Group Title: Computer-based corporate modeling system
Title: A Computer-based corporate modeling system
CITATION PDF VIEWER THUMBNAILS PAGE IMAGE ZOOMABLE
Full Citation
STANDARD VIEW MARC VIEW
Permanent Link: http://ufdc.ufl.edu/UF00097652/00001
 Material Information
Title: A Computer-based corporate modeling system
Physical Description: xi, 146 leaves. : illus. ; 28 cm.
Language: English
Creator: Zant, Robert Franklin, 1943-
Publication Date: 1972
Copyright Date: 1972
 Subjects
Subject: Simulation methods   ( lcsh )
Management games   ( lcsh )
Management and Business Law thesis Ph. D   ( lcsh )
Dissertations, Academic -- Management and Business Law -- UF   ( lcsh )
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
 Notes
Thesis: Thesis -- University of Florida.
Bibliography: Bibliography: leaves 144-145.
Additional Physical Form: Also available on World Wide Web
General Note: Typescript.
General Note: Vita.
 Record Information
Bibliographic ID: UF00097652
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: alephbibnum - 000582609
oclc - 14145450
notis - ADB0986

Downloads

This item has the following downloads:

PDF ( 5 MBs ) ( PDF )


Full Text
















A COMP]UTER-BASED
CORPORATE MODELING SYSTEM






By





ROBERT FRANT}LIN ZANT


A DISSERTATION PRESENTED TO T:E GRADUATE COUT'CIL OF
THE UNIVERSITY OF PLORIDA
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DECREE OP DOCTOR OF PHILOSOPHY





UNIVERSITY OF FLORIDA
1972






























Copyright by

Robert Franklin Zant

1972






























To Sue, Lynn, and Tina












ACKNOWLEDGMENTS


The writer wishes to express his gratitude to the mem-

bers of his supervisory committee: Dr. William V. Wilmot,

Jr., Chairman; Dr. R. H. Blodgett; Dr. E. L. Jackson; and

Dr. W. E. Stone. They have generously contributed their

time and efforts in assisting the writer in this study.

The writer is particularly indebted to Dr. Wilmot,who

as a professor, department head, and supervisory committee

chairman, has contributed immeasurably to the writer's

graduate studies.

Mr. John Kisner, Mr. Joe Gothe, and Mr. Tom Cox of the

Riegel Textile Corporation have also provided much-needed

advice and encouragement throughout the course of this study.

Finally, the writer wishes to acknowledge the self-

denial which was graciously accepted by his wife, Sue, and

daughters, Lynn and Tina. Special appreciation is extended

to his wife who typed all drafts and the final manuscript

for this study.













TABLE OF CONTENTS


Page


ACKNOWLEDGMENTS


LIST OF TABLES. . . . . . . .

LIST OF FIGURES . . . . . .

ABSTRACT. . . . . . . .


. . vii

. . . viii


CHAPTER


I. INTRODUCTION. . . . . . .

Definition of Corporate Models. .
Functions of Corporate Models . .
Developments in Corporate Modeling.
Purpose of Study. . . . . .
Methodology . . . . . .
Overview. . . . . . .

II. MODELING SYSTEMS. . . . . .


. . . 1


* .
* .
* .


Desirable Characteristics of Modeling Systems
State of the Art. . . . . . . .

III. A CORPORATE MODELING SYSTEM . . . . .

Overview of CMS/1 . . . . . . .
General Design Concepts . . . . . .
Control Language . . . . . . .
Logic and Data Specification Language . .
Report Format Specification Language. . .

IV. IMPLEMENTATION OF CMS/ . . . . . .

Deterministic Model . . . . . . .
Sensitivity Analysis . . . . ..
Monte Carlo Simulation. . . . . . .
Interrelated Models . . . . . . .


V. SUMMARY AND RECOMMENDATIONS . . ..

Summary . . . . . . . .
Recommendations for Further Research.


S. 109

S. . 109
. . 110


. .


. . . . . . . .


. . .







TABLE OF CONTENTS

(continued)

Page

APPENDIX A. . . . . . . . . . . 113

BIBLIOGRAPHY. . . . . . . . . . 144

BIOGRAPHICAL SKETCH ............ . .. 146











LIST OF TABLES


Page
1. Summary of Characteristics of
Five Modeling Systems . . ... . 27

2. Summary of Control Language Keywords . . 50

A-1. Special Characters in the CMS/1
Languages . . . . . . . . 121

A-2. Summary of Operators in the Logic and
Data Specification Language . . . 128


vii













LIST OF FIGURES


1. Logical organization of CMS/1. . . .

2. Sample data module . . . . . .

3. Sample logic module. . . . . .

4. Sample report module . . . . .

5. Output from sample model . . . .

6. Sample deterministic model . . . .

7. Results with sum-of-the-years digits
depreciation . . . . . .

8. Results with double declining balance
depreciation . . . . . .

9. Addition to sample model for sensitivity
analysis . . . . . . .

10. Results of sensitivity analysis for the
initial market size. . . . .

11. Results of sensitivity analysis for the
maximum market share . . . .

12. Types of distributions accepted by CMS/1

13. Additions to sample model for Monte
Carlo simulation . . . . .

14. Results of Monte Carlo simulation. . .

15. Logic and report modules for products.

16. Data modules and execute statements
for products . . . . . .

17. Model results for Division One,
Product One . . . . . .

18. Model results for Division One,
Product Two . . . . ..


Page

. . 29

* . 30

. . 31

* . 32

* . 34

. . 65


. 78

. 84


S86

S 88

S 92


S 94


S 96


S 97


viii


* *


* *


* *


* *


. .

* .






LIST OF FIGURES

(continued)
Page


19. Model results for Division Two, Product One... 98

20. Model results for Division Two, Product Two. 99

21. Logic and report modules for divisions . .. 101

22. Data modules and execute statements for
divisions. . . . . . . . ... 102

23. Model results for Division One . . ... 103

24. Model results for Division Two.. . . .. 104

25. Complete corporate level model .. . . 106

26. Results for corporate level model. . . . 108

A-1. Schematic of CMS/1 translation pass. . . 116

A-2. Schematic of CMS/1 execution pass. . . 117

A-3. Example model. . . . . . . .118

A-4. Results from example model . . . . . 119






Abstract of Dissertation Presented to the Graduate Council
of the University of Florida in Partial Fulfillment of
the Requirements for the Degree of Doctor of Philosophy

A COMPUTER-BASED CORPORATE MODELING SYSTEM

By

Robert Franklin Zant


August, 1972



Chairman: Dr. William V. W'ilmot, Jr.
Major Department: Department of Management ahd Business Law


The use of formalized computer-based models in the

management of a firm has been primarily concentrated in

operational areas. There is, however, a growing interest in

the development of models which are broader in scope than

operational models. Models which interrelate all areas of

the firm.- production, finance, and marketing are called

corporate models.

Past efforts in the area of corporate modeling have

demonstrated the need for special purpose computer languages

which facilitate the construction of corporate models. The

purpose of this study is to determine the desirable charac-

teristics of such languages and then to develop prototype

languages which supply the facilities necessary to support

a corporate modeling effort.

A modeling system is developed which is composed of

three languages a control language, a logic and data

specification language, and a report format specification

language. Some of the features of the new system are





1) a high level of user orientation; 2) the ability to per-

form sensitivity analysis; 3) the ability to perform Monte

Carlo simulations; and, 4) the ability to transfer the

values of variables from one model to another.

The high level of user orientation of the modeling

system developed in this study makes the system particularly

well suited for the initial modeling effort of a firm, for

the development of exploratory and/or short-life models,

and as a pedagogical aid in introducing managers to the

concept of modeling.












CHAPTER I

INTRODUCTION


One of the fundamental concepts in management theory

is the concept of models. Managers use models everyday in

forming opinions and in making "intuitive" judgments as well

as in making decisions "scientifically." Indeed, everyone

interprets hi s perceptions in accordance with a conceptual

or mental model known as the person's "view of the world."

Many psychologists maintain that if a person's "view of the

world" can be understood then his behavior can be predicted.

And, a person's behavior can be altered by altering his con-

ceptual model. The implication for management scientists is

that they should be concerned with the formalization of the

models which managers are currently using (a positive theory)

and the models which managers should be using (a normative

theory). Management theory would thus form a base for the

improvement and the development of management techniques.

The formalization of models used by management has been

mostly concentrated in operational areas such as production

scheduling and inventory control systems. Operational prob-

lems are more easily formalized because they tend to be re-

petitive in nature and more well structured than other mana-

gerial problems. There is, however, a great deal of inter-

est in the development of models which are broader in scope







than operational models, models which consider the total

corporation; i.e., corporate models.

Definition of Corporate Models

The activity in the area of corporate modeling has been

so diverse that one author has concluded that there is "no

generally accepted definition of a corporate model..."[11,

p. 43]. Models reported in the literature have varied from

marketing oriented models [12] to production oriented mod-

els [2] to models used only in investment analysis [20 21].

In most cases, however, the models which are purported to

be corporate models have the common trait of considering

the impact of an action over the total organization. This

is usually accomplished by considering the impact on the

corporate financial statements; i.e., the income statement,

the balance sheet, cash flow statement, etc.

The most trivial corporate model would simply be the

relationship "corporate income equals total revenue minus

total expenses." This simple model would be utilized by

estimating the effect of a proposed action on revenues and

on expenses and then computing the estimated corporate in-

come. Of course, a model at this level of aggregation would

be of little use. In order to make the model more meaning-

ful, the detail of the model may be increased by segmenting

the revenues and expenses by divisions and by products.

The model would then consist of relationships such as "cor-

porate income equals the sum of the divisions' net incomes

minus corporate expenses" and "a division's net income






equals the sum of the net contributions per product minus

divisional expenses." The model could then be used to

study the effect of alternatives such as the reallocation

of advertising funds among products; or the effect of a new

pricing policy; or, if the model were sufficiently detailed,

the effect of a change in the production process. In this

way the corporate model could be used to analyze the effects

of various alternative actions on the entire corporation.


Functions of Corporate Models

The functions of corporate models have been defined in

varying ways by several different authors [see 16; 9; 13; and

7]. However, the functions may be generally classified into

three groups. First, corporate models serve as a focus for

planning and decision-making efforts; second, they assist in

the analysis of alternative courses of action; and, third,

they assist in the coordination of the firm's planning and

decision-making activities.

A corporate model serves as a focus for planning and

decision-making efforts during the developmental stage of

the model and serves as a continuing focus as the model is

being used. The development of a corporate model requires

the clear and precise specification of the inter-relation-

ships between elements within the firm. Since the model

is a representation of management's concept of the firm,

the creation of the model will highlight the areas in which

the manager's conceptual models are "fuzzy" or ill-defined.

By focusing attention on these areas, the model encourages







management to clarify their perceptions of the functional

inter-relationships in the organization. The creation of

the model thereby causes management to re-examine and to

improve their understanding of the firm. The continual use

of the model then both encourages and guides the re-evalua-

tion and improvement of this understanding [16, p. 611].

The rephrasing of management's perceptions in the form

of a model also has the effect of defining the information

needed in the planning and decision-making process. "As a

matter of fact, the approach used to develop a corporate

model is very similar to the method used to develop the re-

quirements for an information system, e.g., the identifica-

tion of key variables" [9, p. 33]. A model would define,

for example, the classification of costs or the form of in-

formation such as market forecasts which are needed for

managerial planning and decision-making.

Perhaps the most obvious function of corporate models

is that they may be used in the analysis of alternative

courses of action. A corporate model represents a compre-

hensive and interrelated view of the firm. Consequently,

the use of such a model broadens the scope of the analysis

of an alternative both in terms of the organizational unit

and the time span considered. That is, it is possible to

investigate the long-term effects of an alternative on the

entire organization. Furthermore, since corporate models

are usually solved with little delay by use of a computer,

more alternatives can be considered than would be otherwise

possible. Of course, the shorter response time could be







used to make decisions more rapidly instead of being used

to allow the consideration of more alternatives. Or, a

decision may be delayed until more information is available.

In any event, the use of a corporate model has a significant

impact on the managerial process.

The use of the computer in solving corporate models

also has the advantage of improving the analysis of alter-

natives through improved accuracy, through the determination

of critical variables, and through risk analysis.

Although the saying "Garbage In-Garbage Out" is cer-

tainly true with respect to computers, it is also true that,

given correct information and a correct formulation of the

procedure to be followed, the computer is much less prone to

error than arc humans. This increased accuracy is essential

in a corporate modeling effort because of the number of ma-

nipulations and the magnitude of the data base which is

involved.

The use of a corporate model in conjunction with sensi-

tivity analysis aids in the determination of the critical

variables for a particular decision. Sensitivity analysis

is accomplished by repeatedly solving a model with the value

of one input variable being changed between each solution.

If the behavior of the model changes significantly for small

changes in the input variable, then the system is said to be

sensitive to the value of the input variable. By carrying

out sensitivity analyses over many input variables, the

critical variables may be determined. More effort can then

be expended in estimating and controlling the critical







variables.

Corporate models may also be used in analyzing the risk

involved with the acceptance of various alterations. This

is usually accomplished by the use of Monte Carlo simulation.

Monte Carlo simulation refers to the process of sampling

from probabilistic variables and then using the sample val-

ues in solving a mathematical model. The sampling-solution

process is repeated numerous times in order to develop a

distribution of the responses of the model. In the case of

a corporate model the distribution would typically describe

the profitability of an alternative. The distribution

would depict the risk associated with the alternative by

disclosing the probability of loss as well as the probabil-

ity of receiving less than acceptable returns.

The third function of corporate models is the coordina-

tion of planning and decision-making activities. The devel-

opment of a corporate model requires the explicit considera-

tion of the assumptions that are to be made, the explicit

statement of the inter-relationships that are to be con-

sidered, and the specification of the information which is

needed. By unambiguously specifying the relationships

among variables and the values of variables, a model repre-

sents a common view of the firm which all managers can

comprehend. Planning and decision-making activities are

thereby undertaken with all parties having a common frame

of reference. As McKenney puts it, a model "is discernible

to a variety of managers and therefore discussible" [16,

p. 600].







Models also assist in coordination by virtue of their

comprehensiveness. When an alternative is analyzed, its

effect on the whole firm is considered. If a new policy

is to be considered, "it is possible to study how it re-

verberates and affects the entire company" [9, p. 33].

The use of models can also improve the consistency of

decision-making throughout the firm. The use of a model

over time and the use of a model by different areas in an

organization insures that the same variables are considered

in each decision-making process. If a process and model

are changed, then the new model would represent an explicit

statement of the new assumptions and criteria that are to

be used.

This broad use of modeling for consistency in decision-

making is exemplified by the Monsanto Company. The Mon-

santo Company has developed a modeling language which

affords them a convenient though advanced modeling capa-

bility. Monsanto now requires that all proposals for large

capital projects contain the results of sensitivity and

risk analysis [4, p. 12]. Proposals from all areas of the

firm can now be compared on the basis of common economic

assumptions.

In review, corporate models function as a focus for

the initial activity in a planning effort and as a focus

for the continued improvement of such efforts. Corporate

models aid in the analysis of alternatives because the

use of a computer allows the rapid analysis of complex







models. Corporate models also assist in the determination

of critical values through sensitivity analysis and the

consideration of risk through Monte Carlo simulation.

Finally, corporate models assist in the coordination of

planning and decision-making activities throughout the

organization.

Developments in Corporate Modeling

The construction of formalized corporate models is a

relatively new endeavor. The first modeling efforts began

in the late 1950's, but widespread interest in corporate

modeling did not develop until the late 1960's [9, p. 29].

The recent growth in corporate modeling seems to be a re-

sult of the fact that the initiation of a corporate model-

ing effort is usually an extension of a firm's formal cor-

porate planning activities. Thus, the interest in modeling

has lagged the interest in formal planning vhich experienced

a rapid growth in the late 1950's and early 1960's [9,

p. 30].
Corporate modeling has also been found to be an expen-

sive endeavor. The construction of most models has required

the expenditure of several man-years of effort. One large,

sophisticated model required a total of 23 man-years for

development [10, p. 44]. The large investment necessitated

by a corporate modeling effort has led to the development

of generalized models and to the development of specialized

modeling languages.

The generalized models are usually relatively simple






models which produce pro forma statements according to

common accounting definitions. They allow a varying degree

of freedom in the input of data but allow little or no

freedom in specifying the logic of the model or the form

of the output. Such models have a limited utility since,

in order to gain generality, the models sacrifice detail.

Several corporations such as IBM [14, 19] Dow

Chemicals [17], On-Line Decisions [2], and Monsanto [4] have

developed specialized modeling languages or modeling systems

which are designed to decrease the cost of developing a

corporate model. Modeling systems are not corporate models

themselves, but rather assist in the development of cor-

porate models by providing data structuring routines, pre-

defined mathematical routines, and report writing routines.

IBM's Planning Systems Generator (PSG), for example, pro-

vides a data structure which can contain over 10,000 values,

provides routines for computing the depreciation and the

retirement of investments, and provides capabilities for

printing reports complete with titles and column headings.

These specialized languages, while representing a major

improvement in modeling capabilities, have tended to be

oriented more towards the computer specialist than towards

the manager. Most managers would have difficulty in inter-

preting a corporate model when expressed in one of the

specialized languages. For example, in order to use PSG

the user must be capable of programming in the FORTRAN lan-

guage. Thus, language development has not yet attained the

desirable quality of allowing "the modeler and planner to





10

conceptualize the simulation model in the language it is to

be programmed" [15, p. 172].


Purpose of Study

The purpose of this study is to further the development

of modeling languages that can be used in constructing cor-

porate models. A modeling system consisting of three lan-

guages will be developed which will provide facilities for

data structuring, logic formulation, and report writing.

The system will be comprehensible to managers so that

a model will not have to be interpreted for the manager by

a computer specialist. This user orientation will make the

system particularly well suited for the initial modeling

effort of a firm, for the development of exploratory and/or

short-life models, and as a pedagogical aid in introducing

managers to the concept of modeling.


Methodology

This study will review the literature to determine the

characteristics of currently available modeling systems and

to determine the desirable characteristics of such systems.

A new modeling system will then be developed which supplies

the facilities necessary to support a corporate modeling

effort while retaining a high level of management orienta-

tion.


Overview

The results of this study are reported in four sections.

The first chapter introduces the concept and describes the







functions of corporate models. The growth of corporate

modeling activity is also reviewed. The purpose of this

study is given to be the development of a corporate model-

ing system that is suitable for use in a variety of model-

ing efforts including use as a pedagogical device.

The second chapter describes the desirable characteris-

tics of a modeling system. Several modeling systems current-

ly in use are discussed and are compared in relation to the

desirable characteristics which have been defined.

The third chapter introduces the corporate modeling

system which was developed in this study. General design

concepts are discussed, and then the three languages which

comprise the corporate modeling system are described.

The fourthchapter presents examples of the use of the

modeling system discussed in the previous chapter, while the

fifth chapter presents the conclusions of this study and

recommendations for future research.












CHAPTER II

MODELING SYSTEMS


The initial modeling effort of most firms has been ex-

pended in the development of individualized models [see 18;

4; and 3]. The individualized models could be executed

with different data, but the logic and the reports printed

by the models were "set in concrete.". The logical structure

and the report formats could only be altered with considera-

ble difficulty by reprogramming the model. This was found

to be an important deficiency, since model building is an

evolutionary process. Models are continually altered to

reflect new requirements and changing situations. As Mc-

Kenney states, "An adaptable and changing model is essential

if the model is to be used over an extended period of time"

[15, p. 170]. Thus, the initial modeling efforts high-

lighted the deficiency of the individualized model approach

and, in so doing, helped define the capabilities required

of corporate modeling systems.


Desirable Characteristics of Modeling Systems

The previous discussion of the functions of corporate

models indicated the necessity for the integration of the

modeling effort with the management process. That is, the

models should reflect the manager's view of the organization;

and,hence, the managers should be intimately involved in the






construction and operation of the models. The major diffi-

culty encountered in accomplishing this integration has been

the problem of interfacing managers and computers. McKenney

summarizes the problem thusly,

An impediment to more adequate rapport be-
tween modeler and planner is the state of pres-
ent computer languages. The language of the
program for the model has to be interpreted to
the planner. This interpretation creates am-
biguities and misunderstandings which limit the
effectiveness of present simulations as a tool
for most planners. Hopefully new computer lan-
guages will allow the modeler and planner to
conceptualize the simulation model in the lan-
guage it is to be programmed [15, p. 173].

Modeling systems consist of computer languages and data-

handling routines that simplify the construction of models

and facilitates the manager-computer interface. The desira-

ble characteristics of such systems may be summarized as

follows:

1. The modeling system should be comprehensible to

managers.

2. The logical structure of a model should be easily

translated into computer executable form.

3. The modeling system should have convenient report

generation capabilities.

4. The specification and the alteration of the values

of input variables should be easily accomplished.

5. The creation of the logical structure of a model,

of the report format, and the value of variables

should be independent.

6. The modeling system should support the performance

of simple logical and arithmetical tasks as well





14

as providing the capability of performing special

purpose calculations such as the discounting of

cash flows.

7. The modeling system should facilitate the perform-

ance of sensitivity analyses.

8. The modeling system should facilitate the perform-

ance of Monte Carlo simulations.

9. The communication of the values of variables among

models should be supported.

10. The modeling system itself should be alterable and

expandable.

The first requirement of a modeling system is that the

conceptual design of the system and the use of the system

be comprehensible to managers. A manager should be able to

understand the structure of the system and to develop un-

sophisticated models with a minimum amount of training.

Also, a manager should be able to comprehend the logical

structure ofa model created by others. This requirement

does not mean that all managers will actually be physically

creating computer-based models; nor does it mean that tech-

nically competent personnel will no longer be needed. What

is implied is that managers will be better able to inter-

face with technical personnel and with computers because

the modeling system will function as a common language.

In order for a modeling system to fulfill its role as

a common language, it must provide convenient methods for

the creation and manipulation of the three components of a

model the logical structure, the data structure, and the






reports. The logical structure should be easily expressed

in an "English-like" language that is both understandable

by humans and executable by computers. For example, the

sentence


MARGINAL INCOME EQUALS SALES MINUS VARIABLEEXPENSES


is comprehensible to managers and is an executable state-

ment under the corporate modeling system introduced in the

next chapter.

The data structuring capabilities are an important part

of a modeling system since most models are executed over

time periods rather than for just one time period. For ex-

ample, a budgeting model might be executed for twelve time

periods (i.e., twelve future months),while a long-range

planning model might be executed over five periods corres-

ponding to five future years. A modeling system should thus

accommodate a varying sized data structure.

The need for convenient report generation capabilities

might at first appear to be simply a convenience item, al-

most a "frill," since any computer output could be copied

over into a meaningful form for managers. But a report

generator is more than just a convenience item. The concept

of a modeling system is to increase the integration of

models into the management process by improving the inter-

face between managers and computers. This interface is a

two-way interface; the manager supplies information for the

computer and gets information out of the computer. The

objective is to lessen the need for intermediaries in both







cases. Thus, the output should not have to be interpreted

for the manager. Also, a report generator makes it easy to

alter the formats of reports to reflect changes in the

logical structure of models and new needs for information.

The fifth characteristic summarized above was that the

three components of a model (logic, data, and report) should

be created independently. This does not mean that the three

components are unrelated. Rather, the three components

should be created separately and then linked together when

the model is to be executed. This will allow different data

to be executed with the same logic, different logic to be

executed with the same data, and different reports to be

printed with appropriate logic-data combinations.

The sixth characteristic presented above is, of course,

the primary raison d'etre of models and modeling systems.

The purpose of a model is to perform some prescribed calcu-

lations which will assist the manager in his planning and

decision-making activities. A modeling system must thus

provide the capability of performing logical and arithmeti-

cal tasks and provide certain special purpose functions that

are commonly required in the business environment.

The modeling system should also provide capabilities

beyond simple arithmetical tasks by supplying facilities

for the performance of sensitivity analyses and Monte Carlo

simulations.

Sensitivity analysis requires that a model be repeated-

ly executed with a different value for a particular variable

being used in each execution. The modeling system must





17

therefore provide the facility for denoting the variable to

be used in the sensitivity analysis as well as the succes-

sive values it is to assume.

The requirement for the support of Monte Carlo simula-

tions is much more involved than the requirements for sen-

sitivity analysis. Monte Carlo simulation require repeated

executions as does sensitivity analysis, but it also re-

quires two further capabilities. First, some of the vari-

ables in the model must be expressed as random variables

which either conform to a mathematical distribution (e.g.,

a normal distribution) or conform to some empirical distri-

bution. Secondly, the results of the repeated executions

must be accumulated so that the relative frequency of the

occurrence of the different values for a particular variable

may bo determined. The report generating routines should

then have the capability of depicting these relative fre-

quencies.

A corporate model is typically composed of an inter-

related set of models. For example, a corporate model may

consist of a number of product models whose outputs are

summarized by divisional models whose outputs are in turn

summarized by a model for the total corporation. This

modularization of a corporate model is desirable for a num-

ber of reasons. Modularization allows for some areas of

the organization to be modeled in more detail than others,

allows more decentralization in the development and use of

the models, aids in the alteration and maintenance of the

models, and allows the independent execution of parts of







the corporate model.

The subdivision of a corporate model also gives rise

to a requirement for the ability to communicate the values

of variables among models. The values computed in one

model must be saved in such a manner that they may be ac-

cessed by a model which is executed later. In this way

models for different products may be executed with their

results being saved and later accessed by models which sum-

marize the performance of each division. Likewise, a model

could access values computed by the divisional models and

then summarize the performance of the total organization.

A final requirement of a modeling system is that it

should itself be amenable to alteration and expansion. As

a modeling system is used and as the modeling effort becomes

more sophisticated, it is likely that there will be a need

for capabilities which were not foreseen at the time of the

original development. This growth has been exemplified by

IBM's PSG which was revised to increase the number of

special purpose mathematical routines and to improve the

data strucuturing capabilities. Likewise, the Dow Chemical

Company's PSI has a history of continual alteration and ex-

pansion and is currently being completely rewritten [17].

The original development of a modeling system is the be-

ginning rather than the end. Thus, a modeling system should

be designed for alteration and expansion.


State of the Art

Although a number of corporate models and modeling:




19

systems have been reported in the literature, little detailed

information has been reported. Gershefski has presented in

detail the Sun Oil Company's corporate model [10]. Then

later, in studying the state of the art, he concentrated

more on general attributes such as the number of firms en-

gaged in the modeling activity, the organization and re-

sources required for a successful modeling effort, and the

general characteristics of models [9]. Dickson, Mauriel,

and Anderson have summarized with a bit more detail their

study of twenty models [5]. But, in general, corporate

modeling activity has not been reported in much detail.

In addition to the lack of reported detail, another

difficulty in determining the state of the art is the lack

of distinction in the literature between models and modeling

systems. Dickson et al. distinguish between two "philoso-

phies" of models the rigid structure approach versus the

flexible structure approach but fail to clearly distin-

guish modeling systems.

One philosophy exemplified by our findings noted
above is to adopt a fixed structure model approach
which forces the user to employ existing accounts,
fixed output reports, and a limited set of options
for attaching values to variables in the model.
There is an alternative philosophy--namely, to
build a more general and flexible model which al-
lows the user considerable latitude in choosing
model variables and methods for setting their
values over the projected horizon [5, p. 53].

Rigid or fixed structure models predefine the variables

which are used in the model, predefine the method for assign-

ing values to the variables, and predefine report formats

for the printing of variables. Most corporate models appear







to be of this type [5, p. 58].

A rigid structure model is difficult to alter in re-

sponse to changing situations so that it is desirable to

have a more flexible model structure. Flexibility is usu-

ally obtained by allowing the user to specify the "names"

of the variables used in the model, by allowing the user to

design the format of reports, and by allowing alternative

methods for assigning values to variables (e.g., the ex-

plicit statement of all values, the specification of be-

ginning values and growth rates, or the specification of a

variable as a function of another variable). Flexibility

is also obtained by allowing the input of values to override

values which would normally be calculated. Using these

techniques, considerable flexibility can be built into a

model in that the meaning of the variables, the logic of the

model, and the reports produced by the model can be altered.

There are a number of models of both the rigid and

flexible structure type reported in the literature. Two

easily compared models which are described in sufficient

detail in the literature are the rigid structure model de-

veloped by Dinter [6] and the flexible structure model

FINAN$ developed by General Electric [8]. Both models are

interactive financial models which produce pro forma income

statements, balance sheets, ratio analysis, etc. The logic

in both models is based upon general accounting definitions.

In the Dinter model the logic, variables, and reports

are predefined. The user interacts with the model by speci-

fying the values of variables and then noting the response







evidenced by the printed statements. The user can then

change some of the values of the variables and re-execute

the model. The user is assisted in this process by excep-

tion reports which highlight "out of bounds" conditions and

list possible causes for the exceptions.

The General Electric model does not have the exception

reporting feature but does allow the user more flexibility

in the application of the model. For example, the values

of variables may be assigned in several different ways.

There are three options for specifying the values of antici-

pated sales and four options for specifying the values of

other accounts. Two of these four options allow the user

to express the value of an account as a function of sales

and/or the change in sales. Thus the user has a limited

ability to create some of the logic of the model. There is

also flexibility, though limited, in the specification of

reports. The typesof reports and their formats are prede-

fined, but the user can specify the title that is printed

for each account.

A modeling system is different in philosophy from

either a rigid or flexible structured model. A modeling

system is a set of special purpose computer languages and

facilities which facilitate the construction, alteration,

and execution of models. It is not itself a corporate

model; it is used in the construction of corporate models.

A number of corporate modeling systems have been de-

veloped and reported in the literature. Several of these

systems are reviewed below with the intent of providing a







representative though not exhaustive view of the available

corporate modeling systems.

Perhaps the most widely used corporate modeling system

is the Planning Systems Generator (PSG) developed by Henry

Lande of IBM [14]. PSG consists basically of data input

and data structuring routines, report generation routines,

and arithmetic functions which can be incorporated into a

corporate model. The logic of a model is expressed in the

FORTRAN computer language. PSG is, undoubtedly, one of the

best corporate modeling systems currently available. It

does, however, have some limitations.

The foremost shortcoming is that the modeler must be

capable of programming in the FORTRAN computer language.

FORTRAN is not an extremely difficult language to learn but

it is algebraically oriented rather than being "English"

oriented. This is particularly bothersome in that variables

must be referenced by position rather than by name. For

example, the value of sales cannot be referenced by simply

referring to "SALES," rather the position in a table of values

must be known so that the values can be referenced by speci-

fying the appropriate line number in the table. The user

must thus coordinate the values, the positions, and the

meanings of variables.

Two other limitations of PSG are that there are no

built-in capabilities for the performance of sensitivity

analyses and Monte Carlo simulations. Sensitivity analysis

can, of course, be accomplished by the user repeatedly ini-

tiating the execution of a model after he has altered the




23

value of a variable. It is more desirable, however, for the

user to be able to initiate execution only once and have the

system automatically perform the sensitivity analysis for a

designated variable.

The support required for Monte Carlo simulation is more

involved and cannot easily be compensated for by simple re-

petitive executions. Monte Carlo simulation requires many

more repetitions than does a sensitivity analysis. It is

therefore desirable for the modeling system to be capable of

performing Monte Carlo simulations. This requires the capa-

bility of expressing variables as random variables, the

capability of selecting a value of the random variables to

be used in the calculations, and the capability of counting

the number of occurences of various outcomes. These capa-

bilities are not supported by PSG.

Another modeling system, the Financial Analysis and

Planning System (FAPS), developed by On-Line Decisions, Inc.

has capabilities similar to PSG [2]. In addition, PAPS has

the capability of performing sensitivity analyses and has

extensive capabilities for the statistical and mathematical

manipulation of time series data. But, just as with PSG,

the user must reference variables by position rather than

by the use of a meaningful name; and FAPS does not support

Monte Carlo simulations.

The Dow Chemical Company has developed a modeling sys-

tem called Planning Simulator 1 (PS1) in which variables

may be referenced either by position or by a one to six

character name [17]. PS1 is not, however, as versatile a






system as PSG or FAPS.

One of the major inconveniences of PS1 is that arith-

metical operations cannot be performed in series. For

example, the PS1 statement


Y = ADD(A,B)


denotes that the variable Y should assume a value equal to

the value of A added to the value of B. Likewise, the

statement


Z = SUB(Y,W)


denotes that the variable Z should assume a value equal to

the value of Y minus the value of W. Thus Z would have the

value of A plus B minus W. But the operations "ADD" and

"SJB" cannot be used in series to compute the value of Z

directly. Consequently, either all computations would have

to be broken down into single operations or a multitude of

special operations must be created. PS1 has evolved in

accordance with the latter approach so that there are cur-

rently over one hundred operations in the PS1 language. To

become proficient in the use of the language, the user would

have to familiarize himself with each of these operations.

Another problem in using PS1 is that there appears to

be no convenient method for saving the results from the

execution of a model and then later accessing the results

as inputs into another model. Values can be transferred

from one model to another only when the executions of the

models are contiguous. This is a significant handicap in







corporate modeling since a set of models is often used to

summarize plans on a product-division-corporation continuum.

Without the capability of permanently saving results, the

entire set of models must be run whenever any change is made.

Another shortcoming of PS1 is that the specification of

report formats is integrated with the specification of logic.

Thus, the user is not free to mix logic, data, and report

modules at the time of execution. And, if a report is to

be changed, the logic-report module must be altered.

Finally, PSI, like IBM's PSG, lacks the capability to

automatically perform sensitivity analyses and Konte Carlo

simulations.

A fourth modeling system, the CAPEX Corporation's

AUTOTAB is an interactive system available on General Elec-

tric's Time Sharing Service [1]. AUTOTAB is a relatively

simple system to use and has the advantages of referencing

variables by name and transferring values among models by

storing and retrieving them from permanent files.

One limitation of AUTOTAB is that reports, data, and

logic are contained in a single "package" so that the "pack-

age" must be altered to change any of the components. Also,

a common report could not be used with two different logic

components without duplicating the report specifications.

In addition, AUTOTAB does not support sensitivity

analyses and Monte Carlo simulations.

A final modeling system, reported by Buchman, is Mon-

santo's APEX system [4]. APEX was designed for use in

creating special purpose models rather than for use in





26

building a total corporate model. The system seems to be an

excellent system for its intended use. It obtains a higher

level of user orientation than all the other systems men-

tioned except AUTOTAB. It is simple to understand and easy

to use and yet offers advanced capabilities for sensitivity

analyses and Monte Carlo simulation.

APEX is not as well suited, however, for use in cor-

porate modeling when an interrelated set of models is used.

APEX does not allow the independent creation of logic, data,

and report modules and does not support the transfer of

values among models.

The previous discussion of five representative modeling

systems is summarized in Table 1. The systems are compared

in terms of a few major design concepts with no attempt

being made to discuss the details of implementing any of the

systems. However, the reported usage of PSG and FAPS has

shown that sophisticated modeling systems can be incorporated

into planning and decision-making activities and can make

significant contributions to these efforts. On the other

hand, systems such as AUTOTAB and APEX have demonstrated

the desirability and practicality of maintaining a high

level of user orientation. The next chapter describes a

modeling system that attempts to combine these two lessons.












0 10
02a


0 02,
zr 02


0 0
02a


0 O
o <",
R a


0 0 0
s O o V)
~ r, i= a


0 0 02
S S V>
i= Z >i


0 0 0 0 0
O K O


4'd (
02






-40
cxi0

H
S0



4xi 02
0 20
krg








OH
oa


k C
0 -.r
o CO


SP
oH H
4-3
*H 02



.r


I


cU
0

0 -H
04-
H cd
,--

dr-l


., -


r (.)
.H

1 rI
p44

4 0
0 2


4 0


*H 02
4> cd

4-H
ct E-'
co

*-H 0

024->
cM
Paw



43 4
0

> 0o


0 02


o0













C)
i-,-






0
43
















Pik F
-10
*H
* Hr







IOf





P L4
C H
o a)


Cxi CO

02








* em

*










CHAPTER III

A CORPORATE MODELING SYSTEM


This chapter describes a corporate modeling system

named CMS/1. The structure and the operating characteris-

tics are described first. Second, the major design concepts

of the system are discussed. Finally, more detailed expla-

nations of the characteristics and the use of the system

will be given.

This chapter is intended to be an introduction to the

concepts, capabilities, and use of CMS/1. It is not in-

tended to supply all of the details necessary for the use

of CMS/1. A USER'S MANUAL which does supply such detailed

information is contained in Appendix A.


Overview of CMS/1

The logical organization of CMS/1 is depicted in Fig-

ure 1. The user originates logic, data, and report modules

in the appropriate CMS/1 languages, i.e., the logic and data

specification language or the report format specification

language (see Figures 2, 3, and 4). Then the modules are

entered, usually via cards, into CMS/1 where they are trans-

lated into a more conveniently executed form and stored ona

magnetic disk. The user can then give a command for specif-

ic logic, data, and report modules to be retrieved from the

disk, combined into a complete model, and executed.

















CMS/1


Translatio)
Pass


User


CMS/1
Execution
Pass


Figure 1. Logical organization of CMS/1.


Saved
Results


























*DATA PRODUCT_DATA PERIODS 1 TO 5
SET INVESTMENT FOUAL TO 90CO3CC00. 0 C9 CG'
SET SALVAGE EQUAL TO C0 OC Ce 0* 4250000
SET TAX RATE EQUAL TO .48
SET PRICE EQUAL TO 516
SET FIXEDCOST EQUAL TO 306350
SET VARIABLE COST RATE EQUAL TO 421e65
SET BEGINNING MARKETSIZE EQUAL TO 220000
SET GROWTH RATE EQUAL TO c08, o06. .G033, 02
SET BEGINNINGSHARE EOUAL TO *01
SET MAXIMUM_SHARE EQUAL TO o15
SET DISCOUNTRATE EQUAL TO v12
SET DEPRECIATION EQUAL TO DEPRECIATL(,IENVESTMtENTSALVAGE


Figure 2. Sample data module.


















*LOGIC
SET MARKET SIZE EQUAL TO GROWTH(BEGINNINGMARKET_SIZE, ,
1 GROWTHRATE)
SET MARKETSHARE EQUAL TO LINEAR(BEGINNING SHARE,
I MAXIMUM_SHARE,)
SET UNITS SOLD EQUAL TO MARKETSIZE TIMES MARKET SHARE
SFT SALES EQUAL TO UNITS SOLD TIMES PRICE
SET VARIABLE_COSTS EQUAL TO VARIABLE COST RATE TIMES
1 UNITSSOLD
SET TOTAL_COSTS EQUAL TO FIXED_COST PLUS VARIABLE_COSTS
1 PLUS DEPRECIATION
SET OPERATING INCOME EQUAL TO SALES MINUS TOTAL COSTS
SET TAX EFFECT EQUAL TO TAXRATE TIMES DEPRECIATION
SET CASH FLOW EQUAL TO OPERATING INCOME PLUS SALVAGE
I PLUS DEPRECIATION PLUS TAX EFFECT
SET PRESENTVALUE EQUAL TO DISCOUNT(CASHFLOW,
1 DISCOUNT_RATE)
SET PROFITINDEX EQUAL TO PRESENTVALUE DIVIDED BY
1 INVESTMENT
SET ROI EQUAL TO IN1RLRATE(INVESTMENT, CASHFLOW)

Figure 3. Sample logic module.











*REPORT NEWPRODUCT
TITLE ANALYSIS OF NEW PRODUCT
MARGIN 0
COLUMN SIZES 15, O0 (9)
BEGIN NEW PAGE
SKIP 2 LINES
COLUMN HEADINGS "ACCOUNT". 1972" 1973",
1 1974 ". 1975" 1976"
COLUMN HEADINGS "-------", (" ----")
ITEM "INITIAL MARKET", BEGINNINGMARKETSIZE
ITEM 2, GROWTHRATE
ITEM 2- BEGINNINGSHARE
ITEM 2, MAXIMUM_SHARE
SKIP I L INE
ITEM INVESTMENT
ITEM DEPRECIATION
ITEM "SALVAGE VALUE". SALVAGE
SKIP 1 LINE
ITEM MARKETSIZE
ITEM "SHARE OF MARKET", 3, MARKETSHARE
ITEM UNITS_SOLD
SKIP 1 LINE
ITEM "PRICE PER UNIT". 2, PRICE
ITEM SALES
ITEM VARIABLECOSTS
ITEM FIXEDCOST
ITEM TOTAL COSTS
ITEM "NET INCOME", OPERATINGINCOME
SKIP 1 LINE
ITEM CASH_FLOW
ITEM PRESENTVALUE
ITEM 2. PROFIT INDEX
ITEM 3. ROI


Figure 4. Sample report module.





33
The execution of a model is actually a six-step process.

1. The first step is to examine the specified logic module

and report modules to determine the names of all varia-

bles which will be used.

2. The specified data modules are retrieved and the values

are assigned to the indicated variables. Variables are

ignored if they are contained in a data module but are

not contained in the logic module or in a report module.

3. The third step is to perform the calculations specified

in the logic module.

4. If a sensitivity analysis or Monte Carlo simulation is

in progress, the second and third steps are repeated.

5. If requested, the values of specified variables are

saved by creating a new data module.

6. The requested reports are printed (see Figure 5).


General Design Concepts

The design of CMS/1 is based upon two basic assumptions.

They are:

1) It is both necessary and desirable to improve the

interface between managers and computers with re-

spect to the creation and manipulation of corporate

models.

2) Corporate models are basically data manipulators.

Data must be acquired, "massaged," stored, and

printed out in reports.

Starting with these two assumptions about corporate

models and their environments, a number of more specific











ANALYSIS OF NEW PRODUCT


ACCOUNT

INITIAL MARKET
GROWTH RATE
BEGINNING SHARE
MAXIMUM SHARE

INVESTMENT
DEPRECIATION
SALVAGE VALUE

MARKET SIZE
SHARE OF MARKET
UNITS SOLO

PRICE PER UNIT
SALES
VARIABLE COSTS
FIXED COST
TOTAL COSTS
NET INCOME

CASH FLOW
PRESENT VALUE
PROFIT INDEX
ROI


1972


220000
*
0.01
0015

9000000
950000
0


220000

2200

516 00
1135199
927630
306350
2183979
-1048780

357220
92073i09
1002
0.172


1973


220000
Oeo8
0.01

0.15

0
950000



237600
05, 045
10692

516c 00
5517067
4508277
30'6350
5764627
-247560

1158439
*
*
*


1974


220000






9500010
O 15

0
950000
0

251856
C0 .0 8
20140


516, G0
10396593
8495586
3( 6350
9751936
644657


1975

22000 O


C 15

0
9500 0
C'

25941 1

29832
29832


516.OU
15393451
12578773
3'635(-
13835123
1558328


1976

220000
0.02
0.01
0.15

0
9500U00
4250000

264599
0. 150
39690

516. 0 0
20479968
16735228
31 6350
17991568
24884t'89


2050656 2964327 8144399
*


Figure 5. Output from sample model.







design characteristics can be derived. The design charac-

teristics may be grouped into four categories: 1) the

"point of view" represented in the design of the system,

2) characteristics which promote ease of use, 3) character-

istics which provide flexibility, and 4) technical charac-

teristics.

The primary point of view reflected in the design of a

modeling system should be that of the manager and not the

computer specialist's point of view. This seems self-

evident, but it is a difficult distinction to maintain. It

is tempting to design the modeling system from the stand-

point of the ease with which the system itself can be con-

structed rather than from the standpoint of the ease with

which the system can be utilized. The designer must ask

himself "Vihat facilities are needed by the modeler?" and

not just "What facilities can be easily provided?" In order

to be better able to maintain this distinction, CeTS/1 is

written in the computer language PL/1. PL/1 is a very

powerful computer language developed by IBM. It has ad-

vanced capabilities that are not available in other lan-

guages such as FORTRAN and BASIC which have been used most

often in the development of modeling systems. The use of

PL/1 greatly reduces the number of restrictions imposed on

the modeling system by computer programming considerations.

A second characteristic of the modeling system is that

the system should be easy to use. One of the major contri-

butants to the ease of use is the utilization of "English-

like" languages for communication between the manager and







the computer. The objective is to design a satisfactory

language which falls between the extremes of the verbose,

sometimes imprecise English language and a cryptic, though

precise, numerical code. But the perceived successfulness

of this endeavor depends in part on the user. An experi-

enced user usually prefers a less verbose language than does

a novice. For this reason, CMS/1 is designed with both

"English-like" forms and short-forms that can be used to

designate a required operation.

Another contributant to ease of use is the existence

of default conditions. A system that is designed to be used

in corporate modeling must be capable of performing many

different tasks. However, flexibility increases the diffi-

culty of use since the user must denote which alternative

he wishes to select for each option that is available to

him. This problem may be mitigated by the use of defaults.

If no alternative is specified for an option, a predefined

alternative is assumed by the system. The employment of

selection by default greatly simplifies the use of a system.

Another contributant to ease of use is the provision

of extensive error checking routines. When an error is

detected, it should be diagnosed and noted by the printing

of a precise and clearly worded error message. Good diag-

nostic capabilities are an important part of a modeling

system. The system should be designed to assist the user

in identifying his mistakes as well as being designed to

execute error-free input.

A third design characteristic is flexibility. Model-





37
ing systems should be flexible enough to be used by differ-

ent organizations and to be used for different purposes.

They can be used in preparing budgets, in cash flow analyses,

in facilities planning, in long-range planning, and in spe-

cial projects such as capital investment analysis.

One method of providing the needed flexibility is to

provide the capability of creating models in segments. A

model is segmented into modules which contain only data,

modules which contain only report formats, and a module

which specifies the logical relationship among the variables.

Then, when the user wishes to execute a model, he can select

the appropriate modules and have them combined and executed.

There are several significant effects of this modular

approach. First, the logic module can be created totally

independently of the number of periods over which it will

be executed. A planning model could thus be executed over

three, five, ten, or any other number of years without

changing the logic of the model. The flexibility in the

specification of time periods also makes possible the de-

velopment of models which have different time horizons.

For example, budgeting is usually concerned with twelve

monthly periods, while long-range planning is typically con-

cerned with a five-year period, and capital investment de-

cisions may cover widely different periods. A modeling

system which supports varying horizons can be used in each

of these areas.

Second, the logic module can be created without regard as

to whether it will be executed as a deterministic or as a







stochastic model, or whether it will be executed in con-

junction with a sensitivity analysis. These alternatives

are a function of the data which are used and the manner in

which the model is executed; they need not be reflected in

the logic of the model. For example, the statement


DOLLAR SALES EQUAL UNIT SALES TIMES UNIT PRICE


defines the logical relationship between three variables.

If the two variables "UNIT SALES" and "UNIT PRICE" both are

single-valued variables, then the relationship is determin-

istic. But, if the variable "UNIT SALES" is defined to be

a random variable, then the relationship becomes stochastic.

However, the stated relationship has not been changed; only

the values assigned to the variables have been altered.

A third implication of the modular construction of

models is that multiple report and data modules can be com-

bined with one logic module. Multiple report modules can be

used to print out different reports or to tailor the same

basic report for different users. Multiple data modules can

be used to incorporate into a model the results saved pre-

viously by other models. The use of multiple data modules

also means that all the period values for variables need not

be contained in a single data module. That is, budgeted

values for the current year might be stored in one data

module with estimated values for the next five years stored

in another data module. The data modules could then be com-

bined with logic and report modules to produce reports

covering the current year and a five-year plan.




39

A final implication of the modular approach is that

the modeling system must maintain the modules on computer

accessible storage devices. This is required since the

modules are created independently and a combination is

selected for execution at a later time. The user can

therefore readily access models kept on magnetic Cisks

instead of primarily using cards as a storage medium.

Another method for increasing flexibility is the ref-

erencing of variables by user-supplied names. The use of

names complements the use of modules. A modeler simply uses

the variable's name in different modules. When the modules

are combined for execution, the like names can all be linked.

by the modeling system to the same values. When positions

instead of names are used to identify variables (as in PSG),

the user must insure the positional equivalence of variables

among modules and/or models that are to be interrelated.

Finally, the flexibility of a modeling system can be

increased by allowing arithmetic operations to be used in

the specification of data modules and by allowing values

specified in a data module to supersede values calculated in

a logic module. The use of arithmetic operations in defin-

ing data means that the statement


COST OF GOODS SOLD EQUALS SALES TIMES .75


could be used in a data module to define the values associ-

ated with the account "COST OF GOODS SOLD." Of course, the

values associated with "SALES" would have to be previously

defined. The second method, the superseding of values cal-







culated in the logic module, means simply that if a value

for a variable is given in a data module, then any equation

appearing in the logic module that would alter the value is

ignored. These two methods provide the user great flexi-

bility in temporarily altering the logical structure of a

model without physically altering the logic module. The

user can accomplish the alterations by simply temporarily

adding to the model a data module consisting only of the

desired changes.

The last classification of design characteristics is

composed of two technical considerations. The first is the

consideration of size limitations placed on the model, and

the second deals with the procedure used in executing a

model over several time periods.

CMS/1 is designed so that a user will be restricted by

the physical capacity of his computer rather than by the

modeling system. Storage areas in CMS/1 are allocated dy-

namically so that capacity is limited only by the "counters"

which are used to reference the storage areas. Consequently,

a model may contain as many as 32,767 different variables.

Also, each variable may be defined over a maximum of 32,767

time periods. Thus, CMS/1 has a capacity of over one billion

values. This compares to IBM's PSG which has a capacity of

less than eleven thousand values. The number of statements

which may be included in a model is a function of the size

of the statements. However, over five million "average-

sized" statements could easily be accommodated. All of

these limitations are much in excess of the physical capaci-






ty of current computing equipment.

The second technical consideration deals with the pro-

cedure used in executing a model over several time periods.

A model could be executed from beginning to end for the

first time period, executed again for the second time peri-

od, and so on until all time periods have been accounted

for. Or, each equation could be executed in turn over all

time periods. That is, the first equation would be executed

over each time period, then the second equation, and so on

until all equations have been executed. A third alternative

would be to execute in turn each operation within an equation

over all time periods. Thus, if the statement


SET INTEREST EXPENSE EQUAL TO LOANS TIMES INTEREST RATE


were executed over twelve time periods, the multiplication

would be carried out twelve times; and then the twelve prod-

ucts would be assigned to the variable "INTERESTEXPENSE."

The third alternative would actually yield the most

rapid execution since CMS/1 is executed by an interpreter

rather than by being compiled. This procedure has, however,

some undesirable characteristics. The procedure cannot be

used 1) when a variable is a function of its own values

in previous time periods, 2) when a variable is a function

of another lagged variable and the equation defining the

first variable precedes the equation defining the variable

which is lagged, and 3) when a condition which exists in

one time period alters the sequence in which the equations

are executed.







The first problem can be avoided by using the second

procedure described above, i.e., the execution of each

equation in turn over all time periods. But the second and

third problems would remain. All three problems can be

avoided by executing the complete model over each time

period. This, however, is accomplished at the expense of

longer execution times. Thus, this execution procedure is

followed by CIS/1 only when one or more of the three prob-

lems actually occurs. In all other cases, CKS/1 uses the

more efficient procedure of executing in turn each operation

within an equation over all time periods.

This section has discussed the major design concepts

of CMS/1. The next three sections will discuss the three

CMS/1 languages which embody these concepts.

Control Language

The purpose of the control language is to allow the

user to direct the activity of the modeling system. It is

through the use of the control language that the user insti-

gates the creation of modules and the execution of models.

The general form of a control language statement is


*KEYWORD parameter-list


Each control statement begins with an asterisk in the first

position followed by none, or one or more blanks. The first

non-blank characters encountered after the asterisk must be

one of seven control keywords. Each control keyword unique-

ly designates an activity performed by CMS/1. Following the






control keyword and separated from it by blanks is a list

of parameters. The parameter list provides additional in-

formation which is needed to carry out the activity which

has been requested. The parameter list is composed of sets

of parameter keywords and parameter values. Each set must

be separated from surrounding sets by a comma or by blanks.

In the following discussion and examples of control

language statements, keywords will be denoted by capital

letters whereas user-dependent information will be denoted

by small letters.

There are five types of activities which the user can

specify. The user can specify that a module (logic, data,

or report) is to be created, that modules are to be combined

and executed, that modules are to be destroyed, that no

action is to be taken (i.e., the null activity); and, the

user can govern whether or not the logic, data, and report

statements he supplies to CMS/1 are to be printed.

The first type of activity is the action of creating a

module. A statement such as


*LOGIC


signifies that the following statements conform to the re-

quirements of the logic specification language. The state-

ments are read by CMS/1, checked for errors, and translated

into a more easily executed form. The translated form is

then temporarily stored on a magnetic disk. If the user

wishes to permanently store the logic module, a name for

the module is specified as a parameter on the LOGIC state-





44

ment. The following statement specifies that a logic module

named "sample" is to be created and saved.


*LOGIC sample


The statements for creating data and report modules are

analogous to the LOGIC statement. That is,


*DATA budget


and


*REPORT balance sheet


would be used, respectively, in creating a data module named

"budget" and a report module named "balance_sheet."

The DATA statement may also contain the parameter key-

word PERIODS followed by numbers representing the first and

last periods for which data is given. For example, either

the statement


*DATA budget, PERIODS 1970 TO 1972


or


*DATA budget, PERIODS 1970, 1972


could be used to create a data module named "budget" con-

taining values for the years 1970, 1971, and 1972. If the

periods parameter is not given, CMS/1 assumes the specifica-

tion


PERIODS 1 TO 5






The second type of activity the user may specify is

the activity of combining and executing modules. This

activity is accomplished by the use of the EXECUTE statement.

The simplest form of the statement is


*EXECUTE


The above statement would cause the most recently created

unnamed logic, data, and report modules to be combined and

executed. If an unnamed logic module does not exist, an

error message is printed. If an unnamed report module does

not exist, a default report format is used and the values

of all variables defined in the logic module are printed.

Named (i.e., permanently saved) logic, data, and report

modules may be combined for execution by use of the para-

meter keywords LOGIC, DATA, and REPORT in the following

manner.


*EXECUTE LOGIC sample, DATA budget, REPORT balance sheet


Multiple data and report modules can also be specified.


*EXECUTE LOGIC sample, DATA budget_70, budget_71,
1 REPORT balance sheet, incomestatement


Multiple reports are printed in the same sequence in which

their names appear in the REPORT parameter set. Multiple

data modules are "overlayed" on each other in the same se-

quence in which their names appear in the DATA parameter

set. Thus, if a variable is assigned values in several

different data modules, the resulting values of the varia-





46

bles will be the values assigned by the data module appear-

ing latest in the sequence specified by the DATA parameter

set.

If a named logic module is specified, an unnamed logic

module is, of course, not used. Likewise, the default re-

port format is not used if a named report module is desig-

nated. However, the most recently created unnamed data

module is always used. It is incorporated into a model as

though it were the last named data module in the DATA

parameter set. Consequently, it overlays all named data

modules.

Another parameter set that may appear on an EXECUTE

statement specifies the number of times the model is to be

executed in a sensitivity analysis or in a Monte Carlo sim-

ulation. The form of the parameter set is


ITERATIONS n


where n is the number of times the model is to be executed.

The value of n must be between 1 and 32,767. If this para-

meter set is not given, n is assumed to have a value of 1.

An additional parameter set that may be used in con-

junction with Monte Carlo simulations specifies the "seed"

number for a pseudo random number generator. The form of

the parameter set is


INITIAL RANDOM NUMBER IS n






INITIAL n


where n is the value to be used as the seed. The seed

should be an odd number containing at least five digits.

Its value must be between 1 and 231-1. If a Monte Carlo

simulation is performed without specifying a value for the

seed, a value of 65,549 is assumed.

CMS/1 allows the saving of values computed in one logic

module for later use in another logic module. The values

are stored in a data module that is created by CIIS/1. The

user may name this data module by specifying the desired

name in a SAVE parameter set. The EXECUTE statement


*EXECUTE LOGIC product SAVE results


would execute the logic module named "product" along with

the most recently created unnamed data module. The default

report would be printed, and the values of variables saved

by the logic module "product" would be stored in a data

module named "results."

If variables are saved by a logic module when the SAVE

parameter set is not given on the EXECUTE statement, the

resulting data module is given a name created from the logic

module name and the date.

Finally, headings that are to be printed at the top of

each report may be given on the EXECUTE card. Two headings

may be given the first is printed in the center at the top

of each page of output, and the second is printed against

the right margin at the top of every page. The form of the







HEADINGS parameter set is


HEADINGS "first heading" "second heading"


If only one heading is specified, it is assumed to be the

first heading.

The third type of activity the user may request of CMS/1

is the destroying of named modules. The following DESTROY

statement would erase from the magnetic disk a logic module

named "sample," a data module named "budget," and a report

module named "balance sheet."


*DESTROY LOGIC sample, DATA budget, REPORT balancesheet


More than one logic, or data, or report module can be

erased by simply listing the module names sequentially after

the appropriate parameter keyword. The statement


*DESTROY LOGIC sample, product


would cause the logic modules named "sample" and "product"

to be erased.

The fourth type of activity the governing of the

printing of logic, data, and report statements is con-

trolled by the PRINT statement. The user can have printed

all statements received by CIS/1 or can delete the printing

of logic, data, and/or report specification statements.

Normally, all statements received by CMS/1 are printed. To

stop the printing of logic, data, and report statements, a

PRINT statement with no parameters would be used.






*PRINT


To print only data statements, the PRINT statement


*PRINT DATA


would be used. To begin printing logic, dita, and report

specification statements again, the statement


*PRINT LOGIC, DATA, REPORT


or


*PRINT ALL


would be used.

The final control Janguage statement, the CONTINUE

statement, initializes no system activity. However, it can

terminate previous system activity.. For example, the two-

statement sequence


*DATA
*CONTINUE


begins the creation of an unnamed data module and then ter-

minates the module. The two statements thereby assure that

the most recently created unnamed data module is empty.

This can be useful since the most recently created unnamed

data module is always included in the executed model.

This section has discussed the control language of CMS/1.

The keywords for each statement in the language are summa-

rized in Table 2. The next section describes the logic and













TABLE 2

Summary of Control Language Keywords


Control Keyword


LOGIC

DATA

REPORT

EXECUTE


Parameter Keywords


PERIODS


LOGIC, DATA, REPORT, PERIODS,
ITERATIONS, SAVE, HEADINGS,
INITIAL


LOGIC, DATA, REPORT


ALL, LOGIC, DATA, REPORT


CONTINUE


DESTROY


PRINT


_ __







data specification language.


Logic and Data Specification Language

The contents of a logic and of a data module are ex-

pressed in the logic and data specification language. The

language is composed of four types of statements assign-

ment, group, control, and null statements. Only the first

two types may be used in creating a data module; all four

types may be used in creating a logic module.

The first type of statement, the assignment statement,

is used to calculate the value of an arithmetic expression

and then to relate this value to a named variable. The

assignment statement is a very powerful and flexible in-

strument for performing calculations. Among the operations

which can be performed are addition; subtraction; multi-

plication; division; and predefined procedures for deter-

mining depreciation, present values, and rates of return.

An example of the simplest form of an assignment state-

ment is


SET SALES EQUAL TO 500


In this simple case no arithmetic is performed; the value

500 is just related to the name SALES. Then, if the next

statement executed were


SET MISC EXPENSE EQUAL TO SALES TIMES .05


the variable MISC EXPENSE would be assigned a value of 25.

Since CMS/1 executes a model over multiple periods, the





52

assignment statement may specify multiple values for a vari-

able. The statement


SET SALES EQUAL TO 500, 600, 650, 700, 750


defines a value of SALES over a five-period horizon. If a

model containing this statement were executed over a ten-

period horizon, the values for the last five periods would

be undefined. This type of statement may be used to speci-

fy the initial values of variables in data modules with the

succeeding values being calculated by a logic module.

If a variable is to have the same value in all periods,.

then the value need be expressed only once. Hence, the

following two statements are equivalent over a five-year

horizon.


SET SALES EQUAL TO 600, 600, 600, 600, 600
SET SALES EQUAL TO 600


Note, however, that the second statement assigns a value of

600 to SALES over any horizon.

A second type of data and logic specification statement

is the GROUP statement. This statement is used to associate

a number of variables with a common group name. The whole

group of variables can then be referenced by simply using

the group name. For example, assuming that the variables

LABOR, MATERIALS, OVERHEAD, and SALES have all been assigned

values, the second statement of the two following statements

would sum the values of LABOR, MATERIALS, and OVERHEAD; sub-

tract that sum from the values of SALES; and associate the






resulting value with OPERATINGINCOME.


GROUP LABOR, MATERIALS, OVERHEAD UNDER MPFGEXPENSES
SET OPERATING INCOME EQUAL TO SALES MINUS MFG EXPENSES


The use of a group name (iIlicits a different response

when it represents variables whose values are being computed.

This use of a group name is exemplified by the following

three statements.


GROUP LABOR, MATERIALS, OVERHEAD UNDER MFGEXPENSES,
1 STANDARDS
SET IMFG EXPENSES EQUAL TO SALES TIMES STANDARDS
SET OPERATING INCOME EQUAL TO SALES MINUS FMG_EXPENSES


The second of the three above statements is executed by mul-

tiplying the labor standard times the value of sales, giving

the value of the labor component of the manufacturing ex-

pense; then the materials standard is multiplied times the

value of sales, giving the value of the materials used in

manufacturing; finally, the standard overhead rate is mul-

tiplied times the value of sales, giving the overhead ab-

sorbed by the units sold. The procedure for executing the

third statement is the same as in the previous example.

A single element (i.e., variable) of a group can be

referenced by "qualifying" the element name with the group

name. The previous group statement defines two groups with

three elements each giving a total of six variables. Ref-

erences to the individual variables is accomplished as

follows:







MFG EXPENSES: LABOR
MFG EXPENSES: MATERIALS
MFG EXPENSES: OVERHEAD
STANDARDS: LABOR
STANDARDS: MATERIALS
STANDARDS: OVERHEAD


In this manner the variables may be used individually as

well as by groups.

The third type of statement, the control statement, may

be used only in a logic module. There are three statements

of the control type two which control the sequence in

which statements are executed, and a third which terminates

the execution of a logic module.

In CMS/1 as in most computer languages, statements are

normally executed in the sequence they are encountered.

However, the sequential execution can be altered by "jump-

ing" over statements. The execution can be jumped forward

down the list of statements or backwards to a previous

statement. The method of exercising this type of control

is illustrated by the following sequence of statements.




JUMP TO COMPUTE
SET RATIO) SET RATIO EQUAL TO .25
COMPUTE) SET ACCOUNT EQUAL TO AMOUNT TIMES RATIO



The first statement in the sequence causes the second

statement to be bypassed with control being transferred to

the statement named "COMPUTE." The second statement could






be reached via a JUMP TO statement appearing at another

point in the logic module. However, if the second statement

did not have a name, it could never be reached so an error

message would be printed at the time the logic module is

created.

Sequential execution may also be altered by the condi-

tional statement. The conditional statement performs a com-

parison and then,depending on the results of the comparison,

executes one of two statements. For example, the following

two conditional statements can be used as a simple means of

estimating corporate income taxes.


IF NET INCO"IE IS GREATER THAN 25000THEN SET TAXES EQUAL
1 TO NET INCOME TIMES .48 MINUS 6500 ELSE SET TAXES
1 EQUAL TO NET INCOME TIMES .22
IF NET INCOME IS LESS THAN 0 THEN SET TAXES EQUAL TO O


The second conditional statement does not specify an alter-

native if the net income is not less than zero. In this

event, the value of TAXES computed in the first conditional

statement would not be altered.

The third type of control statement terminates the exe--

cution of the logic module. The form of the statement is

simply


STOP


A logic module does not have to contain a STOP state-

ment. Execution will terminate when the end of the sequence

of statements is reached.







The final type of statement in the logic language is

the null statement. The null statement consists solely of

a statement name. For example,


START)


and


FINISH)


are valid null statements.

The null statement is used to designate a point in the

logic module to which control may be passed by a JUMP TO

statement.

Sample logic and data modules which demonstrate the

use of some of the previously discussed statements are pre-

sented in Figures 2 and 3, respectively.


Report Format Specification Language

The third language in the corporate modeling system is

used to describe the desired form of reports. The language's

statements allow the user to specify the size of the printed

page; the number, size, and headings for the columns of

values; the spacing between lines of information; and the

information that is to be printed on each line of the report.

In addition, the report headings, if any, that were given in

the EXECUTE control statement are printed at the top of each

page.

The page size is determined by three statements. The

default form for each of the statements is given below. The






PAGE LENGTH statement defines the maximum number of lines

that are to be printed on a page excluding the lines con-

tained in a footing, if one is utilized. The LINE LENGTH

statement defines the maximum number of characters, includ-

ing blanks, that may be contained in a printed line. A

printed line may begin in the first physical print position,

or it may begin to the right of the first position. The

extent of indentation is governed by the MARGIN statement.

The margin may be changed within a printed page. Normally,

the page and line length should remain constant for a given

page.


PAGE LENGTH 60
LINE LENGTH 130
MARGIN 2


The number of columns contained in a report is equal to

the number of periods over which the model was executed,

plus two. The two additional columns contain a description

of the variable whose values are being printed and a de-

scription of the units of the values (e.g., tons, gallons,

or dollars).

The width and the headings of columns are specified in

statements of the types shown below. If the sum of the

columns' widths is greater than the line length specified,

the report is automatically continued on another page.

Both column widths and headings may be changed within a

report.







COLUMN SIZES 20, 4, 8, 8, 8
COLUMN HEADINGS "ACCOUNT", "UNIT", "1972", "1973", "1974"


The lines in a report would normally be single spaced,

but the user may also request double or triple spacing over

an entire report or over parts of a report. In addition, a

series of lines may be skipped at any time with the statement


SKIP x LINES


where x is a number. A final method for controlling the

spacing of lines is the use of a statement which causes the

beginning of a new page. The form of the statement is sim-

ply


BEGIN NEW PAGE


The final type of statement in the report format speci-

fication language is the type used to specify the information

to be printed on a line. The user can indicate a page title

that is printed at the top of each page, a footing that is

printed at the bottom of each page, a variable whose values

are to be printed, or just a line of information that is to

be printed.

A report is primarily produced in order to print the

values calculated in a logic module. The stimulus for the

printing of the values of a variable is given by an ITEM

statement. The ITEM statement, such as the one below,

specifies a description of the variable that is to be printed

in the first column of the report, a description of the units

of the values which is printed in the second column of the




59

report, the number of decimal places the printed values are

to have, and lastly the name of the variable as it appears

in the logic module.


ITEM "COST OF GOODS SOLD", "(M$)", 0, CGS


The minimum information that must appear in an ITEM

statement is the keyword "ITEI" and the name of the variable

to be printed. If the description is not specified, the

name of the variable is printed in the first column of the

report. When no units are given, the second column of the

report is left blank. In the event that the number of

decimal places is not given, zero decimal places are assumed.

In order to specify that a line of information other

than the values of variables is to be printed, a statement

is used which consists of the word "LINE" in positions two

through five with the information to be printed appearing

in positions seven through seventy-two. Should more space

be needed, the information may be continued in positions

seven through seventy-two of the next line in the report

module. Thus a maximum of 132 positions (2 times 72-7+1) is

available for specifying a line of information.

Titles and footings that are printed at the top and

bottom of each page, respectively, can be created using

statements similar to the LINE statement. The only differ-

ence in form is that the words "TITLE" and "FOOT" are used

instead of the word "LINE" and the body of a title cannot

begin before the eighth position of the first line, giving

a maximum title length of 131 characters.





60

A sample report module which demonstrates the use of

the report format specification language is presented in

Figure 4.

This chapter has described the structure of CMS/1 and

has discussed the general design concepts and the capabili-

ties of the system. The next chapter demonstrates the im-

plementation of CMS/1 by presenting several models which

depict the capabilities of the system.











CHAPTER IV

IMPLEIMETATION OF CMiS/1


The previous chapters have introduced the concepts of

corporate models and corporate modeling systems. The de-

sired characteristics of corporate modeling systems were

discussed and CMS/1, a new corporate modeling system, was

introduced.

The present chapter demonstrates the capabilities of

CMS/1 by demonstrating its use in two hypothetical situa-

tions in the analysis of the potential profitability of a

new product and in planning for a multi-divisional firm.

The models developed for these hypothetical problems are

relatively simple models. The intention is to demonstrate

the use of CYIS/1, not to develop sophisticated models.

The use of CMS/1 is demonstrated with the simple execu-

tion of a deterministic model, with sensitivity analyses,

with a Monte Carlo simulation, and with the execution of a

set of interrelated models.


Deterministic Model

A deterministic model for the analysis of the potential

profitability of a new product was introduced previously in

Figures 2, 3, and 4. The execution of that model would be

initiated by the control statement


*EXECUTE DATA PRODUCTDATA, REPORT NEW PRODUCT





62

This statement would cause the data module "PRODUCT DATA,"

the report module "NEW_PRODUCT," and the most recently

created unnamed logic and data modules to be combined and

executed. (The results of such an execution are depicted

in Figure 5.)

Deterministic models are usually used to "try out" dif-

ferent alternatives. By changing data and/or logical re-

lationships, various assumptions and "what if" questions

can be investigated. For example, the model of the new

product venture could be used to determine the effects of

using different methods of depreciation.

The results of the sample model, depicted in Figure 5,

are based on the use of straight-line depreciation of the

original investment. The impact of different depreciation

methods can be easily ascertained by altering the model.

The simplest way to alter the model is to add an unnamed

data module containing the new depreciation method. The

following statements, when appended to the previous model,

would create a data module which specifies the use of the

sum-of-the-years-digits method of depreciation (method num-

ber 2), and then initiate the execution of the "new" model.


*DATA PERIODS 1 TO 5
SET DEPRECIATION EQUAL TO DEPRECIATE (2, INVESTMENT,
1 SALVAGE)
*EXECUTE DATA PRODUCT_DATA, REPORT NEW PRODUCT


A complete sequence of modules and EXECUTE statements

for the comparison of straight-line, sum-of-the-years-digits,

and double declining balance methods of depreciation are







presented in Figure 6. The results for each of the three

methods are presented in Figures 5, 7, and 8, respectively.


Sensitivity Analysis

As indicated above, a deterministic model is usually

executed more than once in order to investigate various al-

ternatives. Repetitive execution may also be used to deter-

mine the variables in a model which most affect the behavior

of the model.

An example of the investigation of various alternatives

was given in the previous section. In that case the inves-

tigator had to specify changes for each new alternative and

then initiate a new execution. This procedure may be sim-

plified when the objective is to determine the "sensitive"

variables rather than to investigate new alternatives.

Sensitivity analysis is accomplished by repeatedly exe-

cuting a model with the value of only one variable being

changed for each new execution. Thus, if the multiple

values can be specified for a variable before the repetitive

executions begin, all of the executions may be carried out

automatically. This is accomplished in CMS/1 by the use of

the ITERATE function. The statement


SET BEGINNING MARKET SIZE EQUAL TO ITERATE (180000,
1 260000, 20000)


denotes that the variable BEGINNING MARKET SIZE is to have

the value 180000 the first time the model is executed. The

variable will then be incremented by 20000 in succeeding






























Figure 6. Sample deterministic model.











*LOGIC
SET MARKETSIZE EQUAL TO GROWTH(UEGINNING_MARKET SIZE, *
I GROWTH RATE)
SET MARKET_SHARE EQUAL TO LINEAR(BEGINNINGSHARE,
1 MAXIMUM SHARE )
SET UNITS SOLD EQUAL TO MARKET_SIZE TIMES MARKETSHARE
SET SALES EQUAL TO UNITS SOLD TIMES PRICE
SET VARIABLECOSTS EQUAL TO VARIABLECOSTRATE TIMES
1 UNITS_SOLD
SET TOTAL_COSTS EQUAL TO FIXEDCOST PLUS VARIABLE_COSTS
I PLUS DEPRECIATION
SET OPERATINGINCOME EQUAL TO SALES MINUS TOTAL_COSTS
SET TAX_EFFECT EQUAL TO TAX RATE TIMES DEPRECIATION
SET CASH FLOW EQUAL TO OPERATING INCOME PLUS SALVAGE
1 PLUS DEPRECIATION PLUS TAX_EFFECT
SET PRESENTVALUE EQUAL TO DISCOUNT(CASH FLOW,
1 DISCOUNT_RATE)
SET PROFITINDEX EQUAL TO PRESENT_VALUE DIVIDED BY
I INVESTMENT
SET ROI EQUAL TO INTRLRATE(INVESTMENT, CASH FLOW)
*DATA PRODUCT_DATA PERIODS I TO 5
SET INVESTMENT EQUAL TO 9(COCG.C 0O. OC 0, (
SET SALVAGE EQUAL TO O CD, 0, 0, 4250000
SET TAX_RATE EQUAL TO *48
SET PRICE EQUAL TO 51G
SET FIXED_COST EQUAL TO 306350
SET VARIABLE COST_RATE EQUAL TO 421.65
SET BEGINNINGMARKET_SIZE EQUAL TO 220000
SET GROWTH_RATE EQUAL TO .08, .C6, *03, .02
SET BEGINNINGSHARE EQUAL TO o?1
SET MAXIMUM SHARE EQUAL TO 15
SET DISCOUNTRATE EQUAL TO 12
SET DEPRECIATION EQUAL TO DEPRECIATE( ,INVESTMENTsSALVAGE)
*REPORT NEW_PRODUCT
TITLE ANALYSIS OF NEW PRODUCT
MARGIN 0
COLUMN SIZES 15, 0, (9)
BEGIN NFW PAGE
SKIP 2 LINES
COLUMN HEADINGS "ACCOUNT", 1972", 1973",
1 1974", 1975", 1976"
COLUMN HEADINGS "----- --" (" ----")
ITEM "INITIAL MARKET". BEGINNNINGMARKETSIZE
ITEM 2, GROWTHRATE
ITEM 2, BEGINNINGSHARE
ITEM 2, MAXIMUM SHARE
SKIP 1 LINE
















ITEM INVESTMENT
ITEM DEPRECIATION
ITEM "SALVAGE VALUE", SALVAGE
SKIP I LINE
ITEM MARKET_SIZE
ITEM "SHARE OF MARKET", 3, MARKET_SHARE
ITEM UNITS_SOLD
SKIP 1 LINE
ITEM "PRICE PER UNIT". 2. PRICE
ITEM SALES
ITEM VARIABLE_COSTS
ITEM FIXED_COST
ITEM TOTAL_COSTS
ITEM "NET INCOME", OPERATING_INCOME
SKIP I LINE
ITEM CASH_FLOW
ITEM PRESENT_VALUE
ITEM 2, PROFIT_INDEX
ITEM 3, ROI
'EXECUTE DATA PRODUCT_DATA, REPORT NEW_PRODUCT
*DATA PERIODS 1 TO 5
SET DEPRECIATION EQUAL TO DEPRECIATE(2,INVESTMENTSALVAGE)
*EXECUTE DATA PRODUCT_DATA, REPORT NEW_PRODUCT
*DATA PERIODS 1 TO 5
SET DEPRECIATION EQUAL TO DEPRECIATE(3, INVESTMENT, 5)
*EXECUTE DATA PRODUCT_DATAt REPORT NEW_PRODUCT











ANALYSIS OF NEW PRODUCT


ACCOUNT

INITIAL MARKET
GROWTH RATE
BEGINNING SHARE
MAXIMUM SHARE

INVESTMENT
DEPRECIATION
SALVAGE VALUE

MARKET SIZE
SHARE OF MARKET
UNITS SOLD

PRICE PER UNIT
SALES
VARIABLE COSTS
FIXED COST
TOTAL COSTS
NET INCOME

CASH FLOW
PRESENT VALUE
PROFIT INDEX
ROI


1972

220000
*
0.01
0.15

9000000
1583333
0

22000000
C., 10
2200

516c00
1135199
927630
306350
2817312
-1682113

661220
9330820
IC04
0 180


1973

226(,0C0

0.01
0.15
0 5

0
1266666
0

237600
0 0 45
10692

51600CO
5517067
4508277
306350
6081293
-564226

1310439
*:
*
*


1974

22000 '
0.06
0.01
C* 15

0
950000(
0

251856
00C 80
20148


516 00C
10396593
8495586
306350
9751936
644657

2050656
*
*


1975

220000

C*01
0.15

0
633333
0

259411
0O 115
29832


516o00
15393451
12570773
306350
13518456
1874995

2812327
*
*


1976

220001
e0.02
0.01
9o15

0
316667
4250000

264599
0.150
39690

516.00
20479968
16735228
306350
17358224
3121744

7840409
*
*


Figure 7. Results with sum-of-the-years digits depreciation.












ANALYSIS OF NEW PRODUCT


ACCOUNT

INITIAL MARKET
GROWTH RATE
BEGINNING SHARE
MAXIMUM SHARE

INVESTMENT
DEPRECIATION
SALVAGE VALUE

MARKET SIZE
SHARE OF MARKET
UNITS SOLD

PRICE PEP UNIT
SALES
VARIABLE COSTS
FIXED COST
TOTAL COSTS
NET INCOME

CASH FLOW
PRESENT VALUE
PROFIT INDEX
ROI


1972


2200,00
*

0.15

90C0000
3599999
0

22G0C.0
00010
2200

516oC00
1135199
927630
306350
4833978
-3698779

1629219
10739990
lo19
C 2 59


1973


220000

0.01
0.15

0
2159999
0

2376C0
0,045
10692

516.00
5517067
4508277
306350
6974626
-1457559


1974

220000
Oe06
0o01
0.15

0
1295999
0

251856
0.08C
20148

516.00
10396593
8495586
306350
1C(97935
298658


1739239 2216736
*
*


1975

220000
0.03
o.01
C0015

0
777600
0

259411
C0 115
29832


516,00
15393451
12578773
3'6350
13662722
1730729

2881575
*
*


1976

220000
0.02
0o01
OoC 1
0.15

0
46656')
4250000

264599
0o15C
39690

516c00
2r479968
16735228
306350
17508112
2971856

7912363
*
if


Figure 8. Results with double declining balance depreciation.







executions of the model until a maximum value of 260000 is

reached.

The ITERATE function specifies the values for a varia-

ble but does not control the number of times the model is

executed. The number of repetitions is specified on the

EXECUTE statement. The statement


*EXECUTE DATA PRODUCT_DATA, REPORT NEWPRODUCT,
1 ITERATIONS 5


could be used in conjunction with the previous ITERATE

function to ascertain the effects of varying the variable

BEGINNING MARKET SIZE between the values 180000 and 260000.

Complete sequences of modules and EXECUTE statements

which could be utilized in sensitivity analyses of the vari-

ables "BEGINNING MARKET SIZE" and "MAXIMUMl SHARE" are pre-

sented in Figure 9. (The assumption is made that the modules

depicted in Figure 6 have been previously processed.) The

results of each analysis are presented in Figures 10 and 11,

respectively.


Monte Carlo Simulation

A deterministic model is useful in specifying the basic

structure of a problem, in investigating alternative courses

of action, and in determining the sensitive variables. But

additional information concerning the riskiness of a course

of action is often needed by management.

The deterministic model discussed above indicated that

a return on investment of about 17 percent may be expected


























*DATA PERIODS 1 TO 5
SET BEGINNING_MARKET_SIZE EQUAL TO ITERATE(18t9000 260D000D
1 20r(. 0 )
*EXECUTE DATA PRODUCT DATA, REPORT NEWPRODUCT
1 ITERATIONS 5
*DATA PERIODS 1 TO 5
SET MAXIMUM_SHARE EQUAL TO ITERATE(lO, o18, c02)
*EXECUTE DATA PRODUCTDATAs REPORT NEW_PRODUCT
1 ITERATIONS 5

Figure 9. Addition to sample model for sensitivity
analysis.
































Figure 10. Results of sensitivity analysis for the ini-
tial market size.











ANALYSIS OF NEW PRODUCT


ACCOUNT

INITIAL MARKET
GROWTH RATE
BEGINNING SHARE
MAXIMUM SHARE

INVESTMENT
DEPRECIATION
SALVAGE VALUE

MARKET SIZE
SHARE OF MARKET
UNITS SOLD

PRICE PER UNIT
SALES
VARIABLE COSTS
FIXED COST
TOTAL COSTS
NJET INCOME

CASH FLOW
PRESENT VALUE
PROFIT INDEX
ROI


1972

180000
*
0.01
0.15

9000000
95C00;0
0

180000
S.0 10
1800


516.C0
928800
758970
306350
2015319
-1086519

319481
8169800
090
0*122


1973

18000Q
0 *08
0.01



0
950000
0

194400
o0 045
8748

5160ot0
4513963
3688590
306350
4944940
-430977

975023

*


1974

180000

OoOl
0015


0
95000 C
0

206(C64
0Co080
16485

516. C
8506304
6950934
306350
8207284
299020

1705019

*
*


1975

18C000
0 'j 3
0C01
1. 15

0
950000
C

212246
40115
24408


516a C;
12594642
1(291724
306350
11548074
1046568

2452567
4


1976

18000

0.01
1.15

0
950 00
4250000

216490
0o 150
32474

516 03
16756338
13692457
306350
14948807
1807531

7463530
Y.










ANALYSIS OF NEW PRODUCT


ACCOUNT

INITIAL MARKET
GROWTH RATE
BEGINNING SHARE
MAXIMUM SHARE

INVESTMENT
DEPRECIATION
SALVAGE VALUE

MARKET SIZE
SHARE OF MARKET
UNITS SOLD

PRICE PER UNIT
SALES
VARIABLE COSTS
FIXED COST
TOTAL COSTS
NET INCOME

CASH FLOW
PRESENT VALUE
PROFIT INDEX
ROT


1972

200000



0O15

9000000
950CrCO
0

2000CO
e0010
2000

516CO0
1032000
8433C r:
306350
2099649
-1067649

338351
8638545
0G96
0.147


1973

2COO0;C
Oo08
cl8
0.01
0. 15

0
950 rCO
0

216000
0C045
9720

516.00
51 15515
4098433
366350
5354783
-339268

1C66731
*
*
4


1974

200 (..00
0 06

C, 15

0
95C000
0

228960
S* C 80
18317

516.00
9451451
7723262
306350
8979612
471839

1877838


*


1975

200000
0.03
C .01
0.15



950(000
0

235829
S0.115
27120


516.00
13994049
11435250
306350
1269160( .
1302449

2708448


1976

2000GO
0.02

Oe15

0
950 (001
4250000

240545
0.150
36082

516.00
18618144
15213846
306350
16470196
2147948

7803947











ANALYSIS OF NEW PRODUCT


ACCOUNT

INITIAL MARKET
GROWTH RATE
BEGIIfNING SHARE
MAXIMUM SHARE

INVESTMENT
DEPRECIATION
SALVAGE VALUE

MARKET SIZE
SHARE OF MARKET
UNITS SOLD

PRICE PER UNIT
SALES
VARIABLE COSTS
FIXED COST
TOTAL COSTS
NET INCOME

CASH FLOW
PRESENT VALUE
PROFIT INDEX
ROI


1972

2200C (,

Ce C1
0.15

90CC C('C
950000
0

220000
0.010
22C 0

516e (0
1135199
927630
306350
2183979
-1048780

357220
9207309
1 C 2
0 172


1973

22t COG
0.08
Oo 0 1
0.15

G
0
950000
0

237600
0.045
10692

516.00
551 7r67
450 8277
30(6350
5764627
-247560

1158439
*,
*
*


1974


22COnC
220000
0.06

0,15

0
950000
0

251856
00080
20148


51 6c00
1 396593
8495586
306350
9751936
644657


1975


2200000
0.03
0.01



0
950000
0

259411
0.115
29832


516,00
15393451
12578773
3i 635C'
S3835123
1558328


1976


220000
0.02
0,01
0.15

0
950000
42500C0

264599
C 150
39690

516.00
2C479968
16735228
306353
17991568
2488400


2050656 2964327 8144399
*











ANALYSIS OF NEW PRODUCT


ACCOUNT

INITIAL MARKET
GROWTH RATE
BEGINNING SHAPE
MAXIMUM SHARE

1 INVEST MENT
DEPRECIATION
SALVAGE VALUE

MARKET SIZE
SHARE OF MARKET
UNITS SOLD

PRICE PER UNIT
SALES
VARIABLE COSTS
FIXED COST
TOTAL COSTS
NET INCOME

CASH FLOv
PRESENT VALUE
PROFIT INDEX
POI


1972

240000

0.01
0.15

9C00CO0
950C(C



2400C0
0.010
240C,

51 6. (
1238399
C1011959
3C.635i
22683C9
-1029910

376090
9776057
1.L9
0.197


1973

240000

S95000C 1
0 15



950000



25920C
(- c. 45
11664

516eOC
6C18617
4916119
306350
6174469
-155852


1974

240000

0.01
0.15

0
95CCO0
C

274752
C C80
21980


516. 3
11341740
9267914
3C6350
1C524264


1975

240000





0
950000
C.

282994
. 115
32544


1976

240000
0.02
C.01
0.15

0
950000
425 00C

288654
C 151
43298


516.00 516.00
16792848 22341776
13722299 18256638
3C635L 3C635'
14978649 19512944


817476 1814199 2828832


1250147 2223475 3220198 8484831
*











ANALYSIS OF NEW PRODUCT


ACCOUNT

INITIAL MARKET
GROWTH RATE
BEGINNING SHARE
MAXIMUM SHAPE

INVESTMENT
DEPRECI ATI ON
SALVAGE VALUE

MARKET SIZE
SHARE OF MARKET
UNITS SOLD

PRICE PER UNIT
SALES
VARIABLE COSTS
FIXED COST
TOTAL COSTS
NET INCOME

CASH FLOW
PRESENT VALUE
PROFIT INDEX
ROI


1972


260000

,OC 1
0.15

90OOCOc
950000
0

260000

2600

516o0(0
1341599
1096289
30'6350
2352639
-101 1(40

394960
10344810
1.15
0.221


1973

260000

0.01
0.15

0
950000
0

280800
r rf 45
12636

51 6c C-C
6520169
5327963
3J6350
6584313
-64144

1341855
*
*
*


1974

260000
Oo06
0401
0.15

0
950000
0


297648
0 2080
23812


516co0
12286887
1O04C.?41
316350
S1296591
990296

2396295

*


1975

26C000

0.01
Oel5
0O15

0
950000
0

306577
P0115
35256


516o0
18192256
14865827
306350
16122177
2070079

3476078
*
*


1976

260000
) c02
0.01
0.15

0
950C00
4250C00

312708
C. 150
46906

51 6e00
24203600
19778C00
306350
21034336
3169264

8825263
*
*































Figure 11. Results of sensitivity analysis for the maximum
market share.












ANALYSIS OF NEW PRODUCT


ACCOUNT


INITIAL MARKET
GROWTH RATE
BEGINNING SHARE
MAXIMUM SHARE

INVESTMENT
DEPRECIATION
SALVAGE VALUE

MARKET SIZE
SHARE OF MARKET
UNITS SOLD

PRICE PER UNIT
SALES
VARIABLE COSTS
FIXED COST
TOTAL COSTS
NET INCOME

CASH FLOW
PRESENT VALUE
PROFIT INDEX
ROI


1972


220000
*
0 01
0.10

900C0000
950000



2200(.0
0 .0 1 0
2200

516.00C
1135199
927630,
30635n
21 3979
-1048780

357220
7269483
0081
0C084


1973


220000
0o08
001

0"
o

950000 O
0

237600
o 0 32
7722

5160o r
3984547
3255977
30 6350
4512327
-527780

878220

*
&


1974


220000
0,06
0001
0.10

0
950000
0

251855
Co055
13852

516o000
7147660
5840717
3C06350
7097067
51 593

1456592
*


1975


22000 C
0.03
0.01



0
950000
0

259411
0. r77
20104


516f Cc
10373847
8476999
306350
97333349
640498

2046497
*


1976

220009
Oe01




0
9500 ,
4250000

264599
0. 100
26460

516600
13653313
11156818
306350
12413168
1240145

6896144
&











ANALYSIS OF NEW PRODUCT


ACCOUNT


INITIAL MARKET
GROWTH RATE
BEGINNING SHARE
MAXIMUM SHARE

INVESTMENT
DEPRECIATION
SALVAGE VALUE

MARKET SIZE
SHARE OF MARKET
UNITS SOLD

PRICE PER UNIT
SALES
VARIABLE COSTS
FIXED COST
TOTAL COSTS
NET INCOME

CASH FLOW
PRESENT VALUE
PROFIT INDEX
RO1


1972

220L'rC 0

0.01
O. 12

90000CO
95C0000



220000
0 1 r"
2200

516.00
1135199
92763(0
306350
2183979
-1048780

357220
8044612
0.89
0. 121


1973

22('000
0.08
0.01
0.12

0
950000
C

237600
o 0 37
8910

5156o C'
4597553
3756895
306350
5013245
-415692

990308
*

*


1974

22UG00
0.06
0.01
(Co 12

0
950C00
0

251856
Co 065
16371

516o00
8447232
69 2664
306350
8159014
288218

1694217
*

*


1975

220000
0.03
0.01
C. 12

0
950000



259411
C *. 92
23996

516.0C
12381690
1C117710
31635(;
11374060
10 07630

2413629
*
*


1976

220C00
0.02
0001
0.12

0
95000,0
4250000

264599
0 o 120
31752

516 c, 0Q
16383977
13388183
306350
14644533
1739444

7395443
*
*












ANALYSIS OF NEW PRODUCT


ACCOUNT

INITIAL MARKET
GROWTH RATE
BEGINNING SHARE
MAXIMUM SHARE

INVESTMENT
DEPRECIATION
SALVAGE VALUE

MARKET SIZE
SHARE OF MARKET
UNITS SOLD

PRICE PER UNIT
SALES
VARIABLE COSTS
FIXED COST
T(TAL COSTS
NET INCOME

CASH FLOW
PRESENT VALUE
PROFIT INDEX
R1O


1972

220000
*
0.01
0a 14

90GO000
950000



220W'CO

2200

516eC 0
1135199
927630
3C6350
2183979
-1048780

357220
8819736
0 98
Oo 155


1973

22000

Oc.01
c 14

0
950000
0

237600
C 0 (42
10098

516.00
5210561
4257815
306350
5514 65
-3036Q04

1102395

4


1974

22C0 C
0 006
Oo~1Ol
0.14

0
950000
0

251856
0.075
18889

516e.00
9746806
7964612
306350
9220962
525844

1931843
*

*


197b

22000

0.c03
0.01
0. 14

0
95.0000
0

259411
0Lc 107
27887


516e00
14389530
11758419
306350
13014769
1374761

2780760


1976

220000
p.02
0.01
0o14

0
950000
4250000

264599
o 140
37044

516.00
19114624
15619546
306350
1687588
2238736

7894735
*

*











ANALYSIS OF NEW


ACCOUNT

INITIAL MARKET
GROWTH RATE
BEGINNING SHARE
MAXIMUM SHARE

INVESTMENT
DEPRECIATION
SALVAGE VALUE

MARKET SIZE
SHARE OF MARKET
UNITS SOLD

PRICE PER UNIT
SALES
VARIABLE COSTS
FIXED CUST
TOTAL COSTS
NET INCOME

CASH FLOW
PRESENT VALUE
PROFIT INDEX
ROI


1972




0.01
0.16

9000000
950Ci 0
0

220CC 0
0010
22CC

516.00
1135199
927630
306350
2183979
-1048780

357220
9594882
1.07
Oc 1U9


1973

22C000



0.16

0
95(000
0

237600'
0O047
11286

516.00
5823567
4758734
30635C
601 5' 84
-191517

1214482
*

*


PRODUCT



1974

2200C0

0 .0 1
0. 16

0
950000
0

251856
0,085
21408


516.00
11046380
90'26560
306350
10282910
763470


1975

220000
0.03
C0.01



0
950000
0

259411
0. 122
31778


516.00
16397373
13399129
306350
14655479
1741894


1976

220000

0.01
0.16

0

95000r
4250U00

264599
0.160
42336

516.00
21845296
1785 '896
306350
19107232
2738064


2169469 3147893 8394063
*











ANALYSIS OF NEW PRODUCT


ACCOUNT

INITIAL MARKET
GROWTH RATE
BEGINNING SHARE
MAXIMUM SHARE

INVESTMENT
DEPRECIATION
SALVAGE VALUE

MARKET SIZE
SHARE OF MARKET
UNITS SOLD

PRICE PER UNIT
SALES
VARIABLE COSTS
FIXED COST
TOTAL COSTS
NET INCOME

CASH FLOW
PRESENT VALUE
PROFIT INDEX
ROI


1972

220(C '



0.18

900 C 0 C
950000
0

220000
0 o 0
2200

516. 0
1135199
927630
306350
2183979
-1048780

357220
10369990
lo15
0*220


1973

22000C0

C-o 0 1
Oo08

Cio 1 8

0
950000
0

23760-0
0&052
12474

516o00
6436577
5259655F
306350
6516C n5
-79428

1326571

y,


1974

22000 (
0.06

18

0
950000
0

251856
0o095
23926


516e00
12345955
1CC885C9
366350'
11344859
10C0196


1975

220000
0*03
0.01
0o18

C
950000
0


259411
O 137
35669


516o00
18405200,
1. 5039838
306350
16296188
2109012


1976

22C000
0.02
0. 1
0018

0
950000
425r000

264599
00180
47628

516 00
24575952
20082272
306350
21338608
3237344


24L7095 3515011 8893343
*






from the hypothetical new product venture (see Figure 5).

But no information was provided concerning the riskiness of

the venture. Can the venture lose money? What is the mini-

mum return that may be expected? What is the maximum?

These questions concerning the probability of occurence of

the various possible outcomes cannot be answered by deter-

ministic models.

This type of information can be derived, however, from

Monte Carlo simulations. Monte Carlo simulation requires

that the modeling system be capable of dealing with random

variables and be capable of executing a model repetitively.

CYS/1 can accept four types of random variables. A

random variable may conform to a normal distribution, a

Weibull distribution, a uniform distribution, or an arbi-

trary distribution expressed as a relative frequency distri-

bution (see Figure 12). The normal distribution may often

be used in describing empirical data, particularly the

distribution of averages such as an average per unit cost.

The Weibull distribution has the useful characteristic of

being bounded on one side. This is advantageous for ex-

pressing variables such as percentages as random variables.

In that particular case a Weibull distribution with an

upper bound of 100 percent could be used. If a normal,

Weibull, or uniform distribution is not appropriate, a dis-

tribution may be expressed as a relative frequency distri-

bution. This capability is particularly useful in describ-

ing empirical data which may not conform to any known mathe-

matical distribution; e.g., the age distribution of accounts





















A. Normal Distributions


H
Cd
10
0


X
B. Weibull Distributions


0




cd 0
rl1u


0
o



rA
10
Cd
0
o


X
C. Uniform Distributions


4-D
Cd0"
H0) i


D. Relative Frequency Distributions


Figure 12. Types of distributions accepted by CMS/1.


_r


t-H * ** -- !** I . | I I *


.... m


--


I I


n-~h






receivable or a distribution of the costs associated with

an operation.

In order to perform a Monte Carlo simulation, the num-

ber of times the model is to be executed must be stated.

This is accomplished by the same procedure used in sensi-

tivity analysis. That is, the parameter "ITERATIONS" is

stipulated on the EXECUTE statement.

The sample deterministic model presented in Figure 6

may be altered for execution in a Monte Carlo simulation

with the addition of the modules depicted in Figure 13. The

data module in Figure 13 alters three variables to make them

random variables. In addition, the report module named

"RISK" is added in order to print out the new information

supplied by the Monte Carlo simulation. The results of the

simulation are presented in Figure 14.

The values in Figure 14 that are printed in the format

specified by the report "NEW PRODUCT" are the average values

of the variables for the two hundred iterations. The report

"RISK" displays the distributions of the values of two se-

lected variables (i.e., CASH FLOW and ROI). The distribu-

tions indicate that the average value of the cash flow in-

creases over the years but the distribution of possible

values "spreads out." Also, while the expected return on

investment is high (29.8%), there is more than a 2.5 percent

chance that the rate of return will be negative and approxi-

mately one chance in seven that the rate of return will be

less that 11.5 percent. The Monte Carlo simulation thus

provides information concerning the risk involved in the


















*DATA PERIODS 1 TO 5
SET BEGINNING MARKET_SIZE EQUAL TO NORMAL(18COGO, 2603DCO)
SET MAXIMUMSHARE EQUAL TO WEIBULL(o2U0 *15, .06)
VARIABLE COST_RATE IS DISTRIBUTED(360*.1. 375,.1,
I 390,ll, 405,*12, 420,.13, 435,.13. 450,.11,
1 465,*68, 48C..06, 495,.04, 51Co.02)
*REPORT RISK
TITLE ANALYSIS OF NEW PRODUCT
MARGIN 0
COLUMN SIZES 15, 0. (9)
BEGIN NEW PAGE
SINGLE SPACE
DISTRIBUTION
SKIP 2 LINES
COLUMN HEADINGS "ACCOUNT", 1972", 1973",
1 1974", 1975", 1976"
COLUMN HEADINGS "-------", (" ----.")
ITEM CASHFLOW
BEGIN NEW PAGE
SKIP 2 LINES
COLUMN HEADINGS "ACCOUNT". 1972". 1973".
1 1974", 1975". 1976"
COLUMN HEADINGS "------- (* ----.")
ITEM 3, ROI
*EXECUTE DATA PRODUCT DATA, REPORT NEW PRODUCT, RISK
1 ITERATIONS 200


Figure 13. Additions to sample model for Monte Carlo simu-
lation.
































Figure 14. Results of Monte Carlo simulation.











ANALYSIS OF NEW PRODUCT


ACCOUNT

INITIAL MARKET
GROWTH RATE
BEGINNING SHARE
MAXIMUM SHARE

INVESTMENT
DEPRECIATION
SALVAGE VALUE

MARKET SIZE
SHARE OF MARKET
UNITS SOLD

PRICE PER UNIT
SALES
VARIABLE COSTS
FIXED COST
TOTAL COSTS
NET INCOME

CASH FLOW
PRESENT VALUE
PROFIT INDEX
ROI


1972

219500

O.C1
0.23

9000000
950000
0

219500
0.010
2195

516.CO
1132641
921659
306350
2177963
-1045362

360624
12595430
1.40
0.298


1973

219500
0.08
0.01
0.23

0
950000
0

237061
0.066
15618

516.00
8058922
6558469
306350
7814827
244102

1650076
*
*
*


1974


219500
0006
0.01
0.23

0
950000
0

251284
0.122
30597


516.CO
15788382
12848931
3;6 350
14105287
1683051


1975


219500
0.03
0.01
0.23

0
950000
0

258823
0.178
45979


516.00
23725136
19308256
3C6350
20564608
3160625


1976


219500
e002
0.01
0.23

0
950000
4250000

263999
0.234
61652

516.00
31811712
25889392
306350
27145776
4666165


3089004 4566593 10322124
*





89




ANALYSIS OF NEW PRODUCT


ACCOUNT 1972 1973 1974 1975 1976

CASH FLOW 0.015
186161
0.985 0.215 0.045 0.020
1038575
0.395 0.170 0.055
1890988
0.335 0.195 0.140
2743401
0.050 0.210 0.095
3595814
0.005 0.245 0.180
4448227
0.080 0.125 0.015
5300640
0.050 0.180 0.030
6153053
0.100 0.055
7005466
0.005 0.045 0.115
7857879
0.040 0.065
8710292
0.015 0.120
9562705
0.115
10415118
0.005 0.095
11267531
0.160
12119944
0.085
12972357
0.050
13824770
0.035
14677183
0.040
15529596
0.020



MEAN VALUE 360624 1650076 3C89C04 4566593 10322124
STD. DEV. 87232 652111 1286853 1939049 2603743




University of Florida Home Page
© 2004 - 2010 University of Florida George A. Smathers Libraries.
All rights reserved.

Acceptable Use, Copyright, and Disclaimer Statement
Last updated October 10, 2010 - - mvs