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COST ESTIMATION MODEL FOR ADVANCED

PLANETARY PROGRAMS - FOURTH EDITION

Report No. SAI 1-120-768-C9

by

Daniel J. Spadoni

Science Applications, Inc.1701 East Woodfield Road

Schaumburg, Illinois 60195

for

Earth and Planetary Exploration DivisionOffice of Space Sciences and Applications

NASA HeadquartersWashington, D.C.

Contract NASW-3035

April 1982

https://ntrs.nasa.gov/search.jsp?R=19840014522 2020-05-12T15:11:07+00:00Z

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FOREWORD

This study represents a portion of the work performed by Science

Applications, Inc. within Task 2: Cost Estimation Research of Contract

No. NASW-3035 for the Earth and Planetary Exploration Division (Code EL/4)

of OSSA/NASA Headquarters. The results are intended for use as a

decision-aiding tool to assist NASA in its development of long-rangemission plans for solar system exploration.

The author wishes to express his gratitude to those individualsboth within NASA and the industrial community who graciously provided theinformation vital to his study.

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TABLE OF CONTENTS

Page

FOREWORD iii

ACRONYMS AND ABBREVIATIONS . . . vii

1. INTRODUCTION AND SUMMARY l

1.1 Background and Study Objectives 1

1.2 Cost Model Overview 2

1.3 Summary of Results 3

2. COST MODEL DATABASE 5

2.1 Development Project Cost Data 5

2.2 Development Project Technical Data 10

3. MODEL DEVELOPMENT 13

3.1 Labor/Cost Proxy Analysis 14

3.2 Functional Forms 17

3.3 Test Statistics 19

4. MODEL ANALYSIS 21

4.1 Labor/Cost Conversion Factors 274.2 Error Analysis 27

4.3 Simulation Analysis 31

4.4 Error Analysis Revisited 34

4.5 Benchmark Tests . 36

5. SAMPLE APPLICATIONS . 41

6. CONCLUSIONS AND FURTHER DEVELOPMENT 51

REFERENCES 53

APPENDIX A: Detailed Cost Database

APPENDIX B: Detailed Description of Cost Model Algorithms

APPENDIX C: Inheritance Model

APPENDIX D: Detailed Error Analysis

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Acronyms and Abbreviations

Database Flight Programs

M64 = Mariner Mars 1964SUR = SurveyorLO = Lunar OrbiterM69 = Mariner Mars 1969M71 = Mariner Mars 1971PJS = Pioneer Jupiter/Saturn (10/11)M73 = Mariner Venus/Mercury 1973VLC = Viking Lander CapsuleVKO = Viking OrbiterVGR = VoyagerPV = Pioneer Venus

PVLP = Large ProbePVSP = Small ProbePVBO = Bus/OrbiterPVS = Science Instruments

Cost Model Categories

STD = Structure and DevicesTCP = Thermal Control, Cabling and PyrotechnicsPRP = PropulsionAAC = Attitude and Articulation ControlTCM = TelecommunicationsANT = AntennasCDH = Command and Data HandlingPWR = Radioisotope Thermoelectric Generator (RTG) PowerPWS = Solar/Battery PowerADM = Aerodeceleration ModuleRDR = Landing Radar/AltimeterIML = Line-Scan ImagingIMV = Vidicon ImagingPFI = Particle and Field InstrumentsRSI = Remote Sensing InstrumentsDSI = Direct Sensing/Sampling InstrumentsSYS = System Support and Ground EquipmentL30 = Launch + 30 Days Operations and Ground SoftwareIDD = Imaging Data DevelopmentSDD = Science Data DevelopmentPGM = Program Management/Mission Analysis and EngineeringFO = Flight OperationsDA = Data Analysis

Cost Model Parameters

N = Number of Flight Qualified UnitsDLH = Direct Labor Hours (1000 hours)NRL = Non-recurring Labor Hours (1000 hours)RLH = Recurring Labor Hours (1000 hours)URL = Unit Recurring Labor Hours (1000 hours)M = Subsystem Mass (kilograms)MD = Mission Duration (months)ED = Encounter Duration (months)PPL = Imaging Resolution (pixels per line)

vn

Cost Estimation Model for AdvancedPlanetary Programs - Fourth Edition

1. Introduction and Summary

1.1 Background and Study Objectives

In the decade of the 1980's, the United States' program for

unmanned exploration of the solar system faces increased competition

for the resources required for the achievement of its goals. Oneimportant implication of this situation is that the long-range mission

planning process will involve a greater degree of selectivity than was

seen in the past. This in turn implies that the total cost ofindividual missions must be forecast with a greater sense of confidence

than ever before.

Several techniques are used to develop cost estimates of futuremissions at the pre-Phase A level of mission difinition. Engineering,

or "grassroots", estimation generates cost estimates at the lowest

level of the project's work breakdown structure defined at the time of

the estimate. Analogy estimation derives costs by comparing mission

hardware and scenario definitions with those of similar past projects

and suitably adjusting the known, historical costs for such factors as

differences in requirements and capabilities and for inflation. Model

estimation uses cost estimating relationships (functions relating cost

to requirements/capabilities), derived from historical data, to predict

future costs. In essence, model estimation quantifies the analogy

costing process.

For nearly a decade, Science Applications, Inc. (SAI) has been

involved in cost estimation and analysis of the U.S. planetary exploration

program. The work has encompassed historical cost data collection and

analysis, development and refinement of a cost estimation model based

on the historical data (References 1 and 2), and extensive use of the

model for predicting costs of future missions.

This report discusses the development of the current versionof the SAI Planetary Program Cost Model. The Model was updated toincorporate cost data from the most recent U.S. planetary flightprojects and extensively revised in order to more accurately capturethe information in the historical cost database. The revision was madewith a two-fold objective: to increase the flexibility of the Modelin its ability to deal with the broad scope of scenarios under consid-eration for future missions, and to at least maintain and possiblyimprove upon the confidence in the Model's capabilities with an expected

accuracy of ±20%.

1.2 Cost Model Overview

The SAI Planetary Program Cost Model can be characterized by

the following features.

t The Model is based on all relevant U.S. planetaryprojects from Mariner Mars 1964 through PioneerVenus.

• Inputs to the Model are limited to informationgenerally available at the level of pre-Phase Amission definition. Generally, these consist ofestimates of spacecraft subsystem masses, designheritage, flight time and encounter duration.

• The primary output is manpower, expressed in directlabor hours. Total cost is obtained by use ofappropriate conversion factors which includeinflation indices.

t The Model views a mission program as consisting oftwo distinct phases: The Development Project, whichencompasses all activity through the mission'slaunch + 30 days milestone and the Flight Project,which includes all activity from L + 30 days throughthe nominal end of mission.

• At its most detailed level, the Model deals withcost categories which are derived as compromiseaggregations of the variety of work breakdownstructure definitions found in the cost database.

• The Development Project is further separatedinto hardware-related cost categories andfunctional support cost categories. The hardwarecategories are directly related to the missionspacecraft engineering and science subsystems.

• Hardware categories are further separated intonon-recurring costs (design and development)and recurring costs (fabrication and subsystem-level tests). Inheritance is assumed to affectonly the non-recurring cost,

• The Model is capable of dealing with a wide varietyof spacecraft designs, including inertia! or spinstabilized spacecraft, atmospheric entry probesand highly automated soft landers.

1.3 Summary of Results

The model development effort resulted in an updated and revised

Cost Model which adequately meets the objectives set forth in Section

1.1. Only the Development Project portion of the model was revised;

cost estimates for the Flight Project are generated using algorithmsfrom the previous version of the Model (Ref. 2).

A total of 21 revised cost categories were defined, 16 related

to flight hardware and five to functional support. Two separatealgorithms were derived for each hardware category: one which estimates

total direct labor and another which estimates recurring labor.

Non-recurring labor can be obtained by differencing the two estimates.

The hardware labor algorithms are, in general, power laws or exponential

functions of a single independent variable formed by the product of

the number of flight units and the subsystem (category) mass.

Statistical analysis of the historical cost data resulted in a

conclusion that factors derived as simple ratios can be used to convert

category labor hour estimates to total cost.

An extensive error analysis of the Model measured against theprograms in the database indicated that the information in the database

had been captured with an average error of less than 10%. However,

a simulation of the Model's performance, with number of flight units

as the parameter, showed that predictions made with the Model would

be highly sensitive to the number of flight units. A straight-forward

adjustment procedure was devised that effectively eliminates this

sensitivity but results in an increased average error of just less

than 20% as measured against the database.

2. Cost Model Database

Historical cost data for thirteen unmanned lunar and planetary

flight programs currently comprise the SAI cost model database. Table 1summarizes the present status of this database. For use in modeldevelopment, total program costs were segregated into two independentparts. The first, termed the development project, includes all programcosts incurred through the launch + 30 days milestone. All programcosts after this milestone are termed the flight project. Note thatsome programs in the database have multiple L + 30 milestones that arewidely separated in time (e.g., Pioneer Jupiter/Saturn with launches inMarch, 1972 and April, 1973). This does not present a problem insegregating the costs since it is a simple matter of continuing to trackhardware development of follow-on units after the first launch date.The indications in Table 1 regarding use of the data in model revisionwill be discussed in Section 3.

x

2.1 Development Project Cost Data

Cost data for the programs in Table 1 up to and including Voyagerwere used in developing the previous version of the SAI cost model(Ref. 2 ). At the time, however, the Viking Lander, .Viking Orbiter

and Voyager (then called Mariner Jupiter/Saturn) development projectshad not been completed and the cost data used in modeling were based onestimates to complete. Thus, prior to the present model revision effort,it was necessary to analyze and reduce the actual completion costs whichhad been collected for these three programs into forms useful formodeling.

During this process of data reduction, two issues concerning the

data and its use in modeling became apparent. First, some allocationsof raw cost data into the model's cost categories did not appear to beconsistent. Second, the assumption used in the previous model forseparating non-recurring and recurring costs no longer appeared to be

valid. Both of these issues made it necessary to reevaluate specificelements of the entire database.

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During the process of examining cost allocations, a decision wasmade to broaden and redefine the model cost categories. As with previousmodel versions, these categories are separated into two related areas:flight hardware categories and functional support categories. Thesecategories are defined to be compatible with the wide variety of workbreakdown structure definitions used by the system contractors who

develop the mission hardware. Each specific category definition wasarrived at through an iterative process involving both the cost dataallocation and statistical modeling efforts.

The flight hardware-related categories are defined as follows:t Structure & Devices - Spacecraft main structure, support

trusses, adapter, scan platform, booms, solar panelstructure, miscellaneous mechanisms and other hardware,ballast, bioshield, pressure vessel, landing gear,HGA structure.

• Thermal Control, Cabling & Pyrotechnics - Passive andactive temperature control, cabling and wire harness,pyrotechnic devices.

• Propulsion - Propulsion system inerts.

• Attitude & Articulation Control - Celestial and inertia!sensors, attitude control electronics, articulationdevices and actuators.

• Telecommunication - Transponder, receiver, transmitter,telemetry, modulation/demodulation.

• Antenna - S/X antenna, omni's, low and medium gainantennas, waveguides, feeds, rotary joint.

• Command & Data Handling - Command computer & sequencer,flight data, data storage.

• Power - Solar cells & slide covers, battery, conditioning

and distribution (does not include RTG units).

t Aerodeceleration - Heat shield, aeroshell, parachute andmortar.

• Radar - Altitude marking/terminal descent radarantenna(s) and electronics, radar altimeter.

• Imaging - Camera and electronics (vidicon or line scan).

• Particle & Field - Magnetometers, high-energy radiation,plasma, micrometeroid sensors.

• Remote Sensing - Radiometers and spectrometers.

• Direct Sensing & Sampling - Atmospheric and surfaceinstruments.

Similarly, the functional support categories are defined asfollows:

• Program Management/MAE - Project management and control,

administration and support staff, division reps, preflighttrajectory and navigation analysis, mission engineering,ephemeris development, planetary quarantine support.

• System Support & Ground Equipment - Spacecraft designteams, system configuration, system assembly and testing,quality assurance, reliability, safety, electronic partsacquisition and screening, mission and test computers,ground data system, ground data handling, ground handlingequipment.

• Launch + 30 Days Operation & Ground Software - ETRoperations, command team test and training, simulation,sequence development, flight command and control software.

• Image Data Development - Development of capabilities forimage processing lab, image data software, imaging scienceteam and support (pre-flight).

0 Science Data Development - Development of capabilities forscience teams and team support, science data processingand analysis (pre-flight).

8

The fully reduced and allocated cost data are presented in AppendixA for the seven major programs used in the present model developmenteffort. Although technically speaking, Viking was a single program, theLander capsule and Orbiter are treated separately since each system wasdeveloped under a separate contract. Conversely, all Pioneer Venusspacecraft were procured within the same system contract and thereforethe functional support costs are aggregate for the entire program. Noattempt was made to prorate these costs to the various spacecraft types.

Previous versions of the cost model were predicated on definingthe separation of non-recurring and recurring costs as the point in timein the project schedule when the fully-assembled proof-test-model wasdelivered to the spacecraft test facility for initial system testing.This definition, though arbitrary, was felt to provide an adequateaverage basis for model development.

Recently completed development projects, however, appear toinvalidate the use of this definition. Specifically, several of themajor flight components of the Viking Lander were almost totallyredesigned after initial system tests were started. Conversely, almostall of the Voyager flight hardware was fully fabricated well beforeassembly of the PTM spacecraft. Finally, the Pioneer Venus project didnot fabricate PTM spacecraft. This latest case is also indicative ofcurrent and future project planning, i.e. to not fabricate, assemble andtest a proof-test-model spacecraft.

Since a new definition of the non-recurring/recurring costseparation at the system level could not be found which would adequatelyapply to the projects in the database, it became necessary to analyzethe data at the subsystem/major component level. As a result of thisassessment, it was decided to separate recurring from non-recurringcosts at the start of fabrication of flight qualified hardware. Thisnew definition was applied as closely as possible to the major componentlevel. Occasionally, there was not sufficient information to determine

this breakpoint in cost. For such cases, either a single milestone inthe schedule was applied to all subsystems or considerable direction was

taken from the Work Breakdown Structure (WBS). For example, using WBSsubaccount definitions, non-recurring could be equated with "engineering",

and recurring could be equated with "manufacturing".

Table 2 presents percentage ratios of recurring labor to non-

curring labor, normalized to one flight unit, for each of the hardwarecost categories for the Mariner '69 (M69) through Pioneer Venus (PV)development projects. Cursory examination of this data, as exhibited bythe large standard deviations, leads immediately to the conclusion thatuse of simple ratios for determining recurring cost from non-recurringcost, as had been used in previous model versions, would no longer bevalid. A more complex functional form would be required.

2.2 Development Project Technical Data

Table 3 presents the project-related technical data used in formu-lating the cost model. Except for the number of flight qualified units(N) for each project and imaging resolution (PPL), all other data aresubsystem masses. No other information, such as power requirements, wasfound to be necessary for developing the cost model algorithms.

Note that for those projects that use radioisotope thermoelectricgenerators (RTGs) as the main power source, the mass of the RTG units isnot included. Also, for the Pioneer Venus probes, the masses of thesmall omni antennas are included in the telecommunication subsystemrather than considered separately in the Antenna category (ANT).

Special considerations were required for certain aspects of thePioneer Venus program. For example, many subsystem masses of what isidentified as the Bus/Orbiter are composites of averages of commonhardware components plus components of each vehicle which are unique.This approach was required because of lack of resolution in the detailedcost data between bus hardware and orbiter hardware. The PV probe andorbiter science are treated together as a separate subproject because ofinsufficient resolution in several of the instrument contracts to allowadequate proration of costs to the appropriate PV mission.

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3. Model Development

Results of advanced (pre-Phase A) mission planning and analysisstudies typically involve detailed recommendations regarding sciencerationale, trajectory analysis and mission sequencing. Mission-relatedhardware definitions, however, are usually not as well defined. Grosspayload requirements, in such terms as net mass delivered at the targetor net injected mass at Earth, can be fairly well predicted. However,detailed mass definitions at the subsystem and component level are noteasily obtained. Hence, detailed mass estimates are typicallygenerated prior to Phase B studies by a combination of selectingappropriate subsystems/components from previous successful designsand/or current designs, and use of mass scaling relationships.

This approach can generate reasonable early mass estimates andalso yields information concerning design heritage. However, such anapproach cannot take into account what impact technological advancesand changes in general design philosophy might have on component masses.Furthermore, little, if any, information is generated regarding suchdetails as part counts, number of spare components, reliability, powerprofiles, command structure, communications link parameters, etc. Thatis, spacecraft designs resultant from advanced studies contain none ofthe detailed engineering parameters, required in performing a so-called

bottom-up cost estimate.

The approach for achieving early "top-down" cost estimates, there-fore, is to develop a cost model commensurate with the level of missiondefinition and related flight hardware details generally obtained fromadvanced mission planning studies. On the other hand, as seen inSection 2, the useful database of historical programs will provide onlya relatively small sample size for statistical analysis. Therefore,the individual algorithms which constitute the model should be parsimon-

ious, requiring the smallest possible number of estimated parametersfor adequate representation. Parsimony will thus lead to uncomplicated

functional forms while preserving as many statistical degrees offreedom as practical during the model fitting process. The techniques

13

employed for model fitting are ordinary least squares regression andregression through the origin.

3.1 Labor/Cost Proxy Analysis

All previous versions of the SAI Cost Model have used direct labor

hours as the primary dependent variable for both estimation andprediction, and the present version is no exception. The majorarguments underlying the use of labor hours include decoupling of fore-casts from inflation and ease in costing low volume production.Decoupling from inflation places all forecasts on a normalized,comparable basis, comparable both to past programs in the database andto other forecasts. Mass production techniques have not been utilizedfor deep-space missions and thus the marginal cost of mission hardwareis not substantially decreased through additional production. Themission development effort is labor intensive, and therefore, the costof mission hardware is a direct linear function of the manpowerinvolved in design, manufacturing and testing. This implies that.itmay not be necessary to analyze how development cost breaks down amonglabor, overhead, materials, other direct charges, etc. The relation-ship between mission parameters and resources can be better understoodand evaluated for accuracy when that resource, i.e. manpower, ismodeled explicitly. Finally, forecasting manpower requirements inaddition to project cost provides management with additional informa-tion to aid in the program planning process.

It is not sufficient, however to categorically state thatmanpower, expressed in direct labor hours, should be a reasonable andpractical proxy for cost. Figure 1 compares database averages ofcategory labor hours with category total cost, as percentages of totaldevelopment project hours and cost, for each cost category. On inspec-tion and for most categories, the correlation between labor hours andcost appears to be adequate.

This favorable comparison can be firmly established with astatistical analysis. In a statistical sense, the percentage ratios

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shown in Figure 1 are simply sample means. Therefore, a t-testr ' for

equivalent means can be applied to the data. The assumptions and stepsfor doing so are as follows. Since the sample size is small, normality

must be assumed in the data because tests for normality would not be

powerful enough. (This assumption actually holds throughout the

modeling process for the same reason.) A fairly powerful test isrequired in order to decrease the probability of incorrectly accepting

the hypothesis of equivalent means. Since there is no control oversample size, the best available option is to test at a fairly highsignificance level. For this purpose, 20% was selected.

The t-test is based on equivalent sample variances. Therefore,(2}an F-testv ' for equivalent variances was first performed. Results

from the F-test indicate that for all 21 categories shown in Figure 1,

the variances of the labor hour ratios are equivalent to those of the

cost ratios at the 20% significance level, and therefore the t-test

can be applied.

(1) The t-test evaluates the statistict* = d /TT/s(d)

against the Student's t distribution with n-1 degrees of freedom, to test the hypothesis that twonormally distributed populations with the same unknown variance have the same mean. The test is

performed using n paired observations from the two samples. In the above equation

d = the mean of the differences between the two samplesn = the sample size

s(d) = the standard deviation of the differences between the pairedvalues

For a selected significance level, a, if

|t*| < t(l-a/2; n-1)then we may conclude that the two sample means are equivalent.

(2) F-test, as used in this context, evaluates the statistic

F* = sf/Sa

against the F distribution with n-1 and n-1 degrees of freedom, to test the hypothesis that two

normally distributed populations have the same variance. The test is performed using n pairedobservations from the two samples. In the above equation

Sj = is the standard deviation of the first sample

S2 = is the standard deviation of the second sample

For a selected significance level, a, ifF(a/2;n-l;n-l) < F* < F(l-o/2;n-l;n-l)

then we may conclude that the two samples have the same variance.

16

Results of the t-test show that in 16 of the 21 categories themean ratios of labor hours and total cost are equivalent at 20%

significance. This result is fairly acceptable since at 20%,approximately 4 out of 21 categories are expected to be not equivalent

purely by chance. Relaxing the level of significance to 10% results

in 19 of 21 categories having equivalent means where 2 of 21 are notexpected by chance.

The implications of these results are that direct labor hours

should provide a good proxy to total cost as the primary dependent

variable and that factors derived as simple ratios should be adeqiiate

for conveTt'inq from labor hours to total cost. This latter conclusioneliminates any need to analyze the cost data in terms of breakdowns

among labor, overhead, material, etc.

3.2 Functional Forms

Having argued that 1) the type and amount of independent variables

for cost prediction of advanced planetary missions is limited, 2) that

the cost category algorithms should be parsimonious, and 3) that cost

category direct labor hours should be the primary dependent variable,

the following general relationships are postulated. For the flighthardware categories (subsystems), total direct labor hours (DLH) is a

function of the number of flight qualified units (N) and the subsystem

mass (M):DLH = F (N,M) ,

with separate functions independently derived for each category. Forthe functional support categories, total labor hours is postulated to

be a function of the total hardware labor hours (all categories):

DLH = G (zDLH hardware).Table 4 presents examples of some of the functional forms that

were analyzed for possible relevance to the cost model. The single

parameter type functions attempt to capture the separation between

non-recurring and recurring costs by modeling each quantity separately.

The dual parameter functions essentially ignore this separation and

17

Table 4

EXAMPLES OF POSSIBLE FUNCTIONAL FORMS

Single Parameter Functions

1) DLH = NRL + N*URL

where NRL = f(M)

URL = k*NRL , k constant

2) same as 1) except URL = g(M)

3) DLH = (NRL + URL) + (N-l) * URL

where (NRL + URL) = f(M)

functions f(M) and g(M) are of the general

forms a + bM, aM or exp (a + bM)

Dual Parameter Functions

4) DLH = a + bN + cM

5) DLH = aNb Mc

6) DLH = a + b(NM)

7} DLH = a(NM)b

8) DLH = exp [a + b(NM)]

9) DLH = aMb + cNMd

10) DLH = (a + bN) Mc

18

attempt to directly model category total cost. Function types 1 through

8 in Table 4 can be fitted using the standard techniques of ordinary

least squares regression. In some cases regression through the origin

was used. This technique constrains, on an a priori basis, the estimate

of "a" to be zero in function 6 and to be one in function 7 for example.

Function types 9 and 10 can only be fitted by using non-linear

regression and were only briefly examined.

3.3 Test Statistics

In order to identify which of the possible function forms best fit

the database, some statistical measures of goodness-of-fit are required.For this purpose, two standard measures were used during the regression

analysis: the correlation coefficient of the fitted data and the

t-statistic of the parameter estimates. (The t-statistic in thiscontext is defined as the parameter estimate divided by the standard

error of the estimate. It should not be confused with the t-test

described in Section 3.1, although it is evaluated in a similar manner.)

These measures are fairly adequate for determining a best fit ofthe data at the individual category level, but are not sufficient for

analyzing model performance at the project level. Therefore, twoadditional measures were defined for use at both the category and

project levels: the mean percentage error (MPE) and mean absolute

percentage error (MAPE). Given a theoretical function of the form

y-j = a + b xj , i = 1 , ... , n

the fitted function is then

y-j = a + b x-j ,

and the residual errors due to the fitting process are

The two measures are then simply defined as

MPE = -J- i -^-

and

MAPE = -4- Z -TT"- •

19

Mean percentage error can be viewed as an indication of bias in the

fitted function. Mean absolute percentage error can be viewed as anindication of the gross error inherent to the fitted function and thus

the confidence with which it can be used in making forecasts.

Specific cutoff values of these statistical measures were not used

during the modeling process. For the most part, the "best fit"functions were chosen on the combined basis of having the highest

correlation and t-statistics and lowest MPE and MAPE. However, certain

qualitative, or subjective measures were also applied, such as a desire

to avoid negative constants in linear (straight-line) fits.

20

4. Model Analysis

The first model formulation analyzed was the single parameter type,

which separately models non-recurring and recurring labor hours, for the

obvious reason that this form had been successfully modeled in previousversions. Briefly, however, it was now found that algorithms based on

non-recurring labor as a function of subsystem mass had poor correla-

tion and unacceptably large percentage errors. Furthermore, as might

be expected from the data in Table 2, recurring labor could not be

expressed as a constant fraction of non-recurring labor. Algorithms of

recurring labor as a function of subsystem mass did have moderate

correlation and marginally acceptable percentage errors.

Reexamination of the cost data allocations did not reveal any

obvious discrepancies to which the poor correlation could be attributed.

Therefore, it seemed apparent that a new model formulation would have to

be investigated. Consequently, a wide variety of theoretical functions

based on the dual parameter formulation were analyzed. Multiple lineartype functions, i.e. having two independent variables, exhibited

moderately good correlation, but, in general, the strongest correlation

was observed in algorithms that modeled category total labor as afunction of a single variable formed by the product of subsystem mass

and the number of flight units. Simply restated, total cost is a strong

function of total mass. This is merely a statement of observed

correlation in the data and is not necessarily evidence of a fundamentalrelationship.

Figure 2 illustrates the preceding discussion with the structure and

devices hardware category as an example. Each graph shows the sample

data and the fitted function, in each case a power law. Considerable

scatter and thus poor correlation can be seen in Figure 2a, non-recurring

labor versus unit mass. By comparison, a significant trend is observed

in Figure 2b, total labor versus total mass. Similarly strong correla-

tions between total labor and total mass were/observed for most"all other

hardware cost categories.

21

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All of the available information in the database was not used indeveloping the cost model algorithms. The earliest projects,specifically Mariner '64, Surveyor and Lunar Orbiter, were largelyexcluded from the modeling effort because of the early technologystatus inherent in these projects. Exceptions to exclusion were madewhen a specific cost, category data point correlated strongly with thedata from later projects. Conversely, if a data point from the laterprojects was observed to be an obvious outlier from a significanttrend, that data was excluded from the regression. An example of thisis seen in Figure 2b in which Mariner '64 is included among the fitteddata but Pioneer Jupiter/Saturn is excluded. This selective use ofthe data is not arbitrary. Data included from earlier programs mayindicate that the effort required to design, fabricate and test theparticular subsystem is somewhat independent of the technology in use.Data excluded from recent programs indicates either extreme cost over-runs for high outliers or extremely high heritage for low outliers.

The decision to eliminate outliers led to the complete exclusionof Mariner '73 from the modeling process. This is not surprisingsince this particular project used a considerable amount of residualhardware from previous Mariner projects. Specifically, all hardwarecategory costs for Mariner '73 were observed to lie well below trendsindicated by the other projects in the database.

Even though non-recurring cost could not be successfully modeled,this quantity in terms of labor hours was still needed in order toapply inheritance algorithms (see Appendix C). These algorithms weredeveloped on the premise that design and hardware heritage affectsonly the non-recurring portion of development costs. Developing newinheritance algorithms which would operate on total category cost wasbeyond the scope of the current model development effort. Thus, aprocedure was needed to extract the non-recurring portion from thetotal labor estimate. As was previously mentioned, the recurring

cost data exhibited moderately good correlation with subsystem mass.

Obviously, then, an estimate of non-recurring labor could be recovered

23

by differencing the total labor and recurring labor estimates. Duringthe process of analyzing recurring costs, a slight improvement incorrelation was observed between total recurring cost and total massover that of single-unit recurring cost and unit mass. Algorithms oftotal recurring labor as a function of total subsystem mass were there-fore developed. An estimate of single-unit recurring cost can beobtained by simply dividing by the number of flight units.

In the functional support categories, costs for system supportand ground equipment and for launch + 30 days operations and groundsoftware were found to correlate well with the sum of flight hardwarecategory costs. Costs for pre-launch development of capabilities forimaging data processing and for non-imaging science data analysis wereobserved to correlate with imaging system resolution and non-imagingscience payload mass, respectively. Finally, the cost of programmanagement correlated well with the sum of all other category costs,both hardware and support functions.

Table 5 summarizes the complete set of algorithms developedduring the modeling process. The flight project algorithms from theprevious model version are also presented. Graphs of the data andfitted functions together with associated statistics are presented inAppendix B. Figure 3 is a flow diagram illustrating the key elementsof the cost model.

MissionInput

Parameters

vpCost

CategoryLabor

Algorithms

CostCategoryLabor

Estimates

N

/ Labor Hours \I to 1-\ Labor Cost /

ReducedLabor

Estimates

-4f Programv> 1 «!,„_

VCost

Figure 3. Cost Model Schematic

24

Table 5

Summary of Cost Model Algorithms

Development Project - Flight Hardware

Structure & DevicesDLH = 1.626 (N * M)0.90«t6

RLH = 1.399 (N * M)°'7l+l+5

Thermal Control, Cabling & Pyrotechnics

DLH = exp (4.2702 + 0.00608 N * M)RLH = 3.731 (N * M)0.6082

PropulsionDLH = 56.1878 (N * MjO-^eRLH = 1.0 (N * M)0.90ii

Attitude & Articulation ControlDLH = 21.328 (N * M)°-7230

RLH = 1.932 (N * M)

Telecommunications

DLH = 4.471 (N * M)1-1305

RLH = 1.626 (N * M)1-18^

AntennasDLH = 6.093 (N * M)1-131*8

RLH = 3.339 (N * M)

Command & Data Handling .

DLH = exp (4.2605 + 0.02414 N * M)

RLH = exp (2.8679 + 0.02726 N * M)

RTG PowerDLH = 65.300 (N * M)0.355<*

RLH = 7.88 (N * M)0-7lso

Solar/Battery PowerDLH = exp (3.9633 + 0.00911 N * M)

RLH = exp (2.5183 + 0.01204 N * M)

Aerodeceleration ModuleDLH = 3.481 (N * M)0-8ti6

RLH = 4.662 (N * M)0-5

M in kilogramsDLH, RLH in 1000 hours 25MD, ED in months

Table 5 (continued)

Summary of Cost Model Algorithms

Landing Radar/AltimeterDLH = 11.409 (N * M ) ° - 9 5 7 9

RLH = 1.2227 (N * M)1-23"

Line-Scan Imaging

DLH = 10.069 (N * M ) i . 2 5 ? o

RLH = 1.989 (N * M) 1 ' 1 * 0 8 9

Vidicon Imaging

DLH = 4.463 (N * M)L0369

RLH = 1.0 (N * M ) i - i 5 2 0

Particle & Field InstrumentsDLH = 25.948 (N * M)0.7215

RLH = 0.790 (N * M)1'39™

Remote Sensing InstrumentsDLH = 25.948 (N * M)0.5990

RLH = 0.790 (N * M)°-8393

Direct Sensing/Sampling Instruments

DLH = 6.173 (N * M)1-2737

RLH = 1.0 (N * M)i.^oo

Development Project - Support Functions

System Support & Ground Equipment

DLH-=. 0.36172 (z DLH hardware)0-9815

DLH = 0.5095 (z DLH hardware) [viking Class Missions]

Launch + 30 Days Operations & Ground Software

DLH = 0.09808 (z DLH hardware)

Imaging Data DevelopmentDLH = 0.00124 (PPL)1-629

Science Data DevelopmentDLH = 27.836 (non-imaging science mass)0-3389

Program Management/MA&EDLH = 0.10097 (zDLHall categories^. 9670

Flight Pro.ject

Flight Operations / vn, u / zDLH Hardware \ °-&,,DLH = I 3100 ) (10-7 MD + 27.0 ED)

Data AnalysisDLH = 0.425 (DLH Flight Operations)

26

4.1 Labor/Cost Conversion Factors

As was demonstrated in Section 3.1, simple conversion factors,

derived as average ratios, are all that are needed to convert category

direct labor hours into category total cost. Two independent factors

were derived for this purpose: the first converts labor hours tolabor cost while the second converts labor cost to total cost. These

conversion factors are presented in Table 6 for all cost categories.

Derivation of the labor cost to total cost factors was straight-

forward, involving simple ratios of the allocated cost data. However,derivation of the.labor hours to labor cost factors first required

elimination of the effects of inflation inherent in the raw cost data.i-

For this purpose, the NASA R & D Escalation Indices for Space Systems

Development (February 1979) were used to adjust the annual funding for

each category in each project to a fiscal year 1977 basis. Once this

was accomplished, simple ratios were again used to derive the

conversion factors. Projects completed prior to 1970 were notincluded in this process because such projects, having median funding

nearly a decade or more from the basis, would introduce too much

variance in the ratios.

4.2 Error Analysis

The first step in analyzing model performance was to test the

model against the development projects in the database. In order to

obtain global, consistent measures of model performance, the error

analysis was confined to the seven major projects contributing to the

model development. Errors from cost categories excluded from the

regression analysis (such as PJS structure and devices) are includedin the global error analysis.

Table 7 summarizes the percentage residual errors at the hardware

level (i.e. exclusive of the support categories) of the seven projects.

Also shown for comparison are the hardware level residual errors

obtained from a model based on separate algorithms for non-recurring

27

Table 6

Labor/Cost Conversion Factors

Cost Category Labor Hours to Labor Cost Labor Cost to Total Cost

(FY77 dollars/manhour)Development Project

Structure & Devices 10.45 3.303

Thermal Control, Cabling & Pyrotechnics 10.26 3.317

Propulsion 10.54 3.616

Attitude & Articulation Control 10.63 3.347

Telecommunications 9.99 3.352

Antennas 9.96 3.466

Command & Data Handling 9.68 3.163

RTG Power 9.51 3.177

Solar Battery Power 10.41 3.148

Aerodecleration Module 10.73 3.296

Landing Radar/Altimeter 10.08 3.158

Line-Scan Imaging 10.57 3.604

Vidicon Imaging 9.52 3.586

Particle & Field Instruments 10.62 3.395

Remote Sensing Instruments 10.65 3.286

Direct Sensing/Sampling Instruments 9.55 3.454

System Support & Ground Equipment 10.55 3.076

Launch + 30 Days Ops & Ground S/W 10.71 3.214

Image Data Development 11.46 3.130

Science Data Development 12.76 3.987

Program Management/MA & E 11.57 2.685

Flight Project

Flight Operations 10.44 3.247

Data Analysis 10.44 3.42528

Table 7

Comparison of Hardware - Level Residual Percentage Errors

Hardware Project

M69

M71

PJS

VLC

VKO

V6R

PV

MPE

MAPE

Total DLHModel

1.8-3.1

17.1

-1.2

-5.4

3.0

-8.6

0.6

5.7

Separate NRL/URLModel

26.4

-4.1

26.2

19.8

9.3

8.4

-19.4

9.5

16.2

and recurring labor. The global errors, MPE and MAPE, support expecta-

tions based on the individual algorithm correlations: a model

formulated on estimation of total category DLH would exhibit less bias

and higher confidence than a model based on separate NRL and URL

algorithms.

The next step in the error analysis was to determine the global

errors at the development project level in terms of both percentage

labor hours and total cost. Estimates of support function labor, for

the most part, are based on the summation of hardware category labor.

Thus errors in hardware labor estimates would be expected to propagate'

through the support functions. Also, the factors to convert from

labor hours to total cost are simple averages and the variances

implicit in such averages would also be expected to propagate through

the conversion to cost. These considerations imply that the global

errors at the project cost level might be worse than those observed

at the hardware labor hours level.

Table 8 presents a'summary of the global errors at the project

level. The first grouping, termed "Fitted", are those errors which

are obtained when the actual hardware level labor is used to estimate

29

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the function labor. The group labeled "Estimated" is the errors

observed when the estimated hardware labor is used. The conclusion

from these results is that use of estimated hardware labor in the

function algorithms and averaged conversion factors has only marginal

effect on the hardware level global errors. The model as formulated

captures the database with a marginally conservative bias of approxi-mately 1% overestimation and a gross error less than 10%.

Appendix D contains the details of the error analysis from which

the results in Table 8 were obtained.

4.3 Simulation Analysis

The error analysis discussed in Section 4.2 provides an assessment

of how well the model has captured the data from which it was developed.

However, it provides little information regarding how well the model

will perform in practical applications.

Because of the large number of input variables, a parametricanalysis of the model would be cumbersome to perform and difficult toevaluate. Monte Carlo simulation can provide a feasible method for

assessing the overall performance of the model. By artificiallyviewing subsystem masses as stochastic variables, the simulation

reduces to selecting probability distributions which adequately express

these variables, and randomly sampling from these distributions. A

simplifying assumption is made that individual subsystem masses areindependent of other subsystem masses. The sampled masses from each

trial of the simulation can be input to the model in a deterministic

fashion and the model output can then be averaged over the number of

trials. Given a fixed number of trial runs, the number of flight

units becomes the only control parameter.

Selecting probability distributions is the only matter of specula-

tion. Both uniform and triangular probability density functions were

used to assess what affect, if any, such different assumptions mighthave on the outcome. Figure 4 below represents a typical triangularprobability density function.

31

Pr(M)

'mm max M

Figure 4. Subsystem Mass Probability Density Function

The lower and upper bounds, Mm-jn and Mmax, for each hardware cost

category were chosen as the smallest and largest subsystem masses

observed in the historical database from Table 3. Three different

modes were analyzed for the triangular pdf's: 25%, 50% and 75% of the

difference between Mml-n and Mmax. The number of flight units was

systematically varied from one to five and 1000 trials were run for

each case. Only the hardware total labor was analyzed since the

support categories are direct functions of this quantity and their

inclusion would not provide additional information.

For purpose of comparison, a simulation of the previous version

of the SAI Cost Model was also made. Table 9 summarizes the results

of the two model simulations with number of flight units as the

parameter. That the total simulated mass is the same for each

respective pdf attests to the fact that to maintain consistency the

same pseudo-random number stream was used in each case.

The first point to notice in the data is that the results from

using different probability distributions are consistent. Therefore,

any conclusions which may be drawn from the analysis are not

dependent upon the assumptions used to randomly select subsystem

masses.

The major result indicated by the data in Table 9 is the non-

linear increase in total labor as a function of the number of flight

32

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units. Figure 5 is a plot of the data for the 50% triangular pdf. The

increase in labor for the previous model is linear, which is consistent

with intuitive expectations, ignoring such factors as economy of scale.

Since the hardware cost category algorithms of the new model are a mix

of power laws and exponentials, the increase in labor as a function of

flight units was not expected to be exactly linear. However, the

highly positive slope of the function in Figure 5 was not expected.

To investigate this further, simulations were also made of models

based on formulations 2 and 3 in Table 4. Results of these simulationsare shown as the hashed region in Figure 5. These results exhibit

consistency with the results from the previous model simulation:

increase in total labor is a linear function of the number of flightunits.

4.4 Error Analysis Revisited

From the previous discussion, it appears that the model formula-

tion, as presented in Table 5, is highly sensitive to the number of

flight units. A procedure is needed to reduce this sensitivity while

maintaining the high correlation of the category algorithms with thedatabase. Two considerations indicate that such a procedure may be

feasible. Of the ten hardware systems in Table 3, five consisted oftwo flight qualified units. Furthermore, at N=2, results of the new

model simulation are very near to the other model simulation results.

This suggests a procedure which may be termed anchoring and adjustment,

that is, to anchor an initial cost estimate at two flight units and

adjust this estimate up or down by the appropriate amount of recurring

cost. Expressed mathematically, the procedure is

DLHa = DLH(2,M) + (N-2) * RLH(2,M)/2

where DLH(2,M) and RLH(2,M) are the algorithms of Table 5 evaluated

for N=2. The correct sign for the adjustment term is automatically

accounted for in the (N-2) factor. For N=l, the recurring cost of one

unit is subtracted from the estimate and for N13 the appropriate cost

is added. For N=2, the initial estimate is unaltered.

34

Figure 5

Comparative Summary Results of Simulation Analysis

10

O

Ol

1 5-os_fO

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new model

region ofseparateNRL/URL models"

previous model

triangular mass pdf50% mode

averages of 1000 trials

I2 3 4

Flight Qualified Units

35

Simulations of the new model based on this procedure were performed

and are plotted as the upper bound of the hashed region in Figure 5.

The error analysis of the model database performed in Section 4.2

was reassessed using the adjustment procedure described above.

Table 10 summarizes the results of this analysis. The estimated errors

from Table 8 are repeated for comparison with the errors arising from

the adjustment procedure. The adjusted errors show that, due to the

adjustment procedure, the model still has relatively low bias, but the

gross error has increased. Development projects in the database with

other than two flight units contribute to this increase in mean absolute

percentage error. Despite the factor of two increases, the gross errors

of both labor hours and total cost are still within the acceptance limitof 20%.

4.5 Benchmark Tests

The last step in analyzing model performance was to run benchmarktests of the model against project costs from other sources. Two

sources for such tests are available: actual costs of past projects not

included in the model database, and independent project estimates of

current and future programs.

For the first case, one project is readily available from the SAI

cost database, specifically, Mariner Venus/Mercury 1973. This particu-lar project readily lends itself to the benchmark tests since no data

from it was used during model development, yet it was accomplished

during the same time period as those projects in the model database.

Two projects were selected for the second case: the Galileo Probe to

Jupiter and the Venus Orbital Imaging Radar (VOIR) spacecraft. The

estimated total development cost for the Galileo Probe was obtained

from the POP 80-2 fiscal year cost estimates in Reference 3, adjusted

to FY'82 constant dollars. The benchmark cost of the VOIR development

project was obtained from a JPL cost review (Reference 4 ). The cost

of the synthetic aperture radar system is not included in either the

benchmark cost or the model cost estimates.

36

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37

Table 11 summarizes the results of the benchmark tests. All costs

are given in fiscal year dollars appropriate to the particular benchmark

cost. Subsystem-level heritage was taken into account in generating all

model estimates. Estimates from the cost model are shown first without

using the flight unit adjustment procedure. The model estimate for

Mariner '73 is well within acceptable error limits. However, the

initial estimates for Galileo Probe and VOIR are significantly below

the benchmark costs. Applying the adjustment procedure has no affect on

the Mariner '73 estimate since two units are costed, but the estimates

for Galileo Probe and VOIR are now both within acceptable limits.

Based on the previous analyses, it appears that the revised model

performs well for applications involving either two flight units or one

flight unit with the adjustment procedure. Firm conclusions cannot be

made for applications involving three or more flight units since

appropriate benchmark projects are not available to test such cases.

Considering the results of the benchmark tests, although limitedin number and scope, together with the results of the database error

analysis, the uncertainty of cost predictions obtained from this modelcan be stated as follows. Flight hardware mass estimates associated

with future missions have uncertainties which can vary greatly depending

on the design concept and degree of heritage. Thus, for missionconcepts that generally fall within the scope of missions in the model's

database, the estimated uncertainty in cost predictions is approximately

-20%. For mission concepts which are outside the scope of the database

missions yet can be costed with this model (e.g. penetrators or rovers),

the uncertainty of cost predictions cannot be estimated and must be

treated on an individual basis.

38

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39

"PAGE MISSING FROM AVAILABLE VERSION"

5. Sample Applications

Two sample applications of the cost model are presented to

illustrate its applicability to a variety of project implementation

concepts and mission scenarios.

The first example is a comet rendezvous mission using a low-thrust

trajectory with the stage concept for solar electric propulsion. The

mission is the Hal ley Flyby/Tempel 2 Rendezvous as defined inReference 5 .

The following two pages present completed input data worksheetsfor this mission. The first worksheet defines the project scenario

and provides necessary guidelines for generating the cost estimate.

The guidelines indicate that only the Mission Module spacecraft and

its associated mission operations are to be costed. The second worksheetpresents subsystem mass estimates for the Mission Module flight hardware

together with estimates of the inheritance classifications for each

subsystem.

Figure 6 presents the computer-generated output of the cost model,

showing the raw, detailed cost estimates for the Halley/Tempel 2 Mission

Module hardware development and for the mission flight operations and

data analysis. Within the Development Project subheading, the first

group of columns display individual category labor hour and cost

estimates without accounting for inheritance. The last three columns

show labor and cost estimates after factoring in the effects of

inheritance. The estimated cost of hardware design and fabrication

is approximately $134 Million, after an estimated 30% savings due to

inheritance. The cost reduncing effects of heritage in the flighthardware propagate into the functional support categories and the

flight project categories so that the total development is estimated

to cost $214 Million and the total program is estimated at $292 Million,

without contingency. Table 12 summarizes the cost estimate in a format

which is typical of actual reporting practices. "Science Development"

includes the instrument hardware and imaging and science data development

41

SAI PLANETARY PROGRAM COST MODEL

Input Data Worksheet Page 1Project Scenario Definition

MISSION*: Hatt(Ly flyby/Twpet 2 Rendezvous

HARDWARE CONFIGURATION:

SPACECRAFT ELEMENT*

Module.

NO. OF UNITS* DESIGN HERITAGE

WASA Standard,Gatlte.0, Vo yag eA

LAUNCH VEHICLE: ShuttlfL/lUS

FLIGHT MODE*: Solan Elzc&Uc PJiopul&ion

MISSION PROFILE:

LAUNCH NO.

7

BASE FISCAL YEAR11

LAUNCH DATE*

Ju£(/, 1985

FV79S2

FLIGHT TIME*

4Z MQwtfa

ENCOUNTER TIME*

25

COST SPREAD OPTION PROJECT START DATE:

SPECIAL COST GUIDELINES:

Co4^xS 0($ SEP Stage., SEP OpeAdtionA, and Hattzy P/tobe not -oic£uded.

Module powa/L v-ia Stage..

201

*Necessary Information

42


SPACECRAFT ELEMENT: HF/Tf

ENGINEERING:

Structure & Devices:

Thermal, Cabling & Pyro:

Propulsion Inerts:

Att & Articulation Control

Telecommunications:

Antennas:

Command & Data Handling:

Power*: Solar _v/_ RTG

Aerodeceleration:

Landing Radar/Altimeter:

SCIENCE

Imaging Mass:

Imaging Resolution:

Particle & Field:

Remote Sensing:

Direct Sensing:

Input Data Worksheet Page 2

Flight Hardware Definitions

INHERITANCE CLASS PERCENT BY MASS

199.3 kg

65.0 kg

BLOCK EXACT MINOR MAJORBUY REPEAT MOD MOD

0.0 0.0 33.0 33.0

0.0 0.0 33.0 33.0

NEWDESIGN

34.0

•34.0

-0- -kg

: 59.5

40.7

6.2

45.4

18.6

-0-

15.0

32.0

800

82.2

11.0

-0-

kg

kg

kg

kg

kg

kg

kg

kg

PPL

kg

kg

kg

33.6 49.4 6.0 0.0

20.9 50.6 28.5 0.0

0.0 0.0 100.0 0.0

0.0 100.0 0.0 0.0

0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0

0.0 33.2 0.0 0.0

Vidicon CCD S Fax

0.0 9.5 23.1 27.2

0.0 0.0 40.9 0.0

11.0

0.0

0.0

0.0

100.0

100.0

66.8

40.2

59.1

*For RTG Power Systems, do not include mass of RTG units

43

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categories. "Spacecraft" includes the engineering subsystem categories

and the system support category. With contingency, this program is

estimated to cost $355 Million.

Table 12. Cost Summary for Halley Flyby/Tempel 2 Rendezvous

FY1982 $MProgram Management/MA&E 13.9Science Development 66.9Data Analysis 25.3Spacecraft 119.9Launch + 30 Days Operations 13.2Mission Operations 56.5

Subtotal 295.8APA/Reserve «a 20%) 59.2

Total 355.0

The second sample application deals with a multi-mission projectto deliver atmospheric entry probes to the outer planets Saturn,

Uranus and Neptune. The project implementation scenario is based on

an assumption that all six spacecraft (three probes and three probe

carriers) are developed under a single hardware system contract. The

probe design is assumed to rely heavily on the current Galileo Probewith suitable modifications for use at the other giant outer planets.

The carrier design is assumed to benefit from the contractor's

experience in designing low-cost spinning spacecraft. Other guidelinesinclude a presumption that the project is charged for RTG units, 15%

contingency is to be applied, and only the development cost, i.e.

costs to launch + 30 days, is to be estimated.

Figure 7 presents the cost model output for the Outer Planet

Probes Development Project. Hardware cost categories are shown separately

for the two different spacecraft. Functional categories, however,

are shown as totals for the overall development effort and not prorated

to each spacecraft. The total development is estimated at approximately

$277 Million without contingency. Table 12 summarizes the development

45



Project Scenario Definition

MISSION*: OateA Planet ?nobn

HARDWARE CONFIGURATION:

SPACECRAFT ELEMENT*

P/iofae

Piofae COMA.&L

LAUNCH VEHICLE: Shat££e/ZUS

NO. OF UNITS^

3

3

FLIGHT MODE*:

MISSION PROFILE:

LAUNCH NO.

DESIGN HERITAGE

GaLiie.o P/iofae

Contnactoi'A

BASE FISCAL YEAR*: FY 79*2

COST SPREAD OPTION PROJECT START DATE:

SPECIAL COST GUIDELINES:

System cottt/axc-t:

Co i to Launch •*• 30 dat/4 only

3 RTG'4 (I

15%

46

Suiiwgfay

LAUNCH DATE* FLIGHT TIME* ENCOUNTER TIME*

Ap c£, 1992 (Scutum)

Jan, 1994 (U/ia.nu-4 & Weptane tandem £aanch) .

*Necessary Information




SPACECRAFT ELEMENT:

ENGINEERING:



Propulsion Inerts:

Att & Articulation Control:

Telecommuni cations:

Antennas:


Power*: Solar / RTG

Aerodeceleration:


SCIENCE

Imaging Mass:

Imaging Resolution:

Particle & Field:

Remote Sensing:

Direct Sensing:


58.4

•o: 23.9

-0-

trol: -0-

12.9

-0-

g: 15.6

13.4

9?. 9

r: ~°~

-Q-

-0-

-0-

6.9

21.4

kg

kg

kg

kg

kg

kg

kg

kg

kg

kg

kgPPL

kg

kg

kg

BLOCK EXACT MINOR MAJOR NEWBUY REPEAT MOD MOD DESIGN

0.0 100.0 0.0 0.0 0.0

0.0 100.0 0.0 0.0 0.0

0.0 65.0 35.0 0.0 0.0

0.0 65.0 35.0 0.0 0.0

0.0 100.0 0.0 0.0 0.0

0.0 65.0 35.0 0.0 0.0

Vidicon CCD Fax

0.0 100.0 0.0 0.0 0.0

0.0 62.8 37.2 0.0 0.0


47




SPACECRAFT ELEMENT: fnab<L

ENGINEERING:



Propulsion Inerts:

Att & Articulation Control:

Telecommunications:

Antennas:


Power*: Solar RTG _/

Aerodecelerati on:


SCIENCE

Imaging Mass:

Imaging Resolution:

Particle & Field:

Remote Sensing:

Direct Sensing:


183.6

•o: 52.6

16.2

trol: '21.1

31.0

9.1

g: 50.3

/ 35.5

-0-

r: -0-

-0-

-0-

-0-

-0-

-0-

BLOCK EXACT MINOR MAJORBUY REPEAT MOD MOD

kg S.2 1.0 0.0 90.3

kg 0.0 0.0 0.0 100.0

kg S7.0 1.8 0.0 11.2

kg 34.7 65.9 0.0 0.0

kg 22.2 77.8 0.0 0.0

kg 77.0 77.6 77.4 0.0

kg 24.6 75.4 0.0 0.0

kg 6-4.8 0.0 0.0 35.2

kg

kg

kg

PPL Vidicon CCD Fax

kg

kg

kg

NEWDESIGN

0.0

0.0

0.0

0.0

0.0

0.0

0.0

• 0.0


48

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49

cost estimate for this project. "Science Development" refers only

to the probe science since the carrier spacecraft has no science hard-

ware. "Probe System" and "Carrier System" each include a portion of

the System Support category prorated on the basis of total hardware

cost. With contingency, the total development cost is estimated at$353 Million.

Table 13. Cost Summary for Outer Planet Probe Development Project

FY1982 $MProgram Management/MA&E 18.4Science Development . 39.6Probe System 56.4Carrier System 144.0RTG's 30.0Launch + 30 Days Operations 18.4

Subtotal 306.8APA/Reserve (@ 15%) 46.0Total 352.9

50

6. Conclusions and Further Development

This report has discussed the effort involved in updating and

revising the SAI Planetary Program Cost Model. The Model was updated

to include data from the most recent U.S. planetary missions. It was

functionally revised to increase its flexibility in application to the

broader scope of mission/program scenarios under consideration in

NASA's long-range plans. The Model can be directly applied to cost

such diverse systems as inertial or spin stabilized spacecraft,

atmospheric entry probes and highly autonomous soft landers, and

therefore can more readily extrapolate to systems such as surface

penetrators and rovers.

A detailed error analysis of the Model against the historical

database indicated that, on average, it had captured the information

in the database with an error of less than 10%. In its basic form,

the Model was found to be highly sensitive to the number of hardware

flight units and an adjustment procedure was developed to reduce thissensitivity. This procedure, however, raises the average error of theModel against the database to just less than 20%. Based on this result,

application of 20% contingency is recommended for cost forecasts ofproject definitions which generally fall within the scope of those in

the Model's database. For project definitions that are significantly

different from those in the database, it must be left to the user toassess the Model's validity in generating a cost estimate and to apply

an appropriate contingnecy to the estimate.

In addition to continuing collection and analysis of cost data

from on-going projects, three major development efforts have been

identified to complete the Model and further enhance its capabilities.

The first task is to complete the model revision effort by developingnew algorithms for estimating costs of mission operations and data

analysis. Although historical costs will be used as guidelines, these

new algorithms must take into account the expected effects of planned

51

cost reducing procedures such as the multi-mission end-to-end information

system and reduced cruise phase activity.

The second effort would involve updating the inheritance algorithm

with a more systematic determination of the numerical weighting factors

used in the algorithm. The factors currently in use represent best

estimates of appropriate values. The update will be accomplished by

analyzing cost data from past projects which are known to have benefittedfrom hardware design heritage.

The final task, related to capabilities enhancement, would be to

•develop a new, analytical model to transform a point cost estimate into

annual funding levels. Such a model should account separately for the

different phases of a project, e.g. hardware development versus flight

operations. It must also be capable of dealing with the wide variety

of project implementation scenarios, which can range from relativelysimple Pioneer-class projects to highly complex Viking-class projects.

52

References

1. Pekar, P., A. Friedlander and D. Roberts, "Manpower/Cost Estimation

Model for Automated Planetary Projects", Science Applications, Inc.,September 1973.

2. Kitchen, L. D., "Manpower/Cost Estimation Model for Automated

Planetary Programs -2", Report No. SAI 1-120-399-C2, Science

Applications, Inc., April 1976.

3. Jet Propulsion Laboratory, "Galileo Project Management Report",JPL 1625-92, October 1980.

4. Jet Propulsion Laboratory, "Venus Orbital Imaging Radar: Cost Review",JPL 720-18, June 1978.

5. Jet Propulsion Laboratory, "Comet Rendezvous Development Project:OSS Program Review", JPL 720-32, April 1979.

53

RPPENDIX R

DETfllLED COST DRTRBRSE

Appendix A

Detailed Cost Database

Due to the proprietary nature of the data, this appendix is not in-

cluded in copies of this report intended for distribution external to the

National Aeronautics and Space Administration.

RPPENDIX B

DETRILED DESCRIPTION OFCOST MODEL RLGORITHMS

Appendix B

Detailed Description of

Cost Model Algorithms

The individual algorithms which comprise the SAI Planetary Program

Cost Model are presented in this appendix in both graphical and functional

forms. The function plots also exhibit the data from which the functions

were fitted. Statistics associated with the curve fitting process are alsoshown.

For the hardware-related cost categories, the total labor (DLH)

function is presented with the respective recurring labor (RLH) function

on the facing page. Labor to cost conversion factors are shown for the

total labor functions only.The test statistics associated with the fitted functions include

the correlation coefficient, the t statistics of the estimated coeffi-

cients, and the mean percentage error and mean absolute percentage error

as defined in Section 3.3 of the report.The axes of the function plots are not labeled because of the

proprietary nature of the sample data. Note, however, that the scaling

of each recurring labor plot is the same as its respective total laborplot.

Bl

Index to Algorithms

Cost Category Page

Structure & Devices B4

Thermal Control, Cabling & Pyrotechnics B6

Propulsion B8

Attitude & Articulation Control BIOTelecommunications B12Antennas B14

Command & Data Handling B16Solar/Battery Power B18

RTG Power B20

Aerodeceleration Module B22Landing Radar/Altimeter B24

Line-Scan Imaging B26

Vidicon Imaging B28

Particle & Field Instruments . B30Remote Sensing Instruments B32

Direct Sensing & Sampling Instruments B34System Support & Ground Equipment B36

Launch + 30 Days Operations & Ground Software B37

Image Data Development B38

Science Data Development B39Program Management/MA&E B40

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flPPENDIX C

I N H E R I T R N C E MODELj

Appendix C

Inheritance Model

The following pages define the inheritance classes and associated

cost reduction algorithm used with the SAI Planetary Program Cost Model.

Although the definitions refer to "cost", in practice the inheritancealgorithm is applied individually to the subsystem labor hour estimates

prior to conversion from hours to cost.

The underlying assumption upon which this inheritance model is founded

is that heritage in design philosophy and/or physical hardware affects

only the non-recurring portion of cost. Thus, at the highest extreme of

heritage, a project which uses unaltered residual hardware from a pre-

vious project still incurs a transfer cost exactly equal to the recurring

cost of the hardware item. At intermediate levels of heritage, a fixedpercentage of the non-recurring cost is incurred depending on the in-

heritance class.The inheritance class definitions were determined by mutual consent

of cognizant personnel at NASA, JPL, and SAI. The inheritance class

weighting factors were not derived by analytical techniques but were also

developed by consensus agreement.


INHERITANCE CLASS DEFINITIONS

e Class One: Off-the-Shelf/Block Buy.

The subsystem is taken off of the shelf in working conditionor ordered while the normal production line is operating asan additional unit.

e Inheritance = 100% of non-recurring cost (NRC)

o Cost = recurring cost (RC)

c Class Two: Exact Repeat of Subsystem.

The exact repeat of previous subsystem but to be used inslightly different spacecraft or after line has closed down.Only design work is needed.

e Inheritance = 80% of NRC

t Cost = 20% of NRC + 100% of RC

e Class Three: Minor Modifications of Subsystem.

A previous design is required but it requires minor modifi-cations. Thus, the spacecraft will still incur all thedesign cost and most of the test and development cost inensuring compatibility of the old design and the newminor mods with the new use of the subsystem.

e Inheritance = 25% NRCe Cost = 75% of NRC + 100% of RC

e Class Four: Major Modifications of Subsystem.

A previous design is required but major modifications have tobe made to the design. This gets very close to a new subsystemsince even new subsystems rely on previous design and experience.Some savings in development is possible.

» Inheritance = 5% NRCe Cost = 95% of NRC + 100% RC

Class Five: New Subsystem.

The subsystem is basically new design.

e Inheritance = 0% NRC» Cost = 100% NRC + 100% RC

Cost Reduction Algorithmby Inheritance Classes

Let Xj = Percent of Subsystem Off-the-ShelfX2 = Percent of Subsystem Exact RepeatX3 = Percent of Subsystem Minor ModXit = Percent of Subsystem Major ModX5 = Percent of Subsystem New Design

Thus X], + X2 + X3 + X4 + X5 = 100% of Subsystem Mass

NRC = Non-recurring cost estimate (without inheritance)RC = Recurring cost estimateTC = Total cost estimate (including inheritance effects)Z = Percent cost reduction

If Z = l.OXj + 0.8X2 + 0.25X3 + O.OSX^ + O.OX5

Then TC = (100% - Z) NRC + RC

flPPENDIX D

DETfllLED ERROR RNflLYSIS

Appendix D

Detailed Error Analysis

Due to the proprietary nature of the data, this appendix is not in-

cluded in copies of this report intended for distribution external to the

National Aeronautics and Space Administration.

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