnerstrasse 7,

52062 Aachen/Germany.zChair of Aeronautics and Astronautics and head of institute, Institute of Aeronautics and Astronautics (ILR), RWTH

Aachen University, Wuellnerstrasse 7, 52062 Aachen/Germany.

1 of 21

American Institute of Aeronautics and Astronautics

50th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition09 - 12 January 2012, Nashville, Tennessee

AIAA 2012-0686

Copyright © 2012 by Insitute of Aeronautics and Astronautics, RWTH Aachen University. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.

I. Introduction

Commercial transport aircraft are value creating products.1,2 Hence, it is the main goal to designaircraft for maximum value rather than only for performance or comfort. Experience shows that a high-

performing, absolutely state-of-the-art aircraft, like the Concorde was, does not turn out to be commerciallysuccessful. Instead, well balanced designs turn out to be the most successful ones. Measuring the actualproduct value, however, is not easy in preliminary design. Today, aircraft are assessed based on costs ingeneral, and on direct operating costs (DOC) in particular.1,3, 4 Recently, focus is put more on assessmenton overall life cycle costs (LCC).5{9 Operating costs are to be minimized from the customer’s perspective.The manufacture, however, measures product pro�tability typically in net present value (NPV).10 A designthat shows lowest operating costs does not necessarily lead to highest net present value. Hence, bothparameters have to be assessed already in preliminary aircraft design synthesis. The design space is stillwide in preliminary design. Many di�erent design options are explored and assessed against each other. Thedesign space is then narrowed by down-selection of the most promising concepts. Thus, for decision making,reliable models are already required in preliminary design. It is especially important that such models showsu�cient sensitivity to the relevant design parameters, so that design trade-o�s are mirrored in the results.

Numerous research on cost estimation during preliminary aircraft design have been published in thepast. This holds for single cost items like e.g. manufacturing costs, as well as for the overall life cyclecosts. Comprehensive overviews of past research on this topic are given by Johnson,11 Asiedu and Gu,12

Eaglesham13 or Thokala.14 Existing cost models are either detailed cost accounting methods,15{17 or usesimple semi-empirical cost functions.6,7, 18{21 A drawback of detailed models is that they are generally notapplicable in early design phases, due to still unknown input parameters. Simple empirical functions, on theother hand, do not show the required sensitivities to the relevant design parameters. Tirovolis and Serghides22

published a reliable method for estimating aircraft list price from the most relevant design parameters duringpreliminary design. Their model, however, only determines aircraft list price and does not estimate othercost items. Many of the currently published studies focus on increasing accuracy of cost estimation in earlydesign phase, the general idea is to combine semi-empirical methods with detailed cost accounting methods.For example, within the Implied Cost Evaluation System (ICES), a product data structure was developed byScanlan et al.,23 which contains several examples of costs for design and manufacturing of components, withwhich quite accurate cost estimations can already be derived in an early design phase. Castagne et al.24 havedeveloped a generic model to estimate costs in aircraft design. Therein for di�erent aircraft components,several cost items are de�ned by cost equations, which have to be derived from detailed regression analysis.Hence, an extensive data collection is required. Furthermore, current studies on this topic often considerapproaches to overall cost-bene�t analyses to determine the feasibility of design cases. Lee and Olds25 havedeveloped a conceptual design tool for launch vehicle design, which also includes prediction of key businessindicators for business simulation. Markish10,26 sees the shareholder value, which is obtained by demand,price and costs of manufacturing as well as of taxes and interests, as \the ultimate objective function from theperspective of the �rm designing the aircraft". He has developed a framework wherein a cost model, a revenuemodel, as well as a performance model are linked to determine the net present value of an aircraft programunder consideration of market uncertainties. Based on this work, Peoples and Willcox27 have established anoptimization framework, which { besides technical parameters { also includes �nancial parameters into theoptimization process during conceptual aircraft design. The resulting designs can then be assessed againstbusiness risk due to technical and �nancial uncertainties.

In the scope of this paper, a new approach towards estimating di�erent cost items in preliminary aircraftdesign is presented. It includes estimates for aircraft list price, aircraft unit costs, as well as non-recurring(NRC) and recurring (RC) costs. Furthermore, it allows for assessing the business case by estimating thetargeted units of aircraft that ought to be sold during life cycle, as well as the net present value of thespeci�c aircraft program for given earnings. The proposed methodology is fully integrated into a frameworkfor preliminary aircraft design synthesis, which allows for fast assessment of di�erent designs and for multi-disciplinary design optimization with di�erent target functions. Sensitivity studies that are presented in thescope of this paper show the in uences of di�erent parameters on costs. The proposed model is also appliedin case studies to an existing aircraft program, as well as to preliminary design synthesis and optimization.

2 of 21


II. Overall Methodology for Aircraft Design and Cost Modeling

At the Institute of Aeronautics and Astronautics (ILR) of RWTH Aachen University a design methodologyfor multi-disciplinary preliminary aircraft design28{33 was developed, in which the presented aircraft cost

model is directly integrated for fast and reliable assessment of technology.

A. ILR Preliminary Design Suite

Top-Level Req’s

Aircraft Concept Design

MT

OW

co

nve

rgen

ce

Mu

lti-

dis

cip

lin

ary

op

tim

iza

tio

n

Aircraft Sizing

Wing Sizing Empenage Sizing

Engine Sizing Gear Sizing

Initial Sizing Fuselage Design

optional for design study

Specific Design & Analysis

Systems Stability & Control Structures

Aircraft Assessment

Scenario & Weighting Functions

Monetary Values Noise Emission Custom Criteria

Aircraft Performance Analysis

Performance Analysis

Mass Estimation Polar Estimation

Figure 1. ILR Preliminary Aircraft Design Suite.

From a de�ned set of top-level requirements anddesign speci�cations, a sizing of the aircraft is de-rived in terms of a general arrangement, see �gure 1.Analysis tools are then used to estimate mass andaerodynamic performance of the design. Based onthe general arrangement and estimated performancecharacteristics, the design mission is simulated toderive the required total loaded fuel and to checkfor changes in maximum take-o� weight (MTOW).The design loop is run until convergence is achieved.If started from a white sheet, the design synthesisgenerally takes less than 10 to 15 minutes depend-ing on the computer speed. After convergence, thedesign is assessed based on di�erent criteria suchas fuel e�ciency, costs, or emissions. Di�erent sce-narios as well as weighting functions can be ap-plied in the assessment. Based on the assessment,a multi-disciplinary design optimization (MDO) isconducted in which a de�ned parameter space is in-vestigated. The overall design synthesis has beenimplemented into a fully automated process: theILR Preliminary Aircraft Design Suite (IPADS).Optimized designs can be generated with only aminimum of required user input and required pre-processing e�orts.

B. Cost Modeling in IPADS: A CombinedTop-Down and Bottom-Up Approach

For assessment of commercial transport aircraft,monetary values play an important role. Di�erentitems like aircraft list price (PA=C), aircraft unitcosts (CA=C), recurring (RC) and non-recurring costs (NRC), direct operating costs (DOC) or life cy-cle costs (LCC) have to be considered for assessment of technology, see �gure 2. The di�erent cost itemsare not independent of each other but in uence each other. One example are the DOC, which are directlyin uenced by aircraft list price via depreciation and insurance costs. As highlighted in �gure 2, this paperconcentrates on an aircraft cost model that allows for estimating aircraft list price, unit costs, as well asrecurring and non-recurring costs within preliminary design synthesis. Models for estimating DOC and LCCare already integrated within IPADS but are not the focus of this paper. Before the di�erent underlyingmodels are described in greater detail, the overall methodology is described in the following paragraphs. Thestructural overview of the proposed methodology is illustrated in �gure 3.

1. Schematics of the Proposed Methodology

In a �rst step, aircraft list price is derived from aircraft design parameters with a top-down approach. Aircraftlist price is de�ned as the selling price that a customer pays for the entire aircraft and it is derived fromnon-linear regression analyses of di�erent relevant design parameters with the current list price of variouscommercial aircraft. According to equation 1, aircraft list price can be broken down into targeted aircraftunit costs (CA=C), a margin for possible discounts (D) and earnings (E). Since tax is generally applied

3 of 21


Monetary values

DOC LCCAircraft list price Aircraft units costs Recurring costs Non-recurring costs

Figure 2. Di�erent monetary values for assessment of aircraft designs.

Earnings (E)Aircraft unit costs (CA/C) Discount (D)

Non-recurring costs (NRC)Recurring costs (RC)

Aircraft list price (PA/C)

Top

-do

wn

...

Aircraft design synthesis

Bo

tto

m-u

p ..

.

Component recurring costs

Airframe Engine Systems Assembly

Investment (I) Units (QLC)

Net present value (NPV)

Figure 3. Structural overview of the proposed aircraft cost model.

after the list price in the purchase of commercial aircraft, tax can be neglected in scope of the proposedmodel. Targeted aircraft unit costs can further be split into recurring (RC) and non-recurring costs (NRC),investment costs (IA=C), where the two later are divided by the targeted units (QLC) of aircraft that are oughtto be sold during product life cycle, see equation 2 and �gure 3. Investment costs depend on the interestrates and the investment that was made during product life cycle. For the proposed model a simpli�edapproach was chosen, which assumes constant interest rates (i) over the depreciation period. For constantproduction rates (pA=C), the timeframe of product life cycle can be substituted by the quotient of targetedunits and production rate. Investment costs can be calculated from equation 3.

PA=C = CA=C + D + E (1)

CA=C = RCA=C +NRCA=C

QLC+

IA=C

QLC(2)

IA=C = NRCA=C �h(1 + i)(QLC=pA=C) � 1

i(3)

Hence, for a given PA=C , as well as for given discounts, earnings and investment costs, equation 2 combinesthree unknowns: RCA=C , NRCA=C , and QLC . The targeted recurring costs per unit can then be derivedfrom initial estimates for non-recurring costs and for initial targeted units of aircraft from the followingequation:

RCA=C = CA=C �NRCA=C

QLC� (1 + i)(QLC=pA=C) (4)

To derive the remaining unknown QLC , Beltramo et al.34 provide a model for estimating aircraft recurringcosts from summation of recurring costs of the various components and of �nal assembly. The di�erentcomponent recurring costs are estimated from functions of component weights (WC;i) and aircraft units, seeequation 5. The component weights directly result from overall aircraft design synthesis and are required

4 of 21


input of the proposed methodology. The required aircraft units can then iteratively be computed, so thataccumulated component recurring costs match total targeted recurring costs per aircraft by equalizing resultsfrom equation5 and 4.

RCA=C =

nXi=1

RCC;i =

nXi=1

f(QLC ;WC;i) (5)

It has to be kept in mind that for the top-down approach a starting value for non-recurring costs isused to derive initial targeted recurring costs. For estimating non-recurring costs a model by Roskam7 isproposed. Total aircraft non-recurring costs are estimated from component costs, required man month fordevelopment and applicable labor rates. The estimated component costs from the model by Beltramo et al.34

are fed into the non-recurring costs model and a new estimate for non-recurring costs is derived. The modelsfor recurring and non-recurring costs are then iteratively run until a convergence in costs and the targetedunits is achieved, cf. �gure 3.

2. Net Present Value and Estimating Break-Even

sold units

tota

l ea

rnin

gs

0

earnings

non-recurring costs

recurring costs

revenue

break-even+

-

Figure 4. Dependency of the break-even point.

The ILR cost model also allows for estimating the netpresent value as well as the break-even point of the air-craft development project. In cost-bene�t analysis, thenet present value is a reliable indicator for measuring thebene�t for the company’s stakeholders that is gained froma speci�c product. The net present value is de�ned by therequired investments and the cash ow during product lifecycle.35 In the proposed model it is assumed that the re-quired investments equal the non-recurring costs of theproject. For constant interest rates, the net present valuecan be estimated from the following equation:

NPV = �NRC +

QLCXj=1

PA=C �D �RCA=C

(1 + i)j=pA=C(6)

As shown in �gure 4, break-even is achieved when totalprogram costs match total revenue.36 For break-even thenet present value also equals zero.35 Thus, the required units for break-even can be calculated iterativelyfrom equation 6, so that the net present value equals zero.

III. Underlying Models

In this section the underlying models for estimating the di�erent cost items are described in more detail.Due to the impact of in ation, it is important to relate all prices and costs to the same reference year. In

the proposed model, 2010-USD are consistently used as reference. If prices or costs are given for a di�erentreference year, the consumer price index (CPI) theory37 is used for conversion to 2010-USD. Only the aircraftlist price model was newly derived by the authors. All other implemented models are taken from literatureand were already validated by the di�erent authors. An explicit validation is therefore only provided for theaircraft list price in the scope of this paper. However, the presented sensitivity studies as well as the casestudies proofs plausibility in the obtained results of the proposed methodology.

A. Aircraft List Price

The aircraft list price is estimated from a semi-empirical model that was developed by the authors. Thenecessary data set was established by collecting technical as well as list price data of 101 aircraft. All aircraftthat are included in the regression analysis are listed in table 1. The focus lies on commercial jet airliners.Nevertheless, regional (jets and turboprops) as well as business aircraft are also included; small generalaviation aircraft are not considered.

5 of 21


The technical design and performance parameters mainly originate from publication by the Jane’sGroup38,39 and airport planning manuals (e.g. references40,41) that are published directly by the manu-facturers. For information on aircraft list price, two primary sources { Jane’s Group38,39 and Lloyd’s42 {were used that publish current selling prices of di�erent aircraft models. Information provided by manufac-turer’s press releases (e.g. references43) were also used as secondary sources if available. For most aircraft,a range of list prices is given rather than one speci�c price. This is due to di�erences in equipment andfurnishing between aircraft models as well as due to di�erences in sales discounts for di�erent customers.For the regression analysis, on which the proposed model is based, average list prices for each aircraft modelhave been used.

Table 1. Aircraft used for regression analysis to determine aircraft list price.

Airbus Boeing and McDonnellDouglas

A300 B2-100 A330-200 707 757-200 DC-10-10

A300 B4-200 A330-200F 717-200 757-200F DC-10-30

A300 B4-600 A330-300 727-200 757-300 DC-10-40

A310-200 A340-200 737-300 767-200 MD-11

A310-300 A340-300 737-600 767-200ER MD-11F

A318-100 A340-500 737-700 767-300 MD-80 81

A319-100 A340-600 737-800 767-300ER MD-80 82

A320-200 A380-800 737-900 777-200 MD-80 83

A321-100 747-100 777-200ER MD-80 87

A321-200 747-SP 777-200LR MD-80 90-30

747-200 777-300

747-400 787-3

747-400F 787-8

747-8 787-9

Bombardier Lockheed Embraer Dassault & ATR Others

CRJ-200 L-1011-1 ERJ 135 Dassault Falcon 50 Dornier 328-100

CRJ-700 L-1011-200 ERJ 145 Dassault Falcon 900 Dornier 328 Jet

CRJ-900 L-1011-250 E-Jet 170 Dassault Falcon 2000 Fokker 70

CRJ-1000 L-1011-500 E-Jet 190 Dassault Falcon 7X Fokker 100

Dash 8Q-200 ATR 42-300 Gulfstream V

Dash 8Q-300 ATR 42-500 Gulfstream G400

Dash 8Q-400 ATR 72-500 Gulfstream G500

Global 5000 ATR 72-600 Gulfstream G550

Global Express Gulfstream G650

Challenger 300 Sukhoi SSJ 100-95

Learjet 40 BAE 146-200

Learjet 45

Non-linear regressions between the aircraft list prices, and all technical design and performance param-eters were conducted. Di�erent regression functions were applied to the data and assessed by means ofcoe�cient of determination (R2), which gives direct measure for the correlation of each �t. For each param-eter, the regression function that shows the highest R2 is implemented within the list price model. From alarge set of design and performance parameters only those were implemented that show �ts with a R2 greaterthan 0.85. A total of 27 design parameters were identi�ed that show this close correlation with aircraft listprice, see table 2. All of the identi�ed parameters are already available within preliminary aircraft designsynthesis in IPADS. Also the most important parameters for design trade-o�s are covered, which lets the

6 of 21


Table 2. Technical parameter used for regression analysis.

Weights & Payload Propulsion Geometry Wing Geometry Other Performance

MTOW Type of Engine Sref Stail, Sfin vinitial climb

OWE nengines bwing btail, bfin vapproach

mpayload;max SLST ’25;wing lfuselage Rmax payload

MFW Pengine wfuselage Rmax fuel

PAX lwheel track Rferry

lwheel base TOFL

LFL

estimated aircraft list price show the required sensitivity for assessing design trade-o�s. In �gure 5, theobtained �ts for two design parameters (MTOW and lfuselage) are shown exemplarily.

0

100

200

300

400

0 100 200 300 400 500 600

Aircra

ft lis

t p

rice

, m

. 2

01

0-$

MTOW, 1,000 kg

PMTOW

= 0.0059 · MTOW0.83

R2 = 0.94

Jane’s

Lloyd’s

Others

(a) Maximum take-o� weight

0

100

200

300

400

10 20 30 40 50 60 70 80

Aircra

ft lis

t p

rice

, m

. 2

01

0-$

lfuselage, m

Pl,fuselage = 0.0079 · lfuselage2.43

+ 7.4

R2 = 0.90

Jane’s

Lloyd’s

Others

(b) Fuselage length

Figure 5. Exemplary data �ts for estimating aircraft list price.

From the regression analyses, 27 functions are obtained that all give a non-linear relation between aspeci�c design parameter and aircraft list price. Overall aircraft list price is then estimated by summationof the single obtained price functions (Pi), which are weighted with the corresponding R2, see equation 7.Weighting of the di�erent price functions with the corresponding R2 allows for re ecting the quality ofcorrelation on the in uence of the speci�c design parameter on overall aircraft list price.

PA=C =

Pni=1(R2

i �Pi)Pni=1 R

2i

=R2

MTOW �PMTOW + ::: + R2LFL �PLFLPn

i=1 R2i

(7)

Table 3. Statistical deviation of aircraft list price.

relative mean deviation �PA=C 1.4 % �16:8%

maximum mean deviation �PA=Cmax25.3 %

absolute mean deviation��PA=C

�� 8.0 %

standard deviation STD 8.4 %

The proposed aircraft list price model isdirectly validated against the collected pricedata, for which regression analyses were con-ducted. Figure 6 illustrates relative deviationsof estimated list prices from prices given byboth Jane’s39 and Lloyd’s.42 For the sakeof clarity, only deviations for current Airbusand Boeing aircraft are plotted. For these air-craft, absolute maximum deviation lies well be-low 25 %, whereas absolute mean deviation is 6.8 %. Validation by means of all included aircraft resultsin an absolute mean deviation of 8 % and an absolute maximum deviation of 25.3 %. Statistical standarddeviation (STD) is 8.4 % for the given dataset. Data is randomly scattered about the means and maximumdeviations are within limits, so that statistical normal distribution can be assumed. Thus, a con�dence inter-

7 of 21


vall with a certainty of 95 % that estimated values lie within it, is given by �2 �STD. Statistical data of thevalidation are summarized in table 3. The aircraft list price model shows su�cient accuracy for applicationin preliminary design.

-20

-10

0

10

20

717-

200

737-

300

737-

600

737-

700

737-

800

737-

900

747-

400

757-

200

757-

300

767-

200

767-

200E

R

767-

300

767-

300E

R

767-

400E

R

777-

200

777-

200E

R

777-

200L

R

777-

300

A300B

4-60

0

A310-

300

A318-

100

A319-

100

A320-

200

A321-

100

A330-

200

A330-

300

A340-

200

A340-

300

A340-

500

A340-

600

A380-

800

Devia

tion

, %

Deviation to Price given by Jane’s

Deviation to Price given by Lloyd’s

Figure 6. Relative deviations of estimated list prices for current Airbus and Boeing aircraft.

B. Non-Recurring Costs

For estimating non-recurring costs the semi-empirical model that is given by Roskam7 has been selected.It is based on the �rst Development and Procurement Costs of Aircraft (DAPCA) model developed by theRAND Corporation.44 Since the DAPCA model was developed on the basis of military aircraft data, it wasassessed for application to commercial aircraft against two other semi-empirical models: the latest version ofDAPCA IV6 and the Aircraft Cost Estimation Methodology developed by Burns,20 which both are also basedon military aircraft. Non-recurring costs of twenty commercial aircraft, see table 7, were estimated with thedi�erent methods and compared against published non-recurring costs from literature. Manufacturer’s pressreleases, e.g. published in Flight International45 and Flug Revue,46 were used; all cost data was convertedto 2010-USD. The comparison showed that best results were obtained with the Roskam model.

Accuracy of the Roskam model has been improved further by the authors, by implementation of acommunality factor (CF ) that considers cost reductions when using components from parent aircraft alsofor derivatives. De�nition of the communality factor is based on a break down of non-recurring costs ontocomponent-level that is shown in �gure 8. It is representative for a typical commercial aircraft and originatesfrom Markish.10 Communality in landing gear is neglected due to the small impact of only 1 % in the proposedmodel. Communality factors can be chosen for all other components as user input. They are then multipliedwith the given cost percentage and accumulated to the overall communality factor as shown in the followingequation:

CF = 0:37 �CPfuselage + 0:2 �CPwing + 0:26 �CPsystems + 0:09 �CPempennage + 0:08 �CPengines (8)

For example, if an aircraft is a stretched derivative of an existing aircraft with new engines but no otherchanges: the CP of the fuselage is set to e.g. 80 %, the CP of the engine is set to zero and all other CPs areset to 100 %, which leads to an overall communality factor of 85 %. The non-recurring cost items, which area�ected by communality, are then reduced by this percentage. From the considered cost items as shown in�gure 9, only the production costs of ight test aircraft, except for tooling, are not reduced by communality.

Each cost item that is shown in �gure 9 is covered by a cost estimation relationship (CER), which allowsfor calculating non-recurring costs as function of di�erent aircraft design parameters. The labor costs for theitems: Airframe Engineering and Design, Manufacturing labor, Tooling and Quality Control are derived fromthe di�erent labor rates and the required labor hours. The latter can be estimated by the given CERs whichdepend on aircraft characteristics like maximum operating speed or maximum take-o� weight, as well as onprocess parameters like monthly rate of production or numbers of test aircraft that are produced. The costsfor Development Support & Testing, Flight Test Operations, Test & Simulation Facilities and Manufacturingmaterial are directly estimated by CERs, and are multiplied with the CPI for conversion to the actual year.

8 of 21


Figure 7. Aircraft used for examination of NRC-models.

Boeing Airbus Bombardier Miscellaneous

737-800 A300 CRJ-200 Embraer 170

747-100 A318-100 CRJ-700 Embraer 190

747-8 A319-100 CRJ-1000 Dornier 728

777-200 A320 Gulfstream G650

A330-200 Mitsubishi MRJ

A340-600 Honda HA-420

A380-800

installedengines

8 %

landinggear 1%

wing20 %

tail 9 %

systems26 %

fuselage37 %

Figure 8. Non-recurring cost break downby parts for commercial aircraft.10

Since engines and avionics are treated as supplier parts, the component costs from the recurring cost modelare fed into the corresponding CERs, cf. �gure 3.

Non-recurring costs (NRC)

Airframe Engineering & Design

Development Support & Testing

Flight Test Airplanes

Flight Test Operations

Test & Simulation Facilities

Engine & Avionics

Manufacturing laborManufacturing

materialTooling Quality control

Figure 9. Cost components of non-recurring costs.

C. Recurring Costs

In accordance to �gure3, the implemented model for estimating recurring costs per unit still have to bedescribed in more detail. The break down of overall aircraft recurring costs into component-level is desirablefor assessment of technology and for evaluation of design trade o�s. Models that allow such a break downonto component-level are rare for application in preliminary aircraft design synthesis. Often simple cost breakdowns that are based on a �xed percentage are used instead. Such models, however, do not show the requiredsensitivities for trade studies. One of these cost break downs was for example proposed by Markish.10 Inthe herein presented methodology, a component cost model that was published by Beltramo et al.34 is used.The model allows for estimating recurring costs for the following aircraft components:

� wing,

� fuselage,

� empennage,

� nacelles,

� landing gear,

� propulsion,

� systems, and

� �nal assembly.

The costs for the systems group can be further broken down into system-level by following the ATA-100classi�cation.47 Costs for the following systems are estimated by the implemented model:

9 of 21


� operator items,

� environmental control system (ATA-21),

� power systems (ATA-24, ATA-29, ATA-36 and ATA-49),

� furnishing (ATA-25),

� ight controls (ATA-27),

� avionics (ATA-22, ATA-23, ATA-31 and ATA-34), and

� support systems (ATA-26, ATA-28, ATA-30, ATA-33, ATA-35 and ATA-38).

Estimated component costs as well as system costs for a single-aisle aircraft are exemplarily shown in�gure 10. A break down for airframe, propulsion and total system costs is shown in �gure 10(a), whereas afurther break down of the system costs into system-level is provided in �gure 10(b).

Wing7 %

Fuselage11 %

Assembly20 %

Systems38 %

Engines15 %

Empennage 2 %

Nacelles 5 %

Gear 2 %

(a) Airframe, propulsion and systems

Operator items20 %

ECS 5 %

Power systems

14 %

Furnishing12 %

Flight controls

10 %

Avionics39 %

Support systems< 1 %

(b) Systems group

Figure 10. Component recurring costs break down for a single-aisle aircraft.

As already brie y described in the previous section, the model by Beltramo et al.34 estimates componentrecurring costs as function of component weight (WC;i) and the targeted units (QLC), cf. equation 5.Component weights are derived beforehand within the overall aircraft design synthesis, cf. �gure 1, andare strongly impacted by the relevant design parameters. In the proposed methodology, the model is usedto iteratively determine the targeted number of aircraft that has to be sold during product life cycle, suchthat the targeted recurring costs match the ones that are obtained from equation 4 for a given list price,non-recurring costs as well as given earnings, discounts and investment costs.

IV. Reference Aircraft for Sensitivity and Case Studies

For the sensitivity and case studies that are presented in the scope of this paper, the authors chooseto use a single-aisle aircraft in conventional con�guration and with conventional technology. Its top-levelrequirements, as well as its design speci�cations were speci�ed by Airbus Germany for academic purposes.48

A. General Arrangement

The aircraft is designed for 185 passengers in a typical two-class layout and for a maximum payload of23 t. Its design range is speci�ed to 4000 NM with a design payload of 16.3 t. Since the design rangeis signi�cantly longer than a typical mission during operations, an additional study mission is de�ned forassessment. The study mission has a range of 1000 NM and is own with maximum payload. The ILRPreliminary Aircraft Design Suite was used to design the reference aircraft for the top-level requirements.Minimum fuel burn was chosen as target function for design optimization, which resulted in a wing loadingof 650 kg=m2 and a thrust-to-weight ratio of 0.337, see �gure 11. Key speci�cations as well as calculatedperformance characteristics of the design are summarized in table 4. The general arrangement of the derivedreference aircraft is plotted in �gure 12.

10 of 21


520 540 560 580 600 620 640 660 0.330.34

0.350.36

0.37

0

5

10

15

20∆ Fuel, %

Wing Loading, kg/m 2 Thrust Loading, ---

Figure 11. Optimization of W/S and T/W for thereference aircraft. Figure 12. General arrangement of the reference air-

craft.

Table 4. Key speci�cations and performance characteristics of the reference aircraft.

(a) Design Speci�cations.

Parameter Values Units

MTOW 99,450 kg

OWE 50,986 kg

Wing loading 650 kg=m2

Thrust loading 0.337 |

Cruise mach number 0.80 |

Maximum payload 23,033 kg

Passengers 185 |

ULD devices 12 LD3-45W

(b) Performance for design and study mission.

Parameter Mission Mission Units

(design) (study)

Take-o� weight 99,450 85,506 kg

Payload 16,780 23,033 kg

Mission range 4,000 1,000 NM

Block time 09:20 02:41 hh:mm

Block fuel 27,256 7,657 kg

Total fuel 31,684 11,487 kg

Reserve fuel 4,428 3,830 kg

The level of technology of the propulsion system has signi�cant in uence on fuel e�ciency. For design ofthe reference aircraft, a validated thermodynamic model of the CFM56-5C2 engine is used. Within the designsynthesis, engine decks are slightly scaled by sea level static thrust to match the speci�c thrust requirements,which are derived from the required thrust to weight ratios of the design. The speci�c fuel consumptionremains constant when scaling the engines slightly.

B. Veri�cation of the Reference Design

For veri�cation of the conventional reference design, a preliminary aircraft design that was published byWerner-Spatz48 is used. He used similar top-level requirements and derived his design with the well docu-mented and veri�ed DLR PrADO49 (Preliminary Aircraft Design and Optimization Program) tool. Devia-tions in typical overall aircraft design parameters between the two designs are shown in �gure 13. It can beseen that similar top-level requirements have been used as input data and close agreement between the twodesigns are obtained. For all but one parameter, deviations are well below 5 %. A larger deviation can onlybe seen in the estimated required landing distance, which is highly sensitive to a semi-empirical breakingcoe�cient. Thus, it is likely caused by a di�erent chosen breaking coe�cient. For cost studies that are thescope of this paper, su�cient accuracy can be expected from the IPADS design methodology.

C. Application of Cost Model to Reference Aircraft

The results of the cost model when applied to the reference aircraft are summarized in table 5. As inputtargeted earnings of 3 %, an average discount of 7 % and interest rates of 5 % were assumed. Further,the reference aircraft is treated as entirely new white sheet design that features no commonalities to any

11 of 21


-5

-4

-3

-2

-1

0

1

2

3

4

5

Dev

iati

on, %

T/W

0%

W/S

0%0%

des

ign r

ange

0%

des

ign p

aylo

ad

0%

L/D

cru

ise

+ 1.4%

win

g m

ass

+ 0.5%

fuse

lage

mas

s

- 1.0%

engin

e m

ass

+ 3.4%

OW

E

+ 0.8%

MT

OW

+ 0.2%

trip

fu

el

- 0.7%

take-

off

dis

tance

req

uir

ed

+ 2.7%

landin

gdis

tance

req

uir

ed

- 16 %

input parametersfor aircraft design synthesis

results of design synthesison overall aircraft-level

Figure 13. Comparison between DLR PrADO and ILR IPADS preliminary aircraft designs.

parent aircraft. Compared to today’s single-aisle aircraft, the estimated list price is approximately 20 %higher. This, however, seems realistic, since the reference aircraft features higher payload and higher rangerequirements then the A320 or 737 family, which again leads to e.g. higher design weights. The same istrue for the estimated unit costs and the recurring costs per unit. Total non-recurring costs of 2.4 billionUSD seem to be rather low when compared to other programs. However, this design does not consider anyderivatives, which are common for aircraft families, but one single baseline design only. The same holdsfor the low targeted units during life cycle and for break-even. Other than for a technical assessment of aspeci�c aircraft design, it is important to consider the entire aircraft family for a cost bene�t analysis. Ina case study, which follows later, the cost model is applied examplarily to the entire A320 family to showthe bene�ts of the proposed model. Results show more realistic estimates regarding non-recurring costs andrequired units, other than for the single baseline aircraft.

V. Sensitivities of the Proposed Model

Table 5. Results of Cost Model for Reference Aircraft.

Parameter Result Units

Aircraft list price 90.2 m. 2010-USD

Aircraft unit costs 75.5 m. 2010-USD

Non-recurring costs 2,428 m. 2010-USD

Recurring costs 71.3 m. 2010-USD

Targeted units LC 713 {

Units for break-even 224 {

In the following section, the sensitivities of theproposed model towards selected parameters are dis-cussed. The reference aircraft was used as baselinefor the presented sensitivity studies. The studies arestructured into sensitivities of the model towards:(A) program parameters, (B) aircraft design param-eters, and (C) top-level aircraft requirements, andare intended to show the main in uences on themodel. The aircraft is not re-sized for investigationsof the sensitivities towards program parameters andaircraft design parameters, so that snowball e�ectsfrom overall design synthesis do not impact the re-sults. The reference aircraft was only fully re-sized for capturing the sensitivities of the proposed modeltowards top-level aircraft requirements. Here, changes in top-level requirement �rst have to propagatethrough the entire design synthesis before an in uence on the proposed model can be assessed.

A. Program Parameters

Program parameters are direct input for the proposed model, e.g. labor rates or targeted earnings, butthey do not in uence the aircraft design synthesis in any other way. The strongest sensitivities towards theprogram parameters are summarized in �gure 14. Aircraft list price is determined only from overall aircraftdesign parameters, hence it shows no sensitivity towards the program parameters, cf. �gure 14(a) - 14(d).

12 of 21


-25

-20

-15

-10

-5

0

5

10

15

20

25

-25 -20 -15 -10 -5 0 5 10 15 20 25

∆ R

esu

lts,

%

∆ Discount, %

PA/C

NRC

RC

QLC

QBE

(a) Sensitivities towards discount rates.

-25

-20

-15

-10

-5

0

5

10

15

20

25

-25 -20 -15 -10 -5 0 5 10 15 20 25

∆ R

esu

lts,

%

∆ Earnings, %

(b) Sensitivity towards earnings.

-25

-20

-15

-10

-5

0

5

10

15

20

25

-25 -20 -15 -10 -5 0 5 10 15 20 25

∆ R

esu

lts,

%

∆ Labor Rates, %

(c) Sensitivity towards labor rates.

-25

-20

-15

-10

-5

0

5

10

15

20

25

-25 -20 -15 -10 -5 0 5 10 15 20 25

∆ R

esu

lts,

%

∆ Aircraft Communality, %

(d) Sensitivity towards aircraft communality.

Figure 14. Sensitivities of the cost model towards program parameters.

The assumed discount rate mainly in uences targeted units during life cycle and for break-even, see�gure 14(a). For higher discounts, the margin for amortization of development and capital costs per unitbecomes narrower, and thus more units have to be sold. The slight decrease in recurring cost is not directlycaused by an increase in discount, but by the increase in targeted units. The more aircraft are produced,the lower are the production costs per unit. Sensitivities of the model are closely linear towards changesin discount rate. The model shows the same sensitivities towards changes in interest rate. The impact,however, is of a lower order since interest rates are usually also of a lower order then discount rates.

Sensitivities towards changes in the targeted earnings are shown in �gure 14(b). They also mainlyin uence the targeted units during life cycle and for break-even. The in uence, however, is di�erent for thetwo parameters. Whereas the targeted units during life cycle increase with higher earnings, the requiredunits for break-even decrease. The increase in targeted units is caused by the �xed list price and the resultinglower acceptable unit costs. Recurring costs have to decrease, and thus more aircraft have to be produced.Break-even, however, is reached earlier for higher earnings, since the margin for amortization of programcosts is higher as stated in equation 6.

As shown in �gure 14(c), labor rates have the strongest in uence on the results. Non-recurring costssigni�cantly increase with higher labor rates, recurring costs would have to increase accordingly. Since theproposed methodology assumes targeted recurring costs that are derived from equation 4, estimated recurringcosts cannot increase as direct consequence of increased labor rates. Instead more targeted units are requiredfor keeping recurring costs down despite higher labor costs. However, a non-linear increase also in recurringcosts can be observed from �gure 14(c). It is caused by changes in non-recurring costs and targeted units,

13 of 21


cf. equation 4. The impact of increased labor rates on targeted units and for break-even is high, due to thetwo discussed in uences on non-recurring and recurring costs. Margins for cost amortization decrease forhigher labor costs, and thus more aircraft have to be sold.

Aircraft communality mainly in uences the required non-recurring costs, see �gure 14(d). For increasedcommunality, the required non-recurring costs decrease. Simultaneously, less units have to be sold for break-even and during the life cycle. Recurring costs are only slightly impacted by the decrease in targeted units.Communality is a useful factor for assessing entire aircraft families as it is later shown in the presented casestudy.

B. Aircraft Design Parameters

In a second step, sensitivities towards aircraft design parameters are discussed, see �gure 15. Aircraft listprice shows sensitivity to all parameters that are listed in table 2. Most of these parameters only in uenceaircraft list price and not the other cost items. For these parameters, the proposed model shows the sametrends in its sensitivity. Thus, only one is exemplarily discussed here. It can be observed from �gure 15(a)that the aircraft list price increases with increasing number of passengers. Since the aircraft list price isestimated from a total of 27 parameters, the sensitivity towards changes in one of them is of low order.Non-recurring costs are not in uenced by a change in aircraft list price. For a higher list price and constantnon-recurring costs, the acceptable recurring costs also increase, cf. equation 4. Thus, less units are requiredduring life cycle and for break-even.

Maximum operating speed (VMO) is only an input parameter for the non-recurring model. Thus, itonly directly in uences the estimated non-recurring costs but no other cost items as shown in �gure 15(b).The non-recurring cost model by Roskam7 uses the maximum operating speed as characteristic parameterfor measuring the performance requirements of a design. Accordingly non-recurring costs increase withmaximum operating speed. As discussed beforehand, more units are required for amortization if non-recurring costs increase.

Aircraft list price and non-recurring costs are not impacted by changes in component weights, see�gure 15(c). In the model by Beltramo et al.,34 an increase in component weights would directly leadto an increase in estimated recurring costs for a �xed unit count. Since the acceptable recurring costs aredetermined from equation 4, they are only in uenced slightly. Instead units that are required to keep therecurring costs down are increasing signi�cantly as shown in �gure 15(c). Both the sensitivities of unitsduring life cycle and for break-even are non-linear, due to the exponential characteristic of equation 4.

A change in maximum take-o� weight directly impacts the estimated aircraft list price as well as theestimated non-recurring costs, see �gure 15(d). The impact on list price is much higher than that shownin �gure 15(a). Hence, propagation to recurring costs, as well as to required units during life cycle andfor break-even is much stronger. These impacts, however, are superposed by the increase in non-recurringcosts and its impact that was discussed beforehand for the example of changes in maximum operating speed.The in uence of list price is much stronger, so that the required units decrease and the units for break-evenalmost stay constant. The sensitivity of both parameters is also non-linear.

C. Top-Level Aircraft Requirements

Finally, sensitivities towards changes in top-level aircraft requirements are discussed, see �gure 16. Otherthan before, the reference aircraft is fully re-sized with IPADS according to the changes in top-level require-ments before the cost model is applied. This allows for the changes in top-level requirements to propagatethrough the entire design synthesis. Hence, many aircraft design parameters are simultaneously in uenced,and the trace back of single in uences on the results of the proposed cost model is hardly possible. Both theincrease in design payload and design range for example lead to a quasi \in ation" of the aircraft. Hence,masses (MTOW and component weights), dimensions or thrust requirements increase, whereas the perfor-mance (e.g. TOFL or LFL) degrades. The impact of changes in design payload and design range can beobserved from �gure 16. It is clearly non-linear and superposes the in uence of changes in di�erent designparameters.

The presented sensitivity studies underline that the proposed model shows the required sensitivities to-wards important design parameters. Further, sensitivities are of correct order as well as of correct magnitude.Thus, merging of the single models into the proposed methodology seems reasonable.

14 of 21


-25

-20

-15

-10

-5

0

5

10

15

20

25

-25 -20 -15 -10 -5 0 5 10 15 20 25

∆ R

esu

lts,

%

∆ Passengers, %

PA/C

NRC

RC

QLC

QBE

(a) Sensitivities towards number of passengers.

-25

-20

-15

-10

-5

0

5

10

15

20

25

-25 -20 -15 -10 -5 0 5 10 15 20 25

∆ R

esu

lts,

%

∆ VMO, %

(b) Sensitivity towards maximum operating speed.

-50

-40

-30

-20

-10

0

10

20

30

40

50

-25 -20 -15 -10 -5 0 5 10 15 20 25

∆ R

esu

lts,

%

∆ Component Weights, %

(c) Sensitivity towards component weights.

-25

-20

-15

-10

-5

0

5

10

15

20

25

-25 -20 -15 -10 -5 0 5 10 15 20 25

∆ R

esu

lts,

%

∆ MTOW, %

(d) Sensitivity towards maximum take-o� weight.

Figure 15. Sensitivities of the cost model towards aircraft design parameter.

-20

-16

-12

-8

-4

0

4

8

12

16

20

-25 -20 -15 -10 -5 0 5 10 15 20 25

∆ R

esu

lts,

%

∆ Design Payload, %

PA/C

NRC

RC

QLC

QBE

(a) Sensitivities towards design payload.

-20

-16

-12

-8

-4

0

4

8

12

16

20

-25 -20 -15 -10 -5 0 5 10 15 20 25

∆ R

esu

lts,

%

∆ Design Range, %

(b) Sensitivity towards design range.

Figure 16. Sensitivities of the cost model towards top-level aircraft requirements.

15 of 21


VI. Case Studies

In the following section, case studies are presented that show the application of the proposed model bothto existing aircraft families as well as to overall design synthesis.

A. Application to an Existing Aircraft Family

In a �rst case study, the proposed cost model is applied to an existing aircraft family program for evaluationof costs and bene�ts. The authors choose to apply the model to the Airbus A320-family, since the singleaircraft are well documented and veri�ed in-house IPADS models are available. Results of the cost model foreach aircraft are summarized in table 6. Close communality towards the A320 parent aircraft is assumed forthe derivatives. No reliable data for veri�cation of the cost items is available from literature. However, totalnon-recurring costs of 4.6 billion USD seem to be in the right order for a single-aisle program with a whitesheet design baseline aircraft and three derivatives. For assumed earnings of 3 % the required units duringlife cycle result in 699 aircraft, break-even is achieved for 526 aircaft. Sparaco50 states that at program startAirbus intended to sell only 600 aircraft of that family. Thus, the assumed earnings seem to be realistic forthe given aircraft program. Today Airbus, however, has secured order of nearly 8,000 A320-family aircraft.Comparing the calculated net presented value for 600 aircraft with the one for 8,000 aircraft shows an increasein pro�tability of the program by a factor of 11.

Table 6. Results of the Cost Model for the Airbus A320-family aircraft.

Parameter A320-200 A318-100 A319-100 A321-200 Total family Units

PA=C 67.7 63.4 64.6 74.4 { m. 2010-USD

CA=C 65.7 61.5 62.6 72.2 { m. 2010-USD

NRCA=C 1,960 771 792 1,005 4,598 m. 2010-USD

RCA=C 56.9 54.8 56.5 67.4 { m. 2010-USD

QLC 233 116 132 218 699 {

QBE 188 91 99 148 526 {

1

10

100

1000

10000

0 250 500 750 1000 1250 1500 1750 2000

NP

V, m

. 2010-U

SD

Units

Figure 17. Estimated net present value for A320NEOprogram (logarithmic scale).

Recently Airbus has o�cially launched the re-engined NEO derivatives of its A319, A320 andA321 aircraft. Sparaco50 states total non-recurringcosts of approximately 1.5 billion USD for the NEOprogram, and Airbus51 claims communality in air-frame structure and systems of 95 % compared totodays A320-family aircraft. When neglecting smalldeviations in design parameters, such as changes ine.g. engine weight of the NEO aircraft, the pro-posed model can be used to assess pro�tability ofthe program. The same metrics as for the abovediscussed results were used to obtain the costs es-timates that are listed in table 7. Further, a com-munality in airframe structure and systems of 95 %as well as entirely new engines were assumed. Esti-mated non-recurring costs of 1.56 billion USD matchthose stated by Sparaco well. For targeted earnings of 3 % per aircraft, the program requires 280 aircraft forbreak-even. Targeted units during life cycle are estimated to approximately 450 aircraft. Already today Air-bus has secured orders and commitments for nearly 1,200 NEO aircraft.50 The estimated net present valuesof the program for di�erent assumed units are shown in �gure 17, which expressly underlines pro�tability ofthe program for Airbus.

16 of 21


Table 7. Results of the Cost Model for the A320NEO family aircraft.

Parameter A320NEO A319NEO A321NEO Total family Units

PA=C 67.7 64.6 74.4 { m. 2010-USD

CA=C 65.7 62.6 72.2 { m. 2010-USD

NRCA=C 561 446 552 1,560 m. 2010-USD

RCA=C 61.8 58.5 69.2 { m. 2010-USD

QLC 149 109 189 447 {

QBE 97 75 108 280 {

B. Design Optimization for Fuel E�ciency and Cost

To show the in uences of costs on multi-disciplinary design optimization, the proposed model is appliedwithin preliminary design synthesis in this second case study. It was already mentioned that the referenceaircraft was optimized with respect to minimum fuel consumption on its 1000 NM study mission. Typi-cally, the design is optimized in preliminary design by variation of wing loading and thrust to weight ratio,cf. �gure 11. The design space is hereby limited by constraints of top-level requirements such as for ex-ample required take-o� �eld length. A detailed discussion of these constraints was already published byAnton et al.28

Along with integration of the proposed cost model into IPADS, new possible optimization targets areintroduced into the design environment. In the scope of this paper, a design study was run to show thein uence on overall optimization. A discrete number of designs within the design space were converged, whichresults in contour plots for di�erent target parameters as shown in �gure 18. The dashed lines representiso-lines for constant parameter values. Darker shades represent low values of the speci�c target parameter,whereas lighter shades represent higher values. It is easy to observe from �gure 18(a) that minimum fuelconsumption is achieved for highest possible wing loading combined with the lowest thrust to weight ratio(bottom right corner). As already shown in �gure 11, this corresponds to the initial design point of thereference aircraft.

The proposed model estimates the lowest list price as well as lowest non-recurring and recurring costs forthe initial design point, see �gure 18(b)-18(d). Since deviations from the initial design point lead to a quasi\in ation" of the design, the estimated list price increases accordingly as discussed beforehand. The same istrue for estimated non-recurring costs since they grow along with maximum take-o� weight. Interestingly,it can be observed from �gure 18(e) that the initial design point corresponds with the highest targeted unitsduring life cycle, and not with the lowest ones. This is caused by the low list price and the pressure onacceptable recurring costs; more units have to be produced to keep recurring costs down.

From a manufacturer’s point of view, the most interesting parameter for optimization is the net presentvalue, which is plotted in �gure 18(f). Its maximum is not found at the initial design point but at the topright hand corner of the graph. Reasons for that are the high targeted units during life cycle and the higherlist price, cf. equation 6. For constant wing loadings and increased thrust to weight ratio, the design showsgrowing power reserves that lead to an excess in available performance, e.g. in less required take-o� �eldlength or higher climb speeds. The list price increases and with it also the net present value. Hence, from amanufacturer’s point of view the optimum design point lies at the upper right hand side of the graph. Thisof course implies that customers are willing to pay a higher price for an aircraft, which over-performs andthat shows higher then optimum fuel burn. This, however, is doubtful. Hence, optimization in terms of costsalways has to go hand in hand with reliable market analyses. Results also show that the optimum targetdesign point di�ers for the manufacturer’s and customer’s perspective. In future studies also the impact ondirect operating costs shall be assessed.

VII. Conclusions

In the scope of this paper a new approach towards estimating di�erent cost items and for assessingpro�tability of a design case is presented, which is already applicable in early preliminary design. It includesestimates for: aircraft list price, unit costs, non-recurring and recurring costs, margins for earnings, targeted

17 of 21


units during product life cycle and for break even, as well as net present value. In a combined top-downand bottom-up approach, di�erent models are merged into an overall methodology, which was implementeddirectly into IPADS to allow for fast and reliable assessment in multi-disciplinary aircraft optimization.Special emphasis was put on su�cient sensitivity of the proposed model to relevant design parameters bythe authors. It was the goal to capture the in uence of design trade-o�s on costs, and thus to allow forreliable decision making and down selection of concepts in an early design phase.

The in uence of di�erent parameters on the proposed model was tested in sensitivity studies. It could beobserved that the model shows non-linear sensitivity to relevant design parameters as well as to importanttop-level aircraft requirements. Also, sensitivities are of correct order as well as of correct magnitude, sothat plausibility of the proposed methodology could be shown.

Application of the cost model to the reference aircraft showed that estimated list price lies in the correctorder. Estimated non-recurring costs, however, were too low for this single white sheet design. Commercialtransport aircraft programs generally target entire families of aircraft, which consist of a certain baselineaircraft and various derivatives. Thus, when estimating non-recurring costs for only one aircraft results arenaturally too low. Instead, entire aircraft families have to be considered when assessing costs and bene�tsof a certain program. This has been exemplarily shown with the application of the proposed model to theAirbus A320-family. Results for non-recurring costs as well as for estimated required units now lie in theright order. Close agreement to published data was found. Further, the recently launched NEO programwas assessed. Again, close agreement to published data was found in terms of estimated non-recurring costs.The high pro�tability of both the A320-family as well as for the new A320NEO program could be shown.In a second case study, the direct application of the proposed model to preliminary design synthesis andmulti-disciplinary optimization was presented. Results showed that optimum design point for minimum fuelconsumption corresponds with the optimum of minimum price and minimum costs. However, this designpoint also corresponds with the highest required units that have to be sold during life cycle. Interestingly,highest net present value of the design is not found in this design point, but for designs that feature greaterperformance reserves, and are thus not optimum in terms of fuel e�ciency.

The results of the design optimization show that for decision making the design synthesis have to becombined with reliable market analysis to assure that potential customers are willing to pay a speci�c priceand that the required units can be sold. In future studies direct operating costs and life cycle costs will alsobe incorporated into design optimization. Since direct operating costs are primarily driven by fuel burn anddepreciation costs, minimum direct operating costs will also be found for the design point, which results inminimum fuel burn and minimum list price. As discussed beforehand, this is not the design point for highestnet present value. Hence, the optimum design from the customer’s perspective and from the manufacturer’sperspective do not coincide. Again, market analysis have to provide a correct weighting of the two optima.

For future research, the authors plan to enhance the proposed model by a time axis to properly simulatethe business case. This would then allow for e.g. simulation of time dependent cash ows and of non con-stant discounts or interest. Also the impact of changes in the markets during the product life cycle could beassessed with such a time line model. Peoples and Willcox27 showed already the importance of introducinguncertainties into cost modeling. The authors target to use probabilistic functions for simulation of uncer-tainties such as already successfully applied to design decision making processes by Mavris et al.52,53 So far,the implemented models are for application to aircraft with conventional technology only. Implementationof technology factors could help to model the impact of integration of innovative technologies on costs.

VIII. Acknowledgments

The authors wish to thank all involved colleagues and student assistants of the Institute of Aeronauticsand Astronautics (ILR) of RWTH Aachen University.

18 of 21


520 540 560 580 600 620 640 660

Wing Loading, kg/m2

0.33

0.34

0.35

0.36

0.37

Thr

ust L

oadi

ng, -

--

(a) Resulting fuel consumption.

520 540 560 580 600 620 640 660

Wing Loading, kg/m2

0.33

0.34

0.35

0.36

0.37

Thr

ust L

oadi

ng, -

--

(b) Resulting list price.

520 540 560 580 600 620 640 660

Wing Loading, kg/m2

0.33

0.34

0.35

0.36

0.37

Thr

ust L

oadi

ng, -

--

(c) Resulting non-recurring costs.

520 540 560 580 600 620 640 660

Wing Loading, kg/m2

0.33

0.34

0.35

0.36

0.37

Thr

ust L

oadi

ng, -

--

(d) Resulting recurring costs.

520 540 560 580 600 620 640 660

Wing Loading, kg/m2

0.33

0.34

0.35

0.36

0.37

Thr

ust L

oadi

ng, -

--

(e) Resulting targeted units during life cycle.

520 540 560 580 600 620 640 660

Wing Loading, kg/m2

0.33

0.34

0.35

0.36

0.37

Thr

ust L

oadi

ng, -

--

(f) Resulting net present value.

Figure 18. Multi-disciplinary design optimization of reference aircraft.

19 of 21


References

1Clark, P., Buying the Big Jets, Ashgate Publishing Limited, Hampshire/UK, 2nd ed., 2007.2Doganis, R., Flying O� Course: airline economics and marketing, Routledge, New York, NY 10016, 4th ed., 2010.3Hirst, M., The Air Transport System, Vol. IX of Library of Flight Series, Woodhead Publishing, Reston VA, 1st ed.,

2008.4Stuessel, R., \Der Luftverkehr und seine Anforderungen an zukuenftige Verkehrs ugzeuge," DGLR Jahrbuch 1982 , Vol. I,

Sept. 1983, pp. 024/01{024/32.5Franz, K., Hoernschemeyer, R., Grosse-Boeckmann, M., Schmitt, R., Pollmanns, J., Feldhusen, J., Bueker, K., Reichmuth,

J., Petzoldt, K., Saurer, T., Schneider, C., Ewert, A., and Fromhold-Eisebith, M., \A methodical approach to assess the aircraftlife cycle," ATOS , Delft, Netherlands, April 2011.

6Raymer, D. P., Airplane Design: A Conceptional Approach, AIAA Education Series, American Institute od Aeronauticsand Astronautics, Inc., Restom, VA, 4th ed., 2006.

7Roskam, J., Airplane Cost Estimation: Design, Development, Manufacturing and Operating, Vol. Part VIII of Airplanedesign / Jan Roskam, DARcorporation, Lawrence, Kan., 1st ed., 1990.

8Nicolai, L., Fundamentals of Aircraft Design, Vol. 1st, METS, Inc., San Jose, CA, 1981.9Johnson, V. S., \Minimizing life cycle cost for subsonic commercial aircraft," Journal of Aircraft , Vol. 27, No. 2, Feb.

1990, pp. 139{145.10Markish, J., Valuation Techniques for Commercial Aircraft Program Design, Department of aeronautics and astronautics,

Massachusetts Institute of Technology, Boston, June 2002.11Johnson, V. S., Life Cycle Cost in the Conceptual Design of Subsonic Commercial Aircraft , P.hD. thesis, University of

Kansas, Oct. 1988.12Asiedu, Y. and Gu, P., \Product Life Cycle Cost Analysis: State of the Art Review," International Journal of Production

Research, Vol. 4, No. 36, 1998, pp. 883{908.13Eaglesham, M. A., A Decision Support System for Advanced Composites Manufacturing Cost Estimation, P.hD. thesis,

Virginia Tech, Blacksburg, VA, April 1998.14Thokala, P., Life Cycle Cost Modelling as an Aircraft Design Decision Supoort Tool , Ph.D. thesis, University of South-

hampton, Sept. 2009.15Marx, W. J., Mavris, D. N., and Schrage, D. P., \A Hierarchical Aircraft Life Cycle Cost Analysis Model," AIAA, Los

Angeles, California, Sept. 1995.16Marx, W. J., Mavris, D. N., and Schrage, D. P., \Cost/Time Analysis for Theoretical Aircraft Production," Journal of

Aircraft , Vol. 35, No. 4, July 1998, pp. 637{646.17Kundu, A., Crosby, S., and Raghunathan, S., \Aircraft Component Manufacture Case Studies and Operating Cost

Reduction Bene�t," AIAA, Denver, Colorade, Nov. 2003.18Jenkinson, L., Simpkin, P., and Rhodes, D., Civil Jet Aircraft Design, Arnold, London/UK, 1st ed., 1999.19Kundu, A. K., Aircraft Design, Cambridge Aerospace Series, Cambridge University Press, New York, NY 10013-2473,

USA, 1st ed., 2010.20Burns, W. J., \Aircraft Cost Estimation Methodology and Value of a Pound Derivation for Preliminiary Design Devel-

opment Applications," SAWE, Inc., La Mesa, CA 91942-2110, May 1994.21Hess, R. and Romanho�, H., \Aircraft Airframe Cost Estimating Relationships: All Mission Types," Tech. rep., RAND

Corporation, Santa Monica, CA, Dec. 1987.22Tirovolis, N. L. and Serghides, V. C., \Unit Costs Estimation Methodology for Commercial Aircraft," Journal of Aircraft ,

Vol. 42, No. 6, Dec. 2005, pp. 1377{1386.23Scanlan, J., Hill, T., Marsh, R., Bru, C., Dunkley, M., and Cleevely, P., \Cost Modelling for Aircraft Design Optimiza-

tion," Journal of Engineering Design, Vol. 13, No. 3, 2002, pp. 261{269.24Castange, S., Curran, R., Rothwell, A., Price, M., Bernard, E., and Raghunathan, S., \A Generic Tool for Cost Estimating

in Aircraft Design," Research in Engineering Design, Vol. 4, No. 18, 2008, pp. 149{162.25Lee, H. and Olds, J., \Integration of Cost Modeling and Business Simulation into Conceptual Launch Vehicle Design,"

1997 AIAA Defense & Space Programs Conference & Exhibit , AIAA, Huntsville, AL, Sept. 1997.26Markish, J. and Willcox, K., \Value-Based Multidisciplinary Techniques for Commercial Aircraft System Design," Journal

of Aircraft , Vol. 41, No. 10, Oct. 2003, pp. 2004{2012.27Peoples, R. and Willcox, K., \Value-Based Multidisciplinary Optimization for Commercial Aircraft Design and Business

Risk Assessment," Journal of Aircraft , Vol. 43, No. 4, July 2006, pp. 913{921.28Anton, E., Lammering, T., and Henke, R., \Fast Estimation of Top-Level Requirement Impact on Conceptual Aircraft

Designs," AIAA Aviation Technology, Integration, and Operations (ATIO), Vol. AIAA-2010-9220, AIAA, Fort Worth, Sept.2010.

29Lammering, T., Anton, E., and Henke, R., \Technology Assessment on Aircraft-Level: Modeling of Innovative AircraftSystems in Conceptual Aircraft Design," AIAA Aviation Technology, Integration, and Operations (ATIO), Vol. AIAA-2010-9072, AIAA, Fort Worth, Sept. 2010.

30Lammering, T., Anton, E., Risse, K., and Franz, K., \Impact of Systems Integration on Fuel E�ciency in PreliminaryAircraft Design," 3rd International Workshop on Aircraft System Technologies, Shaker, Hamburg, March 2011, pp. 171{180.

31Risse, K., Anton, E., and Henke, R., \Methodology for Flying Qualities Prediction and Assessment in Preliminary AircraftDesign," AIAA Aviation Technology, Integration, and Operations (ATIO), AIAA, Sept. 2010.

32Lammering, T., Anton, E., Risse, K., Franz, K., and Hoernschemeyer, R., \Gains in Fuel E�ciency: Multi-Stop Missionsvs. Laminar Aircraft," 11th AIAA Aviation Technology, Integration, and Operations (ATIO), AIAA, Virginia Beach, Sept.2011.

20 of 21


33Lammering, T., Anton, E., Risse, K., Franz, K., and Hoernschemeyer, R., \In uence of O�-Design Performance on theDesign of Aircraft with Laminar Flow Technology," 11th AIAA Aviation Technology, Integration, and Operations (ATIO),AIAA, Virginia Beach, Sept. 2011.

34Beltramo, M. N., Trapp, D. L., Kimoto, B. W., and Marsh, D. P., \Parametric Study of Transport Aircraft Systems Costand Weight," Tech. Rep. NASA CR 151970, Science Application, Inc., Los Angeles, California, April 1977.

35Brealey, R. A., Myers, S. C., and Allen, F., Principles of Corporate Finance, Mcgraw-Hill Higher Education, 9th ed.,Nov. 2007.

36Kaesler, C., Kosten- und Leistungsrechnung der Bilanzbuchhalter , Gabler, Wiesbaden, 1st ed., 2010.37\Consumer Price Index," Tech. rep., U.S. Department of Labor, Bureau of Labor Statistics, Washington D.C. 20212,

April 2011.38Jackson, P., Jane’s All the World’s Aircraft 1998-99 , Vol. 89, Jane’s Information Group Limited, Couldsen, U.K., 1st

ed., 1998.39Jackson, P., Munson, K., and Peacock, L., Jane’s All the World’s Aircraft 2007-2008 , Vol. 98, Jane’s Information Group

Limited, Couldsen, U.K., 1st ed., May 2007.40777-200LR/-300ER/-Freighter - Airplane Characteristics for Airport Planning, Boeing Commercial Airplanes, 4th ed.,

Aug. 2009.41Airplane Characteristics - A320 , Airbus S.A.S., Customer Services, 31707 Blagnac Cedex, France, 25th ed., Sept. 2010.42Aircraft Types and Price Guidelines, Lloyd’s Aviation Department, 1990.43\Commercial Airplanes - Jet Prices," Tech. rep., Boeing Commercial Airplanes,

http://www.boeing.com/commercial/prices/index.html, May 2011.44Levenson, G. S., Boren, H. E., Tihansky, D. P., and Timson, F., \Cost-Estimating Relationships for Aircraft Airframes,"

United States Air Force R-761-PR, Santa Monica, CA, 1972.45Kirby, M., \CRJ1000 takes on Embraer 190," Flight International , Feb. 2007.46\Fluggeraet E190/E195," Flug Revue, Dec. 2008.47\ATA Speci�cation 100 - Speci�cation for Manufacturers’ Technical Data," Revision 1999 99100P, Air Transport Asso-

ciation of America (ATA), Inc., Washington D.C. 20004, 1999.48Werner-Spatz, C., Flugzeuggesamtentwurf mit Zirkulationskontrolle am Hochauftriebssystem, Ph.D. thesis, Technische

Universitaet Braunschweig, Shaker Verlag Aachen, 2010.49Strohmeyer, D. and Seubert, R., \Improvement of a Preliminary Design and Optimization Program for the Evaluation

of Future Aircraft Projects," 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization,AIAA, St. Louis, MO, Sept. 1998.

50Sparaco, P., \Muddled Vision," Aviation Week & Space Technology, Vol. 173, No. 33, Nov. 2011, pp. 21.51\A320neo Facts and Figures," Tech. rep., Airbus S.A.S, http://www.airbus.com/aircraftfamilies/passengeraircraft/

a320family/spotlight-on-a320neo/, Dec. 2012.52Mavris, D. N., DeLaurentis, D. A., Bandte, O., and Hale, M. A., \A Stochastic Approach to Multi-disciplinary Aircraft

Analysis and Design," Amercian Institute od Aeronautics and Astronautics (AIAA), Reno, NV, Jan. 1998.53Biltgen, P. T. and Mavris, D. N., \Technique for Concept Selection Using Interactive Probabilistic Multiple Attribute

Decision Making," Journal of Aerospace Computing, Information, and Communication, Vol. 6, No. 1, Jan. 2009, pp. 51{67.

21 of 21


aircraft cost model for preliminary design...

Documents