A model for estimating the cost required to do a cost estimate for Deep Space Network (DSN) projects that range from $0.1 to $100 million is presented. The cost of the cost estimate in thousands of dollars, C(sub E), is found to be approximately given by C(sub E) = K((C(sub p))(sup 0.35)) where C(sub p) is the cost of the project being estimated in millions of dollars and K is a constant depending on the accuracy of the estimate. For an order-of-magnitude estimate, K = 24; for a budget estimate, K = 60; and for a definitive estimate, K = 115. That is, for a specific project, the cost of doing a budget estimate is about 2.5 times as much as that for an order-of-magnitude estimate, and a definitive estimate costs about twice as much as a budget estimate. Use of this model should help provide the level of resources required for doing cost estimates and, as a result, provide insights towards more accurate estimates with less potential for cost overruns

Buchanan, H. R.

Remer, D. S.

English

NASA Technical Reports Server

_..---"_TDA ProgressReport 42-110 August 15, lgg2
I-2 "
A Model for the Cost of Doing a Cost Estimate
D. S. Ramer 1
Harvey MuddCollegeof Engineeringand Science
Claremont,California
H. R. Buchanan
RadioFrequencyand MicrowaveSubsystems Section
This article provides a model for estimating the cost required to do a cost es-
timate for DSN projects that range from $0.1 to $100 million. The cost of the
cost estimate in thousands of dollars, CE, is [ound to be approximately given by
CE - KCp 0'35, where Cp is t_e cost o[ the project being estimated in millions of
dollars and K is a constant depending on the accuracy of the estimate. For an
order-of-magnitude estimate, K = 24; for a budget estimate, K = riO; and for a
definitive estimate, K = 115. That is, for a specific project, the cost of doing a
budget estimate is about 2.5 times as much as that for an order-of-magnitude es-
timate, and a definitive estimate costs about twice as much as a budget estimate.
Use of this model should help provide the level of resources required for doing cost
estimates and, as a result, provide insights towards more accurate estimates with
less potential for cost overruns.
I. Introduction
Large cost overruns for major projects are a frequent
occurrence. For example, the following projects are re-
ported to have had final costs that exceeded the original
cost estimates by over 45 percent. _ These Were Landsat-D
(48 percent), Infrared Astronomical Satellite (60 percent),
Earth Radiation Budget Experiment (61 percent), Gamma
1Consultant to the TDA Planning Section.
2H. W. partma and W. E. Ruldand, Predictin9 Financial Risk Ion"
the Development o.fSpace ['light Projects (internal docuJnent), Jet
Propulsion Laboratory, Pasadena, Callfonfia, September 1988.
Ray Observer (98 percent), Space Telescope (98 percent),
Galileo (100 percent), Tracking and Data Relay Satellite
System (130 percent), and Pegasus (700 percent). Consid-
ering the current emphasis on fiscal responsibility within
NASA and other government agencies, cost overruns are
a major problem. Overruns may lead to cancellation of
the project. In some cases, a potential overrun results in
modifying the project to a design-to-cost task.
There are many reasons for cost overruns, but one of
the key factors is the lack of resources (time, money, and
staffing) spent to do proper up-front cost estimates. An-
278
https://ntrs.nasa.gov/search.jsp?R=19930010247 2020-03-17T08:13:52+00:00Z
other major reason is that the implementers did not do the
estimating. The purpose of this article is to address the
issue of the cost to do a cost estimate. The authors will
report on how others handle this issue, offer suggestions
on how the DSN should estimate the amount to spend on
a cost estimate, and discuss its impact on reducing the
probability of a cost overrun.
The authors will report on their literature search and
actual data from :IPL Procurement on what others charge
the Laboratory for a cost estimate. The goal is to de-
termine guidelines and a methodology for estimating how
much to spend on a cost estimate to achieve a desired ac-
curacy. Underallocation of resources for producing a cost
estimate is not uncommon. All the necessary cost elements
are usually not included because of time constraints. This
leads to cost overruns and/or descoping of the functional
requirements of projects.
II. The Cost of Estimating Accuracy
The cost of doing a cost estimate depends on how well
the project is defined, who is doing the estimate, the
amount of information available, and the level of accuracy
required. An order-of-magnitude estimate will cost much
less than a definitive estimate. The accuracy of a cost
estimate increases within certain limits as the amount of
resources spent on the cost estimate increases. The au-
thors defined a metric for the cost to do a cost estimate as
the percent of the cost of th¢ estimate as compared with
the total cost of the project.
than that sliown m Fig. 1, whereas for smaller p_oje_ts
costing much less than $3 million, the percent spent on
the cost estimate wbuld "be higher. Figure 1 represents a
model typical of thep_s_ndustry; however, the concept
applies to the DSN. The authors will now report on a
recent set of data that is applicable to the DSN.
This second set of data [2] shows the cost to prepare'cost
estimates for three accuracy ranges varying from order of
magnitude, -30 to +50 percent; budget, -15 to +30 per-
cent; and definitive, -5 to +15 percent, for projects rang-
ing from approximately $0.1 to $80 million. Notice that
the high limits of the ranges are greater than the low lim-
its because there is usually a lack of consideration of all
the necessary cost elements. As a result, there is usually
more chance of a Cost overrun than an underrun. By mak-
ing several smoJt__m_pti-ons and updating the data
to 1990 using the NASA I_flation Index [3], the authors
plotted the resulting data set as shown on a log-log plot
in Fig. 2. A model-devel0ped based on these parameters
is described below.
III. Model for the Cost of Estimating
Accuracy
On the log-log plot of Fig. 2, a set of straight lines con-
formed closely to the data points. On a log-log plot, a
straight line represents a convenient power function equa-
tion of the form CE -- KCp R. That is, by taking the log
of both sides of the equation, one gets
cost of a cost estimate (percent) = log Cs=Rlog Cp+log K (:)
cost of the estimate (CE)
total cost of the project (Cp) × 100 percent
Figure 1 shows the relative cost of a cost estimate [1]
as a function of the accuracy of an estimate for a project
costing approximately $3 million that the authors up-
dated to 1990 dollars using the NASA IT_flation Index [3].
For example, an estimate that is accurate enough to be
within 4"30 percent would cost 0.2 percent, or $6 thousand,
whereas an estimate accurate to within -4-10 percent would
cost 1.5 percent, or $45 thousand, of the total project cost.
The more one invests up front in defining the requirements
and the deliverables, the more accurate the finM estimate
will be.
For projects much larger than $3 million, the cost of the
estimate as a percent of the total project cost would be less
This represents a straight line where R is the slope of the
line in Fig. 2 and log K is the Y intercept. The lines
shown in Fig. 2 therefore reflect a convenient power func-
tion equation that can be used as a model.
Cs=KCp R (2)
where
CE -- cost of the cost estimate in thousands of dollars
Cp = cost of theproject being estimated in millions of
dollars
K = a constant depending on the accuracy of the es-
timate
R = slope of lines
Of POORQUALITY
279
Figure 2 shows the slope R andthe constant K for each
class of cost estimate. For each class of estimate, R = 0.35
for project costs in the range of $0.1 to $100 million. The
constant K is 24 for an order-of-magnitude estimate, 60 for
a budget estimate, and 115 for a definitive estimate. Or to
look at it another way, a budget cost estimate costs about
two-and-one-half times as much as an order-of-magnitude
estimate, and a definitive estimate costs about twice as
much as a budget estimate.
IV. Discussion of Levels of Cost Estimates
The levels of cost estimates discussed in this article cor-
relate with the condensed classification of cost estimates
proposed by the American Association of Cost Engineers
[2]. These are as follows:
Class Accuracy, percent
Order of magnitude -30 to +50
Budget -15 to +30
Definitive -5 to +15
An order-of-magnitude level of cost estimate is usually
based on very preliminary statements of requirements.
This is done in the requirements definition stage when
there is a preliminary listing of deliverables. This class
of estimate roughly coincides with that needed for a Level
A design review 3 when a maximum uncertainty of 30 per-
cent is desired.
The budgetary level of a cost estimate is based on sys-
tem functional requirements with at least preliminary de-
liverables, receivables, and schedules presented by a sub-
system. This class of cost estimate is appropriate for
Level B and/or Level C design reviews when a maximum
uncertainty of 20 percent is desired.
The definitive level of a cost estimate is based on a sub-
system functional design, and the deliverables, receivables,
and schedules are carefully defined and final. This class of
cost estimate is appropriate for a Level D design review
with a maximum uncertainty of 10 percent.
3R. P. Mathison and P. T. Westmoreland, "Cost Review Format,"
JPL Interoffice Memorandttm 3300-88-08 (internal document), Jet
Propulsion Laboratory, Pasadena, Califorlnia, January 6, 1988.
280
A more detailed description of the DSN classes of cost
estimates as they relate to design reviews is presented
in the Mathison-Westmoreland J PL Interoffice Memoran-
dum. 4
V. Example Using the Model
Assume that one has to estimate the cost required to
do a cost estimate for a project that is expected, based on
other similar projects, to cost approximately $20 million.
Use Eq. (2) or Fig. 2 where
CE -- KCp R
Cp = 20, R = 0.35, and K = 24, 60, and 115 for an
order-of-magnitude, a budget, and a definitive estimate,
respectively. Using CE = 24 x 20 °'35, one gets $68,000 for
an order-of-magnitude estimate. For a budget estimate,
one gets $171,000 and a definitive estimate costs $328,000.
Armed with these data, a decision can be made to proceed
with the cost estimate after allocating the necessary funds.
This method may reduce underallocation of resources
for producing cost estimates, and thereby more realistic
project cost estimates may be obtained. Of course, if the
actual estimate of the project turns out to be more or
less than the so-called ballpark guesstimate, the budget
for doing the cost estimate can be adjusted accordingly.
In the next section, data obtained from JPL Procurement
will be presented on the cost of actual cost estimates.
VI. JPL Procurement Data for Cost
Estimates
The authors obtained data based on JPL procure-
ments for outside contractors to do cost estimates for DSN
projects. 5,6,_ These data points are summarized in Ta-
ble 1. The first data point reflects a Motorola estimate
4 Ibid.
5R. S. Hughes, personal communication, Radio Frequency and Mi-
crowave Subsystems Section, Jet Propulsion Laboratory, Pasadena,
California, December 5, 1989.
6R. L. White, personal communication, Ground Antennas and Fa-
cilities Engineering Section, ,let Propulsion Laboratory, Pasadena,
California, January 23, 1991
7L. H. Kushner, personal communication, Ground Antennas and Fa-
cilities Engineering Section, Jet Propulsion Laboratory, Pasadena,
California, February 5, 1991.
for a significant supplement to an existing Motorola con-
tract. The second and third data points show the costs
of two externally generated Preliminary Engineering Re-
ports (PER's) that developed the estimated costs for cost-
of-facilities projects
The costs of the cost estimates for these three projects
varied from 1.3 to 2.9 percent of the total project cost.
The high value of 2.9 percent was for a relatively small
project of about $2 million, whereas the lower values of 1.3
to 1.5 percent were for projects in the $11 to $24 million
range. These results fall into the band of curves shown in
Fig. 2. This provides an independent check of the model
proposed earlier.
=
Vii. Summary
A model for estimating how much should be allocated
to do DSN cost estimates for new c_pabilities has been
developed. This model may help the DSN make better cost
estimates and thereby reduce the possibility of producing
cost estimates that are too low. These low cost estimates
could lead to cost overruns or reduction of some functional
requirements or both.
References
[1] W. R. Park, Cost Engineering Analysis--A Guide to the Economic Evolution of
Engineering Projects, New York: John Wiley & Sons, p. 133, 1973.
[2] K. K. Humphreys, Jelen's Cost and Optimization Engineering, third edition, New
York: McGraw-Hill, p. 370, 1991.
[3] National Aeronautics and Space Administration, Cost _E6bhbmic Analysis
Branch, NASA Inflation Index, Washington, DC, February 28, 1991.
P
r
281
_!
Table 1. JPL procured cost estimates in FY'90 dollars,
Source Cp, millions of dollars C,_, thousands of dollars (CE/Cp)lO0, percent
Motorola _ 1.96 56 2.9
Section 332 b PER c 10.83 146 1.3
Section 332 PER d 24.00 360 1.5
Modification to Motorola contract (Magellan ground hardware) for adding C-band upllnk capability
to DSN receiver-exclter subsystems.
b Ground Antennas and Facilities Engineering Section.
c For 34-m antenna JPL support effort plus contractor production of PER.
d For new Telecommunication Researc_.h Laboratory (building).
282
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0.50 -
0.20 -
0.10 -
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t I I I
I I l I
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ACCURACY OF ESTIMATE, PERCENT PLUS OR MINUS
Fig. 1. Cost of a cost estimate for a $3 million (1990) project [1].
50
ct_
n-
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z
co
L_
102
101
10-1
DEFINITIVE
CE = KCOp.35 (-5 TO +15
BUDGET
K= 115 (-5
K= 60
ORDER OF MAGNITUDE
(-30 TO +50 PERCENT)
K= 24
L l I I x = i I{ ,I ,l I I J ; I I( =
100 101
Cp, COST OF PROJECT, MILLIONS OF DOLLARS
Fig. 2. Cost of e coal estimate for three accuracy ranges.
I I I I I I I
10 2
283
"IDAProgress Report 42-110 %. August15, 1992
Referees
The following people have refereed articles for The Telecommunications and Data
Acquisition Progress Report. By attesting to the technical and archival value of the
articles, they have helped to maintain the excellence of tiffs publication during the
past year.
H. Ansari G.J. Dick J.W. Layland M. Simon
D. A. Bathker J.O. Dickey F. Manshadi R. Syndor
J. S. Border R. Dickinson R.J. McEliece R.C. Tausworthe
D. Boussalis S. Dolinar F. Pollara S.W. Thurman
S. Butman W. Gawronski R. Rasmussen J.S. Ulvestad
A. Cha R.A. Jacobson E.H. Satorius C.L. Zygielbaum
P. A. Clements A.S. Konopliv J. Shell
284
OF POOR QUALITY


A model for the cost of doing a cost estimate

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