U.S. patent application number 09/938874 was filed with the patent office on 2008-03-20 for method of and system for analyzing, modeling and valuing elements of a business enterprise.
Invention is credited to Jeff S. Eder.
Application Number | 20080071588 09/938874 |
Document ID | / |
Family ID | 46328236 |
Filed Date | 2008-03-20 |
United States Patent
Application |
20080071588 |
Kind Code |
A1 |
Eder; Jeff S. |
March 20, 2008 |
Method of and system for analyzing, modeling and valuing elements
of a business enterprise
Abstract
An automated system (100) and method for analyzing, modeling and
valuing elements of a business enterprise on a specified valuation
date. The performance of the elements are analyzed using search
algorithms and induction algorithms to determine the value drivers
associated with each element. The induction algorithms are also
used to create composite variables that relate element performance
to enterprise revenue, expenses and changes in capital. Predictive
models are then used to determine the correlation between the value
drivers and the enterprise revenue, expenses and changes in
capital. The correlation percentages for each value driver are then
multiplied by capitalized value of future revenue, expenses and
changes in capital, the resulting numbers for each value driver
associated with each element are then added together to calculate a
value for each element.
Inventors: |
Eder; Jeff S.; (Mill Creek,
WA) |
Correspondence
Address: |
ASSET TRUST, INC.
2020 MALTBY ROAD, SUITE 7362
BOTHELL
WA
98021
US
|
Family ID: |
46328236 |
Appl. No.: |
09/938874 |
Filed: |
August 27, 2001 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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08999245 |
Dec 10, 1997 |
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09938874 |
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Current U.S.
Class: |
705/7.31 ;
705/7.11; 705/7.37 |
Current CPC
Class: |
G06Q 10/06 20130101;
G06Q 10/06375 20130101; G06Q 40/02 20130101; G06Q 30/0202 20130101;
G06Q 30/02 20130101; G06Q 10/063 20130101; G06Q 40/00 20130101;
G06Q 40/12 20131203 |
Class at
Publication: |
705/7 |
International
Class: |
G06F 9/44 20060101
G06F009/44 |
Claims
1-78. (canceled)
79. A computer based method of building predictive models from
transaction data, comprising: aggregating data from a plurality of
transaction systems covering a series of time periods for one or
more elements of value and one or more aspects of financial
performance; transforming said element of value data in accordance
with one or more pre-programmed functions; establishing a plurality
of input nodes, a plurality of hidden nodes and an output node for
a neural network model for each aspect of financial performance;
inputting the raw and transformed transaction data into each neural
network model using a separate input node for untransformed
transaction data and each pre-programmed transformation function by
element of value for all time periods in the series; training each
neural network model using said inputs until an error function
associated with an output value that corresponds to an aspect of
financial performance is minimized; and using one or more weights
from the trained neural network models to identify a set of raw and
transformed transaction data by element of value and output that
will be used as an input to an element of value summary for each of
one or more predictive models normalizing each of the one or more
sets of raw and transformed transaction data by element of value,
refining the sets of raw and transformed transaction data by
element of value, creating a summary of the refined transaction
data set for each element of value, and using the element of value
summaries as inputs to a predictive model for each of the one or
more aspects of enterprise financial performance where the aspects
of financial performance are selected from the group consisting of
revenue, expense, capital change, cash flow and combinations
thereof, and where the predictive models of aspects of financial
performance are useful for completing tasks selected from the group
consisting of optimizing a current operation financial performance
for a business, predicting an impact of one or more changes to a
current operation financial performance, calculating a value for an
element of value and combinations thereof.
80. The method of claim 79 where a plurality of input nodes is set
equal to one plus the number of elements of value times one plus
the number of pre-programmed functions used to transform
transaction data.
81. The method of claim 79 where a plurality of hidden nodes is set
equal to one plus the number of input nodes.
82. The method of claim 79, where an error function further
comprises ERR(W)k=1/2(Rk-Y(W)).sup.2.
83. The method of claim 79 where a set of raw and transformed
transaction data that will be used as an input to a predictive
model further comprises a set of numbers.
84. The method of claim 79 where one or more predictive models
further comprises one or more neural network models.
85. The method of claim 79 where training a neural network model
further comprises using a genetic algorithm to complete the
training where a population being analyzed is partitioned into a
plurality of subpopulations, with each subpopulation being
processed by a genetic algorithm independently of the others and
where a migration mechanism produces a chromosome exchange between
the subpopulations.
86. The method of claim 79 where training a neural network model
further comprises using a back propagation algorithm to complete
the training.
87. The method of claim 79 where one or more elements of value
further comprise one or more elements of value selected from the
group consisting of brands, customers, employees, partners, vendors
and combinations thereof.
88. The method of claim 79 where a plurality of transaction systems
comprise systems selected from the group consisting of advanced
financial systems, basic financial systems, operation management
systems, sales management systems, human resource systems, accounts
receivable systems, accounts payable systems, capital asset
systems, inventory systems, invoicing systems, payroll systems,
purchasing systems, the Internet and combinations thereof.
89. The method of claim 79 where a series of time periods contains
time periods selected from the group consisting of historical time
periods, future time periods and combinations thereof.
90. The method of claim 79 where the one or more pre programmed
functions are selected from the group consisting of average,
rolling average, time delay, trend, average time delay, rolling
average time delay, ratio, average ratio, rolling average ratio,
slope, average slope, rolling average slope and combinations
thereof.
91. The method of claim 79 where training a neural network model
further comprises using a genetic algorithm to complete the
training where a population being analyzed is partitioned into a
plurality of subpopulations, with each subpopulation being
processed by a genetic algorithm independently of the others and
where a selective crossover and a fitness measure rescaling
produces a chromosome exchange between the subpopulations.
92. A program storage device readable by a computer, tangibly
embodying a program of instructions executable by at least one
computer to perform the steps in method, comprising: aggregating
data from a plurality of transaction systems covering a series of
time periods for one or more elements of value and one or more
aspects of financial performance; transforming said element of
value data in accordance with one or more pre-programmed functions;
establishing a plurality of input nodes, a plurality of hidden
nodes and an output node for a neural network model for each aspect
of financial performance; inputting the raw and transformed
transaction data into each neural network model using a separate
input node for untransformed transaction data and each
pre-programmed transformation function by element of value for all
time periods in the series; training each neural network model
using said inputs until an error function associated with an output
value that corresponds to an aspect of financial performance is
minimized; and using one or more weights from the trained neural
network models to identify a set of raw and transformed transaction
data by element of value and output that will be used as an element
of value summary for use as an input to each of one or more
predictive models normalizing each of the one or more sets of raw
and transformed transaction data by element of value, refining the
sets of raw and transformed transaction data by element of value,
creating a summary of the refined transaction data set for each
element of value, and using the element of value summaries as
inputs to a predictive model for each of the one or more aspects of
enterprise financial performance where the aspects of financial
performance are selected from the group consisting of revenue,
expense, capital change, cash flow and combinations thereof, and
where the predictive models of aspects of financial performance are
useful for completing tasks selected from the group consisting of
optimizing a current operation financial performance for a
business, predicting an impact of one or more changes to a current
operation financial performance, calculating a value for an element
of value and combinations thereof.
93. The program storage device computer readable medium of claim 92
where a plurality of input nodes is set equal to one plus the
number of elements of value times one plus the number of
pre-programmed functions used to transform transaction data.
94. The program storage device of claim 92 where a plurality of
hidden nodes is set equal to one plus the number of input
nodes.
95. The program storage device of claim 92, where an error function
further comprises ERR(W)k=1/2(Rk-Y(W)).sup.2.
96. The program storage device of claim 92 where a set of raw and
transformed transaction data that will be used as an input to a
predictive model further comprises a set of numbers.
97. The program storage device of claim 92 where one or more
predictive models further comprises one or more neural network
models.
98. The program storage device of claim 92 where training a neural
network model further comprises using a genetic algorithm to
complete the training where a population being analyzed is
partitioned into a plurality of subpopulations, with each
subpopulation being processed by a genetic algorithm independently
of the others and where a migration mechanism produces a chromosome
exchange between the subpopulations.
99. The program storage device of claim 92 where training a neural
network model further comprises using a back propagation algorithm
to complete the training.
100. The program storage device computer readable medium of claim
92 where one or more elements further comprise elements of value
selected from the group consisting of brands, customers, employees,
partners, vendors and combinations thereof.
101. The program storage device of claim 92 where a plurality of
transaction systems comprise systems selected from the group
consisting of advanced financial systems, basic financial systems,
operation management systems, sales management systems, human
resource systems, accounts receivable systems, accounts payable
systems, capital asset systems, inventory systems, invoicing
systems, payroll systems, purchasing systems, the Internet and
combinations thereof.
102. The program storage device of claim 92 where a series of time
periods contains time periods selected from the group consisting of
historical time periods, future time periods and combinations
thereof.
103. The program storage device of claim 92 where the one or more
pre programmed functions are selected from the group consisting of
average, rolling average, time delay, trend, average time delay,
rolling average time delay, ratio, average ratio, rolling average
ratio, slope, average slope, rolling average slope and combinations
thereof.
104. The program storage device of claim 92 where the method
further comprises: training a neural network by using a genetic
algorithm to complete the training where a population being
analyzed is partitioned into a plurality of subpopulations, with
each subpopulation being processed by a genetic algorithm
independently of the others and where a selective crossover and a
fitness measure rescaling produces a chromosome exchange between
the subpopulations.
105. An apparatus for building predictive models from transaction
data, comprising: a plurality of transaction systems, means for
preparing data from said systems for use in processing for a series
of time periods for one or more elements of value and one or more
aspects of financial performance; means for transforming said
element of value data in accordance with one or more pre-programmed
functions; means for establishing a plurality of input nodes, a
plurality of hidden nodes and an output node for a neural network
model for each aspect of financial performance; means for inputting
the raw and transformed transaction data into each neural network
model using a separate input node for untransformed transaction
data and each pre-programmed transformation function by element of
value for all time periods in the series; means for training each
neural network model using said inputs until an error function
associated with an output value that corresponds to an aspect of
financial performance is minimized; and means for using one or more
weights from the trained neural network models to identify a set of
raw and transformed transaction data by element of value and output
that will be used as an element of value summary for use as an
input to each of one or more predictive models normalizing each of
the one or more sets of raw and transformed transaction data by
element of value, refining the sets of raw and transformed
transaction data by element of value, creating a summary of the
refined transaction data set for each element of value, and using
the element of value summaries as inputs to a predictive model for
each of the one or more aspects of enterprise financial performance
where the aspects of financial performance are selected from the
group consisting of revenue, expense, capital change, cash flow and
combinations thereof.
106. The apparatus of claim 105 where a plurality of input nodes is
set equal to one plus the number of elements times one plus the
number of pre-programmed functions used to transform transaction
data.
107. The apparatus of claim 105 where a plurality of hidden nodes
is set equal to one plus the number of input nodes.
108. The apparatus of claim 105, where an error function further
comprises ERR(W)k=1/2(Rk-Y(W)).sup.2.
109. The apparatus of claim 105 where a set of raw and transformed
transaction data that will be used as an input to a predictive
model further comprises a set of numbers.
110. The apparatus of claim 105 where one or more predictive models
further comprises one or more neural network models.
111. The apparatus of claim 105 where training a neural network
model further comprises using a genetic algorithm to complete the
training where a population being analyzed is partitioned into a
plurality of subpopulations, with each subpopulation being
processed by a genetic algorithm independently of the others and
where a migration mechanism produces a chromosome exchange between
the subpopulations.
112. The apparatus of claim 105 where training a neural network
model further comprises using a back propagation algorithm to
complete the training.
113. The apparatus of claim 105 where one or more elements further
comprise elements of value selected from the group consisting of
brands, customers, employees, partners, vendors and combinations
thereof.
114. The apparatus of claim 105 where one or more outputs further
comprise a plurality of transaction systems comprise systems
selected from the group consisting of advanced financial systems,
basic financial systems, operation management systems, sales
management systems, human resource systems, accounts receivable
systems, accounts payable systems, capital asset systems, inventory
systems, invoicing systems, payroll systems, purchasing systems,
the Internet and combinations thereof.
115. The apparatus of claim 105 where a series of time periods
contains time periods selected from the group consisting of
historical time periods, future time periods and combinations
thereof.
116. The apparatus of claim 105 where the one or more pre
programmed functions are selected from the group consisting of
average, rolling average, time delay, trend, average time delay,
rolling average time delay, ratio, average ratio, rolling average
ratio, slope, average slope, rolling average slope and combinations
thereof.
117. The apparatus of claim 105 where preparing data for use in
processing further comprises integrating, converting and storing
data from a plurality of systems in accordance with a common data
dictionary.
118. The apparatus of claim 105 that further comprises: means for
training a neural network by using a genetic algorithm to complete
the training where a population being analyzed is partitioned into
a plurality of subpopulations, with each subpopulation being
processed by a genetic algorithm independently of the others and
where a selective crossover and a fitness measure rescaling
produces a chromosome exchange between the subpopulations.
119. A data processing method, comprising: organizing business
transaction data by enterprise into one or more components of value
and two or more elements of value where at least one element of
value is intangible; determining a relative contribution of each of
two or more elements of value to a value of a business by analyzing
at least a portion of the data; and reporting the relative
contribution of each element of value and the value of the
business.
120. The data processing method of claim 119 wherein determining a
relative contribution for each of the two or more elements to a
value of the business further comprises: deriving an element of
value weighting factor for each element of value by enterprise; and
weighting the data concerning each of one or more elements of value
according to the element of value weighting factors for each
enterprise, where the relative value contribution is the sum of the
weighted element of value data for all enterprises within the
business.
121. The data processing method of claim 119 wherein the intangible
elements of value are selected from the group consisting of brands,
customers, employees, partners, vendors and combinations
thereof.
122. The data processing method of claim 119 wherein reporting the
value of the business and the relative contribution by element of
value further comprises the use of a paper document or an
electronic display.
123. The data processing method of claim 119 wherein a value of the
business is market value.
124. The data processing method of claim 119 wherein at least one
of the two or more elements of value contain items that are
optionally clustered into sub-elements of value for more detailed
analysis.
125. The data processing method of claim 119 wherein transaction
data is obtained from the group consisting of advanced financial
systems, basic financial systems, operation management systems,
sales management systems, human resource systems, accounts
receivable systems, accounts payable systems, capital asset
systems, inventory systems, invoicing systems, payroll systems,
purchasing systems and combinations thereof.
126. The data processing method of claim 119 wherein at least a
portion of the data is obtained from the Internet.
127. The data processing method of claim 119 wherein an enterprise
is defined by a revenue component of value together with an
optional component of value selected from the group consisting of
expense, capital change and combinations thereof.
128. The data processing method of claim 119 wherein a revenue
component of value that defines an enterprise can include the
revenue from a single product, a group of products, a division or
an entire company.
129. The data processing method of claim 119 wherein each of one or
more components of value can be divided into subcomponents of value
for more detailed analysis.
130. The data processing method of claim 119 wherein a relative
contribution for each element of value further comprises a relative
contribution for a specified point in time within a sequential
series of points in time.
131. The data processing method of claim 119 wherein a relative
contribution of each element of value to a value of a business is
determined by a relative impact of the element of value on the
components of value and the other elements of value by
enterprise.
132. The data processing method of claim 120 wherein deriving one
or more element of value weighting factors further comprises:
determining an initial weighting factor with a predictive neural
network model; and finalizing the element of value weighting
factors with a model selected from the group consisting of entropy
minimization, lagrange and path analysis.
133. The data processing method of claim 132 wherein the method
further comprises using genetic algorithms to evolve each model to
an optimal configuration before completing each method step.
134. A program storage device having sequences of instructions
stored therein, which when executed causes the processor in a
computer to perform a data processing method, comprising:
organizing business data by enterprise into one or more components
of value and two or more elements of value where at least one
element of value is intangible; determining a relative contribution
of each of two or more elements of value to a value of the business
by analyzing at least a portion of the data; and reporting the
relative contribution of each element of value and the value of the
business.
135. The program storage device of claim 134 wherein determining a
relative contribution for each of the two or more elements to a
value of the business further comprises: deriving an element of
value weighting factor for each element of value by enterprise; and
weighting the data concerning each of one or more elements of value
according to the element of value weighting factors for each
enterprise, where the relative value contribution is the sum of the
weighted element of value data for all enterprises within the
business.
136. The program storage device of claim 134 wherein an intangible
element of value is selected from the group consisting of brands,
customers, employees, partners, vendors and combinations
thereof.
137. The program storage device of claim 134 wherein reporting the
value of the business and the relative contribution by element of
value further comprises the use of a paper document or an
electronic display.
138. The program storage device of claim 134 wherein a value of the
business is market value.
139. The program storage device of claim 134 wherein at least one
of the two or more elements of value contain items that are
optionally clustered into sub-elements of value for more detailed
analysis.
140. The program storage device of claim 134 wherein transaction
data is obtained from the group consisting of advanced financial
systems, basic financial systems, operation management systems,
sales management systems, human resource systems, accounts
receivable systems, accounts payable systems, capital asset
systems, inventory systems, invoicing systems, payroll systems,
purchasing systems and combinations thereof.
141. The program storage device of claim 134 wherein at least a
portion of the data is obtained from the Internet.
142. The program storage device of claim 134 wherein an enterprise
is defined by a revenue component of value together with an
optional component of value selected from the group consisting of
expense, capital change and combinations thereof.
143. The program storage device of claim 134 wherein a revenue
component of value that defines an enterprise can include the
revenue from a single product, a group of products, a division or
an entire company.
144. The program storage device of claim 134 wherein each of one or
more components of value can be divided into subcomponents of value
for more detailed analysis.
145. The program storage device of claim 134 wherein a relative
contribution for each element of value further comprises a relative
contribution for a specified point in time within a sequential
series of points in time.
146. The program storage device of claim 134 wherein a relative
contribution of each element of value to a value of a business is
determined by a relative impact of the element of value on the
components of value and the other elements of value by
enterprise.
147. The program storage device of claim 135 wherein deriving one
or more element of value weighting factors further comprises:
determining an initial weighting factor with a predictive neural
network model; and finalizing the element of value weighting
factors with a model selected from the group consisting of entropy
minimization, lagrange and path analysis.
148. The program storage device of claim 147 wherein the method
further comprises using genetic algorithms to evolve each model to
an optimal configuration before completing each method step.
149. A financial system, comprising: networked computers each with
a processor having circuitry to execute instructions; a storage
device available to each processor with sequences of instructions
stored therein, which when executed cause the processors to:
integrate transaction data from a plurality of enterprise
management systems, analyze at least a portion of the integrated
data to identify one or more events that drive enterprise value
creation and a business context that is associated with said
events, and using transaction data associated with said events to
develop a computational model of enterprise financial
performance.
150. The system of claim 149 wherein a computational model further
comprises up to three network component of value models where a
plurality of tangible and intangible elements of value are
connected to a level of each component of value over time and where
automated analysis through computational techniques is
supported.
151. The system of claim 149 wherein a computational model further
comprises a causal model that supports automated analysis through
computational techniques.
152. The system of claim 149 wherein one or more intangible
elements of value are selected from the group consisting of brands,
customers, employees, intellectual capital, partners, vendors,
vendor relationships and combinations thereof.
153. The system of claim 149 wherein one or more tangible elements
of value further comprise production equipment.
154. The system of claim 149 where one or more components of value
are selected from the group consisting of revenue, expense, capital
change and combinations thereof.
155. The system of claim 149 that produces useful results selected
from the group consisting a valuation for one or more elements of
value, an impact quantification for one or more elements of value,
one or more financial performance forecasts that do not require
reconciliations, one or more forecasts of the expected impact of
change to an element value driver and combinations thereof.
156. A program storage device readable by a computer, tangibly
embodying a program of instructions executable by at least one
computer to perform the steps in a neural network development
method, comprising: a) preparing a plurality of input data and
ouput data for a population for use in neural network processing,
b) defining a structure for a neural network comprising a plurality
of input nodes, a plurality of hidden nodes, an output node, a
connection between each input node and each hidden node and a
connection between each hidden node and the output node, c)
assigning a random weight value to the connections between each
node and a target fitness level d) creating a plurality of
chromosomes that encode the weights between each node, e)
generating a successor set of weight values from said initial set
of weight values by evolving the chromosomes with a genetic
algorithm, the input data and the output data until the target
fitness level is achieved, f) implementing said neural network with
the set of weight values that achieved the target fitness level
where the population being analyzed is partitioned into a plurality
of subpopulations, with each subpopulation being processed by a
genetic algorithm independently of the others and where a selective
crossover produces a chromosome exchange between the
subpopulations, and where the selective crossover occurs between
two or more successive generations.
157. The program storage device of claim 156, wherein a neural
network model connects one or more elements of value of a business
enterprise to one or more aspects of financial performance of said
business enterprise, where each input node represents an element of
value, where each output node represents an aspect of financial
performance, where the weights between nodes represent a plurality
of relationships where each relationship is a function of the
impact of an element of value on other elements of value or on an
aspect of financial performance, and where one or more aspects of
financial performance are selected from the group consisting of
revenue, expense, capital change, market value and combinations
thereof.
158. The program storage device of claim 156, wherein a neural
network model further comprises a business event network model.
159. A computer implemented neural network modeling method,
comprising: a) preparing a plurality of input data and ouput data
for a population for use in neural network processing, b) defining
a structure for a neural network comprising a plurality of input
nodes, a plurality of hidden nodes, an output node, a connection
between each input node and each hidden node and a connection
between each hidden node and the output node, c) assigning a random
weight value to the connections between each node and a target
fitness level d) creating a plurality of chromosomes that encode
the weights between each node, e) generating a successor set of
weight values from said initial set of weight values by evolving
the chromosomes with a genetic algorithm, the input data and the
output data until the target fitness level is achieved, f)
implementing said neural network with the set of weight values that
achieved the target fitness level where the population being
analyzed is partitioned into a plurality of subpopulations, with
each subpopulation being processed by a genetic algorithm
independently of the others and where a selective crossover
produces a chromosome exchange between the subpopulations, and
where the selective crossover occurs between two or more successive
generations.
160. The method of claim 159, wherein a neural network model
connects one or more elements of value of a business enterprise to
one or more aspects of financial performance of said business
enterprise, where each input node represents an element of value,
where each output node represents an aspect of financial
performance, where the weights between nodes represent a plurality
of relationships where each relationship is a function of the
impact of an element of value on other elements of value or on an
aspect of financial performance, and where one or more aspects of
financial performance are selected from the group consisting of
revenue, expense, capital change, market value and combinations
thereof.
161. The method of claim 159, wherein a neural network model
further comprises a business event network model.
Description
CONTINUATION AND CROSS REFERENCE TO RELATED PATENT
[0001] This application is a continuation of application Ser. No.
08/999,245 filed Dec. 10, 1997. The subject matter of this
application is also related to the subject matter of U.S. Pat. No.
5,615,109 for "Method of and System for Generating Feasible, Profit
Maximizing Requisition Sets", by Jeff S. Eder, the disclosure of
which is incorporated herein by reference.
BACKGROUND OF THE INVENTION
[0002] This invention relates to a method of and system for
business valuation, more particularly, to an automated system that
analyzes elements of a business to identify their value drivers,
models the value creation impact of the elements and computes a
valuation of each of the elements on a specified date.
[0003] The valuation of a business is a complex and time-consuming
undertaking. Business valuations determine the price that a
hypothetical buyer would pay for a business under a given set of
circumstances. The volume of business valuations being performed
each year is increasing significantly. A leading cause of this
growth in volume is the increasing use of mergers and acquisitions
as vehicles for corporate growth. Business valuations are
frequently used in setting the price for a business that is being
bought or sold. Another reason for the growth in the volume of
business valuations has been their increasing use in areas other
than supporting merger and acquisition transactions. For example,
business valuations are now being used by financial institutions to
determine the amount of credit that should be extended to a
company, by courts in determining litigation settlement amounts and
by investors in evaluating the performance of company
management.
[0004] In most cases, a business valuation is completed by an
appraiser or a Certified Public Accountant (hereinafter, appraiser)
using a combination of judgment, experience and an understanding of
generally accepted valuation principles. The two primary types of
business valuations that are widely used and accepted are income
valuations and asset valuations. Market valuations are also used in
some cases but their use is restricted because of the difficulty
inherent in trying to compare two different companies.
[0005] Income valuations are based on the premise that the current
value of a business is a function of the future value that an
investor can expect to receive from purchasing all or part of the
business. Income valuations are the most widely used type of
valuation. They are generally used for valuing businesses that are
expected to continue operating for the foreseeable future. In these
valuations the expected returns from investing in the business and
the risks associated with receiving the expected returns are
evaluated by the appraiser. The appraiser then determines the value
whereby a hypothetical buyer would receive a sufficient return on
the investment to compensate the buyer for the risk associated with
receiving the expected returns. Income valuation methods include
the capitalization of earnings method, the discounted future income
method, the discounted cash flow method, the economic income method
and other formula methods.
[0006] Asset valuations consider the business to be a collection of
assets which have an intrinsic value to a third party in an asset
sale. Asset valuations are typically used for businesses that are
ceasing operation and for specific type of businesses such as
holding companies and investment companies. Asset valuation methods
include the book value method, the adjusted book value method, the
economic balance sheet method and the liquidation method.
[0007] Market valuations are used to place a value on one business
by using valuations that have been established for comparable
businesses in either a public stock market or a recent transaction.
This method is difficult to use properly because no two companies
are exactly the same and no two transactions are completed for the
exact same reasons. Market valuation methods include the price to
earnings method, the comparable sales method, industry valuation
methods and the comparable investment method.
[0008] When performing a business valuation, the appraiser is
generally free to select the valuation type and method (or some
combination of the methods) in determining the business value.
Under the current procedures, there is no correct answer, there is
only the best possible informed guess for any given business
valuation. There are several difficulties inherent in this
approach. First, the reliance on informed guessing places a heavy
reliance on the knowledge and experience of the appraiser. The
recent increase in the need for business valuations has strained
the capacity of existing appraisal organizations. As a result, the
average experience level of those performing the valuations has
decreased. The situation is even worse for many segments of the
American economy where experienced appraisers don't exist because
the industries are too new. Another drawback of the current
procedures for completing a valuation is that the appraiser is
typically retained and paid by a party to a proposed transaction.
It is difficult in this situation to be certain that the valuation
opinion is unbiased and fair. Given the appraiser's wide latitude
for selecting the method, the large variability of experience
levels in the industry and the high likelihood of appraiser bias,
it is not surprising that it is generally very difficult to compare
the valuations of two different appraisers--even for the same
business. These limitations in turn serve to seriously diminish the
usefulness of business valuations to business managers, business
owners and financial institutions.
[0009] The usefulness of business valuations to business owners and
managers is limited for another reason--valuations typically
determine only the value of the business as a whole. To provide
information that would be useful in improving the business, the
valuation would have to furnish supporting detail that would
highlight the value of different elements of the business. An
operating manager would then be able to use a series of business
valuations to identify elements within a business that have been
decreasing in value. This information could also be used to
identify corrective action programs and to track the progress that
these programs have made in increasing business value. This same
information could also be used to identify elements that are
contributing to an increase in business value. This information
could be used to identify elements where increased levels of
investment would have a significant favorable impact on the overall
health of the business.
[0010] Another limitation of the current methodology is that
financial statements and accounting records have traditionally
provided the basis for most business valuations. Appraisers
generally spend a great deal of time extracting, aggregating,
verifying and interpreting the information from accounting systems
as part of the valuation process. Accounting records do have the
advantage of being prepared in a generally unbiased manner using
the consistent framework of Generally Accepted Accounting
Principles (hereinafter, GAAP). Unfortunately, these accounting
statements have proved to be increasingly inadequate for use in
evaluating the financial performance of modern companies.
[0011] Many have noted that traditional accounting systems are
driving information-age managers to make the wrong decisions and
the wrong investments. Accounting systems are "wrong" for one
simple reason, they track tangible assets while ignoring intangible
assets. Intangible assets such as the skills of the workers,
intellectual property, business infrastructure, databases, and
relationships with customers and suppliers are not measured with
current accounting systems. This oversight is critical because in
the present economy the success of an enterprise is determined more
by its ability to use its intangible assets than by its ability to
amass and control the physical ones that are tracked by traditional
accounting systems.
[0012] The recent experience of several of the most important
companies in the U.S. economy, IBM, General Motors and DEC,
illustrates the problems that can arise when intangible asset
information is omitted from corporate financial statements. All
three were showing large profits using current accounting systems
while their businesses were falling apart. If they had been forced
to take write-offs when the declines in intangible assets were
occurring, the problems would have been visible to the market and
management would have been forced to act on them much sooner. These
deficiencies of traditional accounting systems are particularly
noticeable in high technology companies that are highly valued for
their intangible assets and their options to enter new markets
rather than their tangible assets.
[0013] The accounting profession itself recognizes the limitations
of traditional accounting systems. A group of senior financial
executives, educators and consultants that had been asked to map
the future of financial management by the American Institute of
Certified Public Accountants (AICPA) recently concluded that:
[0014] a) Operating managers will continue to lose confidence in
traditional financial reporting systems, [0015] b) The motto of
CFOs in the future will likely be "close enough is good enough",
and [0016] c) The traditional financial report will never again be
used as the exclusive basis for any business decisions.
[0017] The deficiency of traditional accounting systems is also one
of the root causes of the short term focus of many American firms.
Because traditional accounting methods ignore intangible assets,
expenditures that develop a market or expand the capabilities of an
organization are generally shown as expenses that only decrease the
current period profit. For example, an expenditure for technical
training which increases the value of an employee to an enterprise
is an expense while an expenditure to refurbish a piece of
furniture is capitalized as an asset.
[0018] Even when intangible assets have been considered, the
limitations in the existing methodology have severely restricted
the utility of the valuations that have been produced. All known
prior efforts to value intangible assets have been restricted to
independent valuations of different types of intangible assets with
only limited attempts to measure the actual impact of the asset on
the enterprise that owns it. Some of the intangible assets that
have been valued separately in this fashion are: brand names,
customers and intellectual property. Problems associated with the
known methods for valuing intangible assets include: [0019] 1.
Interaction between intangible assets is ignored, for example the
value of a brand name is in part a function of the customers that
use the product--the more prestigious the customers, the stronger
the brand name. In a similar fashion the stronger the brand name,
the more likely it will be that customers will stay a long time.
Valuing either of these assets in isolation will give the wrong
answer; and, [0020] 2. The value of an intangible asset is a
function of the benefit that it provides the enterprise. Therefore,
measuring the value of an intangible asset requires a method for
measuring the actual impact of the asset on the
enterprise--something that is missing from known existing
methods
[0021] The historical dependence on accounting records for valuing
business enterprises has to some extent been a matter of simple
convenience. Because corporations are required to maintain
financial records for tax purposes, accounting statements are
available for virtually every company. At the same time, the high
cost of data storage has until recently prevented the more detailed
information required for valuing intangibles from being readily
available. In a similar manner, the absence of integrated corporate
databases within corporations and the home-grown nature of most
corporate systems has until recently made it difficult to compare
similar data from different firms. Unfortunately, even the firms
that have established integrated business management systems find
that retrieving the information required to perform an integrated
analysis of their data is a cumbersome task. These firms also find
that there are few tools that facilitate the analysis of the
information after it is gathered together in one place.
[0022] The lack of a consistent, well accepted, realistic method
for measuring all the elements of business value also prevents some
firms from receiving the financing they need to grow. Most banks
and lending institutions focus on book value when evaluating the
credit worthiness of a business seeking funds. As stated
previously, the value of many high technology firms lies primarily
in intangible assets and growth options that aren't visible under
traditional definitions of accounting book value. As a result,
these businesses generally aren't eligible to receive capital from
traditional lending sources, even though their financial prospects
are generally far superior to those of companies with much higher
tangible book values.
[0023] In light of the preceding discussion, it is clear that it
would be advantageous to have an automated financial system that
measured the financial performance of all the elements of business
value for a given enterprise. Ideally, this system would be capable
of generating detailed valuations for businesses in new
industries.
SUMMARY OF THE INVENTION
[0024] It is a general object of the present invention to provide a
novel and useful system that calculates and displays a
comprehensive and accurate valuation for the elements of an
enterprise that overcomes the limitations and drawbacks of the
prior art that were described previously.
[0025] A preferable object to which the present invention is
applied is the valuation of elements of a high technology
commercial enterprise where a significant portion of the business
value is associated with intangible assets.
[0026] The present invention eliminates a great deal of
time-consuming and expensive effort by automating the extraction of
transaction data from the databases, tables, and files of the
existing computer-based corporate finance, operation, sales, and
human resource software databases as required to operate the
system. In accordance with the invention, the automated extraction,
aggregation and analysis of transaction data from a variety of
existing computer-based systems significantly increases the scale
and scope of the analysis that can be completed. The system of the
present invention further enhances the efficiency and effectiveness
of the business valuation by automating the retrieval, storage and
analysis of information useful for valuing intangible assets from
external databases and publications via the internet or other
external networks.
[0027] Uncertainty over which method is being used for completing
the valuation and the resulting inability to compare different
valuations is eliminated by the present invention by consistently
utilizing different valuation methodologies for valuing the
different elements of the enterprise as shown in Table 1.
TABLE-US-00001 TABLE 1 Enterprise element Valuation methodology
Excess Cash & Marketable Securities GAAP Total
current-operation value (COPTOT): Income valuation*
Current-operation: Cash & Marketable Securities GAAP (CASH)
Current-operation: Accounts Receivable (AR) GAAP Current-operation:
Inventory (IN) GAAP Current-operation: Prepaid Expenses (PE) GAAP
Current-operation: Production Equipment (PEQ) If correlation value
> liquidation value, then use correlation valuation, else use
liquidation value Current-operation: Other Physical Assets (OPA)
Liquidation Value Current-operation: Other Assets (OA) GAAP
Current-operation: Intangible Assets (IA): Customers Correlation to
component(s) of value Employees Correlation to component(s) of
value Vendor Relationships Correlation to component(s) of value
Strategic Partnerships Correlation to component(s) of value Brand
Names Correlation to component(s) of value Other Intangibles
Correlation to component(s) of value Current-operation: General
going concern value GCV = COPTOT - CASH - AR - (GCV) IN - PE - PEQ
- OPA - OA - IA Growth options Option pricing algorithms *The user
also has the option of specifying the total value
The value of an enterprise operation is calculated by summing items
from Table 1 as shown in Table 2.
TABLE-US-00002 [0028] TABLE 2 Enterprise Value = Current value of
enterprise excess cash and marketable securities + Value of
current-operation + Value of growth options
[0029] As shown in Table 1, the growth opportunities of the firm
are valued using option pricing algorithms. Option pricing
algorithms are improvements over traditional methods as they
correct two inaccurate assumptions implicit in traditional
discounted cash flow analyses of business growth opportunities,
namely: the assumption that investment decisions are reversible,
and the assumption that investment decisions can not be delayed. In
reality, a firm with a project that requires an investment has the
right but not the obligation to buy an asset at some future time of
its choosing. However, once the investment is made it is often
irreversible--a situation analogous to a call option. Because
option valuation algorithms explicitly recognize that investments
of this type are often irreversible and that they can be delayed,
the asset values calculated using these algorithms are more
accurate than valuations created using more traditional approaches.
The use of option pricing analysis for valuing growth opportunities
(hereinafter, growth options) gives the present invention a
distinct advantage over traditional approaches to business
valuation.
[0030] The innovative system has the added benefit of providing a
large amount of detailed information concerning both tangible and
intangible elements of enterprise business value. The system also
gives the user the ability to track the changes in elements of
business value and total business value over time by comparing the
current valuation to previously calculated valuations. As such, the
system also provides the user with an alternative mechanism for
tracking financial performance. To facilitate its use as a tool for
improving the value of an enterprise, the system of the present
invention produces reports in formats that are similar to the
reports provided by traditional accounting systems. The method for
tracking the elements of value for a business enterprise provided
by the present invention eliminates many of the limitations
associated with current accounting systems that were described
previously.
BRIEF DESCRIPTION OF DRAWINGS
[0031] These and other objects, features and advantages of the
present invention will be more readily apparent from the following
description of the preferred embodiment of the invention in
which:
[0032] FIG. 1 is a block diagram showing the major processing steps
of the present invention;
[0033] FIG. 2 is a diagram showing the files or tables in the
application database of the present invention that are utilized for
data storage and retrieval during the processing that values
elements of the enterprise;
[0034] FIG. 3 is a block diagram of an implementation of the
present invention;
[0035] FIG. 4 is a diagram showing the data windows that are used
for receiving information from and transmitting information to the
user during system processing;
[0036] FIG. 5A and FIG. 5B are block diagrams showing the sequence
of steps in the present invention used for extracting, aggregating
and storing information utilized in system processing from: user
input, the basic financial system database, the operation
management system database, the advanced financial system database,
the sales management system database, external databases via the
internet and the human resource information system database;
[0037] FIG. 6A, FIG. 6B, FIG. 6C, FIG. 6D and FIG. 6E are block
diagrams showing the sequence of steps in the present invention
that are utilized in identifying the value drivers and defining the
composite variables;
[0038] FIG. 7 is a block diagram showing the sequence of steps in
the present invention used for the specification and valuation of
growth options;
[0039] FIG. 8 is a block diagram showing the sequence of steps
associated with the analyzing the components of enterprise
value;
[0040] FIG. 9A and FIG. 9B are block diagrams showing the sequence
of steps in the present invention that are utilized in the
specification and optimization of the predictive models that
determine the relationships between value drivers and the revenue,
expense and capital components of enterprise value;
[0041] FIG. 10 is a diagram illustrating the processing of a
feed-forward neural network;
[0042] FIG. 11 is a diagram illustrating the processing of a
Kohonen neural network;
[0043] FIG. 12 is a block diagram showing the sequence of the steps
in the present invention used for calculating the percentage of the
revenue, expense and capital components attributed to the elements
and sub-elements of value;
[0044] FIG. 13 is a block diagram showing the sequence of steps in
the present invention used in preparing, displaying and optionally
printing reports;
[0045] FIG. 14 is a sample Value Map.TM. report from the present
invention showing the calculated value for all elements of value in
the total company on the valuation date; and
[0046] FIG. 15 is a sample Value Creation report from the present
invention detailing the changes in the elements of value and total
company value from a prior date to the valuation date.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
[0047] FIG. 1 provides an overview of the processing completed by
the innovative system for business valuation. In accordance with
the present invention, an automated method of and system (100) for
business valuation is provided. Processing starts in this system
(100) with a block of software (200) that extracts, aggregates and
stores the transaction data and user input required for completing
a valuation. This information is extracted via an interconnection
network (25) from a basic financial system database (10), an
operation management system database (15), an advanced financial
system database (30), a sales management system database (35), and
a human resource information system database (40). Information can
also be extracted from an on-line external database such as those
found on an internet (5) via a communications link (45). These
information extractions and aggregations are guided by a user (20)
through interaction with a user-interface portion of the
application software (900) that mediates the display and
transmission of all information to the user (20) from the system
(100) as well as the receipt of information into the system (100)
from the user (20) using a variety of data windows tailored to the
specific information being requested or displayed in a manner that
is well known. While only one database of each type (10, 15, 30, 35
& 40) is shown in FIG. 1, it is to be understood that the
system (100) can extract data from multiple databases of each type
via the interconnection network (25).
[0048] All extracted information concerning revenue, expenses,
capital and elements of value is stored in a file or table
(hereinafter, table) within an application database (50) as shown
in FIG. 2. The application database (50) contains tables for
storing user input, extracted information and system calculations
including a system settings table (140), a revenue data table
(141), an expense data table (142), a capital data table (143), an
equity data table (144), a physical asset ID table (145), an asset
liquidation price table (146), an account number structure table
(147), an equity forecast table (148), a data dictionary table
(149), a revenue component definition table (150), an expense
component definition table (151), a capital component definition
table (152), an element of value definition table (153), a
sub-element definition table (154), an enterprise definition table
(155), a composite variable table (156), a sub-element weights
table (157), a revenue model gene table (158), a revenue model
weights table (159), an expense model gene table (160), an expense
model weights table (161), a capital model gene table (162), a
capital model weights table (163), a revenue component percentage
table (164), an expense component percentage table (165), a capital
component percentage table (166), a composite variable location
table (167), a composite variable data table (168), a normalized
composite variable data table (169), an enterprise value table
(170), an economic equity values table (171), a reports table
(172), a tax data table (173), a debt data table (174), a growth
option definition table (175), a growth option overlap table (176),
a growth option scenario table (177), a growth option value table
(178), a revenue driver table (179), an expense driver table (180),
a capital driver table (181), an excluded variable table (182) and
a driver genes table (183). The application database (50) can
optionally exist as a datamart, data warehouse or departmental
warehouse. The system of the present invention has the ability to
accept and store supplemental or primary data directly from user
input, a data warehouse or other electronic files in addition to
receiving data from the databases described previously. The system
of the present invention also has the ability to complete the
necessary calculations without receiving data from one or more of
the specified databases. However, in the preferred embodiment all
required information is obtained from the specified databases
(5,10,15, 30, 35 & 40).
[0049] As shown in FIG. 3, the preferred embodiment of the present
invention is a computer system (100) illustratively comprised of a
client personal computer (110) connected to an application server
personal computer (120) via an interconnection network (25). The
application server personal computer (120) is in turn connected via
the interconnection network (25) to a database-server personal
computer (130).
[0050] The database-server personal computer (130) has a CPU (136),
a keyboard (132), a CRT display (133), a printer (137), a hard
drive (131) for storage of the basic financial system database
(10), the operation management system database (15), the advanced
financial system database (30), the sales management system
database (35) and the human resource information system database
(40), a communications bus (134) and a read/write random access
memory (135).
[0051] The application-server personal computer (120) has a CPU
(127), a keyboard (123), a mouse (126), a CRT display (124), a
printer (128), a hard drive (122) for storage of the application
database (50) and the majority of the application software (200,
300, 400, 500, 600, 700 and 800) of the present invention, a
communications bus (125) and a read/write random access memory
(121). While only one client personal computer is shown in FIG. 3,
it is to be understood that the application-server personal
computer (120) can be networked to fifty or more client personal
computers (110) via the interconnection network (25). The
application-server personal computer (120) can also be networked to
fifty or more server, personal computers (130) via the
interconnection network (25). It is to be understood that the
diagram of FIG. 3 is merely illustrative of one embodiment of the
present invention.
[0052] The client personal computer (110) has a CPU (117), a
keyboard (113), a mouse (116), a CRT display (114), a printer
(118), a modem (119), a hard drive (112) for storage of a client
data-base (49) and the user-interface portion of the application
software (900), a communications bus (115) and a read/write random
access memory (111).
[0053] The application software (200, 300, 400, 500, 600, 700, 800
and 900) controls the performance of the central processing unit
(127) as it completes the calculations required to calculate the
detailed business valuation. In the embodiment illustrated herein,
the application software program (200, 300, 400, 500, 600, 700, 800
and 900) is written in a combination of PowerScript, C++ and Visual
Basic.RTM.. The application software (200, 300, 400, 500, 600, 700,
800 and 900) also uses Structured Query Language (SQL) for
extracting data from other databases (10, 15, 30, 35 and 40) and
then storing the data in the application database (50) or for
receiving input from the user (20) and storing it in the client
database (49). The other databases contain information regarding
historical financial performance (10), operation management records
(15), forecast financial performance (30), sales prospects and
transactions (35) and the company employees (40) that are used in
the operation of the system (100). The user (20) provides the
information the application software requires to determine which
data need to be extracted and transferred from the database-server
hard drive (131) via the interconnection network (25) to the
application-server computer hard drive (122) by interacting with
user-interface portion of the application software (900). The
extracted information is combined with input received from the
keyboard (113) or mouse (116) in response to prompts from the
user-interface portion of the application software (900) before
processing is completed.
[0054] User input is initially saved to the client database (49)
before being transmitted to the communication bus (125) and on to
the hard drive (122) of the application-server computer via the
interconnection network (25). Following the program instructions of
the application software, the central processing unit (127)
accesses the extracted data and user input by retrieving it from
the hard drive (122) using the random access memory (121) as
computation workspace in a manner that is well known.
[0055] The computers (110, 120 and 130) shown in FIG. 3
illustratively are IBM PCs or clones or any of the more powerful
computers or workstations that are widely available. Typical memory
configurations for client personal computers (110) used with the
present invention should include at least 32 megabytes of
semiconductor random access memory (111) and at least a 2 gigabyte
hard drive (112). Typical memory configurations for the
application-server personal computer (120) used with the present
invention should include at least 64 megabytes of semiconductor
random access memory (121) and at least a 50 gigabyte hard drive
(122). Typical memory configurations for the database-server
personal computer (130) used with the present invention should
include at least 128 megabytes of semiconductor random access
memory (135) and at least a 200 gigabyte hard drive (131).
[0056] Using the system described above, the value of the
enterprise will be further broken down into tangible and intangible
elements of value. As shown in Table 1, the value of the
current-operation will be calculated using an income valuation
model. An integral part of most income valuation models is the
calculation of the present value of the expected cash flows, income
or profits associated with the current-operation. The present value
of a stream of cash flows is calculated by discounting the cash
flows at a rate that reflects the risk associated with realizing
the cash flow. For example, the present value (PV) of a cash flow
of ten dollars ($10) per year for five (5) years would vary
depending on the rate used for discounting future cash flows as
shown below.
Discount rate = 25 % _ PV = 10 1.25 + 10 ( 1.25 ) 2 + 10 ( 1.25 ) 3
+ 10 ( 1.25 ) 4 + 10 ( 1.25 ) 5 = 26.89 ##EQU00001## Discount rate
= 35 % _ PV = 10 1.35 + 10 ( 1.35 ) 2 + 10 ( 1.35 ) 3 + 10 ( 1.35 )
4 + 10 ( 1.35 ) 5 = 22.20 ##EQU00001.2##
[0057] The first step in evaluating the elements of
current-operation value is separating the underlying formula that
defines the value of the current-operation as shown in Table 3.
TABLE-US-00003 TABLE 3 Value of current-operation = (R) Value of
expected revenue from current-operation + (E) Value of expected
expense for current-operation + (C) Value of capital required to
support current-operation* *Note: (C) can have a positive or
negative value
[0058] The three components of current-operation value will be
referred to as the revenue value (R), the expense value (E) and the
capital value (C). Examination of the equation in Table 3 shows
that there are three ways to increase the value of the
current-operation--increase the revenue, decrease the expense or
decrease the capital requirements (note: this statement ignores a
fourth way to increase value--decrease interest rate used for
discounting future cash flows).
[0059] While it is possible to break each component down into a
large number of sub-components for analysis, the preferred
embodiment has a pre-determined number of sub-components for each
component of value. The revenue value is not subdivided. The
expense value is subdivided into five sub-components: the cost of
raw materials, the cost of manufacture or delivery of service, the
cost of selling, the cost of support and the cost of
administration. The capital value is subdivided into six
sub-components: cash, non-cash financial assets, production
equipment, other assets (non financial, non production assets),
financial liabilities and equity. The production equipment and
equity sub-components are not used directly in evaluating the
elements of value.
[0060] The components and sub-components of current-operation value
will be used in calculating the value of the tangible and
intangible elements of value. For the calculations completed by the
present invention, an element of value will be defined as "an
identifiable entity or group that as a result of past transactions
has provided and is expected to provide economic benefit to the
enterprise." An item will be defined as a single member of the
group that defines an element of value. For example, an individual
salesman would be an "item" in the "element of value" sales staff.
Predictive models are used to determine the percentage of: the
revenue value, the expense value sub-components, and the capital
value sub-components that are attributable to each element of
value. The resulting values will then be added together to
determine the valuation for different elements as shown by the
example in Table 4.
TABLE-US-00004 TABLE 4 Valuation of the Large, Loyal Customer
Element Revenue value = $120M 13% attributed to large, Value =
$15.6 M loyal customers Expense value = ($80M) 10% attributed to
large, Value = ($8) M loyal customers Capital value = ($5M) 12%
attributed to large, Value = ($.6) M loyal customers Total value =
$35M Large, Loyal Customer Element Value = $ 7 M
[0061] The valuation of an enterprise using the approach outlined
above is completed in seven distinct stages. The first stage of
processing (block 200 from FIG. 1) extracts, aggregates and stores
the data from user input, existing internal databases (10, 15, 30,
35 or 40) and external databases (5) required for the calculation
of enterprise business value as shown in FIG. 5A and FIG. 5B. The
second stage of processing (block 300 from FIG. 1) identifies the
item variables and item performance indicators that drive the
components of value (revenue, expense and changes in capital) and
calculates composite variables that characterize the performance of
the elements of value, as shown in FIG. 6A FIG. 6B, FIG. 6C, FIG.
6D, FIG. 6E, FIG. 10 and FIG. 11. The third stage of system
processing (block 400 from FIG. 1) values the growth options by
enterprise using option pricing algorithms as shown in FIG. 7. The
fourth stage of system processing (block 500 from FIG. 1) values
the revenue, expense and capital components and calculates the
current operation value using the information prepared in the
previous stage of processing as shown in FIG. 8. The fifth stage of
system processing (block 600 from FIG. 1) specifies and optimizes
predictive models to determine the relationship between the value
drivers and the revenue, expense and capital values as shown in
FIG. 9A, FIG. 9B and FIG. 10. The sixth stage of processing (block
700 from FIG. 1) combines the results of the fourth and fifth
stages of processing to determine the value of each element as
shown in FIG. 12. The seventh and final stage of processing (block
800 from FIG. 1) determines the relationship between equity and
calculated total value as shown in FIG. 13 and displays the results
of the prior calculations in specified formats as shown in FIG. 14
and FIG. 15.
Extraction and Aggregation of Data
[0062] The flow diagrams in FIG. 5A and FIG. 5B detail the
processing that is completed by the portion of the application
software (200) that extracts, aggregates and stores the information
required for system operation from: the basic financial system
database (10), operation management system database (15), advanced
financial system database (30), sales management system database
(35), human resource information system database (40), external
databases found on the internet (5) and the user (20). A brief
overview of the different databases will be presented before
reviewing each step of processing completed by this portion (200)
of the application software.
[0063] Corporate financial software systems are generally divided
into two categories, basic and advanced. Advanced financial systems
utilize information from the basic financial systems to perform
financial analysis, financial planning and financial reporting
functions. Virtually every commercial enterprise uses some type of
basic financial system as they are required to use these systems to
maintain books and records for income tax purposes. An increasingly
large percentage of these basic financial systems are resident in
microcomputer and workstation systems. Basic financial systems
include general-ledger accounting systems with associated accounts
receivable, accounts payable, capital asset, inventory, invoicing,
payroll and purchasing subsystems. These systems incorporate
worksheets, files, tables and databases. These databases, tables
and files contain information about the company operations and its
related accounting transactions. As will be detailed below, these
databases, tables and files are accessed by the application
software of the present invention as required to extract the
information required for completing a business valuation. The
system is also capable of extracting the required information from
a data warehouse (or datamart) when the required information has
been pre-loaded into the warehouse.
[0064] General ledger accounting systems generally store only valid
accounting transactions. As is well known, valid accounting
transactions consist of a debit component and a credit component
where the absolute value of the debit component is equal to the
absolute value of the credit component. The debits and the credits
are posted to the separate accounts maintained within the
accounting system. Every basic accounting system has several
different types of accounts. The effect that the posted debits and
credits have on the different accounts depends on the account type
as shown in Table 5.
TABLE-US-00005 TABLE 5 Account Type: Debit Impact: Credit Impact:
Asset Increase Decrease Revenue Decrease Increase Expense Increase
Decrease Liability Decrease Increase Equity Decrease Increase
General ledger accounting systems also require that the asset
account balances equal the sum of the liability account balances
and equity account balances at all times.
[0065] The general ledger system generally maintains summary,
dollar only transaction histories and balances for all accounts
while the associated subsystems, accounts payable, accounts
receivable, inventory, invoicing, payroll and purchasing, maintain
more detailed historical transaction data and balances for their
respective accounts. It is common practice for each subsystem to
maintain the detailed information shown in Table 6 for each
transaction.
TABLE-US-00006 TABLE 6 Subsystem Detailed Information Accounts
Vendor, Item(s), Transaction Date, Amount Owed, Due Payable Date,
Account Number Accounts Customer, Transaction Date, Product Sold,
Quantity, Receivable Price, Amount Due, Terms, Due Date, Account
Number Capital Asset ID, Asset Type, Date of Purchase, Purchase
Price, Asset Useful Life, Depreciation Schedule, Salvage Value
Inventory Item Number, Transaction Date, Transaction Type,
Transaction Qty, Location, Account Number Invoicing Customer Name,
Transaction Date, Item(s) Sold, Amount Due, Due Date, Account
Number Payroll Employee Name, Employee Title, Pay Frequency, Pay
Rate, Account Number Purchasing Vendor, Item(s), Purchase Quantity,
Purchase Price(s), Due Date, Account Number
As is well known, the output from a general ledger system includes
income statements, balance sheets and cash flow statements in well
defined formats which assist management in measuring the financial
performance of the firm during the prior periods when data input
have been completed.
[0066] Advanced financial systems, including financial planning
systems, generally use the same format used by basic financial
systems in forecasting income statements, balance sheets and cash
flow statements for future periods. Management uses the output from
financial planning systems to highlight future financial
difficulties with a lead time sufficient to permit effective
corrective action and to identify problems in company operations
that may be reducing the profitability of the business below
desired levels. These systems are most often developed by
individuals within companies using 2 and 3 dimensional spreadsheets
such as Lotus 1-2-3.RTM., Microsoft Excel.RTM. and Quattro
Pro.RTM.. In some cases, financial planning systems are built
within an executive information system (EIS) or decision support
system (DSS). For the preferred embodiment of the present
invention, the advanced financial system database is the financial
planning system database detailed in U.S. Pat. No. 5,165,109 for
"Method of and System for Generating Feasible, Profit Maximizing
Requisition Sets", by Jeff S. Eder, the disclosure of which is
incorporated herein by reference.
[0067] While advanced financial systems are similar between firms,
operation management systems vary widely depending on the type of
company they are supporting. These systems typically have the
ability to not only track historical transactions but to forecast
future performance. For manufacturing firms, operation management
systems such as Enterprise Requirements Planning Systems (ERP),
Material Requirement Planning Systems (MRP), Purchasing Systems,
Scheduling Systems and Quality Control Systems are used to monitor,
coordinate, track and plan the transformation of materials and
labor into products. These systems will generally track information
about the performance of the different vendors that supply
materials to the firm including the information shown in Table
7.
TABLE-US-00007 TABLE 7 Operation Management System - Vendor
Information 1. Vendor Name 2. Vendor Number 3. Commodity Code(s) 4.
Year to date dollar volume 5. Historical dollar volume 6.
Percentage of deliveries rejected by QC 7. Percentage of deliveries
accepted out of specification 8. Compliance with ISO 9000 9. Actual
lead time required for purchases 10. Terms and conditions for
purchases 11. Average Delivery Quantity Variance 12. Average
Delivery Date Variance 13. EDI* vendor - Yes or No *EDI =
Electronic Data Interchange
Systems similar to the one described above may also be useful for
distributors to use in monitoring the flow of products from a
manufacturer.
[0068] Operation Management Systems in manufacturing firms may also
monitor information relating to the production rates and the
performance of individual production workers, production lines,
work centers, production teams and pieces of production equipment
including the information shown in Table 8.
TABLE-US-00008 TABLE 8 Operation Management System - Production
Information 1. ID number (employee id/machine id) 2. Actual hours -
last batch 3. Standard hours - last batch 4. Actual hours - year to
date 5. Actual/Standard hours - year to date % 6. Actual setup time
- last batch 7. Standard setup time - last batch 8. Actual setup
hours - year to date 9. Actual/Standard setup hrs - yr to date %
10. Cumulative training time 11. Job(s) certifications 12. Actual
scrap - last batch 13. Scrap allowance - last batch 14. Actual
scrap/allowance - year to date 15. Rework time/unit last batch 16.
Rework time/unit year to date 17. QC rejection rate - batch 18. QC
rejection rate - year to date
Operation management systems are also useful for tracking requests
for service to repair equipment in the field or in a centralized
repair facility. Such systems generally store information similar
to that shown below in Table 9.
TABLE-US-00009 TABLE 9 Operation Management System - Service Call
Information 1. Customer name 2. Customer number 3. Contract number
4. Service call number 5. Time call received 6. Product(s) being
fixed 7. Serial number of equipment 8. Name of person placing call
9. Name of person accepting call 10. Promised response time 11.
Promised type of response 12. Time person dispatched to call 13.
Name of person handling call 14. Time of arrival on site 15. Time
of repair completion 16. Actual response type 17. Part(s) replaced
18. Part(s) repaired 19. 2nd call required 20. 2nd call number
[0069] Sales management systems are similar to operation management
systems in that they vary considerably depending on the type of
firm they are supporting and they generally have the ability to
forecast future events as well as track historical occurrences. In
firms that sell customized products, the sales management system is
generally integrated with an estimating system that tracks the flow
of estimates into quotations, orders and eventually bills of lading
and invoices. In other firms that sell more standardized products,
sales management systems generally are used to track the sales
process from lead generation to lead qualification to sales call to
proposal to acceptance (or rejection) and delivery. All sales
management systems would be expected to store information similar
to that shown below in Table 10.
TABLE-US-00010 TABLE 10 Sales Management System - Information 1.
Customer/Potential customer name 2. Customer number 3. Address 4.
Phone number 5. Source of lead 6. Date of first purchase 7. Date of
last purchase 8. Last sales call/contact 9. Sales call history 10.
Sales contact history 11. Sales history: product/qty/price 12.
Quotations: product/qty/price 13. Custom product percentage 14.
Payment history 15. Current A/R balance 16. Average days to pay
[0070] Computer based human resource systems are increasingly used
for storing and maintaining corporate records concerning active
employees in sales, operations and the other functional specialties
that exist within a modern corporation. Storing records in a
centralized system facilitates timely, accurate reporting of
overall manpower statistics to the corporate management groups and
the various government agencies that require periodic updates. In
some cases human resource systems include the company payroll
system as a subsystem. In the preferred embodiment of the present
invention, the payroll system is part of the basic financial
system. These systems can also be used for detailed planning
regarding future manpower requirements. Human resource systems
typically incorporate worksheets, files, tables and databases that
contain information about the current and future employees. As will
be detailed below, these databases, tables and files are accessed
by the application software of the present invention as required to
extract the information required for completing a business
valuation. It is common practice for human resource systems to
store the information shown in Table 11 for each employee.
TABLE-US-00011 TABLE 11 Human Resource System Information 1.
Employee name 2. Job title 3. Job code 4. Rating 5. Division 6.
Department 7. Employee No./(Social Security Number) 8. Year to date
- hours paid 9. Year to date - hours worked 10. Employee start date
- company 11. Employee start date - department 12. Employee start
date - current job 13. Training courses completed 14. Cumulative
training expenditures 15. Salary history 16. Current salary 17.
Educational background 18. Current supervisor
[0071] External databases such as those found on the internet (5)
can be used for obtaining information that enables the
categorization and valuation of assets such as brand names,
trademarks and service marks (hereinafter, referred to as brand
names). In some cases it can also be used to supplement information
obtained from the other databases (10, 15, 30, 35 and 40) that are
used in categorizing and evaluating employee groups and other
elements of value. In the system of the present invention, the
retrieval of information from the internet (5) can be: [0072] a)
targeted to specific on-line publications that provide information
relevant to the element being evaluated, [0073] b) restricted to a
simple count of the number of matches a specific trademark
generates when entered into a general purpose internet
search-engine such as Yahoo!, Lycos, AltaVista or HotBot, or
WebCrawler, and [0074] c) specific searches using commercially
available software agents and/or text mining products to determine
both the number and the type of references (favorable, unfavorable
or information only) that have been made concerning a specific
trademark in all discovered references.
[0075] System processing of the information from the different
databases (5, 10, 15, 30, 35 and 40) described above starts in a
block 201, FIG. 5A, which immediately passes processing to a
software block 202. The software in block 202 prompts the user via
the system settings data window (901) to provide system setting
information. The system setting information entered by the user
(20) is transmitted via the interconnection network (25) back to
the application server (120) where it is stored in the system
settings table (140) in the application database (50) in a manner
that is well known. The specific inputs the user (20) is asked to
provide at this point in processing are shown in Table 12.
TABLE-US-00012 TABLE 12 System Settings 1. Mode of operation -
stand-alone valuation or comparison to previous valuation 2. Date
of business valuation calculation (valuation date) 3. Date of
previous valuation (if any) 4. Location (address) of basic
financial system data dictionary and data 5. Location (address) of
advanced financial system data dictionary and data 6. Location
(address) of human resource information system data dictionary and
data 7. Location (address) of operation management system data
dictionary and data 8. Location (address) of sales management
system data dictionary and data 9. Location (address) of any
external databases used in the valuation calculation 10. The
maximum acceptable age of a valuation (in days) 11. The maximum
number of generations to be processed without improving fitness 12.
Base currency 13. Currency conversions for any non-base currencies
used in the financial systems 14. Weighted average cost of capital
(to be used in discounting cash flows) 15. Simplified analysis (no
sub-components of expense or capital value) 16. Number of months a
product is considered new after it is first produced 17. Define
composite variables? (Yes or No) 18. Amount of cash and marketable
securities required for day to day operations
The application of these system settings will be explained as part
of the detailed explanation of the system operation.
[0076] The software in block 202 uses the valuation date specified
by the user (20) to determine the time periods (months) that
require data in order to complete the valuation of the current
operation and the growth options and stores the resulting date
range in the system settings table (140). The valuation of the
current operation by the system requires sales, operation, finance,
external database and human resource data for the three year period
before and the four year period after the specified valuation date.
Because of the difficulties inherent in forecasting from the
perspective of the past or the future, the specified valuation date
is generally within a month of the current system date. After the
storage of system setting data is complete, processing advances to
a software block 203 where the data dictionaries from the basic
financial system database (10), the operation management system
database (15), the advanced financial system database (30), the
sales management system database (35) and the human resource
information system database (40) are extracted and saved in the
data dictionary table (149) in the application database (50) and
processing advances to a software block 204.
[0077] The software in block 204 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a comparison to a prior valuation or if it is a
stand-alone calculation. If the calculation involves a comparison
with a prior valuation, then the software in block 204 retrieves
the previously defined account structure, data definitions,
enterprise definitions and component definitions and saves them in
the appropriate tables for use in the current calculation before
processing advances to a software block 209. Alternatively, if the
calculation is a stand-alone, then processing advances to a
software block 205.
[0078] The software in block 205 interacts with an account
structure and data dictionary data window (902) that prompts the
user for any input that is required to define data fields for the
extracted data dictionaries and the data dictionary of the
application software of the present invention. This input is also
saved to the data dictionary table (149). The software in block 205
also prompts the user (20) via the account structure and data
dictionary data window (902) for information that edits or defines
the account structure used in the financial system databases. It is
common practice for account numbers to have several segments where
each segment represents a different set of subgroups as shown below
in Table 13.
TABLE-US-00013 TABLE 13 Account Number 01- 800- 901- 677- 003
Segment Company Division Department Account Sub- account Subgroup
Products Workstation Marketing Labor P.R. Position 5 4 3 2 1
[0079] As will be detailed below, the different account number
segments are used for separating the financial information for
analysis.
[0080] After the account structure information is stored in the
account number structure table (147) in the application database
(50), processing advances to a block 206 where the software in the
block interacts with an enterprise definition data window (903) to
prompt the user (20) to specify the account number segment or
segments that will be used to define the enterprise being valued by
the innovative system of the present invention. For example, the
user (20) could specify that each division is to be analyzed as a
separate enterprise. In this case, if the total company had two
business units with six divisions, then the user could specify up
to six enterprises as shown in Table 14.
TABLE-US-00014 TABLE 14 Products Business Unit Software Business
Unit 1. PC Division 5. Application Software Division 2. Workstation
Division 6. Operating System Software Division 3. Mainframe
Division 4. Peripherals Division
[0081] The specified enterprises are then displayed to the user
(20) by the software in block 206 via the enterprise definition
data window (903). At this point, the user (20) is given the option
of combining the enterprises or leaving them in the initial
configuration. For example, the user (20) could combine the
Personal Computer Product enterprise and the Workstation Product
enterprise into one enterprise for the business valuation
calculation. When the user (20) indicates that all enterprises have
been defined, the resulting specifications are stored in the
enterprise definition table (155) in the application database
(50).
[0082] After the enterprise definitions are stored, processing
advances to a software block 207 where the software in the block
prompts the user (20) via a component definition data window (904)
to specify the account segment or segments that will be used to
define the expense and capital sub-components for each enterprise.
Only account segments with position numbers below those of the
segment used for enterprise specification can be used for expense
and capital sub-component specification. Continuing the example
shown above for a valuation calculation, departments, accounts and
sub-accounts are the only segments that can be utilized for expense
or capital component and sub-component specification. This
limitation is applicable because their position numbers 3, 2 and 1
respectively are below 4, the position number of the division
segment that was the lowest position used to define the enterprise.
As discussed previously, there is only one revenue component per
enterprise; therefore, the enterprise definition automatically
defines the revenue component.
[0083] For the normal analysis, each enterprise has: one revenue
component, five expense sub-components (cost of raw materials, the
cost of manufacture or delivery of service, the cost of sales, the
cost of support and other costs), four capital sub-components used
in the valuation calculation (cash, non-cash financial assets,
other (non-financial, non-production) assets, liabilities), and two
capital sub-components that are not used directly in the valuation
calculation (production equipment and equity). The software in
block 207 via the component definition data window (904) will
accept all logical combinations of account number segment
specifications for a sub-component while preventing the reuse of
the same segment for more than one sub-component specification in
each enterprise. Sub-component definitions are required even if the
user (20) has chosen to run a simplified analysis (i.e., one
without sub-components). Table 15 provides examples of expense and
capital sub-component definitions.
TABLE-US-00015 TABLE 15 Sub-component Definition Expense: Cost of
materials Departments 10-18, accounts 500 to 505 Expense: Cost of
manufacturing Departments 10-18, accounts 506 to 999 Expense: Cost
of sales Department 21, accounts 500 to 999 Capital: Cash Account
100, all departments Capital: Liabilities Accounts 200-299, all
departments
[0084] The software in block 207 saves the new or updated revenue
component definitions to the revenue component definition table
(150), expense sub-component definitions to the expense component
definition table (151) and capital sub-component definitions to the
capital component definition table (152). The production equipment
and other asset definitions are also used to populate the physical
asset ID table (145) and the asset liquidation price table (146)
with the names of all assets used by all enterprises.
[0085] After the definitions for the revenue, expense and capital
components have been stored in the application database (50),
processing advances to a software block 209. Processing can also
advance to block 209 directly from block 204 if the calculation is
a comparison to a prior valuation. The software in block 209 checks
to determine if all the available financial data have been included
in a revenue, expense, or capital component or sub-component. In
the example shown above, block 209 would check to determine that
the financial data for all divisions, departments, account numbers
and sub-account numbers have been assigned to a component. If the
software in block 209 determines that all financial data have been
assigned to a component, then processing advances to a software
block 210. Alternatively, if the software in block 209 determines
that some financial data have not been assigned to a component,
then processing advances to a software block 208. The software in
block 208 prompts an edit component definition data window (905) to
display a screen that provides the user (20) with the ability to
redefine previously stored component and sub-component definitions
to include the unassigned financial data. The revised component
definition(s) are then saved in the appropriate definition table(s)
(150, 151 or 152) in the application database (50) and processing
returns to block 209 and from there to software block 210.
[0086] The software in block 210 retrieves the debit or credit
balances from the basic financial system database (10) and the
advanced financial system database (30) in account segment position
order, lowest position to highest position, for the revenue,
expense and capital components for the time periods determined by
the software in block 202 and stored in the system settings table
(140). Continuing the example, the software in block 210 would
first retrieve and total debits and credits in each required period
for the sub-components that have sub-account specifications. The
higher level specifications, account number, department and
division, are observed when data are retrieved for the
sub-components with sub-account specifications. The software in
block 210 would then retrieve the required data for the
sub-components with account number specifications. The higher level
specifications, department and division, are observed when data are
retrieved for the sub-components with account number
specifications. The software in block 210 would finally retrieve
the required data for the sub-components with department number
specifications. The higher level specification, division, is
observed when data are retrieved for these sub-components. This
same procedure is completed for each enterprise and the resulting
totals are then saved in the appropriate data tables (141-revenue,
142-expense and 143-capital) in the application database (50).
[0087] After all the financial data have been extracted and stored
in the application database (50), system processing advances to a
software block 212. The software in block 212 determines if any of
the components or sub-components are missing data for any of the
required periods. Missing data is defined as the condition when
there is a null value for a sub-component financial data field in a
required period. If the software in block 212 determines that all
components have the required data in all periods, then processing
advances directly to a software block 221. Alternatively, if data
are missing, then processing advances to a software block 213 where
the user (20) is prompted by a missing financial data window (906)
to provide the missing data or the location of the missing data. In
some cases the user (20) may simply replace the null value with a
zero. After the user (20) provides the missing data or the location
of the missing data, the appropriate data tables (141-revenue,
142-expense and/or 143-capital) in the application database (50)
are updated and processing advances to software block 221.
[0088] The next step in system processing is completed by software
block 221 where the software in the block prompts the user (20) via
an element of value specification data window (907) to define the
elements of value for each enterprise, to indicate the maximum
number of sub-elements for each element and to identify the
identity and location of transaction data and other information
that are related to each element of value. Elements of value with
sample specifications are shown below in Table 16.
TABLE-US-00016 TABLE 16 Element of Value: Maximum Name/ Sub Element
of Value Data Definition Elements Identity and Location Customers/
10 Account payment data (10); Communications Customer numbers
history (15), Date of first order (35); Order history - 1-21,877
line items, products/services, revenue, returns, delivery (10 &
35); Invoice adjustment history (10 & 35), Service call history
- first time and repeat (15); Technical support call history -
first time and repeat (15). Employees Production/ 0 Date of first
employment (40), Employee Job codes: suggestion history (15);
Employee training data 17, 18, 19 and 33 (40); Employee production
data - hours, piece quantity (15); Employee pay data including
benefits (10, 30 & 40). Brand names/ 50* Monthly average price
premium/(discount) vs. Name(s) industry average price (35), Monthly
number of favorable mentions in trade press (5), Monthly number of
hits on corporate web site (5), Monthly spending on advertising
(10), Monthly average cost per 1,000 for advertising (10). *Default
system limit
The information entered by the user (20) defining the elements of
value is stored in the element of value definition table (153), the
location of the element of value data is stored in the composite
variable location table (167), and an index of the element of value
data is stored in the composite variable data table (168) in the
application database (50), before processing advances to a software
block 222.
[0089] The software in block 222 prompts the user (20) via a growth
option definition data window (908) to specify the growth options
that will be valued for each enterprise. The specification of each
growth option includes: an option name; the financial resources
consumed or generated by the growth option by component of value;
the resources associated with the growth option by element of
value, and the number of scenarios that will be analyzed as part of
the growth option valuation. A growth option specification example
is shown below in Table 17.
TABLE-US-00017 TABLE 17 Growth Option Example Specification Option
name VRML Equipment Revenue Component None Expense Sub-Component:
Department 17, accounts 500 to 505, Raw Materials after 6/97
Expense Sub-Component: All expenses, department 87 Other Capital
Sub-Component: All assets, department 87 Other Assets Element of
Value: All employees, department 87 Other Employees
If the system (100) is calculating a business valuation comparison,
then the input from the user (20) regarding growth options is
limited to defining new growth options. After the user's input is
stored in the growth option definition table (175) in the
application database (50), processing advances to a software block
223. The software in block 223 retrieves data from the different
databases in accordance with the specifications provided by the
user(20) in the previous two steps. After this information is
stored in the application database (50) processing advances to a
software block 225.
[0090] The software in block 225 prompts the user (20) via a tax
information data window (910) to provide an overall tax rate for
the company and detailed schedules for federal income taxes plus
any other taxes as shown in Table 18.
TABLE-US-00018 TABLE 18 Tax Example Schedule Federal Income Tax 15%
of first $250,000 in profit 25% of next $500,000 in profit 35% of
profit over $750,000 State Tax 2.25% of revenue Overall Tax Rate
33% of GAAP operating profit
After the information the user (20) provides is stored in the tax
data table (173) in the application database (50), processing
advances to a software block 226. The software in block 226 prompts
the user (20) via an equity information data window (911) to
provide historical and forecast (Fcst) information for each account
included in the equity sub-component specification stored in the
capital component definition table (152) as shown in Table 19.
TABLE-US-00019 TABLE 19 Actual/ Equity Account Example Schedule
Fcst 301 - Preferred stock 100,000 shares @ $40/ A share Sep. 1,
1987 with yield 5% 250,000 shares @ $90/ F share Mar. 31, 1998 with
yield 8% 302 - Common Stock 1,000,000 shares @ $20/ A share on
valuation date Price history for last 5 years A 303 - Dividends
Actual dividends last 5 years A
After the information the user (20) provides is stored in the
equity data table (144) in the application database (50),
processing advances to a software block 227.
[0091] The software in block 227 prompts the user (20) via a
liability information data window (912) to provide historical and
forecast information concerning each account included in the
financial liability sub-component stored in the capital component
definition table (152) as shown in Table 20.
TABLE-US-00020 TABLE 20 Actual/ Liability Account Example Schedule
Fcst 201 - Accounts NA Payable 203 - Accrued Salary NA 205 - Short
Term Debt $150,000 @ 12% annual, Dec. 31, 1991 A $250,000 @ 11.7%
annual, A Mar. 17, 1993 $250,000 @ 11% annual, Jun. 30, 1999 F 215
- Lon Term Debt $2,500,000 @ 8.5% annual, A Sep. 1, 1993
After the information the user (20) provides is stored in the debt
data table (174) in the application database (50), processing
advances to a software block 228.
[0092] The software in block 228 calculates the current weighted
average cost of capital using the information stored in the debt
and equity tables (174 and 144, respectively) using Formula 1 shown
below.
Weighted average cost of
capital=((D/V).times.R.sub.D)(1-T)+(E/V.times.R.sub.E) Formula
1
Where:
[0093] D=Value of Debt, E=Value of Equity, R.sub.D=Weighted Average
Interest Rate of Debt, T=Tax Rate, [0094] R.sub.E=Rate of Return on
Equity (based on historical information provided) and V=(D+E) After
the calculation is completed, processing advances to a software
block 229. The software in block 229 compares the calculated value
to the value previously specified by the user (20) in the system
settings table (140). If the two values are different, then
processing advances to a software block 230 which prompts the user
via a cost of capital selection data window (913) to select the
cost of capital figure to use for future calculations. The cost of
capital specified by the user (20) is stored in the system settings
table (140) and processing returns to block 229 and on to a
software block 232. System processing passes directly to block 232
if the calculated and specified values of the cost of capital are
identical.
[0095] The software in block 232 checks the asset liquidation price
table (146) to determine if there are "current" (as defined
previously) liquidation prices for all physical assets listed in
the physical asset ID table (145). If there are "current" prices
for all physical assets listed in the physical asset ID table
(145), then processing advances to a software block 302 where the
identification of the value drivers begins. If, on the other hand,
there are not "current" prices for all physical assets, then
processing advances to a software block 235. The software in block
235 prompts the user (20) via a liquidation price entry data window
(914) to provide liquidation prices for all physical assets that
don't have "current" values. The user (20) is given the option of
specifying a liquidation value as a fixed price, as a percentage of
original purchase price or as a percentage of book value (as stored
in the basic financial system database (10)). After the required
information has been entered by the user (20) and stored in the
asset liquidation price table (146) in the application database
(50), system processing advances to a software block 302.
Identify Value Drivers by Element
[0096] The flow diagrams in FIG. 6A, FIG. 6B, FIG. 6C, FIG. 6D and
FIG. 6E detail the processing that is completed by the portion of
the application software (300) that identifies the item variables
and item performance indicators that drive revenue, expense and
changes in capital by element for all defined enterprises. The item
variables and item performance indicators identified during this
processing are collectively referred to as "value drivers".
[0097] Processing begins in software block 302. The software in
block 302 checks the composite variable table (156) and the revenue
driver table (179) in the application database (50) to determine if
all enterprise revenue components have "current" drivers and
composite variables for all elements. If all enterprise revenue
components have "current" drivers for all elements, then processing
advances to a software block 306. Alternatively, if there are any
revenue components without "current" drivers for at least one
element, then processing advances to a software block 303. The
software in block 303 uses the element of value definition table
(153) and excluded variables table (182) to guide the retrieval of
information required to specify the next revenue driver model that
is being updated. All information related to the enterprise element
being examined less any information identified in the excluded
variable table (182) is retrieved by block 303 from the primary
databases including: the basic financial system database (10), the
operation management system database (15), the advanced financial
system database (30), the sales management system database (35),
the human resource information system database (40), and external
databases found on the internet (5) by item. For example, if the
element being modeled was the customer element that was defined by
the customer numbers in the range from 1 to 21,877, then all
numeric and date fields in data records containing a customer
number, save those listed in the excluded variable table (182),
would be retrieved and stored in the revenue driver table (179) by
item. The numeric and date field data are collectively referred to
as "item variables". When all item variables have been stored in
the revenue driver table (179), processing advances to a software
block 304.
[0098] The software in block 304 calculates expressions by item for
each numeric data field including: cumulative total value, the
period to period rate of change in value, the rolling average value
and the time lagged value of each numeric item variable. In a
similar fashion the software in block 304 calculates expressions
for each date field including time since last occurrence,
cumulative time since first occurrence, average frequency of
occurrence and the rolling average frequency of occurrence. The
numbers calculated from numeric and date fields are collectively
referred to as "item performance indicators". After the item
performance indicators are calculated and stored in the revenue
driver table (179) in the application database (50), processing
advances to a software block 305.
[0099] The software in block 305 creates a predictive time series
neural net model for the revenue driver. More specifically, the
software in the block creates a neural network model that relates
the item variables and item performance indicators for a given
enterprise to the revenue component. Neural networks are
increasingly being used for statistically modeling the
relationships between sets of data. One of the main reasons for the
increase in their use is that they are effective in modeling
relationships even when there are nonlinear relationships and
interactions between independent variables. Neural networks consist
of a number of processing elements (hereinafter, referred to as
nodes) that send data to one another via connections. The strengths
of the connections between the nodes are referred to as weights. As
shown in FIG. 10, there are three types of nodes, input nodes
(710-x), hidden nodes (720-x) and output nodes (730). Input nodes
receive data values from input variables (701). A threshold node
(710-THRESH) is a special class of input node (710-x) with a
constant weight of 1 on the connection to a hidden node (720-x).
Hidden nodes (720-x) create intermediate representations of the
relationship between input data and the output values. It is
important to note that while the diagram in FIG. 10 shows only one
layer of hidden nodes (703), in many cases a network model will
contain several layers of hidden nodes. Finally, output nodes (730)
produce output variables (705).
[0100] The action of a neural network is determined by two things:
the architecture, that is how many input, hidden and output nodes
it has; and the values of the weights. A neural network "learns" by
modifying its weights (706 and 707) to minimize the difference
between the calculated output value (705) and the actual output
value. The difference between the calculated output value and the
actual output value is defined as the error function for the
network. For revenue components such as those specified by the
software in block 305, the error function is defined by Formula
2.
ERR ( W ) k = 1 / 2 ( R k - Y ( W ) ) 2 _ Where: W = a set of
weight values ERR ( W ) k = error function for W for period k R k =
actual / forecast revenue for period k Y ( W ) = output value for W
Formula 2 ##EQU00002##
The process for minimizing the error function will be detailed
after the specification of the network architecture is
explained.
[0101] The software in block 305 determines the number of the input
nodes and hidden nodes for each network as a function of the number
of item variables and item performance indicators specified by the
software in blocks 303 and 304. There are also additional input
nodes for prior period revenue and for a threshold node. For the
system of the present invention, the number of hidden nodes is
derived by adding one (1) to the number of input nodes. Table 21
shows the calculation of the number of nodes in an example
predictive revenue model.
TABLE-US-00021 TABLE 21 Potential Value Drivers - Element to
Revenue Model by Item Quantity Item Variables 6 Item Performance
Indicators 48 Subtotal Input Nodes: 54 Threshold & Prior Period
Nodes 2 Total Input Nodes: 56 Hidden Node Adder 1 Total Hidden
Nodes: 57
The software in block 305 sets the initial number of hidden layers
to one. The software in block 305 also establishes one output node
for the revenue and sets all weights to random numbers between 0
and 1 (except the threshold node weight which is fixed at 1).
[0102] The processing completed by all of the network nodes (710-x,
720-x and 730) is similar. The input nodes (710-x) receive their
input of item variables and item performance indicators by item by
period while the hidden node (720-x) receives its input from the
input nodes and the output nodes (730-x) receive their input from
the hidden nodes. Each node multiplies the received input by the
corresponding weight (706 or 707) to produce a weighted sum. The
network applies a sigmoid or linear function to the weighted sum to
determine the state of the node. The state of each node is then
passed on to the next layer along a weighted connection or it is
used to generate an output variable. When the network architecture
including the nodes has been specified by the software in block
305, then processing advances to a software block 325 where network
optimization begins.
[0103] The normal operation of a neural network requires the use of
very large amounts of data to train the network to minimize the
error function and then test the networks predictive capabilities.
The preferred embodiment of the present invention minimizes the
need for very large data sets by using genetic algorithms to find
the weights (W) that reduce the error function to an acceptable
level before optimizing the network using the backpropagation
algorithm to determine the "best fit". The software in a block 325
uses genetic algorithms to find solutions for the current error
minimization problem by evolving a set of solutions toward the
desired goal of having an error function value of zero. More
specifically, the genetic algorithms in block 325 create and
maintain a population of the software equivalent of DNA chromosomes
(hereinafter, chromosomes) that "evolve" toward the specified goal
by using selective crossover and random mutation to generate new
chromosomes. For this application, the chromosomes (see Table 22
below) encode the network weights.
TABLE-US-00022 TABLE 22 ##STR00001##
Each individual "gene" represents a weight between two sets of
nodes. The fitness of each chromosome in the population is
evaluated by the proximity of the resulting solution to the
expected objective function maximum (the maximum of the objective
function corresponds to the minimum error level of the neural
network). Selective crossover in a genetic algorithm gives a
preference to the chromosomes (sets of weights) that are the most
fit (e.g., have lowest error and highest objective function
outputs). Crossover is a form of reproduction that separates each
of two individual chromosomes into two separate pieces at a random
break point. Crossover is completed when the algorithm recombines
the top piece from the first chromosome with the bottom piece of
the second chromosome and the bottom piece from the first
chromosome with the top piece from the second chromosome to produce
two new chromosomes that have a mix of "genes" from each of the
original chromosomes. Giving a preference to the most fit
chromosomes increases the likelihood that the new chromosomes will
produce more fit solutions than the precursor chromosomes. Mutation
is the random change in the value of a randomly selected "gene".
Mutation occurs to "genes" during crossover. It also occurs in
individual chromosomes within the population. When a population of
chromosomes has been crossed over and mutated, a new generation of
the population is created. The fitness of the chromosomes within
the new population is evaluated and unless one of the chromosomes
produces an acceptable solution (a solution where the error level
is below the target), the process is repeated. Over time the
selective crossover will increase the relative fitness of the
population and decrease the difference between the best and worst
chromosomes.
[0104] The evolutionary process is enhanced in the present
invention using three separate mechanisms. First, the fitness
measures for individual chromosomes are re-scaled before crossover
by the software in block 325 whenever the difference between the
fitness of the top 10% of population and the bottom 10% of the
population is less than 5% of the expected solution. To accomplish
this, the fitness of the chromosome(s) with the lowest fitness is
arbitrarily changed to 10% of the target value and the fitness of
the chromosome(s) with the highest fitness is set to 95% of the
target value. The remaining chromosomes fitness values are adjusted
accordingly. This adjustment has the effect of restoring the
relative advantage that the fitter chromosomes have in being
selected for crossover.
[0105] The second mechanism for speeding the evolutionary process
is to pick only the fittest members of a population for inclusion
in the next generation. For this procedure, the current generation
is combined with the two preceding generations and the fittest
third from the combined population is carried forward for crossover
and mutation in the next generation by the software in block 325.
Finally, the sensitivity of the solution to the inclusion of all
"genes" is tested when the fitness of a chromosome reaches the
target level or the fitness of the population fails to increase for
the maximum number of successive generations specified by the user
(see System Settings, Table 12). The highest level of fitness
achieved is established as the new target and processing advances
to a block 330 after the resulting genes are stored in the driver
genes table (183). The software in block 330 creates parallel
populations where the "genes" (weights) associated with one item
variable or item-performance indicator are removed from each
chromosome before processing advances to a software block 335.
[0106] The software in a block 335 repeats the evolution process
using the parallel population with the highest initial average
fitness. If the fitness level of a chromosome in the parallel
populations equals or exceeds the target value after a minimum
number of generations (equal to the user specified maximum--see
System Settings, Table 12) or the fitness of the population fails
to increase for the user specified maximum number of successive
generations, then processing advances to a block 340. If the
software in block 340 determines that a chromosome in the parallel
population has reached a new target level, then the genes are
stored in the driver genes table (183) and the processing returns
to a block 330 where process of creating parallel populations by
removing potential driver "genes" is repeated. The overall process
of evolution and removal of item variables and item performance
indicators continues in this manner until the new parallel
populations fail to reach a new target level at which point
processing is advanced to a software block 345.
[0107] The software in block 345 uses the three chromosomes that
achieved the highest fitness to initialize three distinct induction
algorithms or causal models. While the neural network software in
blocks 325 and 335 is capable of determining which item variables
and item performance indicators correlate most strongly with
changes in revenue, their configuration does not provide for
identification of the item variables and item performance
indicators that are causing changes in revenue (i.e. the "value
drivers"). The item variables and item performance indicators that
didn't correlate strongly with changes in revenue were "pruned"
during the evolution of the high fitness chromosomes. As a result,
the chromosomes in the three most "fit" chromosomes contain the
item variables and item performance indicators that correlate most
strongly with revenue changes. Eliminating low correlation factors
from the initial configuration of the induction algorithms
increases their processing efficiency.
[0108] A brief description of the three algorithms initialized by
the software in block 345 are presented below in Table 23.
TABLE-US-00023 TABLE 23 Induction Algorithm Description Entropy
Starting with nothing, add variables to composite variable
Minimization formula as long as they increase the explainability of
result LaGrange Algorithm designed to identify the behavior of
dynamic systems uses linear regression of the time derivatives of
the system variables. Path Essentially equivalent multiple linear
regression that Analysis finds the least squares rule for more than
one predictor variable.
In addition to identifying the value drivers, these algorithms
produce formulas that summarize the performance of the element
being examined in causing changes in revenue.
[0109] After the models are initialized by the software in block
345, processing passes to a software block 350. The software in
block 350 sub-divides the item variable, item performance indicator
and revenue data into ten (10) distinct subgroups before processing
passes to a block 355. The software in block 355 uses a model
selection algorithm to identify the induction algorithm that best
fits the data for the element being examined. For the system of the
present invention, a cross validation algorithm is used for model
selection. The software in block 355 optimizes each of the
induction algorithms using nine (9) of the ten (10) sets of data.
As part of this processing, the duplication of the information
related to each item is eliminated as only the strongest causal
factor variables are included in the final solution. The resulting
equation from each induction algorithm is then tested using the
data from the remaining set to identify the causal model that
produces the best fit for that set of test data. The equations
produced by the induction algorithms will hereinafter be referred
to as composite variables. This process is repeated ten (10) times
which allows each subgroup to be used as the basis for validating
model performance. The composite variables and value drivers from
the induction algorithm that produced the best results are then
saved in the composite variable table (156), composite variable
data table (168) and revenue driver table (179) in the application
database (50) and processing returns to a block 302.
[0110] If the software in block 302 determines that there are
elements that still require new revenue value driver models, then
the processing described in the preceding paragraphs is repeated.
Alternatively, if the software in block 302 determines that there
are "current" revenue value drivers for all elements in all
enterprises, then processing advances to a software block 306. The
software in block 306 checks the composite variable table (156) and
the expense driver table (180) in the application database (50) to
determine if all enterprise expense components have "current"
drivers and composite variables for all elements. If all enterprise
expense components have "current" drivers and composite variables
for all elements, then processing advances to a software block 312.
Alternatively, if there are expense components without "current"
drivers or composite variables for at least one element, then
processing advances to a software block 307. The software in block
307 uses the element of value definition table (153) and excluded
variable table (182) to guide the retrieval of information required
to specify the next expense driver model that is being updated. All
information related to the enterprise element being examined less
any information identified in the excluded variable table (182) is
retrieved by block 307 from the primary databases including: the
basic financial system database (10), the operation management
system database (15), the advanced financial system database (30),
the sales management system database (35), the human resource
information system database (40), and external databases found on
the internet (5) by item. When all item variables have been stored
in the expense driver table (180), processing advances to a
software block 308.
[0111] The software in block 308 calculates expressions by item for
each numeric data field and each date field in manner identical to
that described previously for software block 304. After the item
performance indicators are calculated and stored in the expense
driver table (180) in the application database (50), processing
advances to a software block 309. The software in block 309 creates
a predictive time series neural net model for the expense driver in
a manner similar to that described previously for block 305. After
the expense value driver predictive model has been specified,
processing proceeds through blocks 325, 330, 335, 340, 345, 350 and
355 in a manner identical to that described above for the
processing of the revenue value driver model before returning to
block 306.
[0112] If the software in block 306 determines that there are
elements that still require new expense value driver models, than
the processing described in the preceding paragraphs is repeated.
Alternatively, if the software in block 306 determines that there
are "current" expense value drivers for all elements in all
enterprises, then processing advances to a software block 312. The
software in block 312 checks the composite variable table (156) and
the capital driver table (181) in the application database (50) to
determine if all enterprise capital components have "current"
drivers and composite variables for all elements. If all enterprise
capital components have "current" drivers and composite variables
for all elements, then processing advances to a software block 375.
Alternatively, if there are capital components without "current"
drivers or composite variables for at least one element, then
processing advances to a software block 313. The software in block
313 uses the element of value definition table (153) and excluded
variables table (182) to guide the retrieval of information
required to specify the next capital driver model that is being
updated. All information related to the enterprise element being
examined less any information identified in the excluded variable
table (182) is retrieved by block 313 from the primary databases
including: the basic financial system database (10), the operation
management system database (15), the advanced financial system
database (30), the sales management system database (35), the human
resource information system database (40), and external databases
found on the internet (5) by item. When all item variables have
been stored in the capital driver table (181), processing advances
to a software block 314.
[0113] The software in block 314 calculates expressions by item for
each numeric data field and each date field in manner identical to
that described previously for software blocks 304 and 308. After
the item performance indicators are calculated and stored in the
capital driver table (181) in the application database (50),
processing advances to a software block 315. The software in block
315 creates a predictive time series neural net model for the
capital driver in a manner similar to that described previously for
block 305 and 309. After the capital value driver predictive model
has been specified, processing proceeds through blocks 325, 330,
335, 340, 345, 350 and 355 in a manner identical to that described
above for the processing of the expense and revenue value driver
models before returning to block 312.
[0114] If the software in block 312 determines that there are
elements that still require new capital value driver models, than
the processing described in the preceding paragraphs is repeated.
Alternatively, if the software in block 312 determines that there
are "current" capital value drivers for all elements in all
enterprises, then processing advances to a software block 317. The
software in block 317 checks the element of value definition table
(153) and sub-element definition table (154) to determine if the
user (20) has specified that there will be sub-elements of value
for any of the elements. If the user (20) has specified that there
will be no sub-elements of value, then processing advances to a
block 375 where the elements are checked for interaction.
Alternatively, if there are elements of value with sub-elements,
then processing advances to a software block 318. The software in
block 318 checks the element of value definition table (153) to
determine the number of elements that have sub-elements before
advancing processing to a block 319.
[0115] The software in block 319 retrieves the element of value
definition for the next element with defined sub-elements from the
element of value definition table (153) before advancing processing
to a block 321. The software in block 321 checks the sub-element
definition table (154) to determine if the sub-elements assignments
for all items within the element are "current". If the sub-element
assignments are "current", then processing returns to block 318
which checks to see if all elements with sub-elements have been
reviewed in the current cycle of processing. If the software in
block 318 determines that all elements have been reviewed, then
system processing advances to a software block 332. Alternatively,
if there are elements still need to be reviewed, then processing
returns to block 319 as described previously. If the software in
block 319 determines that the sub-element assignments are not
"current", then processing advances to a block 326 where the
sub-element assignments are completed.
[0116] The software in block 326 checks the system settings table
(140) to determine if the calculation being completed is a
stand-alone calculation or a comparison to a prior calculation. If
the software in block 326 determines that the current calculation
is not being used for a comparison, then the processing advances to
a software block 322. The software in block 322 retrieves the value
driver data by item for the element being analyzed from the
composite variable data table (168) before creating a normalized
set of value driver data for each item within the element of value
being analyzed. The normalized value for each value driver data
element for each item in each period is then calculated using
Formula 3 shown below.
Normalized Value = Current value - MN ( MP - MN ) Formula 3
##EQU00003##
[0117] Where: MN=minimum positive or most negative data value for
all element items [0118] MP=maximum positive data value for all
element items After the normalized data are saved in the normalized
composite variable data table (169) in the application database
(50), system processing advances to a software block 323. The
software in block 323 uses an unsupervised "Kohonen" neural network
that uses competitive learning to create a clustering scheme and
segment the element of value. As shown in FIG. 11 a "Kohonen"
network has only two layers--an input layer (712) and an output
layer (713). The input layer (712) holds the input nodes (750-x)
where the different inputs are sequentially entered. The input
patterns are transmitted to an output layer (713) which has one
node (760-x) for each possible output category. The input layer and
the output layer are fully interconnected as shown in FIG. 11. The
different variables are defined in Table 24.
TABLE-US-00024 [0118] TABLE 24 Variable Definition P The number of
items for the element. Equals the number of different patterns that
will be presented to the network M The number of variables the in
the composite variable for the element as well as the number of
input nodes (750-1 through 750-M) N The maximum number of
sub-elements for this element (default is 20) as well as the number
of output nodes (760-1 through 760-N) .omega..sub.ij Represents the
connection strength between unit j of the input layer (712) and
unit i of the output layer (713) X.sub.j Represents the input
vector which is the normalized value of the "j.sup.th" item
composite variables V.sub.i Matching value - measures how closely
the weights of a given node matches the input vector
[0119] "Kohonen" network processing begins when the software in
block 323 initializes at random the weights (716) between the
output layer (713) and the input layer (712) with small values. In
the next step the system starts sequentially entering the
normalized composite variable data from the normalized composite
variable data table (169) into the input layer (712). The
normalized value for each variable is entered into a different
input node (750-x) and transmitted from there to the output layer
(713). The nodes in the output layer (760-x) each compute their
matching values (V.sub.i) using Formula 4 shown below:
v.sub.I=.SIGMA.(.omega..sub.ij-x.sub.j).sup.2 Formula 4
The matching value (V.sub.i) essentially represents the distance
between the vectors (.omega..sub.i) and x. Therefore, the output
node (760) with the lowest matching value is also the node that
most closely matches the input vector. The unit that is closest to
the input is declared the winner and its weight (.omega..sub.ij)
along with the weights of the neighboring output nodes are updated.
The change in weight for the winning node and its neighbors is
calculated using Formula 5 shown below.
.DELTA..omega..sub.ij=.alpha.(x.sub.j-.omega..sub.ij) Formula 5
where: .alpha. represents the learning rate (see Formula 6) The
application of this formula diminishes the difference between the
weights of the output nodes and the weights of the input vectors.
Output nodes that are not neighbors of the winning node are not
updated. The output nodes are updated after each input and over
time the application of the formulas shown above will tend to
create clusters of similar nodes.
[0120] The input vectors (data patterns) are cycled through the
"Kohonen" network a pre-determined number of times which are
referred to as epochs. The total number of epochs (T) will be set
by the software to somewhere between 500 and 10,000 depending upon
the number of composite sort variables used for the element. The
neighborhood size, that is the quantity of adjacent nodes that are
considered to be neighbors, is adjusted downward from its initial
value of 75% of the value of N by one node at a time as the number
of epochs increases from zero (0) to its maximum number (T). The
learning rate (.alpha.) is determined by Formula 6 shown below.
.alpha.=0.2.times.(1-(T/10,000)) Formula 6
Once the Kohonen network processing has been completed for the
specified number of epochs (T), the software in block 323
arbitrarily assigns a number to each output node (760-x). The
software in block 323 then calculates the distance between the
input vector (x) of each item and the weight in each output node
(760-x) using Formula 4. The software in block 323 then assigns the
number of the closest output node (760-x) to the item and stores
the resulting information in the sub-element definition table (154)
in the application database (50). The software in block 323 also
stores the final value of all network weights in the sub-element
weights table (157) in application database (50).
[0121] After the network weights and information assigning each
item to a sub-element have been stored in the appropriate tables in
the application database (50), processing returns to software block
318 and the process described above is repeated until all elements
with sub-elements of value have been reviewed.
[0122] If the software in block 326 determines that the calculation
being completed is a comparison to a prior valuation, then
processing advances to a software block 327. The software in block
327 retrieves the sub-element weights from the previous calculation
from the sub-element weights table (157) and reestablishes the
prior sub-element assignments by using Formula 4 to determine the
appropriate sub-element assignment for each item. When this
processing has been completed, processing advances to a software
block 328.
[0123] The software in block 328 checks the composite variable data
table (168) to see if there are any new items for elements being
analyzed. If there are no new items, then processing returns to
block 318 as described previously. Alternatively, if the software
in block 328 determines that there are new items, then processing
advances to a software block 329.
[0124] The software in block 329 determines the appropriate
sub-element assignment for each new item by calculating the
normalized value of the input vector for each new item and using
formula 4 to determine which output node (i.e., which sub-element
from the previous calculation) each item should be assigned to. The
inputs for these calculations are stored in the normalized
composite variable data table (169) and the results are stored in
the composite variable data table (168) in the application database
before processing returns to block 318 as described previously.
[0125] When the software in block 318 determines that all elements
have been reviewed, processing advances to block 332 as described
previously. The software in block 332 checks the composite variable
(156), revenue driver (179), expense driver (180) and capital
driver (181) tables to determine if the value drivers and composite
variables for all sub-elements are current. If they are current,
then processing advances to a software block 375. Alternatively, if
the sub-element drivers and composite variables are not current,
then processing advances to a block 337.
[0126] The software in block 337 checks the revenue driver table
(179) in the application database (50) to determine if all
enterprise revenue sub-components have "current" drivers and
composite variables for all elements. If all enterprise revenue
sub-components have "current" drivers and composite variables for
all elements, then processing advances to a software block 341.
Alternatively, if there are any revenue components without
"current" drivers for at least one element, then processing
advances to a software block 338. The software in block 338 uses
the sub-element definition table (154) and excluded variables table
(182) to guide the retrieval of information required to specify the
next revenue driver model that is being updated. All information
related to the enterprise element being examined less any
information identified in the excluded variable table (182) is
retrieved by block 338 from the primary databases including: the
basic financial system database (10), the operation management
system database (15), the advanced financial system database (30),
the sales management system database (35), the human resource
information system database (40), and external databases found on
the internet (5) by item. When all item variables have been stored
in the revenue driver table (179), processing advances to a
software block 339.
[0127] The software in block 339 calculates performance indicators
by item for each date and numeric data field in a manner similar to
that described for block 304. After the item performance indicators
are calculated and stored in the revenue driver table (179) in the
application database (50), processing advances to a software block
305. The software in block 305 creates a predictive time series
neural net model for the revenue driver as described previously.
After the revenue value driver predictive model has been specified,
processing proceeds through blocks 325, 330, 335, 340, 345, 350 in
a manner identical to that described previously for the processing
of the revenue value driver model before advancing to a block
360.
[0128] The software in block 360 uses a model selection algorithm
to identify the induction algorithm that best fits the data for the
element being examined. For the system of the present invention, a
cross validation algorithm is used for model selection. The
software in block 360 optimizes each of the induction algorithms
using nine (9) of the ten (10) sets of data. As part of this
processing, the duplication of the information related to each item
is eliminated as only the strongest causal factor variables are
included in the final solution. The composite variable from each
induction algorithm is then tested using the data from the
remaining set to identify the causal model that produces the best
fit for that set of test data. The previously calculated composite
variable for the revenue element is also compared to the
sub-element composite variables as part of this processing. This
process is repeated ten (10) times which allows each subgroup to be
used as the basis for validating model performance. The composite
variable and value drivers that produced the best results are then
saved in the composite variable table (156), composite variable
data table (168) and revenue driver table (179) in the application
database (50) and processing returns to a block 337.
[0129] If the software in block 337 determines that there are
sub-elements that still require new revenue value driver models,
then the processing described in the preceding paragraphs is
repeated. Alternatively, if the software in block 337 determines
that there are "current" revenue value drivers for all sub-elements
in all enterprises, then processing advances to a software block
341. The software in block 341 checks the expense driver table
(180) in the application database (50) to determine if all
enterprise expense components have "current" drivers for all
elements. If all enterprise expense components have "current"
drivers for all sub-elements, then processing advances to a
software block 352. Alternatively, if there are expense
sub-components without "current" drivers for at least one element,
then processing advances to a software block 342. The software in
block 342 uses the sub-element definition table (154) and excluded
variables table (182) to guide the retrieval of information
required to specify the next expense driver model that is being
updated. All information related to the enterprise sub-element
being examined less any information identified in the excluded
variable table (182) is retrieved by block 342 from the primary
databases including: the basic financial system database (10), the
operation management system database (15), the advanced financial
system database (30), the sales management system database (35),
the human resource information system database (40), and external
databases found on the internet (5) by item. When all item
variables have been stored in the expense driver table (180),
processing advances to a software block 343.
[0130] The software in block 343 calculates expressions by item for
each numeric data field and each date field in manner identical to
that described previously for software block 339. After the item
performance indicators are calculated and stored in the expense
driver table (180) in the application database (50), processing
advances to a software block 309. The software in block 309 creates
a predictive time series neural net model for the expense driver as
described previously. After the expense value driver predictive
model has been specified, processing proceeds through blocks 325,
330, 335, 340, 345, 350 and 360 in a manner identical to that
described above for the processing of the revenue value driver
model before returning to block 341.
[0131] If the software in block 341 determines that there are
sub-elements that still require new expense value driver models,
then the processing described in the preceding paragraphs is
repeated. Alternatively, if the software in block 341 determines
that there are "current" expense value drivers for all sub-elements
in all enterprises, then processing advances to a software block
352. The software in block 352 checks the capital driver table
(181) in the application database (50) to determine if all
enterprise capital components have "current" drivers for all
elements. If all enterprise capital components have "current"
drivers for all sub-elements, then processing advances to software
block 375. Alternatively, if there are capital sub-components
without "current" drivers for at least one element, then processing
advances to a software block 353. The software in block 353 uses
the sub-element definition table (154) and excluded variables table
(182) to guide the retrieval of information required to specify the
next capital driver model that is being updated. All information
related to the enterprise sub-element being examined less any
information identified in the excluded variable table (182) is
retrieved by block 353 from the primary databases including: the
basic financial system database (10), the operation management
system database (15), the advanced financial system database (30),
the sales management system database (35), the human resource
information system database (40), and external databases found on
the internet (5) by item. When all item variables have been stored
in the capital driver table (181), processing advances to a
software block 354.
[0132] The software in block 354 calculates expressions by item for
each numeric data field and each date field in manner identical to
that described previously for software blocks 339 and 343. After
the item performance indicators are calculated and stored in the
capital driver table (181) in the application database (50),
processing advances to a software block 315. The software in block
315 creates a predictive time series neural net model for the
capital driver as described previously. After the capital value
driver predictive model has been specified, processing proceeds
through blocks 325, 330, 335, 340, 345, 350 and 360 in a manner
identical to that described above for the processing of the revenue
value driver model before returning to block 352.
[0133] If the software in block 352 determines that there are
sub-elements that still require new capital value driver models,
then the processing described in the preceding paragraphs is
repeated. Alternatively, if the software in block 352 determines
that there are "current" capital value drivers for all sub-elements
in all enterprises, then processing advances to software block
375.
[0134] The software in block 375 checks the value drivers to
determine if there is any interaction between drivers for different
elements. If interaction between element drivers is discovered,
then processing advances to a block 402 where the valuation of
growth options is started. Alternatively, if no interaction between
element drivers is found, then system processing is completed in
accordance with the specification of U.S. patent application Ser.
No. 08/779,109.
Growth Option Valuation
[0135] The flow diagram in FIG. 7 details the processing that is
completed by the portion of the application software (400) that
calculates the value of the growth options for each enterprise.
Processing begins in block 402 where the software in the block
checks the growth option value table (178) to determine if there
are "current" valuations for all defined growth options. If there
are no defined growth options or no growth options that require a
new valuation, then processing advances to a transfer block 403 and
on to a block 501 where the components of value are analyzed.
[0136] If there are growth options that need a value calculation
completed, then system processing advances to a software block 404
where the software in the block retrieves the previously stored
data from the growth option definition table (175) for the next
growth option and then advances processing to a block 405. The
software in block 405 checks the growth option definition table
information to determine if there are multiple scenarios for the
growth option being analyzed. If there is only one scenario for the
growth option being analyzed, then processing advances to a block
407. Alternatively, if there are multiple scenarios, then
processing advances to a block 408.
[0137] The software in block 408 prompts the user (20) via the
growth option scenario definition data window (916) to input or
update: the number of scenarios, the name of the scenarios and the
probability that each scenario will occur for the growth option
being valued. The probability of each scenario is specified as a
percentage. The sum of the scenario probabilities must equal 100%
for each growth option. The user (20) is allowed to change the
scenarios even if the system (100) is calculating a business
valuation comparison as the comparison will be made at the growth
option level not at the scenario level. The input from the user
(20) is stored in the growth option definition table (175) in the
application database (50) before processing advances to a block
409.
[0138] The software in block 409 checks the growth option scenario
table (177) in the application database (50) to determine if there
are "current" data for every scenario listed in the growth option
definition table (175). If there are "current" data for all
scenarios, then processing advances to block 407 where the value of
the total deductions from revenue components, expense
sub-components, capital sub-components and elements of value for
the growth option are calculated. Alternatively, if some or all of
the scenarios for the growth option being examined don't have
"current" data, then processing advances to a block 410. The
software in block 410 retrieves the information for the scenario
from the growth option scenario table (177) and advances processing
to a software block 411.
[0139] The software in block 411 generates a form that is displayed
using a scenario revenue and expense data window (917) for the user
(20) to complete. The form identifies the time periods that revenue
and expense forecasts are required for the growth option scenario
in accordance with the calculations previously completed by the
application software and stored in the system settings table (140).
The forecast information the user (20) provides is saved to the
growth option scenario table (177) in the application database
(50). If the scenario being examined is the first scenario for the
growth option, then the user (20) is also asked to quantify any
growth-option related expenses by account number that were incurred
in prior periods. The user (20) is not asked to identify any prior
period growth option revenue as a growth option is by definition a
project that will lead to revenue at some date in the future. The
information regarding prior period expenses is saved in expense
data table (142) in the application database (50). The user (20) is
also asked to identify the months where the prior expenses and/or
the forecast revenue and the forecast expenses for the growth
option scenario were included in the overall company totals. The
users input regarding the overlapping periods for the scenario is
saved in the growth option overlap table (176) in the application
database (50) and processing advances to a software block 412.
[0140] The software in block 412 prompts the user (20) via a
scenario capital data window (918) to edit or provide a forecast of
the capital requirements for the scenario by month for the time
periods required for growth option valuation in accordance with the
calculations previously completed by the application software and
stored in the system settings table (140). The forecast information
the user (20) provides is saved to the growth option scenario table
(177) in the application database (50). If the scenario being
examined is the first scenario for the growth option, then the user
(20) is also asked to quantify any growth-option related capital
investments by account number that were present in prior periods.
The information regarding prior period capital requirements is
saved in the capital data table (143) in the application database
(50). The user (20) is also asked to identify the months where the
prior period actual and/or forecast capital requirements for the
growth option scenario were included in the overall company totals.
The users input regarding overlapping periods for the scenario is
saved in the growth option overlap table (176) in the application
database and processing advances to a software block 413.
[0141] The software in block 413 prompts the user (20) via a
scenario element of value data window (919) to edit or provide a
forecast of element of value usage by month for the time periods
required for growth option valuation in accordance with the
calculations previously completed by the application software. The
forecast information the user (20) provides is saved to the growth
option scenario table (177) in the application database (50). If
the scenario being examined is the first scenario for the growth
option, then the user (20) is also asked to identify any
growth-option related element of value usage that occurred in prior
periods. The information regarding prior period use of the elements
of value is saved in the element of value data table (144). The
user (20) is also asked to identify the months where the prior
period and/or forecast element of value usage for the growth option
were included in the overall company totals. The users input
regarding overlapping periods is saved in the growth option overlap
table (176) in the application database and processing returns to a
software block 409.
[0142] If the software in block 409 determines that there are still
scenarios that don't have "current" data, then the processing
sequence described above is repeated until all scenarios have
"current" data. When all scenarios for the growth option being
analyzed have "current" data, processing advances to block 407. The
software in block 407 calculates the total value of revenue,
expense, capital and element of value deductions for each scenario.
The software in block 407 also calculates the weighted average
forecast of total growth option revenues, expenditures, capital and
element of value deductions for each period by multiplying the
forecast revenue, capital and element of value deductions for each
scenario by the probability of that scenario realization. The
totals for the growth option revenue, expense, capital, and element
of value deductions are then saved in the appropriate data tables
(141 through 146) in the application database (50). After the data
have been stored, processing advances to a software block 406 where
the value of the growth option is calculated using dynamic
programming algorithms in a manner that is well known. The process
described in the preceding paragraphs is repeated until all growth
options have "current" valuations and processing advances to block
502 as described previously.
Analyze Components of Value
[0143] The flow diagram in FIG. 8 details the processing that is
completed by the portion of the application software (500) that
analyzes the components and sub-components of value. Processing
begins in a software block 502. The software in block 502 checks
the enterprise value table (170) in the application database (50)
to determine if there are "current" valuations for all enterprises
for the date for which the current valuation is being calculated.
If there are "current" valuations for all enterprises, then
processing advances to a software block 515 where the software in
the block calculates the total company current operation value.
However, if some or all of the enterprises don't have "current"
valuations, then processing advances to a software block 503.
[0144] The software in block 503 retrieves the definition for the
next enterprise that doesn't have a "current" valuation from the
enterprise definition table (155) in the application database (50).
Processing then advances to a software block 504. The software in
block 504 checks the data from the revenue component definition
table (150) for the revenue component of the enterprise being
valued to determine if there is a "current" valuation for the
component. If there is a "current" valuation for the revenue
component, then processing advances to a software block 507 where
the values of the expense component or expense sub-components for
the enterprise are checked to determine if they are "current".
However, if the revenue component valuation isn't "current", then
processing advances to a software block 505. The software in block
505 retrieves the information for the revenue component from the
revenue data table (141) and processing advances to a software
block 506. In accordance with the present invention, the revenue
component value is calculated for the specified date of valuation
using Formula 7 shown below.
Formula 7
[0145]
Value=F.sub.f1/(1+K)+F.sub.f2/(1+K).sup.2+F.sub.f3/(1+K).sup.3+F.s-
ub.f4/(1+K).sup.4+(F.sub.f4.times.(1+g))/((K-g).times.(1+K).sup.4)
Where:
[0146] F.sub.fx= Forecast revenue, expense or capital for year x
after valuation date(from advanced financial system) [0147] K= Cost
of capital -% per year (from system settings) [0148] g= Forecast
growth rate to perpetuity -% per year (from advanced financial
system)
After the valuation of the revenue component is complete, the
result is stored in the revenue component definition table (150) in
the application database (50) and processing advances to a software
block 507.
[0149] The software in block 507 checks the expense component
definition table (151) in the application database (50) to
determine if there are "current" valuations for all expense
components or sub-components in the enterprise being valued. If the
user (20) has previously stored information in the system settings
table (140) specifying a "simplified" analysis, then the expense
component values will be checked. Alternatively, if the user (20)
has not selected a simplified analysis, then the expense
sub-component values will be checked. If there are "current"
valuations for the expense components or all sub-components, then
processing advances to a block 510 where the values of the capital
components for the company are checked to determine if they are
"current". However, if some or all of the expense components or
sub-components don't have "current" valuations, then processing
advances to a software block 508. The software in block 508
retrieves the information from the expense data table (142) for the
expense component or the next expense sub-component that doesn't
have a "current" valuation. Processing then advances to a software
block 509. In accordance with the present invention the valuation
of the expenses is calculated for the specified date of valuation
using Formula 7. After the valuation of the expense component or
expense sub-component has been completed, the results are stored in
the expense component definition table (151) in application
database (50) and processing returns to a software block 507. If
there are still expense sub-components that don't have current
valuations, then the processing described above is repeated for the
next sub-component. Alternatively, if the expense component or all
expense sub-components have current valuations, then processing
advances to a software block 510.
[0150] The software in block 510 checks the capital component
definition table (152) in the application database (50) to
determine if there are "current" valuations for all capital
components. If the user (20) has previously stored information in
the system settings table (140) specifying a "simplified" analysis,
then the capital component value for the enterprise will be
checked. Alternatively, if the user (20) has not selected a
simplified analysis, then the standard capital sub-components will
be checked. If there are "current" valuations for all capital
components, then processing advances to a software block 514 where
the enterprise current operation value is calculated. If the
valuation for the capital component or some of the capital
sub-components is not "current", then processing advances to a
software block 511. The software in block 511 retrieves the
information required for valuation of the next capital component or
sub-component that doesn't have a "current" valuation from the
capital data table (143) in the application database (50).
Processing then advances to a software block 512. The software in
block 512 calculates the value of the capital component or capital
sub-component using Formula 7. After the valuation of the capital
component or a capital sub-component is complete, the results are
stored in the capital component definition table (152) in the
application database (50) and system processing returns to block
510. If there are still capital sub-components that don't have
current valuations, then the processing described above is repeated
for the next sub-component. Alternatively, if the capital component
or all capital sub-components have current valuations, then
processing advances to a software block 514.
[0151] The software in block 514 calculates the current operation
value of each enterprise by summing the previously calculated
component and sub-component values as shown in Table 4. The
calculated value for the enterprise current operation is stored in
the enterprise value table (170) in the application database (50)
and processing returns to block 502 which again checks the
enterprise value table (170) in the application database (50) to
determine if all enterprises have "current" values. If there are
still enterprises without "current" values, then processing
advances to block 503 and the processing described in the preceding
paragraphs is repeated for another enterprise. Alternatively, if
all the enterprises have "current" values, then processing
transfers to a block 515 where the software in the block adds the
enterprise values together to calculate the value of the
current-operation for the total company. The total company
current-operation value is stored in the enterprise value table
(170) in the application database (50) and processing advances to a
software block 602 where the predictive model specification and
optimization is started.
Predictive Model Specification and Optimization
[0152] The flow diagrams in FIG. 9A and FIG. 9B detail the
processing that is completed by the portion of the application
software (600) that uses predictive models to determine the
relationship between the value drivers and the revenue, expense and
capital components of all defined enterprises. Processing begins in
software block 602. The software in block 602 checks the revenue
model weights table (159) in the application database (50) to
determine if the revenue components for all enterprises have
"current" models. If there are revenue components without "current"
predictive models, then processing advances to a software block 603
where the information specifying the next revenue component is
retrieved from the revenue component definition table (150) in the
application database (50). After the revenue component definition
has been retrieved, processing advances to a software block 604
where the software in the block creates a predictive time series
neural net model for the revenue component. More specifically, the
software in the block creates a neural network model that relates
the value drivers for a given enterprise to the revenue
component.
[0153] The software in block 604 determines the number of the input
nodes and hidden nodes for the network as a function of the number
of value drivers associated with the enterprise revenue component.
There are also additional input nodes for prior period revenue and
for a threshold node. The software in block 604 sets the initial
number of hidden layers to one. The software in block 604 also
establishes one output node for the revenue and sets all weights to
random numbers between 0 and 1 (except the threshold node weight
which is fixed at 1). The processing completed by the network nodes
(710-x, 720-x and 730) was described previously. After the network
architecture including the nodes has been specified by the software
in block 604, processing advances through blocks 325, 330, 335 and
340 as described previously.
[0154] The process of evolving a preliminary solution continues
until the new parallel populations fail to reach a new target level
and processing is then advanced to a block 625. As part of this
processing revenue model genes are stored in the expense model
genes table (160) in a manner identical to that described
previously for the storage of model genes. The software in block
625 uses the chromosome that achieved the highest fitness to
initialize a feed-forward neural network. In a manner that is well
known, the network is then trained by the software in a block 630
using a traditional backpropagation algorithm to further minimize
the error function associated with the network. The resulting
weights for the enterprise are then saved in the revenue model
weights table (159) in the application database (50) and processing
returns to a block 602.
[0155] If the software in block 602 determines that there are
"current" revenue models for all enterprises, then processing
advances to a software block 605. The software in block 605 checks
the expense model weights table (161) in the application database
(50) to determine if the expense component or all expense
sub-components have "current" models. If the user (20) has
previously stored information in the system settings table (140)
specifying a "simplified" analysis, then the expense component
model will be checked before processing advances to another block.
Alternatively, if the user (20) has not selected a simplified
analysis, then the expense sub-component models will be checked
before processing advances to another block. In either case,
processing will advance to block 607 if the expense models aren't
"current" and to block 611 if they are "current".
[0156] The software in block 607 retrieves the information
specifying the expense component or the next expense sub-component
from the expense component definition table (151) in the
application database (50). After the required information is
retrieved, processing advances to a block 608 where the predictive
expense model is specified in a manner similar to that described
previously for the predictive revenue model. From block 608,
processing advances to blocks 325, 330, 335, 340, 625 and 630 where
the genetic evolution of the fittest solution is completed in a
manner similar to that described above for the predictive revenue
model. As part of this processing expense model genes are stored in
the expense model genes table (160) in a manner identical to that
described previously for the storage of revenue model genes. If
there are sub-components, then the process described above is
repeated until all expense sub-components have "current" models.
When all expense components or all expense sub-components have
"current" models, processing advances to a software block 611.
[0157] The software in block 611 checks the capital model weights
table (163) in the application database (50) to determine if the
capital component or all capital sub-components have "current"
models. If the user (20) has previously stored information in the
system settings table (140) specifying a "simplified" analysis,
then the capital component model will be checked before processing
advances to another software block. Alternatively, if the user (20)
has not selected a simplified analysis, then the standard capital
sub-component models will be checked before processing advances to
another software block 613. In either case, processing will advance
to block 613 if the models aren't "current" and to block 772 if
they are "current".
[0158] The software in block 613 retrieves the information
specifying the capital component or the next capital sub-component
from the capital component definition table (152) in the
application database (50). After the required information is
retrieved, processing advances to a block 614 where the predictive
capital model is specified in a manner similar to that described
previously for the predictive revenue and expense models. From
block 614, processing advances to blocks 325, 330, 335, 340, 625
and 630 where the genetic evolution of the fittest solution is
completed in a manner similar to that described above for the
predictive revenue and expense model. As part of this processing,
capital model genes are stored in the capital model genes table
(162) in a manner identical to that described previously for the
storage of revenue and expense model genes. If there are
sub-components, then the process described above is repeated until
all capital sub-components have "current" models. When all capital
components or all capital sub-components have "current" models,
processing advances to a block 772 where valuations are calculated
for the elements and sub-elements of value.
Value All Elements and Sub-Elements of Value
[0159] The flow diagram in FIG. 12 details the processing that is
completed by the portion of the application software (700) that
values all elements and sub-elements of current-operation value for
all enterprises. Processing begins in software block 772. The
software in block 772 checks the revenue component percentage table
(164) in the application database (50) to determine if the revenue
component models for all enterprises have "current" percentages. If
there are revenue components without "current" percentages, then
processing advances to a block 773 where the information specifying
the next revenue component is retrieved from the revenue component
definition table (150) and the revenue model weights table (159) in
the application database (50).
[0160] After the revenue component information is retrieved,
processing advances to a block 774 where relationships between the
elements and sub-elements of value and the revenue component are
determined. The software in block 774 uses the network weights (706
and 707) previously stored in the revenue model weights table (159)
to segregate the hidden-layer (703) to output-layer (704)
connection weights (707) for each hidden node (720-x) into the
components associated with each input node (710-x). The portion of
the output attributable to each input node is then determined by
Formula 8 (shown below).
( k = 1 k = m j = 1 j = n I jk .times. O k / j = 1 j = n I ik ) / k
= 1 k = m j = 1 j = n I jk .times. O k Formula 8 ##EQU00004##
Where
[0161] I.sub.jk=Absolute value of the input weight (706) from input
node j to hidden node k
[0162] O.sub.k=Absolute value of output weight (707) from hidden
node k
[0163] m=number of hidden nodes
[0164] n=number of input nodes
After Formula 8 is solved by the software in block 774, the portion
of the revenue value attributable to each element or sub-element of
value is calculated by adding together the percentages from all
value drivers associated with each element or sub-element of value.
The result of these calculations is then stored in the revenue
component percentage table (164) in the application database (50).
The portion of the revenue value that can't be attributed to an
element or sub-element of value is generally the portion that is
attributed to the prior period revenue. This portion of the value
will be referred to as going concern revenue component. After the
storage of the revenue component percentages has been completed,
processing returns to block 772. The software in block 772 checks
the application database (50) to determine if all revenue
components have "current" model percentages. If there are still
revenue components without "current" percentages, then the system
repeats the processing described in the preceding paragraphs.
Alternatively, if all of the revenue component models have
"current" percentages, then processing advances to a software block
775.
[0165] The software in block 775 checks the expense component
percentage table (165) in the application database (50) to
determine if all expense component or sub-component models for all
enterprises have "current" percentages. If the user (20) has
previously stored information in the system settings table (140)
specifying a "simplified" analysis, then the expense component
percentages will be checked. Alternatively, if the user (20) has
not selected a simplified analysis, then the expense sub-component
percentages will be checked. If there are expense components or
sub-components without "current" percentages, then processing
advances to a software block 776 where the information specifying
the next expense component or sub-component is retrieved from the
expense component definition table (151) and the expense model
weights table (161) in the application database (50). After the
expense component or sub-component information is retrieved,
processing advances to a software block 777 where the percentages
of value for the expense component or sub-component are calculated
in a manner identical to that described previously for revenue
components. The portion of the expense value that can't be
attributed to an element or sub-element of value is generally the
portion that is attributed to the prior period expense. This
portion of the value will be referred to as going concern expense
component. After the storage of the percentages of the expense
component or sub-component to the expense component percentage
table (165) has been completed, processing returns to block 775.
The software in block 775 checks the expense component percentage
table (165) in the application database (50) to determine if all
expense component or sub-component models have "current"
percentages. If there are still expense component or sub-component
models without "current" percentages, then the system repeats the
processing described above. Alternatively, if all of the expense
component or sub-component models have "current" percentages, then
processing advances to a software block 778.
[0166] The software in block 778 checks the capital component
percentage table (166) in the application database (50) to
determine if all capital component or sub-component models for all
enterprises have "current" percentages. If the user (20) has
previously stored information in the system settings table (140)
specifying a "simplified" analysis, then the capital component
percentages will be checked. Alternatively, if the user (20) has
not selected a simplified analysis, then the capital sub-component
percentages will be checked. If there are capital component or
sub-component models without "current" percentages, then processing
advances to a software block 779 where the information specifying
the next capital component or sub-component is retrieved from the
capital component definition table (152) and the capital model
weights table (163) in the application database (50). After the
capital component or sub-component information is retrieved,
processing advances to a software block 780 where the percentages
of value for the capital component or sub-component are calculated
in a manner identical to that described previously for revenue and
expense components. The portion of the capital element or
sub-element value that can't be attributed to an element or
sub-element of value is generally the portion that is attributed to
the prior period capital requirements. This portion of the value
will be referred to as going concern capital value. After the
storage of the percentages of the capital component or
sub-component to the capital component percentage table (166) has
been completed, processing returns to block 778. The software in
block 778 checks the capital component percentage table (166) in
the application database (50) to determine if all capital
components or sub-components have "current" percentages. If there
are still capital component or sub-component models without
"current" percentages, then the system repeats the processing
described above (779 and 780). Alternatively, if all of the capital
components or sub-components have "current" percentages, then
processing advances to a software block 781.
[0167] The software in block 781 combines all the revenue
component, expense component or sub-component and capital component
or sub-component values together to calculate the overall value for
each element or sub-element of value by enterprise as shown in
Table 4. As part of the processing in this block, the calculated
value of production equipment element (or sub-elements) of value is
compared to the liquidation value for the equipment in the element.
The stored value for the element (or sub-elements) will be the
higher of liquidation value or calculated value. After the
calculations are completed, processing advances to a software block
782 where the residual going concern value is calculated using
Formula 9.
Residual Going Concern Value=Total Current-Operation Value-.SIGMA.
Financial Asset Values-.SIGMA. Elements of
Value-.SIGMA.Sub-Elements of Value Formula 9
After the residual going concern value is calculated for each
enterprise, the values calculated for each element and sub-element
of value (including going concern value) by the software in blocks
781 and 782 are stored by enterprise in the enterprise value table
(170) in the application database (50). System processing then
advances to a software block 802 where the preparation of the
management reports is started.
Display and Print Results
[0168] The flow diagram in FIG. 13 details the processing that is
completed by the portion of the application software (800) that
creates, displays and optionally prints financial management
reports. The primary management report, the Value Map.TM. report,
summarizes information about the elements and sub-elements of
business value on the valuation date. If a comparison calculation
has been completed, a Value Creation report can be generated to
highlight changes in the elements and sub-elements of business
value during the period between the prior valuation and the current
valuation date.
[0169] System processing in this portion of the application
software (800) begins in block 802. At this point in system
processing, virtually all of the information required to produce
the Value Map.TM. report has been calculated using the methods
outlined in Table 1 as detailed in the preceding sections. As a
result, the only computation that needs to be made is the
calculation of economic equity. The software in block 802 retrieves
the required information from the enterprise value table (170),
debt data table (174) and equity data table (144) in the
application database (50) and then calculates the economic equity
for the business as a whole using Formula 10 (shown below).
Economic Equity = ( Current Operation Value ) + ( Growth Option
Values ) - ( Current Liabilities ) - ( Current Dept ) - ( Book *
Equity Value ) * calculated in accordance with GAAP Formula 10
##EQU00005##
An equity value for each enterprise is then calculated by dividing
the combined book and economic equity as required to balance the
Value Map.TM. report totals in accordance with Formula 11 (shown
below).
[0170] Enterprise Equity = ( Enterprise Current Operation Value ) +
( Enterprise Growth Option Values ) - ( Current Enterprise
Liabilities ) - ( Current Enterprise Debt ) where ( Enterprise
Equity ) = Book * Equity + Economic Equity * calculated in
accordance with GAAP Formula 11 ##EQU00006##
After the economic equity value and the enterprise equity values
are calculated and stored in the economic equity values table
(171), a summary Value Map.TM. report (see FIG. 14 for format) for
the entire company is created and stored in the reports table (172)
and processing advances to a software block 803. The software in
block 803 checks the system settings table (140) to determine if
the current valuation is being compared to a previous valuation. If
the current valuation is not being compared to a previous
valuation, then processing advances to a software block 805.
Alternatively, if the current valuation is being compared to a
previously calculated valuation, then processing advances to a
software block 804.
[0171] The software in block 804 calculates Value Creation
Statements (see FIG. 15 for format) for each enterprise and for the
business as a whole for the specified time period. After the Value
Creation Statements are stored in the reports table (172) in the
application database (50), processing advances to a software block
805. The software in block 805 displays the summary Value Map.TM.
report to the user (20) via a report data window (909).
[0172] After displaying the summary Value Map.TM. report, system
processing advances to a software block 806 where the user is
prompted via a report selection data window (915) to designate
additional reports for creation, display and/or printing. The
report selection data window (915) also gives the user (20) the
option of having a report created to analyze the relationship
between the market value of the business and the calculated
business value. The user (20) has the option of creating,
displaying or printing the Value Map.TM. reports for the company as
a whole and/or for any combination of the enterprises within the
company. The user (20) can also choose to create, display or print
a Value Creation Statement for the business as a whole and/or for
any combination of enterprises if a comparison calculation were
being made. The software in block 806 creates and displays all
Value Map.TM. reports and Value Creation Statements requested by
the user (20) via the report selection data window (915). After the
user (20) has completed the review of displayed reports and the
input regarding equity analysis and reports to print has been
stored in the reports table (172), processing advances to a
software block 807. The software in block 807 transfers processing
to a software block 808 if the user (20) has chosen to have the
relationship between market value and calculated business value
examined. The software in block 808 compares the market value of
the business to the calculated value by completing the Formula 12
for each complete valuation stored in the reports table (172).
((.SIGMA.E.times.N)-D)=(Y.times.V) Formula 12
Where:
[0173] E=Market price of equity for valuation date [0174] N=Number
of shares of equity outstanding on valuation date [0175] D=Market
value of debt on valuation date [0176] Y=Market value/calculated
business value ratio [0177] V=Total calculated business value on
the valuation date
The average ratio of market value to calculated business value and
the standard deviation of the ratio are then calculated using
standard regression analysis methods and stored in the equity
forecast table (148) in the application database.
[0178] If the date of the current valuation is more than 60 days
after the current system date, then the software in block 808 will
calculate a range for equity prices on the valuation date by
combining the results of previous calculations of the relationship
between equity value and calculated value with the forecast of
future value that was just completed. The software will calculate
the future equity value range using both the average ratio of total
business value to total market value. The software in block 808
then prepares a report summarizing the results of the preceding
calculations that is stored in the reports table (172) in the
application database (50) and processing advances to a software
block 809. If the user (20) elects not to complete the calculated
valuation versus equity price analysis, then the software in block
807 advances processing directly to a software block 809.
[0179] The software in block 809 checks the reports tables (172) to
determine if any reports have been designated for printing. If
reports have been designated for printing, then processing advances
to a block 810 which sends the designated reports to the printer
(118). After the reports have been sent to the printer (118),
processing advances to a software block 811 where processing stops.
If no reports were designated for printing then processing advances
directly from block 809 to 811 where processing stops.
[0180] Thus, the reader will see that the system and method
described above transforms extracted transaction data and
information into detailed valuations for specific elements of a
business enterprise. The level of detail contained in the business
valuations allows users of the system to monitor and manage efforts
to improve the value of the business in a manner that is superior
to that available to users of traditional accounting systems and
business valuation reports. The user also has the option of
examining the relationship between the calculated business value
and the market price of equity for the business.
[0181] While the above description contains many specificity's,
these should not be construed as limitations on the scope of the
invention, but rather as an exemplification of one preferred
embodiment thereof. Accordingly, the scope of the invention should
be determined not by the embodiment illustrated, but by the
appended claims and their legal equivalents.
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