**CHAPTER 1**

**INTRODUCTION, BACKGROUND,**

**AND**

**DEFINITIONS**

**CHAPTER I**

**INTRODUCTION, BACKGROUND, AND DEFINITIONS**

**[INTRODUCTION]{.underline}**

This *Handbook* is intended to be a living document. As advances are
made in parametric estimating methodology, they will be introduced into
the body of this material. Changes suggested from experienced users are
solicited, as well as recommendations from other experts in the field.
However, the *Handbook* is primarily intended for the beginning
parametrics practitioner and to be used to enhance parametric training
in the field. When using the *Handbook*, however, we assume that the
reader has a basic understanding of algebra and statistics.

Defined, a parametric cost estimate is one that uses Cost Estimating
Relationships (CER's) and associated mathematical algorithms (or logic)
to establish cost estimates. For example, detailed cost estimates for
manufacturing and test of an end item (for instance, a hardware
assembly) can be developed using very precise Industrial Engineering
standards and analysis. Performed in this manner, the cost estimating
process is laborious and time consuming. However, if history has
demonstrated that test (as the dependent variance) has normally been
valued at about 25% of the manufacturing value (the independent
variable), then a detailed test estimate need not be performed and can
simply be computed at the 25% (CER) level. It is important, though, that
any CER's used be carefully tested for validity using standard
statistical approaches. An exploration of certain statistical approaches
relevant to CER development is included later in this *Handbook*.

The need to reengineer business processes and reduce cost in the
Department of Defense (DoD) has led to a parametric cost estimating
initiative. In every corner of every aspect of defense contracting,
Business Process Reengineering (BPR) has become a nineties buzzword. The
cumbersome techniques that evolved into the development of the "normal"
cost estimating processes of today are beginning to yield to more
efficient and less costly approaches to achieve the same, or superior
results. Parametric estimating approaches fit very well into overall BPR
methods.

The importance of Business Process Reengineering was recently
underscored by Lloyd K. Mosemann, II, Deputy Assistant Secretary of the
Air Force, in his closing Keynote Address entitled "Predictability," to
the Software Technology Conference in Salt Lake City, Utah, on Thursday,
April 14, 1994. Although addressing the software process, Mr. Mosemann's
comments are relevant to the cost estimating process in general. In
summary, he said, in part:

"There seems to be an inability within the software community, in
general, to predict how much a software system will cost, when it will
become operational, and whether or not it will satisfy user
requirements. We need to deliver on our promises.

"We have a poor track record regarding predictions. A 1979 GAO report
concluded that only two percent of software contracted for was useable
exactly as delivered. Twenty different studies have come to the same
conclusion. Therefore, we in the DoD are focusing our attention on
process improvement: These include: specific metrics usage plans, reuse
plans, peer inspections, process controls, proposed architectures in
executable code, and government access to contractor on-line development
environments.

"This emphasis on process will give all of us in the software community
a greater confidence that the prospective contractor will deliver the
promised product on time and on budget."

Mr. Mosemann's emphasis on process improvements to improve the quality
of predictability of cost and schedule fits nicely with the concept of
expanding the use of parametric tools in the cost estimating workplace.
Parametrics can play a role in the BPR process as was underscored by
Anthony A. DeMarco in his article, [CAPE (Computer Aided Parametric
Estimating for Business process Re-Engineering]{.underline}, in the
[PRICE Newsletter]{.underline}, October 1994. In his article, in
summary, Mr. DeMarco states that:

"Business Processing Reengineering (BPR) is the reengineering of an
organization by examining existing processes and then revamping and
revising them for incremental improvement. It is doing more with less
and sometimes entails "starting over."

There are five phases to BPR. They are:

1\. Create an organization for improvement,

2\. Develop an understanding of the process,

3\. Streamline the process,

4\. Model, implement, measure, and control, and,

5\. Design and implement continuous improvement.

"Parametric tools can assist BPR. On one level, they can improve and
streamline the BPR phases. On another level, parametric technology is
the 'best practice' for estimating. Parametric tools bring speed,
accuracy and flexibility to estimating processes, processes that are
often bogged down in bureaucracy and unnecessary detail."

The need to reengineer the DoD cost estimating process (Acquisition
Reform initiatives) became self evident to certain people from both
government and industry. A Steering Committee was chartered by
government and industry executives to explore the role played by
parametrics in the cost estimating process. One goal was to determine
what barriers, if any, exist to expanding the role of parametrics, and
to develop the action plans to overcome those barriers. The committee
consists of representatives from all of the Armed Services, the oversite
community, selected contractors. This *Handbook* has been authorized by
that Steering Committee.

The *Handbook* is intended to be used by both model developers and model
reviewers, their management or oversite, either technical or financial.
Government and industry cost analysts and auditors who utilize CER's
and/or parametric cost models to develop or evaluate an estimate
generated with these parametric tools should find the *Handbook* useful.
It is also intended to be utilized as a source document by trainees
within a generic parametric cost estimating training program.

This *Handbook* includes basic information concerning data collection,
Cost Estimating Relationship (CER) development, parametric cost models,
and statistical techniques.

Parametric techniques are a credible cost estimating methodology that
can provide accurate and supportable contractor estimates, lower cost
proposal processes, and more cost-effective estimating systems.

An estimating workbench context model is shown in Figure I-1. The model
indicates the tools required within the estimating community of
contractors, customers and government agencies. Figure I-2 is a
graphical representation of the complete parametric cost estimating
process. The figure indicates the process from inputs through modeling
and into a post processor phase. The post processor allows for the
conversion of parametric output into a cost proposal.

**[BACKGROUND]{.underline}**

The origins of parametric cost estimating date back to World War II. The
war caused a demand for military aircraft in numbers and models that far
exceeded anything the aircraft industry had manufactured before. While
there had been some rudimentary work from time to time to develop
parametric techniques for predicting cost, there was no widespread use
of any cost estimating technique beyond a laborious buildup of
labor-hours and materials. A type of statistical estimating had been
suggested in 1936 by T. P. Wright in the [Journal of Aeronautical
Science]{.underline}. Wright provided equations which could be used to
predict the cost of airplanes over long production runs, a theory which
came to be called the learning curve. By the time the demand for
airplanes had exploded in the early years of World War II, industrial
engineers were using Wright's learning curve to predict the unit cost of
airplanes.

In the late 1940's, the DoD, and, especially, the United States Air
Force began a study of multiple scenarios concerning how the country
should proceed into the age of jet aircraft, missiles and rockets. The
Military saw a need for a stable, highly skilled cadre of analysts to
help with the evaluation of such alternatives. Around 1950, the military
established the Rand Corporation in Santa Monica, California, as a civil
"think-tank" for independent analysis. Over the years, Rand's work
represents some of the earliest and most systematic studies of cost
estimating in the airplane industry.

The first assignments given to Rand concerned studies of first and
second generation ICBM's, jet fighters and jet bombers. While the
learning curve technique still proved useful for predicting the behavior
of recurring cost, there were still no techniques other than detailed
labor-hour and material estimating for projecting what the first unit
cost might be (a key input to the

**ESTIMATING WORKBENCH**

**CONTEXT MODEL**

PARAMETRIC

MODELS/TOOLS

REQUEST FOR

SUPPORTING DATA

SOLICITATION RESPONSE

ESTIMATING

WORKBENCH

• PROPOSAL/COST DATA,

• HISTORICAL DATA,

• CONTRACTOR DATA,

• COST ELEMENT ANALYSIS

• CERs

• OTHER PARAMETRIC DATA

• OTHER NONCOST DATA

• EXTERNAL DATA -

COST COMPARISONS

• BACKGROUND DATA -

HISTORICAL DATA **USERS**

**FIGURE I-1**

![](media/image1.emf){width="6.509027777777778in"
height="5.522222222222222in"}

learning curve equation). Worse still, no methods were available for
quickly estimating the non-recurring costs associated with research,
development, testing and evaluation (RDT&E). In the defense business in
the early to mid 1950's, RDT&E had suddenly become a much more important
consideration. There were two reasons for that fact. First, a shrinking
defense budget (between World War II and the Korean War) had cut the
number of production units for most military programs, and second, the
cost of new technology had greatly magnified the cost of development.
The inability to quickly, and accurately, estimate RDT&E and first unit
production costs had become a distinct problem.

Fortunately, within Rand, a cost analysis department had been started in
1950. This group proved to be prolific contributors to the art and
science of cost analysis \-- so much so that the literature of aerospace
cost estimating of the 1950's and 1960's is dominated by the scores of
Rand cost studies that were published during that time. In the mid
1950's, Rand developed the most basic tool of the cost estimating
discipline, the Cost Estimating Relationship (CER), and merged the CER
with the learning curve to form the foundation of parametric aerospace
estimating. This estimating approach is still used today.

By 1951, Rand derived CER's for aircraft cost as a function of such
variables as speed, range, and altitude. Acceptable statistical
correlations were observed. When the data was segregated by aircraft
types (e.g., fighters, bombers, cargo aircraft, etc.), families of
curves were discovered. Each curve corresponded to different levels of
product or program complexity. This Parametric stratification especially
helped clarify development cost trends. Eventually, a useable set of
predictive equations were derived which were quickly put to use in Air
Force planning activities.

> The use of CER's and data stratification were basic breakthroughs in
> cost estimating, especially for RDT&E and first unit costs. For the
> first time, cost analysts saw the promise of being able to estimate
> relatively quickly and accurately the cost of proposed new systems.
> Rand extended the methods throughout the 1950's, and by the early
> 1960's, the techniques were being applied to all phases of aerospace
> systems.
>
> Since these rather humble beginnings, the state-of-the-art in
> parametric estimating has been steadily improving by an explosive
> growth in the number of practitioners, important methodological
> improvements, and greatly expanded databases. All of the major
> aerospace contractors and government aerospace organizations have
> dedicated staffs of parametricians who maintain and expand databases,
> develop parametric cost models, and utilize the tools of parametrics
> to make estimates of new and ongoing programs. NASA and the DoD
> routinely use parametric estimates to form the basis of new project
> cost commitments to Congress. The contractor community also routinely
> uses parametric cost models, especially during product concept
> definition. These estimates are used for decision making regarding bid
> strategies and are used as submittals to the government. It is only at
> the production and full scale development phase that parametrics are
> not commonly utilized for official proposal submissions (although
> contractors still frequently use parametrics to generate target costs
> and cross-checks on the labor-material/buildup estimates).
>
> Over the past several years industry and professional estimating
> associations (e.g., International Society of Parametric Analyst
> (ISPA), Society of Cost Estimating and Analysis (SCEA), and the Space
> Systems Cost Analysis Group (SSCAG)) have been actively working with
> both Defense Contract Management Command (DCMC) and Defense Contract
> Audit Agency (DCAA) to explore the expanded opportunities for the use
> of parametric cost estimating techniques in firm business proposals.
> ISPA was formed in 1978 when a parametric estimating user's group
> evolved into a more generic Society. The Space Systems Cost Analysis
> Group formed in 1977 under the sponsorship of the U.S. Air Force Space
> Division, with a mission to:
>
> 1\. Promote Cost Analysis Research
>
> 2\. Develop new tools to improve cost estimating techniques
>
> 3\. Promote sound practices, and
>
> 4\. Provide a forum for government and industry cost analysts
> concerned with the development and production of space-design hardware
> and software.
>
> Then, in April 1994, a joint Industry and Government workshop on
> Parametric Cost Estimating occurred at the Defense Contract Audit
> Institute in Memphis, TN. Under the initiative and leadership of the
> DCMC, the DCAA, and industry proponents, a group of knowledgeable
> government and industry executives, policy formulators, and parametric
> practitioners were assembled to evaluate why there is not greater use
> of parametric cost estimating in DoD and NASA business proposals;
> identification of the barriers to expanded use of parametrics; and,
> action planning to take advantage of identified opportunities.
>
> At the conclusion of the workshop, it became clear to the participants
> that there were no barriers which precluded further implementation and
> use of parametric cost estimating by contractors in DoD or NASA
> business proposals. Rather, barrier analysis and actions recommended
> focused on the need for industry leaders to demonstrate that
> parametric estimating systems can be relied upon by the Government
> customers, and the need for the Government to train employees so that
> there exists a clear message that valid parametric estimates are a
> useful and often cost effective estimating approach.
>
> **[DEFINITIONS AND TERMS]{.underline}**
>
> A complete glossary of parametric terminology, taken from numerous
> sources, is included in this *Handbook* as Appendix A. A few of the
> more important definitions are noted in this chapter.
>
> There are several definitions of parametric estimating, but for the
> purpose of this *Handbook*, the formal one adopted is as follows: A
> technique employing one or more CER'S and associated mathematical
> relationships and logic. The technique is used to measure and/or
> estimate the cost associated with the development, manufacture, or
> modification of a specified end item. The measurement is based on the
> technical, physical, or other end item characteristics.
>
> This definition establishes the clear linkage between cost and a
> product's (or end item) technical parameters. Without this linkage, a
> product cost cannot be effectively defined. Non-parametric estimating
> systems generally do not connect technical (parametric) and cost
> elements with any substantial precision.
>
> And, a Parametric Cost Model is defined as: A parametric cost model is
> a group of cost estimating relationships used together to estimate
> entire cost proposals or significant portions thereof. These models
> are often computerized and may include many inter-related CER's, both
> cost-to-cost and cost-to-noncost. Some models use a very limited
> number of independently estimated values and a series of Parametric
> inter-related cost-to-cost and cost-to-noncost estimating
> relationships to predict complex proposal cost structures.
>
> Parametric cost estimating is a technique used by both contractors and
> the Government in planning, budgeting, and performance stages of the
> acquisition process. The technique is used by contractors to expedite
> the development of cost estimates when discrete estimating techniques
> would require inordinate amounts of time and resources and would
> produce similar results. Reliance on properly developed and carefully
> evaluated CER's and parametric cost models to produce realistic cost
> estimates can save both Industry and the Government time and resources
> in the evaluation and definitization cycle of the proposal or
> contract.
>
> The concept includes the use of cost-to-cost CER's such as engineering
> labor overhead rates and material overhead rates which when reviewed
> using traditional evaluation criteria, are considered valid estimators
> by the government. However, the technique also uses cost-to-noncost
> CER's which require additional analysis to determine their validity
> and acceptability as estimating tools.
>
> Parametric techniques focus on the cost drivers, not the miscellaneous
> details. The drivers are the controllable system design or planning
> characteristics and have a predominant effect on system cost.
> Parametrics uses the few important parameters that have the most
> significant cost impact on the product(s), hardware or software, being
> estimated.

**[COST REALISM]{.underline}**

A widely used term today is "cost realism." Now, no one expects a cost
estimate to precisely predict what a hardware or software product or a
time and material service will cost. So, cost realism is not about the
exact cost estimate. It's about the system of logic, the assumptions
about the future, and the reasonableness of the historical basis of the
estimate. That is, it's about the things that make up the
***foundation*** of the estimate.

Cost realism analysis answers questions such as:

\* Are the assumptions used in the estimating process reasonable?

\* Has the historical data base used been normalized to account for
environmental parameters such as inflation?

\* Is the cost estimate logical? Does it make sense in the context of
the hardware or software product or service being estimated?

\* Does the estimate display a bias toward being too low or too high? If
so, how is this bias displayed in the estimate?

\* Is the cost estimating organization motivated to produce an
inordinately high or low estimate in order to serve their own purposes?

\* If the product is fixed price sole source, has the historical basis
data been "cherry picked" to insure the cost estimate obtained is
unreasonably high (contractor) or unreasonably low (auditor or
government customer)?

\* If the program is competitive, has the contractor or government
program office created program expectations that are far too optimistic?

The cost estimator or analyst must ensure that they are working toward
the goal of cost realism. It doesn't matter whether or not they are
employed by private industry, or the customer as a cost analyst or an
auditor. If a contractor chooses to accept a management challenge in a
competitive procurement, that's certainly acceptable. However, the basis
for the challenge should be clearly identified.

There is no easy answer to the cost realism dilemma we all face.
Unreasonable biases and expectations from contractor and customer have
driven the cost estimating process in the past, and personal and
programmatic motivations may continue to drive it in the future. But one
thing is certain: the cost estimating process will continue to confront
future unknowns. These unknowns are what make the cost estimating job
one of the most difficult there is. But sound assumptions, high quality
historical data, and unbiased analysts and estimators will improve the
process for all.