U.S. patent application number 10/645099 was filed with the patent office on 2008-06-12 for automated method of and system for identifying, measuring and enhancing categories of value for a value chain.
Invention is credited to Jeff Scott Eder.
Application Number | 20080140549 10/645099 |
Document ID | / |
Family ID | 32176726 |
Filed Date | 2008-06-12 |
United States Patent
Application |
20080140549 |
Kind Code |
A1 |
Eder; Jeff Scott |
June 12, 2008 |
Automated method of and system for identifying, measuring and
enhancing categories of value for a value chain
Abstract
An automated method and system (100) for identifying, measuring
and enhancing categories of value for the different levels of a
value chain on a continual basis. The categories of value are
analyzed at each level in the value chain using predictive models
and vector creation algorithms to define the enterprise and element
vectors before valuing the organization, each enterprise in the
organization and the elements of value in each enterprise. The
relative strengths of the intangible elements of value are used in
evaluating the real options of each enterprise and in determining
the allocation of industry real options to the enterprise and the
organization before summary reports are prepared, displayed and
optionally printed. The system then generates potential value
improvements which the user (20) optionally accepts, rejects or
modifies before simulations are completed to analyze the value
impact of the enhancements.
Inventors: |
Eder; Jeff Scott; (Mill
Creek, WA) |
Correspondence
Address: |
ASSET TRUST, INC.
2020 MALTBY ROAD, SUITE 7362
BOTHELL
WA
98021
US
|
Family ID: |
32176726 |
Appl. No.: |
10/645099 |
Filed: |
August 21, 2003 |
Related U.S. Patent Documents
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Application
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Filing Date |
Patent Number |
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09940450 |
Aug 29, 2001 |
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10645099 |
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09421553 |
Oct 20, 1999 |
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09940450 |
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09358969 |
Jul 22, 1999 |
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09421553 |
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09295337 |
Apr 21, 1999 |
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09358969 |
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09293336 |
Apr 16, 1999 |
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09295337 |
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09135983 |
Aug 17, 1998 |
6321205 |
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09293336 |
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08999245 |
Dec 10, 1997 |
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09135983 |
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08779109 |
Jan 6, 1997 |
6393406 |
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08999245 |
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Current U.S.
Class: |
705/35 |
Current CPC
Class: |
G06Q 10/06 20130101;
G06Q 40/06 20130101; G06Q 40/02 20130101; G06Q 40/00 20130101; G06Q
30/06 20130101; G06Q 40/04 20130101; G06Q 30/0202 20130101; G06Q
10/04 20130101 |
Class at
Publication: |
705/35 |
International
Class: |
G06Q 40/00 20060101
G06Q040/00 |
Claims
1-24. (canceled)
25. A finance method, comprising: integrating data from
organization transaction databases in accordance with a common
schema for an organization with one or more enterprises; and using
at least a portion of the data to develop a model that identifies a
net contribution of one or more elements of value to an
organization share price by a category of value and a plurality of
tools for organization financial management selected from the group
consisting of one or more category of value models, one or more
component of value models, one or more market value models, one or
more network models, one or more optimization models, a plurality
of segmentation models, a plurality of simulation models, one or
more value chain models, a plurality of management reports, one or
more lists of changes that will optimize one or more aspects of
organization financial performance; a system for automated trading
of an organization equity security based on a market sentiment
value and combinations thereof where the categories of value are
current operation and a category segment of value selected from the
group consisting of real options, market sentiment and combinations
thereof.
26. The method of claim 25 where an element of value is selected
from the group consisting of alliances, brands, channels,
customers, customer relationships, employees, employee
relationships, equipment intellectual property, partnerships,
processes, supply chains, vendors, vendor relationships and
combinations thereof.
27. The method of claim 25 where developing a model that identifies
a net contribution of one or more elements of value to an
organization share price value by a category of value further
comprises: creating performance indicators for each element of
value using at least a portion of the data, training models of
historical and forecast data for one or more aspects of financial
performance using said indicators to identify value driver
candidates by element of value by enterprise, analyzing historical
and forecast data for one or more aspects of financial performance
using induction algorithms and said value driver candidates to
identify value drivers and create element impact summaries by
enterprise, and using said element impact summaries to quantify a
contribution of each of one or more elements of value to an
organization share price value by category of value by
enterprise.
28. The method of claim 27 where an aspect of financial performance
is selected from the group consisting of revenue, expense, capital
change, market value, alliance value, brand value, channel value,
customer value, customer relationship value, employee value,
employee relationship value, intellectual property value,
partnership value, process value, supply chain value, vendor value,
vendor relationship value and combinations thereof.
29. The method of claim 27 where a contribution of an element of
value to a category of value is a net contribution of the element
of value to the category of value and the other elements of
value.
30. The method of claim 25 that further comprises using a model
that identifies a net contribution of one or more elements of value
to an organization share price by a category of value to complete
activities selected from the group consisting of identifying
changes to one or more element value drivers that will optimize one
or more aspects of organization financial performance, identifying
the impact of value driver changes on one or more aspects of
organization financial performance in an interactive manner,
reporting organization market and share price value by element of
value, reporting organization market and share price value by
category of value, identifying a price point for trading
organization shares and combinations thereof.
31. The method of claim 25 where an organization transaction
database is selected from the group consisting of advanced
financial system databases, basic financial system databases,
alliance management system databases, brand management system
databases, business intelligence system databases, customer
relationship management system databases, channel management system
databases, estimating system databases, intellectual property
management system databases, process management system databases,
supply chain management system databases, vendor management system
databases, operation management system databases, enterprise
resource planning systems (ERP), material requirement planning
systems (MRP), quality control system databases, sales management
system databases, human resource system databases, accounts
receivable system databases, accounts payable system databases,
capital asset system databases, inventory system databases,
invoicing system databases, payroll system databases, purchasing
system databases, web site system databases, the Internet, external
databases, user input and combinations thereof.
32. The method of claim 25 where a transaction is any event that is
logged or recorded.
33. A computer readable medium having sequences of instructions
stored therein, which when executed cause a processor in a computer
to perform a learning method, comprising: integrating data from
organization transaction databases in accordance with a common
schema for an organization with one or more enterprises;
identifying a set of data records that are associated with each of
one or more aspects of enterprise financial performance from said
integrated data that can be used for training a plurality of
cluster models for each aspect of enterprise financial performance,
and generating a plurality of cluster models that identify a
plurality of segments for each aspect of financial performance, by
learning from at least a portion of the data where said cluster
models when taken together comprise an overall model for each
aspect of financial performance, and where the aspects of financial
performance are selected from the group consisting of category of
value, component of value, element of value, market value and
combinations thereof.
34. The computer readable medium of claim 33, wherein identifying a
plurality of segments for an element of value further comprises:
creating a plurality of performance indicators for each element of
value using at least a portion of the data, evolving a plurality of
models of historical and forecast data for one or more aspects of
financial performance using said indicators to learn which
indicators are value driver candidates by enterprise, evolving a
plurality of induction models of historical and forecast data for
one or more aspects of enterprise financial performance using said
candidates to learn which indicators are value driver candidates
while creating a plurality of element impact summaries from said
value drivers, and using said element impact summaries to identify
a plurality of segments for each element of value with a clustering
algorithm.
35. The computer readable medium of claim 34 where a contribution
of each of one or more elements of value to a value of a business
is segmented by a category of value where the categories of value
are selected from the group consisting of current operation, real
options, market sentiment and combinations thereof.
36. The computer readable medium of claim 33, wherein a component
of value is selected from the group consisting of revenue, expense,
capital change and combinations thereof.
37. The computer readable medium of claim 33, wherein the method
further comprises using a genetic algorithm to evolve a plurality
of models.
38. The computer readable medium of claim 33 where learning from
the data further comprises activities selected from the group
consisting of identifying previously unknown value drivers,
identifying previously unknown relationships between elements of
value, identifying previously unknown relationships between element
value drivers and combinations thereof.
39. The computer readable medium of claim 33, wherein an element of
value is selected from the group consisting of alliances, brands
channels, customers, customer relationships, employees, employee
relationships, equipment intellectual property, partnerships,
processes, supply chains, vendors, vendor relationships and
combinations thereof.
40. The computer readable medium of claim 33, wherein a cluster
model is developed using algorithms selected from the group
consisting of "Kohonen" neural network, K-nearest neighbor,
Expectation Maximization and the segmental K-means algorithm.
41-48. (canceled)
49. A computer readable medium having sequences of instructions
stored therein, which when executed cause the processor in a
computer to perform a composite application method for data
processing, comprising: using two or more independent components of
application software to produce one or more useful results by
processing a set of data where said data has been integrated from
two or more systems in an automated fashion accordance with a
common model or schema defined by a common metadata standard.
50. The computer readable medium of claim 49, wherein two or more
independent components of application software can be flexibly
combined as required to support the development of one or more
useful results.
51. The computer readable medium of claim 49, wherein a common
metadata standard is selected from the group consisting of xml,
metadata coalition standard and corba.
52. The computer readable medium of claim 49, wherein an
independent component of application software completes processing
selected from the group consisting of: data analysis, attribute
derivation, capitalization, causal analysis, classification,
clustering, count linkages, data acquisition, data conversion, data
storage, data transformation, element life estimation, indicator
selection, induction, keyword counting, keyword search, linkage
location, relative strength determination, statistical learning,
valuation, vector generation and combinations thereof.
53. The computer readable medium of claim 49, wherein one or more
useful results are selected from the group consisting of: an
element contribution determination, an element impact
quantification, an element valuation, an enterprise financial
performance analysis, an enterprise financial performance
optimization, a keyword location identification, an enterprise
financial performance simulation, a future market value
optimization, a future market value quantification, a management
report production, a real option discount rate calculation, a real
option valuation, a share price valuation, an element of value
segmentation, a target share price determination, a keyword count
and combinations thereof.
54. The computer readable medium of claim 49, wherein two or more
systems are selected from the group consisting of accounts
receivable systems, accounts payable systems, advanced financial
systems, basic financial systems, alliance management systems,
brand management systems, customer relationship management systems,
channel management systems, estimating systems, intellectual
property management systems, process management systems, supply
chain management systems, vendor management systems, operation
management systems, sales management systems, human resource
systems, capital asset systems, inventory systems, invoicing
systems, payroll systems, purchasing systems, web site management
systems, the Internet, external databases and combinations
thereof.
55. The computer readable medium of claim 49, wherein a plurality
of data are integrated from two or more systems in accordance with
a common model or schema defined by a common metadata standard
using metadata mapping.
56. The computer readable medium of claim 49, wherein two or more
independent components of application software further comprise two
or more bots.
57. A computer readable medium having sequences of instructions
stored therein, which when executed cause the processor in a
computer to perform a data method, comprising: automatically
integrating data from a plurality of disparate sources into a
common database using a metadata standard where the plurality of
disparate sources further comprise data sources selected from the
group consisting of a plurality of database management systems
associated with a plurality of transactions systems for one or more
commercial enterprises, one or more external databases, an Internet
and combinations thereof and where a metadata standard is selected
from the group consisting of xml and metadata coalition
standard.
58. The computer readable medium of claim 57, wherein a plurality
of data from a plurality of disparate data sources are
automatically integrated into a common database using metadata
mapping.
59. The computer readable medium of claim 57, wherein a plurality
of enterprise transactions systems are selected from the group
consisting of accounts receivable systems, accounts payable
systems, advanced financial systems, basic financial systems,
alliance management systems, brand management systems, customer
relationship management systems, channel management systems,
estimating systems, intellectual property management systems,
process management systems, supply chain management systems, vendor
management systems, operation management systems, sales management
systems, human resource systems, capital asset systems, inventory
systems, invoicing systems, payroll systems, purchasing systems,
web site management systems and combinations thereof.
60. The computer readable medium of claim 57, wherein the method
further comprises performing a search for one or more keywords and
making a set of results from said search available using an
electronic display.
61. The computer readable medium of claim 61, wherein a keyword
further comprises a word selected from a category consisting of
company name, brand name, trademark and combinations thereof.
Description
CROSS REFERENCE TO RELATED PATENTS
[0001] This application is a continuation of application Ser. No.
09/940,450 filed Aug. 29, 2001. Application Ser. No. 09/940,450 is
a continuation of Ser. No. 09/421,553, filed Oct. 20, 1999 which is
incorporated herein by reference. Application Ser. No. 09/421,553
was a continuation-in-part of application Ser. No. 09/358,969,
filed Jul. 22, 1999, of application Ser. No. 09/295,337, filed Apr.
21, 1999, application Ser. No. 09/293,336, filed Apr. 16, 1999,
application Ser. No. 09/135,983 filed Aug. 17, 1998, application
Ser. No. 08/999,245, filed Dec. 10, 1997 and application Ser. No.
08/779,109, filed Jan. 6, 1997 which are incorporated herein by
reference. 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 also
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
identifies, evaluates and helps improve the management of the
categories of value for a value chain and for each enterprise in
the value chain on a continual basis.
[0003] The internet has had many profound effects on global
commerce. The dramatic increase in the use of email, the explosion
of e-commerce and the meteoric rise in the market value of internet
firms like eBay, Amazon.com and Yahoo! are some of the more visible
examples of the impact it has had on the American economy. Another
impact of the internet has been that it has enabled the "virtual
integration" of companies in different locations and different
industries. Companies can now join together in a matter of days
with essentially no investment to form a "virtual value chain" for
delivering products and services to consumers.
[0004] The virtual value chain may appear to the consumer as a
single entity, when in reality a number of enterprises from
different continents have joined together to complete the
preparation and delivery of the good or service that is ultimately
being purchased. Virtual value chains allow each firm in the value
chain to focus on their own specialty, be it manufacturing, design,
distribution or marketing while reaping the benefits of the
increased scale and scope inherent in the alliance. Enabled by the
low cost communication capability provided by the internet, the
virtual value chain is really just an extreme form of a phenomenon
that has been sweeping American industry for many years--the
electronic linkage of businesses.
[0005] Despite the widespread acceptance and use of "virtual value
chains" as a mechanism for efficiently and effectively responding
to customer demands, there is no known method or system for
systematically evaluating the value of these new types of
organizations. In a similar manner there is no known method or
system for evaluating the contribution of the different enterprises
in the "virtual value chain".
[0006] The need for a systematic approach for evaluating "virtual
value chains" is just part of a larger need that has recently
appeared for a new method for systematically evaluating the
financial performance of a commercial business. The need for a new
approach has been highlighted in the past two years by the
multi-billion dollar valuations being placed on internet companies
like Amazon.com, E trade and eBay that have never earned a dollar
of profit and that have no prospect of earning a dollar of profit
any time soon. The most popular traditional approaches to valuation
are all based on some multiple of accounting earnings (a price to
earnings ratio or P/E ratio)--with no corporate earnings in the
past or the foreseeable future--these methods are of course useless
in evaluating the new companies.
[0007] The inability of traditional methods to provide a framework
for analyzing "virtual value chains" and internet firms are just
two glaring examples of the weakness of traditional financial
systems. Numerous academic studies have demonstrated that
accounting earnings don't fully explain changes in company
valuations and the movement of stock prices. Many feel that because
of this 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.
[0008] Consultants from McKinsey & Company recently completed a
three year study of companies in 10 industry segments in 12
countries that confirmed the importance of intangible assets as
enablers of new business expansion and profitable growth. The
results of the study, published in the book The Alchemy of Growth,
revealed three common characteristics of the most successful
businesses in today's economy: [0009] 1. They consistently utilize
"soft" or intangible assets like brand names, customers and
employees to support business expansion; [0010] 2. They
systematically generate and harvest real options for growth; and
[0011] 3. Their management focuses on 3 distinct "horizons"--short
term (1-3 years), growth (3-5 years out) and options (beyond 5
years).
[0012] The experience of several of the most important companies in
the U.S. economy, IBM, General Motors and DEC, in the late 1980's
and early 1990's illustrates the problems that can arise when
intangible asset information is omitted from corporate financial
statements and companies focus only on the short term horizon. All
three companies were showing large profits using current accounting
systems while their businesses were deteriorating. 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 to correct the
problems much more quickly than they actually did. 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 growing markets
rather than their tangible assets.
[0013] The appearance of a new class of software applications, soft
asset management applications, is further evidence of the
increasing importance of "soft" or intangible assets. Soft asset
management applications (or systems) include: alliance management
systems, brand management systems, customer relationship management
systems, channel management systems, intellectual property
management systems, process management systems and vendor
management systems. While these systems enhance the day to day
management of the individual "soft" assets, there is currently no
mechanism for integrating the input from each of these different
systems in to an overall organization or enterprise asset
management system. As a result, the organization or enterprise can
be (and often is) faced with conflicting recommendations as each
system tries to optimize the asset it is focused on without
considering the overall financial performance of the organization
or enterprise.
[0014] A number of people have suggested using business valuations
in place of traditional financial statements as the basis for
measuring and managing financial performance. Unfortunately, using
current methods, 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.
[0015] Income valuations are the most common type of valuation.
They 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. 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. One difficulty with this method is
determining the length of time the company is expected to generate
the expected returns that drive the valuation. Most income
valuations use an explicit forecast of returns for some period,
usually 3 to 5 years, combined with a "residual". The residual is
generally a flat or uniformly growing forecast of future returns
that is discounted by some factor to estimate its value on the date
of valuation. In some cases the residual is the largest part of the
calculated value.
[0016] One of the problems inherent in a steady state "residual"
forecast is that returns don't continue forever. Economists
generally speak of a competitive advantage period or CAP
(hereinafter referred to as CAP) during which a given firm is
expected to generate positive returns. Under this theory, value is
generated only during the CAP. After the CAP ends, value creation
goes to zero or turns negative. Another change that has been
produced by the internet economy is that the CAP for most
businesses is generally thought to be shrinking with the exception
of companies whose products possess network externalities that tie
others to the company and its products or services. These latter
companies are thought to experience increasing returns as time goes
by rather than having a finite CAP. Because the CAP is hard to
calculate, it is generally ignored in income valuations however,
the simplification of ignoring the CAP greatly reduces the utility
of the valuations that are created with large residuals.
[0017] 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. The
usefulness of these valuations is limited because there is no
correct answer, there is only the best possible informed guess for
any given business valuation. The usefulness of business valuations
to business owners and managers is restricted 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
categories of value within the business. An operating manager would
then be able to use a series of business valuations to identify
categories within a business that have been decreasing in value.
This information could also be used to help 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 categories that are contributing to an increase
in business value. This information could be used to identify
categories where increased levels of investment would have a
significant favorable impact on the overall health of the
business.
[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
(similar to the individual soft asset management systems discussed
previously). Intangible assets that have been valued separately in
this manner include: brand names, customers and intellectual
property. Problems associated with existing methods for valuing
intangible assets include: [0019] 1. interactions between the
different intangible assets are ignored, [0020] 2. the actual
impact of the asset on the enterprise isn't measured, [0021] 3. the
relative strength of the intangible asset within the industry is
just as important (and in some cases more important) than any
absolute measure of its strength, and [0022] 4. there is no
systematic way for determining the life of the assets. Typically,
intangible asset valuations also ignore the real options for growth
that are intimately inter-related and dependent upon the intangible
assets being evaluated. In addition to having a direct influence on
the valuation of a given real option the enterprise may possess,
intangible assets can affect the market's perception of which
company is likely to receive the lions share of future growth in a
given industry. This, in turn affects the allocation of industry
options to the market price for equity in the enterprise.
[0023] The lack of a consistent, well accepted, realistic method
for measuring all the categories 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 real 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.
[0024] In light of the preceding discussion, it is clear that it
would be advantageous to have an automated financial system that
valued all the assets and options for a given organization.
Ideally, this system would be capable of generating detailed
valuations for businesses in new industries while prioritizing and
coordinating the management of the different soft assets that the
organization is tracking.
SUMMARY OF THE INVENTION
[0025] It is a general object of the present invention to provide a
novel and useful system that continuously calculates and displays a
comprehensive and accurate valuation for all the categories of
value for a virtual organization that overcomes the limitations and
drawbacks of the existing art that were described previously.
[0026] A preferable object to which the present invention is
applied is the valuation and coordinated management of the
different categories of value within an organization that consists
of two or more commercial enterprises that have come together to
form a "virtual value chain" for the purpose of delivering products
or services to customers where a large portion of the
organization's business value is associated with intangibles and
real options.
[0027] The present invention also provides the ability to calculate
and display a comprehensive and accurate valuation for the
categories of value for each commercial enterprise within the
virtual value chain. The ability to "drill down" for more detailed
analysis extends to each element of value within each enterprise in
the "virtual value chain" as illustrated in Table 1.
TABLE-US-00001 TABLE 1 Level Valuation Categories Organization
Current Operation: Assets/Liabilities Current Operation: Enterprise
Contribution & Joint: Real options/Contingent Liabilities
Enterprise Current Operation: Assets/Liabilities Current Operation:
Elements of Value Real Options/Contingent Liabilities & Market
Sentiment Element of Value Sub-elements of value
[0028] The present invention eliminates a great deal of
time-consuming and expensive effort by automating the extraction of
data from the databases, tables, and files of existing
computer-based corporate finance, operations, human resource and
"soft" asset management system databases as required to operate the
system. In accordance with the invention, the automated extraction,
aggregation and analysis of 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 categories of value from
external databases and publications and the internet. 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 the
same set of valuation methodologies for valuing the different
categories of organization value as shown in Table 2.
TABLE-US-00002 TABLE 2 Organization Categories of Value Valuation
methodology Total current-operation value (COPTOT): Income
Valuation Current Operation Cash & Marketable Securities GAAP
for portion of assets/liabilities Assets/Liabilities: (CASH),
Inventory (IN), from each enterprise that are devoted Accounts
Receivable (AR), to the organization Prepaid Expenses (PE), Other
Assets (OA); Accounts Payable (AP), Notes Payable (NP), Other
Liabilities (OL) Current Operation Production Equipment Replacement
Value for portion of Assets/Liabilities: (PEQ), Other Physical
Assets assets from each enterprise that are (OPA) devoted to the
organization Current Operation Enterprise contribution to System
calculated value Enterprise virtual value chain (VVCC)
Contribution: Current Operation General going concern GGCV = COPTOT
- CASH - AR - Enterprise element of value (GGCV) IN - PE - PEQ -
OPA - OA - VVCC Contribution: Real options/Contingent Liabilities
Real option algorithms + allocation of industry real options based
on relative industry position *The user also has the option of
specifying the total value
[0029] The present invention takes a similar approach to enterprise
value analysis by consistently utilizing the same set of valuation
methodologies for valuing the different categories of enterprise
value as shown in Table 3.
TABLE-US-00003 TABLE 3 Enterprise Categories of Value Valuation
methodology Total current-operation value (COPTOT): Income
Valuation Current-operation Cash & Marketable Securities GAAP
Assets/Liabilities: (CASH), Inventory (IN), Accounts Receivable
(AR), Prepaid Expenses (PE), Other Assets (OA), Accounts Payable
(AP), Notes Payable (NP), Other Liabilities (OL) Current-operation
Production Equipment Replacement Value Assets/Liabilities: (PEQ),
Other Physical Assets (OPA) Current Operation Alliances, Brand
Names, System calculated value Elements of Value Channel Partners,
(EV): Customers, Employees, Industry Factors*, Infrastructure,
Intellectual Property, Information Technology, Processes and
Vendors Current Operation General going concern GCV = COPTOT - CASH
- AR - IN - PE - Element of Value: (GCV) PEQ - OPA - OA - .SIGMA.EV
Real options/Contingent Liabilities Real option algorithms +
allocation of industry real options based on relative strength of
elements of value (EV) Market Sentiment Enterprise Market Value -
(COPTOT + .SIGMA.Real option Values) *Note: Industry Factors
(regulation, concentration, etc.) are analyzed like an element of
value
There is no market sentiment calculation at the organization level
because the market value of each enterprise in the organization
generally includes non-value chain related activities and the firm
level market sentiment for each enterprise can not readily be
sub-divided in to value chain and non-value chain sentiment. The
market value of each enterprise in the organization is calculated
by adding the market value of all debt and equity as shown in Table
4.
TABLE-US-00004 TABLE 4 Enterprise Market Value = .SIGMA. Market
value of enterprise equity + .SIGMA. Market value of company
debt
[0030] One benefit of the novel system is that the market value of
every enterprise in the organization is subdivided in to at least
three distinct categories of value: current operation assets,
elements of value and real options. As shown in the table 5, these
three value categories match the three distinct "horizons" for
management focus the McKinsey consultants reported on in The
Alchemy of Growth.
TABLE-US-00005 TABLE 5 System Value Categories Three Horizons
Current Operation Assets Short Term Elements of Value Growth Real
Options Options
[0031] The utility of the valuations produced by the system of the
present invention are further enhanced by explicitly calculating
the lives of the different elements of value as required to remove
the inaccuracy and distortion inherent in the use of a large
residual.
[0032] As shown in Tables 2 and 3, growth opportunities and
contingent liabilities are valued using real option algorithms.
Because real option algorithms explicitly recognize whether or not
an investment is reversible and/or if it can be delayed, the values
calculated using these algorithms are more realistic than
valuations created using more traditional approaches like Net
Present Value. The use of real option analysis for valuing growth
opportunities and contingent liabilities (hereinafter, real
options) gives the present invention a distinct advantage over
traditional approaches to business valuation.
[0033] The innovative system has the added benefit of providing a
large amount of detailed information concerning both tangible and
intangible elements of value. Because intangible elements are by
definition not tangible, they can not be measured directly. They
must instead be measured by the impact they have on their
surrounding environment. There are analogies in the physical world.
For example, electricity is an "intangible" that is measured by the
impact it has on the surrounding environment. Specifically, the
strength of the magnetic field generated by the flow of electricity
through a conductor is used to determine the amount of electricity
that is being consumed. The system of the present invention
measures intangible elements of value by identifying the attributes
that, like the magnetic field, reflect the strength of the element
in driving the components of value (revenue, expense and change in
capital) and are easy to measure. Once the attributes related to
each element's strength are identified, they are summarized into a
single expression (a composite variable or vector). The vectors for
all elements are then evaluated to determine their relative
contribution to driving each of the components of value. The system
of the present invention calculates the product of each element's
relative contribution and forecast life to determine the
contribution to each of the components of value. The contributions
to each component of value are then added together to determine the
value of each element (see Table 7).
[0034] The system also gives the user the ability to track the
changes in categories of value by comparing the current valuations
to previously calculated valuations. As such, the system provides
the user with an alternative to general ledger accounting systems
for tracking financial performance. To facilitate its use as a tool
for improving the value of a commercial 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 categories 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
[0035] 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:
[0036] FIG. 1 is a block diagram showing the major processing steps
of the present invention;
[0037] 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 the
categories of value within the organization;
[0038] FIG. 3 is a block diagram of an implementation of the
present invention;
[0039] FIG. 4 is a diagram showing the data windows that are used
for receiving information from and transmitting information to the
user (20) during system processing;
[0040] FIG. 5A, FIG. 5B, FIG. 5C, FIG. 5D, FIG. 5E and FIG. 5F are
block diagrams showing the sequence of steps in the present
invention used for specifying system settings and for initializing
and operating the data bots that extract, aggregate, store and
manipulate information utilized in system processing from: user
input, the basic financial system database, the operation
management system database, the human resource information system
database, external databases, the advanced financial system
database, soft asset management system databases and the
internet;
[0041] FIG. 6A, FIG. 6B and FIG. 6C are block diagrams showing the
sequence of steps in the present invention that are utilized for
initializing and operating the analysis bots;
[0042] FIG. 7 is a block diagram showing the sequence of steps in
the present invention used for the analyzing enterprise market
sentiment;
[0043] FIG. 8 is a block diagram showing the sequence of steps in
the present invention used in trading organization stock and in
preparing, displaying and optionally printing reports; and
[0044] FIG. 9 is a block diagram showing the sequence of steps in
the present invention used for generating lists of value enhancing
changes and calculating, displaying and optionally printing
simulations of the effects of user-specified and/or system
generated changes in business value drivers on the financial
performance and the future value of the organization and the
enterprises in the organization;
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
[0045] 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 the specification of system settings and the
initialization and activation of software data "bots" (200) that
extract, aggregate, manipulate and store the data and user (20)
input required for completing system processing. This information
is extracted via a network (45) from a basic financial system
database (5), an operation management system database (10), a human
resource information system database (15), an external database
(25), an advanced financial system database (30), soft asset
management system databases (35) and the internet (40). These
information extractions and aggregations may be influenced by a
user (20) through interaction with a user-interface portion of the
application software (700) that mediates the display, transmission
and receipt of all information to and from a browser (800) that the
user (20) interacts with. While only one database of each type (5,
10, 15, 25, 30 and 35) 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 network (45). The preferred embodiment of the
present invention contains a soft asset management system for each
element of value being analyzed. Automating the extraction and
analysis of data from each soft asset management system ensures
that the management of each soft asset is considered and
prioritized within the overall financial models for the
organization and for each enterprise in the organization. It should
also be understood that it is possible to complete a bulk
extraction of data from each database (5, 10, 15, 25, 30 and 35)
via the network (45) using data extraction applications such as
Aclue from Decisionism and Power Center from Informatica before
initializing the data bots. The data extracted in bulk could be
stored in a single datamart or datawarehouse where the data bots
could operate on the aggregated data.
[0046] All extracted information 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 metadata mapping table
(141), a conversion rules table (142), a basic financial system
table (143), an operation system table (144), a human resource
system table (145), an external database table (146), an advanced
finance system table (147), a soft asset system table (148), a bot
date table (149), a keyword table (150), a classified text table
(151), a geospatial measures table (152), a composite variables
table (153), an industry ranking table (154), an element of value
definition table (155), a component of value definition table
(156), a cluster ID table (157), an element variables table (158),
a vector table (159), a bot table (160), a cash flow table (161), a
real option value table (162), an enterprise vector table (163), a
report table (164), an equity purchase table (165), an enterprise
sentiment table (166), a value driver change table (167), a
simulation table (168) and a sentiment factors table (169). 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 data sources (5, 10, 15, 25, 30, 35 and
40).
[0047] As shown in FIG. 3, the preferred embodiment of the present
invention is a computer system (100) illustratively comprised of a
user-interface personal computer (110) connected to an application
server personal computer (120) via a network (45). The application
server personal computer (120) is in turn connected via the network
(45) to a database-server personal computer (130). The user
interface personal computer (110) is also connected via the network
(45) to an internet browser appliance (90) that contains browser
software (800) such as Microsoft Internet Explorer or Netscape
Navigator.
[0048] The database-server personal computer (130) has a read/write
random access memory (131), a hard drive (132) for storage of the
application database (50), a keyboard (133), a communications bus
(134), a CRT display (135), a mouse (136), a CPU (137) and a
printer (138).
[0049] The application-server personal computer (120) has a
read/write random access memory (121), a hard drive (122) for
storage of the non user interface portion of the application
software (200, 300, 400, 500 and 600) of the present invention, a
keyboard (123), a communications bus (124), a CRT display (125), a
mouse (126), a CPU (127) and a printer (128). 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 network (45).
The application-server personal computer (120) can also be
networked to fifty or more server, personal computers (130) via the
network (45). It is to be understood that the diagram of FIG. 3 is
merely illustrative of one embodiment of the present invention.
[0050] The user-interface personal computer (110) has a read/write
random access memory (111), a hard drive (112) for storage of a
client data-base (49) and the user-interface portion of the
application software (700), a keyboard (113), a communications bus
(114), a CRT display (115), a mouse (116), a CPU (117) and a
printer (118).
[0051] The application software (200, 300, 400, 500, 600 and 700)
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 and 700) is
written in a combination of C++ and Visual Basic.RTM.. The
application software (200, 300, 400, 500, 600 and 700) can use
Structured Query Language (SQL) for extracting data from the
databases and the internet (5, 10, 15, 25, 30, 35 and 40). The user
(20) can optionally interact with the user-interface portion of the
application software (700) using the browser software (800) in the
browser appliance (90) to provide information to the application
software (200, 300, 400, 500, 600 and 700) for use in determining
which data will be extracted and transferred to the application
database (50) by the data bots.
[0052] 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
network (45). 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.
[0053] 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 256 megabytes of
semiconductor random access memory (111) and at least a 50 gigabyte
hard drive (112). Typical memory configurations for the
application-server personal computer (120) used with the present
invention should include at least 1028 megabytes of semiconductor
random access memory (121) and at least a 100 gigabyte hard drive
(122). Typical memory configurations for the database-server
personal computer (130) used with the present invention should
include at least 2056 megabytes of semiconductor random access
memory (135) and at least a 500 gigabyte hard drive (131).
[0054] Using the system described above, the value of the
organization, each enterprise within the organization and each
element of value can be broken down into the value categories
listed in Table 1. As shown in Table 2 and Table 3, the value of
the current-operation will be calculated using an income valuation.
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 % ##EQU00001## PV = 10 1.25 + 10 ( 1.25 ) 2 + 10
( 1.25 ) 3 + 10 ( 1.25 ) 4 + 10 ( 1.25 ) 5 = 26.89 ##EQU00001.2##
Discount rate = 35 % ##EQU00001.3## PV = 10 1.35 + 10 ( 1.35 ) 2 +
10 ( 1.35 ) 3 + 10 ( 1.35 ) 4 + 10 ( 1.35 ) 5 = 22.20
##EQU00001.4##
[0055] One of the first steps in evaluating the elements of
current-operation value is extracting the data required to complete
calculations in accordance with the formula that defines the value
of the current-operation as shown in Table 6.
TABLE-US-00006 TABLE 6 Value of current-operation = (R) Value of
forecast revenue from current-operation (positive) + (E) Value of
forecast expense for current-operation (negative) + (C)* Value of
current operation capital change forecast *Note: (C) can have a
positive or negative value
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 6 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).
[0056] In the preferred embodiment, the revenue, expense and
capital requirement forecasts for the current operation, the real
options and the contingent liabilities are obtained from an
advanced financial planning system database (30) from an advanced
financial planning system similar to the one disclosed in U.S. Pat.
No. 5,615,109. The extracted revenue, expense and capital
requirement forecasts are used to calculate a cash flow for each
period covered by the forecast for the organization and each
enterprise in the organization by subtracting the expense and
change in capital for each period from the revenue for each period.
A steady state forecast for future periods is calculated after
determining the steady state growth rate the best fits the
calculated cash flow for the forecast time period. The steady state
growth rate is used to calculate an extended cash flow forecast.
The extended cash flow forecast is used to determine the
Competitive Advantage Period (CAP) implicit in the enterprise
market value.
[0057] While it is possible to use analysis bots to sub-divide each
of the components of current operation value into a number of
sub-components for analysis, the preferred embodiment has a
pre-determined number of sub-components for each component of value
for the organization and each enterprise in the organization. The
revenue value is not subdivided. In the preferred embodiment, 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.
[0058] The components and sub-components of current-operation value
will be used in calculating the value of: enterprise contribution,
elements of value and sub-elements of value. Enterprise
contribution will be defined as "the economic benefit that as a
result of past transactions an enterprise is expected to provide to
an organization." In a similar fashion, an element of value will be
defined as "an identifiable entity or group of items that as a
result of past transactions has provided and is expected to provide
economic benefit to an 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. The data associated with performance of an
individual item will be referred to as "item variables".
[0059] Analysis bots are used to determine enterprise and element
of value lives and the percentage of: the revenue value, the
expense value, and the capital value that are attributable to each
element of value. The resulting values are then be added together
to determine the valuation for different elements as shown by the
example in Table 7.
TABLE-US-00007 TABLE 7 Element Gross Value Percentage Life/CAP Net
Value Revenue value = $120M 20% 80% Value = $19.2 M Expense value =
($80M) 10% 100% Value = ($8.0) M Capital value = ($5M) 5% 80% Value
= ($0.2) M Total value = $35M Net value for this element: Value =
$11.0 M
[0060] The valuation of an organization and the enterprises in the
organization using the approach outlined above is completed in five
distinct stages. As shown in FIG. 5A, FIG. 5B, FIG. 5C, FIG. 5D,
FIG. 5E and FIG. 5F the first stage of processing (block 200 from
FIG. 1) programs bots to continually extract, aggregate, manipulate
and store the data from user input and databases and the internet
(5, 10, 15, 25, 30, 35 or 40) as required for the analysis of
business value. Bots are independent components of the application
that have specific tasks to perform. As shown in FIG. 6A, FIG. 6B
and FIG. 6C the second stage of processing (block 300 from FIG. 1)
programs analysis bots to continually: [0061] 1. identify the item
variables, item performance indicators and composite variables for
each enterprise, element of value and sub-element of value that
drive the components of value (revenue, expense and changes in
capital), [0062] 2. create vectors that summarize the performance
of the item variables and item performance indicators for each
enterprise contribution, element of value and sub-element of value,
[0063] 3. determine the appropriate cost of capital and value the
organization and enterprise real options; [0064] 4. determine the
appropriate cost of capital, value and allocate the industry real
options to each organization or enterprise on the basis of relative
element strength; [0065] 5. determine the expected life of each
element of value and sub-element of value; [0066] 6. calculate the
organization and enterprise current operation values and value the
revenue, expense and capital components said current operations
using the information prepared in the previous stage of processing;
[0067] 7. specify and optimize predictive models to determine the
relationship between the vectors determined in step 2 and the
revenue, expense and capital values determined in step 6, [0068] 8.
combine the results of the fifth, sixth and seventh stages of
processing to determine the value of each, enterprise contribution,
element and sub-element (as shown in Table 7); The third stage of
processing (block 400 from FIG. 1) analyzes the market sentiment
associated with each enterprise as shown in FIG. 7. The fourth
stage of processing (block 500 from FIG. 1) displays the results of
the prior calculations in specified formats and optionally
generates trades in enterprise stock as shown in FIG. 8. The fifth
and final stage of processing (block 600 from FIG. 1) identifies
potential improvements in organization and enterprise operation and
analyzes the impact of proposed improvements on financial
performance and business value for the organization and each
enterprise as shown in FIG. 9.
System Settings and Data Bots
[0069] The flow diagrams in FIG. 5A, FIG. 5B, FIG. 5C, FIG. 5D,
FIG. 5E and FIG. 5F detail the processing that is completed by the
portion of the application software (200) that extracts,
aggregates, transforms and stores the information required for
system operation from: the basic financial system database (5),
operation management system database (10), human resource
information system database (15), external database (25), advanced
financial system database (30), soft asset management system
database (35), the internet (40) 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.
[0070] 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.
[0071] 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 8.
TABLE-US-00008 TABLE 8 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.
[0072] 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 9 for each
transaction.
TABLE-US-00009 TABLE 9 Subsystem Detailed Information Accounts
Vendor, Item(s), Transaction Date, Amount Owed, Due Payable Date,
Account Number Accounts Customer, Transaction Date, Product Sold,
Quantity, Price, Receivable Amount Due, Terms, Due Date, Account
Number Capital Asset ID, Asset Type, Date of Purchase, Purchase
Price, Assets 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
[0073] 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 and system processing have been completed.
[0074] While basic 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 Resource 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. Systems similar to the one described above may
also be useful for distributors to use in monitoring the flow of
products from a manufacturer.
[0075] 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 10.
TABLE-US-00010 TABLE 10 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
[0076] 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 11.
TABLE-US-00011 TABLE 11 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
[0077] Computer based human resource systems may some times be
packaged or bundled within enterprise resource planning systems
such as those available from SAP, Oracle and Peoplesoft. 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 12 for
each employee.
TABLE-US-00012 TABLE 12 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
[0078] External databases can be used for obtaining information
that enables the definition and evaluation of a variety of things
including elements of value, sentiment factors, industry real
options and composite variables. In some cases information from
these databases can be used to supplement information obtained from
the other databases and the internet (5, 10, 15, 30, 35 and 40). In
the system of the present invention, the information extracted from
external databases (25) can be in the forms listed in Table 13.
TABLE-US-00013 Types of information a) numeric information such as
that found in the SEC Edgar database and the databases of financial
infomediaries such as FirstCall, IBES and Compustat, b) text
information such as that found in the Lexis Nexis database and
databases containing past issues from specific publications, c)
multimedia information such as video and audio clips, and d)
geospatial data.
The system of the present invention uses different "bot" types to
process each distinct data type from external databases (25). The
same "bot types" are also used for extracting each of the different
types of data from the internet (40). The system of the present
invention must have access to at least one external database (25)
that provides information regarding the equity prices for each
enterprise in the organization and the equity prices and financial
performance of competitors.
[0079] Advanced financial systems may also use information from
external databases (25) and the internet (40) in completing their
processing. Advanced financial systems include financial planning
systems and activity based costing systems. Activity based costing
systems may be used to supplement or displace the operation of the
expense component analysis segment of the present invention as
disclosed previously. 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
similar to 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.
[0080] While advanced financial planning systems have been around
for some time, soft asset management systems are a relatively
recent development. Their appearance is further proof of the
increasing importance of "soft" assets. Soft asset management
systems include: alliance management systems, brand management
systems, customer relationship management systems, channel
management systems, intellectual property management systems,
process management systems and vendor management systems. Soft
asset management systems are similar to operation management
systems in that they generally have the ability to forecast future
events as well as track historical occurrences. Customer
relationship management systems are the most well established soft
asset management systems at this point and will the focus of the
discussion regarding soft asset management system data. In firms
that sell customized products, the customer relationship 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, customer relationship 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 customer relationship management
systems would be expected to track all of the customer's
interactions with the enterprise after the first sale and store
information similar to that shown below in Table 14.
TABLE-US-00014 TABLE 14 Customer Relationship 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
[0081] System processing of the information from the different
databases and the internet (5, 10, 15, 25, 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 (20) via the system settings data window (701) to
provide system setting information. The system setting information
entered by the user (20) is transmitted via the network (45) 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 15.
TABLE-US-00015 TABLE 15 1. New run or structure revision? 2.
Continuous, If yes, frequency? (hourly, daily, weekly, monthly or
quarterly) 3. Structure of virtual organization (organization,
enterprises and sub-elements) 4. Organization checklist 5.
Enterprise checklist 6. Base acount structure 7. Metadata standard
(XML, MS OIM, MDC) 8. Location of basic financial system database
and metadata 9. Location of advanced financial system database and
metadata 10. Location of human resource information system database
and metadata 11. Location of operation management system database
and metadata 12. Location of soft asset management system databases
and metadata 13. Location of external database and metadata 14.
Location of account structure 15. Base currency 16. Location of
database and metadata for equity information 17. Location of
database and metadata for debt information 18. Location of database
and metadata for tax rate information 19. Location of database and
metadata for currency conversion rate information 20. Geospatial
data? If yes, identity of geocoding service. 21. The maximum number
of generations to be processed without improving fitness 22.
Default clustering algorithm (selected from list) and maximum
cluster number 23. Amount of cash and marketable securities
required for day to day operations 24. Weighted average cost of
capital (optional input) 25. Number of months a product is
considered new after it is first produced 26. Organization industry
segments (SIC Code) 27. Enterprise industry segments (SIC Code) 28.
Primary competitors by industry segment 29. Management report types
(text, graphic, both) 30. Default reports 31. Trading in enterprise
equity authorized? 32. On-line equity trading account information
33. Default Missing Data Procedure 34. Maximum time to wait for
user input
The organization and enterprise checklists are used by a "rules"
engine (such as the one available from Neuron Data) in block 202 to
influence the number and type of items with pre-defined metadata
mapping for each category of value. For example, if the checklists
indicate that the organization and enterprises are focused on
branded, consumer markets, then additional brand related factors
will be pre-defined for mapping. The application of these system
settings will be further explained as part of the detailed
explanation of the system operation.
[0082] The software in block 202 also uses the current system date
to determine the time periods (months) that require data in order
to complete the current operation and the real option valuations
and stores the resulting date range in the system settings table
(140). In the preferred embodiment the valuation of the current
operation by the system utilizes basic finance, advanced financial,
soft asset management, external database and human resource data
for the three year period before and the three year forecast period
after the current date.
[0083] After the storage of system setting data is complete,
processing advances to a software block 203. The software in block
203 prompts the user (20) via the metadata and conversion rules
window (702) to map metadata using the standard specified by the
user (20) (XML, Microsoft's Open Information Model of the Metadata
Coalitions specification) from the basic financial system database
(5), the operation management system database (10), the human
resource information system database (15), the external database
(25), the advanced financial system database (30) and the soft
asset management system database (35) to the organizational
hierarchy stored in the system settings table (140) and to the
pre-specified fields in the metadata mapping table (141).
Pre-specified fields in the metadata mapping table include, the
revenue, expense and capital components and sub-components for the
organization and each enterprise and pre-specified fields for
expected value drivers. Because the bulk of the information being
extracted is financial information, the metadata mapping often
takes the form of specifying the account number ranges that
correspond to the different fields in the metadata mapping table
(141). Table 16, shows the base account number structure that the
account numbers in the other systems must align with. For example,
using the structure shown below, the revenue component for the
organization could be specified as organization 01, any enterprise
number, any department number, accounts 400 to 499 (the revenue
account range) with any sub-account.
TABLE-US-00016 TABLE 16 Account Number 01 - 800 - 901 - 677 - 003
Segment Organization Enterprise Department Account Sub- account
Subgroup Products Workstation Marketing Labor P.R. Position 5 4 3 2
1
As part of the metadata mapping process, any database fields that
are not mapped to pre-specified fields are defined by the user (20)
as component of value. elements of value or non-relevant attributes
and "mapped" in the metadata mapping table (141) to the
corresponding fields in each database in a manner identical to that
described above for the pre-specified fields. After all fields have
been mapped to the metadata mapping table (141), the software in
block 203 prompts the user (20) via the metadata and conversion
rules window (702) to provide conversion rules for each metadata
field for each data source. IConversion rules will include
information regarding currency conversions and conversion for units
of measure that may be required to accurately and consistently
analyze the data. The inputs from the user (20) regarding
conversion rules are stored in the conversion rules table (142) in
the application database. When conversion rules have been stored
for all fields from every data source, then processing advances to
a software block 204.
[0084] The software in block 204 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a new calculation or a structure change. If the
calculation is not a new calculation or a structure change then
processing advances to a software block 212. Alternatively, if the
calculation is new or a structure change, then processing advances
to a software block 207.
[0085] The software in block 207 checks the bot date table (149)
and deactivates any basic financial system data bots with creation
dates before the current system date and retrieves information from
the system setting table (140), metadata mapping table (141) and
conversion rules table (142). The software in block 207 then
initializes data bots for each field in the metadata mapping table
(141) that mapped to the basic financial system database (5) in
accordance with the frequency specified by user (20) in the system
settings table (140). Bots are independent components of the
application that have specific tasks to perform. In the case of
data acquisition bots, their tasks are to extract and convert data
from a specified source and then store it in a specified location.
Each data bot initialized by software block 207 will store its data
in the basic financial system table (143). Every data acquisition
bot for every data source contains the information shown in Table
17.
TABLE-US-00017 TABLE 17 1. Unique ID number (based on date, hour,
minute, second of creation) 2. The data source location 3. Mapping
information 4. Timing of extraction 5. Conversion rules (if any) 6.
Storage Location (to allow for tracking of source and destination
events) 7. Creation date (day, hour, minute, second)
[0086] After the software in block 207 initializes all the bots for
the basic financial system database, processing advances to a block
208. In block 208, the bots extract and convert data in accordance
with their preprogrammed instructions in accordance with the
frequency specified by user (20) in the system settings table
(140). As each bot extracts and converts data from the basic
financial system database (5), processing advances to a software
block 209 before the bot completes data storage. The software in
block 209 checks the basic financial system metadata to see if all
fields have been extracted. If the software in block 209 finds no
unmapped data fields, then the extracted, converted data is stored
in the basic financial system table (143). Alternatively, if there
are fields that haven't been extracted, then processing advances to
a block 210. The software in block 210 prompts the user (20) via
the metadata and conversion rules window (702) to provide metadata
and conversion rules for each new field. The information regarding
the new metadata and conversion rules is stored in the metadata
mapping table (141) and conversion rules table (142) while the
extracted, converted data is stored in the basic financial system
table (143). It is worth noting at this point that the activation
and operation of bots that don't have unmapped fields continues.
Only bots with unmapped fields "wait" for user input before
completing data storage. The new metadata and conversion rule
information will be used the next time bots are initialized in
accordance with the frequency established by the user (20). In
either event, system processing passes, on to software block
212.
[0087] The software in block 212 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a new calculation or a structure change. If the
calculation is not a new calculation or a structure change then
processing advances to a software block 224. Alternatively, if the
calculation is new or a structure change, then processing advances
to a software block 221.
[0088] The software in block 221 checks the bot date table (149)
and deactivates any operations management system data bots with
creation dates before the current system date and retrieves
information from the system setting table (140), metadata mapping
table (141) and conversion rules table (142). The software in block
221 then initializes data bots for each field in the metadata
mapping table (141) that mapped to the operations management system
database (10) in accordance with the frequency specified by user
(20) in the system settings table (140). Each data bot initialized
by software block 221 will store its data in the operations system
table (144).
[0089] After the software in block 221 initializes all the bots for
the operations management system database, processing advances to a
block 222. In block 222, the bots extract and convert data in
accordance with their preprogrammed instructions in accordance with
the frequency specified by user (20) in the system settings table
(140). As each bot extracts and converts data from the operations
management system database (10), processing advances to a software
block 209 before the bot completes data storage. The software in
block 209 checks the operations management system metadata to see
if all fields have been extracted. If the software in block 209
finds no unmapped data fields, then the extracted, converted data
is stored in the operations system table (144). Alternatively, if
there are fields that haven't been extracted, then processing
advances to a block 210. The software in block 210 prompts the user
(20) via the metadata and conversion rules window (702) to provide
metadata and conversion rules for each new field. The information
regarding the new metadata and conversion rules is stored in the
metadata mapping table (141) and conversion rules table (142) while
the extracted, converted data is stored in the operations system
table (144). It is worth noting at this point that the activation
and operation of bots that don't have unmapped fields continues.
Only bots with unmapped fields "wait" for user input before
completing data storage. The new metadata and conversion rule
information will be used the next time bots are initialized in
accordance with the frequency established by the user (20). In
either event, system processing then passes, on to software block
224.
[0090] The software in block 224 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a new calculation or a structure change. If the
calculation is not a new calculation or a structure change then
processing advances to a software block 228. Alternatively, if the
calculation is new or a structure change, then processing advances
to a software block 225.
[0091] The software in block 225 checks the bot date table (149)
and deactivates any human resource management system data bots with
creation dates before the current system date and retrieves
information from the system setting table (140), metadata mapping
table (141) and conversion rules table (142). The software in block
225 then initializes data bots for each field in the metadata
mapping table (141) that mapped to the human resource management
system database (15) in accordance with the frequency specified by
user (20) in the system settings table (140). Each data bot
initialized by software block 225 will store its data in the human
resource system table (145).
[0092] After the software in block 225 initializes all the bots for
the human resource management system database, processing advances
to a block 226. In block 226, the bots extract and convert data in
accordance with their preprogrammed instructions in accordance with
the frequency specified by user (20) in the system settings table
(140). As each bot extracts and converts data from the human
resource management system database (15), processing advances to a
software block 209 before the bot completes data storage. The
software in block 209 checks the human resource management system
metadata to see if all fields have been extracted. If the software
in block 209 finds no unmapped data fields, then the extracted,
converted data is stored in the human resource system table (145).
Alternatively, if there are fields that haven't been extracted,
then processing advances to a block 210. The software in block 210
prompts the user (20) via the metadata and conversion rules window
(702) to provide metadata and conversion rules for each new field.
The information regarding the new metadata and conversion rules is
stored in the metadata mapping table (141) and conversion rules
table (142) while the extracted, converted data is stored in the
human resource system table (145). It is worth noting at this point
that the activation and operation of bots that don't have unmapped
fields continues. Only bots with unmapped fields "wait" for user
input before completing data storage. The new metadata and
conversion rule information will be used the next time bots are
initialized in accordance with the frequency established by the
user (20). In either event, system processing then passes, on to
software block 228.
[0093] The software in block 228 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a new calculation or a structure change. If the
calculation is not a new calculation or a structure change then
processing advances to a software block 244. Alternatively, if the
calculation is new or a structure change, then processing advances
to a software block 241.
[0094] The software in block 241 checks the bot date table (149)
and deactivates any external database data bots with creation dates
before the current system date and retrieves information from the
system setting table (140), metadata mapping table (141) and
conversion rules table (142). The software in block 241 then
initializes data bots for each field in the metadata mapping table
(141) that mapped to the external database (25) in accordance with
the frequency specified by user (20) in the system settings table
(140). Each data bot initialized by software block 241 will store
its data in the external database table (146).
[0095] After the software in block 241 initializes all the bots for
the external database, processing advances to a block 242. In block
242, the bots extract and convert data in accordance with their
preprogrammed instructions. As each bot extracts and converts data
from the external database (25), processing advances to a software
block 209 before the bot completes data storage. The software in
block 209 checks the external database metadata to see if all
fields have been extracted. If the software in block 209 finds no
unmapped data fields, then the extracted, converted data is stored
in the external database table (146). Alternatively, if there are
fields that haven't been extracted, then processing advances to a
block 210. The software in block 210 prompts the user (20) via the
metadata and conversion rules window (702) to provide metadata and
conversion rules for each new field. The information regarding the
new metadata and conversion rules is stored in the metadata mapping
table (141) and conversion rules table (142) while the extracted,
converted data is stored in the external database table (146). It
is worth noting at this point that the activation and operation of
bots that don't have unmapped fields continues. Only bots with
unmapped fields "wait" for user input before completing data
storage. The new metadata and conversion rule information will be
used the next time bots are initialized in accordance with the
frequency established by the user (20). In either event, system
processing then passes, on to software block 244.
[0096] The software in block 244 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a new calculation or a structure change. If the
calculation is not a new calculation or a structure change then
processing advances to a software block 248. Alternatively, if the
calculation is new or a structure change, then processing advances
to a software block 245.
[0097] The software in block 245 checks the bot date table (149)
and deactivates any advanced financial system data bots with
creation dates before the current system date and retrieves
information from the system setting table (140), metadata mapping
table (141) and conversion rules table (142). The software in block
245 then initializes data bots for each field in the metadata
mapping table (141) that mapped to the advanced financial system
database (30) in accordance with the frequency specified by user
(20) in the system settings table (140). Each data bot initialized
by software block 245 will store its data in the advanced financial
system database table (147).
[0098] After the software in block 245 initializes all the bots for
the advanced financial system database, processing advances to a
block 246. In block 246, the bots extract and convert data in
accordance with their preprogrammed instructions in accordance with
the frequency specified by user (20) in the system settings table
(140). As each bot extracts and converts data from the advanced
financial system database (30), processing advances to a software
block 209 before the bot completes data storage. The software in
block 209 checks the advanced financial system database metadata to
see if all fields have been extracted. If the software in block 209
finds no unmapped data fields, then the extracted, converted data
is stored in the advanced financial system database table (147).
Alternatively, if there are fields that haven't been extracted,
then processing advances to a block 210. The software in block 210
prompts the user (20) via the metadata and conversion rules window
(702) to provide metadata and conversion rules for each new field.
The information regarding the new metadata and conversion rules is
stored in the metadata mapping table (141) and conversion rules
table (142) while the extracted, converted data is stored in the
advanced financial system database table (147). It is worth noting
at this point that the activation and operation of bots that don't
have unmapped fields continues. Only bots with unmapped fields
"wait" for user input before completing data storage. The new
metadata and conversion rule information will be used the next time
bots are initialized in accordance with the frequency established
by the user (20). In either event, system processing then passes,
on to software block 248.
[0099] The software in block 248 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a new calculation or a structure change. If the
calculation is not a new calculation or a structure change then
processing advances to a software block 264. Alternatively, if the
calculation is new or a structure change, then processing advances
to a software block 261.
[0100] The software in block 261 checks the bot date table (149)
and deactivates any soft asset management system data bots with
creation dates before the current system date and retrieves
information from the system setting table (140), metadata mapping
table (141) and conversion rules table (142). The software in block
261 then initializes data bots for each field in the metadata
mapping table (141) that mapped to a soft asset management system
database (35) in accordance with the frequency specified by user
(20) in the system settings table (140). Extracting data from each
soft asset management system ensures that the management of each
soft asset is considered and prioritized within the overall
financial models for the organization and each enterprise in the
organization. Each data bot initialized by software block 261 will
store its data in the soft asset system table (148).
[0101] After the software in block 261 initializes bots for all
soft asset management system databases, processing advances to a
block 262. In block 262, the bots extract and convert data in
accordance with their preprogrammed instructions in accordance with
the frequency specified by user (20) in the system settings table
(140). As each bot extracts and converts data from the soft asset
management system databases (35), processing advances to a software
block 209 before the bot completes data storage. The software in
block 209 checks the metadata for the soft asset management system
databases to see if all fields have been extracted. If the software
in block 209 finds no unmapped data fields, then the extracted,
converted data is stored in the soft asset system table (148).
Alternatively, if there are fields that haven't been extracted,
then processing advances to a block 210. The software in block 210
prompts the user (20) via the metadata and conversion rules window
(702) to provide metadata and conversion rules for each new field.
The information regarding the new metadata and conversion rules is
stored in the metadata mapping table (141) and conversion rules
table (142) while the extracted, converted data is stored in the
soft asset system table (148). It is worth noting at this point
that the activation and operation of bots that don't have unmapped
fields continues. Only bots with unmapped fields "wait" for user
input before completing data storage. The new metadata and
conversion rule information will be used the next time bots are
initialized in accordance with the frequency established by the
user (20). In either event, system processing then passes, on to
software block 264.
[0102] The software in block 264 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a new calculation or a structure change. If the
calculation is not a new calculation or a structure change then
processing advances to a software block 276. Alternatively, if the
calculation is new or a structure change, then processing advances
to a software block 265.
[0103] The software in block 265 prompts the user (20) via the
identification and classification rules window (703) to identify
keywords such as company names, brands, trademarks, competitors for
pre-specified fields in the metadata mapping table (141). The user
(20) also has the option of mapping keywords to other fields in the
metadata mapping table (141). After specifying the keywords, the
user (20) is prompted to select and classify descriptive terms for
each keyword. The input from the user (20) is stored in the keyword
table (150) in the application database before processing advances
to a software block 266.
[0104] The software in block 266 checks the bot date table (149)
and deactivates any internet text bots with creation dates before
the current system date and retrieves information from the system
settings table (140), the metadata mapping table (141) and the
keyword table (150). The software in block 266 then initializes
internet text bots for each field in the metadata mapping table
(141) that mapped to a keyword in accordance with the frequency
specified by user (20) in the system settings table (140) before
advancing processing to a software block 267.
[0105] Bots are independent components of the application that have
specific tasks to perform. In the case of text bots, their tasks
are to locate, count and classify keyword matches from a specified
source and then store their findings in a specified location. Each
text bot initialized by software block 266 will store the location,
count and classification data it discovers in the classified text
table (151). Multimedia data can be processed using bots with
essentially the same specifications if software to translate and
parse the multimedia content is included in each bot. Every
internet text bot contains the information shown in Table 18.
TABLE-US-00018 TABLE 18 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (day, hour, minute,
second) 3. Storage location 4. Mapping information 5. Home URL 6.
Keyword 7. Descriptive term 1 To 7 + n. Descriptive term n
[0106] In block 267 the text bots locate and classify data from the
external database (25) in accordance with their programmed
instructions in accordance with the frequency specified by user
(20) in the system settings table (140). As each text bot locates
and classifies data from the internet (40) processing advances to a
software block 268 before the bot completes data storage. The
software in block 268 checks to see if all keyword hits are
associated with descriptive terms that have been classified. If the
software in block 268 doesn't find any unclassified "hits", then
the address, count and classified text are stored in the classified
text table (151). Alternatively, if there are terms that haven't
been classified, then processing advances to a block 269. The
software in block 269 prompts the user (20) via the identification
and classification rules window (703) to provide classification
rules for each new term. The information regarding the new
classification rules is stored in the keyword table (150) while the
newly classified text is stored in the classified text table (151).
It is worth noting at this point that the activation and operation
of bots that don't have unclassified fields continues. Only bots
with unclassified fields will "wait" for user input before
completing data storage. The new classification rules will be used
the next time bots are initialized in accordance with the frequency
established by the user (20). In either event, system processing
then passes, on to a software block 270.
[0107] The software in block 270 checks the bot date table (149)
and deactivates any external database text bots with creation dates
before the current system date and retrieves information from the
system settings table (140), the metadata mapping table (141) and
the keyword table (150). The software in block 270 then initializes
external database text bots for each field in the metadata mapping
table (141) that mapped to a keyword in accordance with the
frequency specified by user (20) in the system settings table (140)
before advancing processing to a software block 271. Every text bot
initialized by software block 270 will store the location, count
and classification data it discovers in the classified text table
(151). Every external database text bot contains the information
shown in Table 19.
TABLE-US-00019 TABLE 19 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (day, hour, minute,
second) 3. Storage location 4. Mapping information 5. Data Source
6. Keyword 7. Descriptive term 1 To 7 + n. Descriptive term n
[0108] In block 271 the text bots locate and classify data from the
external database (25) in accordance with its programmed
instructions with the frequency specified by user (20) in the
system settings table (140). As each text bot locates and
classifies data from the external database (25) processing advances
to a software block 268 before the bot completes data storage. The
software in block 268 checks to see if all keyword hits are
associated with descriptive terms that have been classified. If the
software in block 268 doesn't find any unclassified "hits", then
the address, count and classified text are stored in the classified
text table (151). Alternatively, if there are terms that haven't
been classified, then processing advances to a block 269. The
software in block 269 prompts the user (20) via the identification
and classification rules window (703) to provide classification
rules for each new term. The information regarding the new
classification rules is stored in the keyword table (150) while the
newly classified text is stored in the classified text table (151).
It is worth noting at this point that the activation and operation
of bots that don't have unclassified fields continues. Only bots
with unclassified fields "wait" for user input before completing
data storage. The new classification rules will be used the next
time bots are initialized in accordance with the frequency
established by the user (20). In either event, system processing
then passes, on to software block 276.
[0109] The software in block 276 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a new calculation or a structure change. If the
calculation is not a new calculation or a structure change then
processing advances to a software block 280. Alternatively, if the
calculation is new or a structure change, then processing advances
to a software block 277.
[0110] The software in block 277 checks the system setting table
(140) to see if there is geocoded data in the application database
(50) and to determine which on-line geocoding service (Centrus.TM.
from QM Soft or MapMarker.TM. from MapInfo) is being used. If
geospatial data is not being used, then processing advances to a
block 291. Alternatively, if the software in block 277 determines
that geospatial data is being used, processing advances to a
software block 278.
[0111] The software in block 278 prompts the user (20) via the
geospatial measure definitions window (709) to define the measures
that will be used in evaluating the elements of value. After
specifying the measures, the user (20) is prompted to select the
geospatial locus for each measure from the data already stored in
the application database (50). The input from the user (20) is
stored in the geospatial measures table (152) in the application
database before processing advances to a software block 279.
[0112] The software in block 279 checks the bot date table (149)
and deactivates any geospatial bots with creation dates before the
current system date and retrieves information from the system
settings table (140), the metadata mapping table (141) and the
geospatial measures table (152). The software in block 279 then
initializes geospatial bots for each field in the metadata mapping
table (141) that mapped to geospatial data in the application
database (50) in accordance with the frequency specified by user
(20) in the system settings table (140) before advancing processing
to a software block 280.
[0113] Bots are independent components of the application that have
specific tasks to perform. In the case of geospatial bots, their
tasks are to calculate user specified measures using a specified
geocoding service and then store the measures in a specified
location. Each geospatial bot initialized by software block 279
will store the measures it calculates in the application database
table where the geospatial data was found. Tables that could
include geospatial data include: the basic financial system table
(143), the operation system table (144), the human resource system
table (145), the external database table (146), the advanced
finance system table (147) and the soft asset system table (148).
Every geospatial bot contains the information shown in Table
20.
TABLE-US-00020 TABLE 20 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (day, hour, minute,
second) 3. Mapping information 4. Storage location 5. Geospatial
locus 6. Geospatial measure 7. Geocoding service
[0114] In block 280 the geospatial bots locate data and complete
measurements in accordance with their programmed instructions with
the frequency specified by the user (20) in the system settings
table (140). As each geospatial bot retrieves data and calculates
the geospatial measures that have been specified, processing
advances to a block 281 before the bot completes data storage. The
software in block 281 checks to see if all geospatial data located
by the bot has been measured. If the software in block 281 doesn't
find any unmeasured data, then the measurement is stored in the
application database (50). Alternatively, if there are data
elements that haven't been measured, then processing advances to a
block 282. The software in block 282 prompts the user (20) via the
geospatial measure definition window (709) to provide measurement
rules for each new term. The information regarding the new
measurement rules is stored in the geospatial measures table (152)
while the newly calculated measurement is stored in the appropriate
table in the application database (50). It is worth noting at this
point that the activation and operation of bots that don't have
unmeasured fields continues. Only the bots with unmeasured fields
"wait" for user input before completing data storage. The new
measurement rules will be used the next time bots are initialized
in accordance with the frequency established by the user (20). In
either event, system processing then passes on to a software block
291.
[0115] The software in block 291 checks: the basic financial system
table (143), the operation system table (144), the human resource
system table (145), the external database table (146), the advanced
finance system table (147), the soft asset system table (148), the
classified text table (151) and the geospatial measures table (152)
to see if data is missing from any of the periods required for
system calculation. The range of required dates was previously
calculated by the software in block 202. If there is no data
missing from any period, then processing advances to a software
block 293. Alternatively, if there is missing data for any field
for any period, then processing advances to a block 292.
[0116] The software in block 292, prompts the user (20) via the
missing data window (704) to specify the method to be used for
filling the blanks for each item that is missing data. Options the
user (20) can choose from for filling the blanks include: the
average value for the item over the entire time period, the average
value for the item over a specified period, zero, the average of
the preceeding item and the following item values and direct user
input for each missing item. If the user (20) doesn't provide input
within a specified interval, then the default missing data
procedure specified in the system settings table (140) is used.
When all the blanks have been filled and stored for all of the
missing data, system processing advances to a block 293.
[0117] The software in block 293 calculates attributes by item for
each numeric data field in the basic financial system table (143),
the operation system table (144), the human resource system table
(145), the external database table (146), the advanced finance
system table (147) and the soft asset system table (148). The
attributes calculated in this step include: cumulative total value,
the period to period rate of change in value, the rolling average
value and a series of time lagged values. In a similar fashion the
software in block 293 calculates attributes for each date field in
the specified tables including time since last occurrence,
cumulative time since first occurrence, average frequency of
occurrence and the rolling average frequency of occurrence. The
numbers derived from numeric and date fields are collectively
referred to as "item performance indicators". The software in block
293 also calculates pre-specified combinations of variables called
composite variables for measuring the strength of the different
elements of value. The item performance indicators are stored in
the table where the item source data was obtained and the composite
variables are stored in the composite variables table (153) before
processing advances to a block 294.
[0118] The software in block 294 uses attribute derivation
algorithms such as the AQ program to create combinations of the
variables that weren't pre-specified for combination. While the AQ
program is used in the preferred embodiment of the present
invention, other attribute derivation algorithms such as the LINUS
algorithms, may be used to the same effect. The software creates
these attributes using both item variables that were specified as
"element" variables and item variables that were not. The resulting
composite variables are stored in the composite variables table
(153) before processing advances to a block 295.
[0119] The software in block 295 uses Data Envelopment Analysis
(hereinafter, DEA) to determine the relative industry ranking of
the organization and enterprises being examined using the composite
variables calculated in block 293. For example, DEA can be used to
determine the relative efficiency of a company in receiving
favorable press mentions per dollar spent on advertising. When all
pre-specified industry rankings have been calculated and stored in
the industry ranking table (154), processing advances to a software
block 296.
[0120] The software in block 296 uses pattern-matching algorithms
to assign pre-designated data fields for different elements of
value to pre-defined groups with numerical values. This type of
analysis is useful in classifying purchasing patterns and/or
communications patterns as "heavy", "light", "moderate" or
"sporadic". The assignments are calculated using the "rolling
average" value for each field. The classification and the numeric
value associated with the classification are stored in the
application database (50) table where the data field is located
before processing advances to a block 297.
[0121] The software in block 297 retrieves data from the metadata
mapping table (141), creates and then stores the definitions for
the pre-defined components of value in the components of value
definition table (155). As discussed previously, the revenue
component of value is not divided into sub-components, the expense
value is divided 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) and
the capital value is divided into six sub-components: (cash,
non-cash financial assets, production equipment, other assets,
financial liabilities and equity) in the preferred embodiment. When
data storage is complete, processing advances to a software block
302 to begin the analysis of the extracted data using analysis
bots.
Analysis Bots
[0122] The flow diagrams in FIG. 6A, FIG. 6B and FIG. 6C detail the
processing that is completed by the portion of the application
software (300) that programs analysis bots to: [0123] 1. identify
the item variables, item performance indicators and composite
variables for each enterprise, element of value and sub-element of
value that drive the components of value (revenue, expense and
changes in capital), [0124] 2. create vectors that summarize the
performance of the item variables and item performance indicators
for each enterprise contribution, element of value and sub-element
of value, [0125] 3. determine the appropriate cost of capital and
value the organization and enterprise real options; [0126] 4.
determine the appropriate cost of capital, value and allocate the
industry real options to each organization or enterprise on the
basis of relative element strength; [0127] 5. determine the
expected life of each element of value and sub-element of value;
[0128] 6. calculate the organization and enterprise current
operation values and value the revenue, expense and capital
components said current operations using the information prepared
in the previous stage of processing; [0129] 7. specify and optimize
predictive models to determine the relationship between the vectors
determined in step 2 and the revenue, expense and capital values
determined in step 6, [0130] 8. combine the results of the fifth,
sixth and seventh stages of processing to determine the value of
each, enterprise contribution, element and sub-element (as shown in
Table 7);
[0131] Processing in this portion of the application begins in
software block 302. The software in block 302 checks the system
settings table (140) in the application database (50) to determine
if the current calculation is a new calculation or a structure
change. If the calculation is not a new calculation or a structure
change then processing advances to a software block 3110.
Alternatively, if the calculation is new or a structure change,
then processing advances to a software block 303.
[0132] The software in block 303 retrieves data from the meta data
mapping table (141) and the soft asset system table (148) and then
assigns item variables, item performance indicators and composite
variables to each element of value using a two step process. First,
item variables and item performance indicators are assigned to
elements of value based on the soft asset management system they
correspond to (for example, all item variables from a brand
management system and all item performance indicators derived from
brand management system variables are assigned to the brand element
of value). Second, pre-defined composite variables are assigned to
the element of value they were assigned to measure in the metadata
mapping table (141). After the assignment of variables and
indicators to elements is complete, the resulting assignments are
saved to the element of value definition table (155) and processing
advances to a block 304.
[0133] The software in block 304 checks the bot date table (149)
and deactivates any clustering bots with creation dates before the
current system date. The software in block 304 then initializes
bots as required for each component of value. The bots: activate in
accordance with the frequency specified by the user (20) in the
system settings table (140), retrieve the information from the
system settings table (140), the metadata mapping table (141) and
the component of value definition table (156) as required and
define segments for the component of value data before saving the
resulting cluster information in the application database (50).
[0134] Bots are independent components of the application that have
specific tasks to perform. In the case of predictive model bots,
their primary task is to segment the component and sub-component of
value variables into distinct clusters that share similar
characteristics. The clustering bot assigns a unique id number to
each "cluster" it identifies and stores the unique id numbers in
the cluster id table (157). Every item variable for every component
and sub-component of value is assigned to one of the unique
clusters. The cluster id for each variable is saved in the data
record for each item variable in the table where it resides. The
item variables are segmented into a number of clusters less than or
equal to the maximum specified by the user (20) in the system
settings. The data is segmented using the "default" clustering
algorithm the user (20) specified in the system settings. The
system of the present invention provides the user (20) with the
choice of several clustering algorithms including: an unsupervised
"Kohonen" neural network, K-nearest neighbor, Expectation
Maximization (EM) and the segmental K-means algorithm. For
algorithms that normally require the number of clusters to be
specified the bot will iterate the number of clusters until it
finds the cleanest segmentation for the data. Every clustering bot
contains the information shown in Table 21.
TABLE-US-00021 TABLE 21 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (day, hour, minute,
second) 3. Mapping information 4. Storage location 5. Component or
subcomponent of value 6. Clustering algorithm type 7. Maximum
number of clusters 8. Variable 1 . . . 8 + n. Variable n
When bots in block 304 have identified and stored cluster
assignments for the item variables associated with each component
and subcomponent of value, processing advances to a software block
305.
[0135] The software in block 305 checks the bot date table (149)
and deactivates any predictive model bots with creation dates
before the current system date. The software in block 305 then
retrieves the information from the system settings table (140), the
metadata mapping table (141), the element of value definition table
(155) and the component of value definition table (156) required to
initialize predictive model bots for each component of value at
every level in the organization.
[0136] Bots are independent components of the application that have
specific tasks to perform. In the case of predictive model bots,
their primary task is determine the relationship between the item
variables, item performance indicators and composite variables
(collectively hereinafter, "the variables") and the components of
value (and sub-components of value) by cluster at each level of the
organization. A series of predictive model bots are initialized at
this stage because it is impossible to know in advance which
predictive model type will produce the "best" predictive model for
the data from each commercial enterprise. The series for each model
includes 9 predictive model bot types: neural network; CART;
projection pursuit regression; generalized additive model (GAM),
redundant regression network; boosted Naive Bayes Regression; MARS;
linear regression; and stepwise regression. The software in block
305 generates this series of predictive model bots for the levels
of the organization shown in Table 22.
TABLE-US-00022 TABLE 22 Predictive models by organization level
Organization: Enterprise variables relationship to organization
revenue component of value by cluster Enterprise variables
relationship to organization expense subcomponents of value by
cluster Enterprise variables relationship to organization capital
change subcomponents of value by cluster Enterprise: Element
variables relationship to enterprise revenue component of value by
cluster Element variables relationship to enterprise expense
subcomponents of value by cluster Element variables relationship to
enterprise capital change subcomponents of value by cluster Element
of Value: Sub-element of value variables relationship to element of
value
Every predictive model bot contains the information shown in Table
23.
TABLE-US-00023 TABLE 23 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (day, hour, minute,
second) 3. Mapping information 4. Storage location 5. Component or
subcomponent of value 6. Cluster (ID) 7. Enterprise, Element or
Sub-Element ID 8. Predictive Model Type 9. Variable 1 . . . 9 + n.
Variable n
[0137] After predictive model bots for each level in the
organization are initialized, the bots activate in accordance with
the frequency specified by the user (20) in the system settings
table (140). Once activated, the bots retrieve the required data
from the appropriate table in the application database (50) and
randomly partition the item variables, item performance indicators
and composite variables into a training sets and a test set. The
software in block 305 uses "bootstrapping" where the different
training data sets are created by re-sampling with replacement from
the original training set, so data records may occur more than
once. The same sets of data will be used to train and then test
each predictive model bot. When the predictive model bots complete
their training and testing, processing advances to a block 306.
[0138] The software in block 306 uses a variable selection
algorithm such as stepwise regression (other algorithms can be
used) to combine the results from the predictive model bot analyses
for each model to determine the best set of variables for each
model. The models having the smallest amount of error as measured
by applying the mean squared error algorithm to the test data are
given preference in determining the best set of variables. As a
result of this processing the best set of variables contain the
item variables, item performance indicators and composite variables
that correlate most strongly with changes in the components of
value. The best set of variables will hereinafter be referred to as
the "value drivers". Eliminating low correlation factors from the
initial configuration of the vector creation algorithms increases
the efficiency of the next stage of system processing. Other error
algorithms alone or in combination may be substituted for the mean
squared error algorithm. After the best set of variables have been
selected and stored in the element variables table (158) for all
models at all levels, the software in block 306 tests the
independence of the value drivers at the enterprise, element and
sub-element level before processing advances to a block 307.
[0139] The software in block 307 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a new calculation, a structure change or if the
interaction between value drivers has changed from being highly
correlated to being independent. If the calculation is not a new
calculation, a structure change or a change to independent value
driver status, then processing advances to a software block 310.
Alternatively, if the calculation is new, a structure change or a
change to independent status, then processing advances to a
software block 308. The software in block 308 checks the bot date
table (149) and deactivates any induction bots with creation dates
before the current system date. The software in block 308 then
retrieves the information from the system settings table (140), the
metadata mapping table (141), the component of value definition
table (156) and the element variables table (158) as required to
initialize induction model bots for each enterprise, element of
value and sub-element of value at every level in the organization
in accordance with the frequency specified by the user (20) in the
system settings table (140) before processing advances to a block
309.
[0140] Bots are independent components of the application that have
specific tasks to perform. In the case of induction bots, their
primary tasks are to refine the item variable, item performance
indicator and composite variable selection to reflect only causal
variables and to produce formulas, (hereinafter, vectors) that
summarize the relationship between the item variables, item
performance indicators and composite variables and changes in the
component or sub-component of value being examined. (Note: these
variables are simply grouped together to represent an element
vector when they are dependent). A series of induction bots are
initialized at this stage because it is impossible to know in
advance which induction algorithm will produce the "best" vector
for the best fit variables from each model. The series for each
model includes 4 induction bot types: entropy minimization,
LaGrange, Bayesian and path analysis. The software in block 308
generates this series of induction bots for each set of variables
stored in the element variables table (158) in the previous stage
in processing. Every induction bot contains the information shown
in Table 24.
TABLE-US-00024 TABLE 24 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (day, hour, minute,
second) 3. Mapping information 4. Storage location 5. Component or
subcomponent of value 6. Cluster ID 7. Enterprise, Element or
Sub-Element ID 8. Variable Set 9. Induction algorithm type
[0141] After the induction bots are initialized by the software in
block 308 processing passes to a software block 309. In block 309
bots activate in accordance with the frequency specified by the
user (20) in the system settings table (140). Once activated, they
retrieve the element variable information for each model from the
element variable table (158) and sub-divides the variables into two
sets, one for training and one for testing. The same set of
training data is used by each of the different types of bots for
each model. After the induction bots complete their processing for
each model, the software in block 309 uses a model selection
algorithm to identify the vector that best fits the data for each
enterprise, element or sub-element being analyzed. For the system
of the present invention, a cross validation algorithm is used for
model selection. The software in block 309 saves the best fit
vector in the vector table (159) in the application database (50)
and processing returns to advances to a block 310. The software in
block 310 tests the value drivers or vectors to see if there are
"missing" value drivers that are influencing the results. If the
software in block 310 doesn't detect any missing value drivers,
then system processing advances to a block 322. Alternatively, if
missing value drivers are detected by the software in block 310,
then processing advances to a software block 321.
[0142] The software in block 321 prompts the user (20) via the
variable identification window (710) to adjust the specification(s)
for the affected enterprise, element of value or subelement of
value. After the input from the user (20) is saved in the system
settings table (140) and/or the element of value definition table
(155), system processing advances to a software block 323. The
software in block 323 checks the in the system settings table (140)
and/or the element of value definition table (155) to see if there
any changes in structure. If there have been changes in the
structure, then processing advances to a block 205 and the system
processing described previously is repeated. Alternatively, if
there are no changes in structure, then processing advances to a
block 325.
[0143] The software in block 325 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a new calculation or a structure change. If the
calculation is not a new calculation or a structure change, then
processing advances to a software block 329. Alternatively, if the
calculation is new or a structure change, then processing advances
to a software block 326.
[0144] The software in block 326 checks the bot date table (149)
and deactivates any option bots with creation dates before the
current system date. The software in block 326 then retrieves the
information from the system settings table (140), the metadata
mapping table (141), the basic financial system database (143), the
external database table (146) and the advanced finance system table
(147) as required to initialize option bots for the organization,
the industry and each enterprise in the organization before
processing advances to a block 327.
[0145] Bots are independent components of the application that have
specific tasks to perform. In the case of option bots, their
primary tasks are to calculate the cost of capital (if the user
(20) hasn't specified the cost of capital in the system settings
table (140)) and value the real options for the industry, the
organization, and each enterprise in the organization. The base
cost of capital is calculated using a well known formula for the
industry and each enterprise. The bots then use the data regarding
the similarity of the "soft" asset profiles between the proposed
real option activity and the existing industry, organization and
enterprise profiles to determine the multiple on the cost of
capital that will be used in valuing the real option. The closer
the real option profile is to the existing profile, the closer the
multiple is to one. If sufficient data is available, pattern
matching algorithms can be used to replace the assessment by the
user (20). After the cost of capital multiple has been determined,
the value of the real option is calculated using dynamic
programming algorithms in a manner that is well known and stored in
the real option value table (162). Real option values are
calculated using dynamic programming algorithms. The real option
can be valued using other algorithms including binomial, neural
network or Black Scholes algorithms. The software in block 326
generates option bots for the industry, the organization and each
enterprise in the organization.
[0146] Option bots contain the information shown in Table 25.
TABLE-US-00025 TABLE 25 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (day, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
or Enterprise ID 6. Real Option Type (Industry, Organization or
Enterprise) 7. Real Option 8. Allocation % (if applicable)
[0147] After the option bots are initialized by the software in
block 326 processing passes to a block 327. In block 327 the bots
activate in accordance with the frequency specified by the user
(20) in the system settings table (140). After being activated, the
bots retrieve information for the organization, the industry and
each enterprise in the organization from the basic financial system
database (143), the external database table (146) and the advanced
finance system table (147) as required to complete the option
valuation. After the cost of capital multiple has been determined
the value of the real option is calculated using dynamic
programming algorithms in a manner that is well known. The
resulting values are then saved in the real option value table
(162) in the application database (50) before processing advances
to a block 328.
[0148] The software in block 328 uses the item performance
indicators produced by DEA analysis in blocks 304, 308 and 314 and
the percentage of industry real options controlled by the
enterprise to determine the allocation percentage for industry
options. The more dominant the organization and enterprise--as
indicated by the industry rank for the intangible element
indicators, the greater the allocation of industry real options.
After the software in block 328 saves the information regarding the
allocation of industry real options to the organization and each
enterprise in the organization to the real option value table (162)
in the application database (50) before advancing processing to a
block 329.
[0149] The software in block 329 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a new calculation or a structure change. If the
calculation is not a new calculation or a structure change, then
processing advances to a software block 333. Alternatively, if the
calculation is new or a structure change, then processing advances
to a software block 330.
[0150] The software in block 330 checks the bot date table (149)
and deactivates any cash flow bots with creation dates before the
current system date. The software in block 326 then retrieves the
information from the system settings table (140), the metadata
mapping table (141) and the component of value definition table
(156) as required to initialize cash flow bots for the organization
and each enterprise in the organization in accordance with the
frequency specified by the user (20) in the system settings table
(140) before processing advances to a block 331.
[0151] Bots are independent components of the application that have
specific tasks to perform. In the case of cash flow bots, their
primary tasks are to calculate the cash flow for the organization
and each enterprise in the organization for every time period where
data is available and to forecast a steady state cash flow for the
organization and each enterprise in the organization. Cash flow is
calculated using a well known formula where cash flow equals period
revenue minus period expense plus the period change in capital plus
non-cash depreciation/amortization for the period. The steady state
cash flow is calculated for the organization and each enterprise in
the organization using forecasting methods identical to those
disclosed previously in U.S. Pat. No. 5,615,109 to forecast
revenue, expenses, capital changes and depreciation separately
before calculating the cash flow. The software in block 326
generates cash flow bots for the organization and each enterprise
in the organization.
[0152] Every cash flow bot contains the information shown in Table
26.
TABLE-US-00026 TABLE 26 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (day, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
or Enterprise ID 6. Components of value
[0153] After the cash flow bots are initialized by the software in
block 330 processing passes to a block 331. In block 331 the bots
activate in accordance with the frequency specified by the user
(20) in the system settings table (140). After being activated the
bots retrieve the component of value information for the
organization and each enterprise in the organization from the
component of value definition table (156). The cash flow bots then
complete the calculation and forecast of cash flow for the
organization and each enterprise in the organization before saving
the resulting values by period in the cash flow table (161) in the
application database (50) before processing advances to a block
333.
[0154] The software in block 333 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a new calculation or a structure change. If the
calculation is not a new calculation or a structure change, then
processing advances to a software block 343. Alternatively, if the
calculation is new or a structure change, then processing advances
to a software block 341.
[0155] The software in block 341 checks the bot date table (149)
and deactivates any element life bots with creation dates before
the current system date. The software in block 341 then retrieves
the information from the system settings table (140), the metadata
mapping table (141) and the element of value definition table (155)
as required to initialize element life bots for each element and
sub-element of value in the organization before processing advances
to a block 342.
[0156] Bots are independent components of the application that have
specific tasks to perform. In the case of element life bots, their
primary task is to determine the expected life of each element and
sub-element of value for each enterprise in the organization. There
are three methods for evaluating the expected life of the elements
and sub-elements of value. Elements of value that are defined by a
population of members (such as: channel partners, customers,
employees and vendors) will have their lives estimated by analyzing
and forecasting the lives of the members of the population. The
forecasting of member lives will be determined by the "best" fit
solution from competing life estimation methods including the Iowa
type survivor curves, Weibull distribution survivor curves,
Gompertz-Makeham survivor curves, polynomial equations and the
forecasting methodology disclosed in U.S. Pat. No. 5,615,109.
Elements of value (such as some parts of Intellectual
Property--patents) that have legally defined lives will have their
lives calculated using the time period between the current date and
the expiration date of the element or sub-element. Finally,
elements of value and sub-element of value (such as brand names,
information technology and processes) that do not have defined
lives and that do not consist of a collection of members will have
their lives estimated by comparing the relative strength and
stability of the element vectors with the relative stability of the
enterprise CAP. The resulting values are stored in the element of
value definition table (155) for each element and sub-element of
value of each enterprise in the organization.
[0157] Every element life bot contains the information shown in
Table 27.
TABLE-US-00027 TABLE 27 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (day, hour, minute,
second) 3. Mapping information 4. Storage location 5. Element of
Sub-Element of Value 6. Life Estimation Method (population
analysis, date calculation or relative CAP)
[0158] After the element life bots are initialized by the software
in block 341 processing passes to block 342. In block 342 the
element life bots activate in accordance with the frequency
specified by the user (20) in the system settings table (140).
After being activated, the bots retrieve information for each
element and sub-element of value from the element of value
definition table (155) as required to complete the estimate of
element life. The resulting values are then saved in the element of
value definition table (155) in the application database (50)
before processing advances to a block 343.
[0159] The software in block 343 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a new calculation or a structure change. If the
calculation is not a new calculation or a structure change, then
processing advances to a software block 402. Alternatively, if the
calculation is new or a structure change, then processing advances
to a software block 345.
[0160] The software in block 345 checks the bot date table (149)
and deactivates any component capitalization bots with creation
dates before the current system date. The software in block 341
then retrieves the information from the system settings table
(140), the metadata mapping table (141) and the component of value
definition table (156) as required to initialize component
capitalization bots for the organization and each enterprise in the
organization before processing advances to a block 346.
[0161] Bots are independent components of the application that have
specific tasks to perform. In the case of component capitalization
bots, their task is to determine the capitalized value of the
components of value, forecast revenue, expense or capital
requirements, for the organization and for each enterprise in the
organization in accordance with the formula shown in Table 28.
TABLE-US-00028 TABLE 28 Value = F.sub.f1/(1 + K) + F.sub.f2/(1 +
K).sup.2 + F.sub.f3/(1 + K).sup.3 + F.sub.f4/(1 + K).sup.4 +
(F.sub.f4 .times. (1 + g))/(1 + K).sup.5) + (F.sub.f4 .times. (1 +
g).sup.2)/ (1 + K).sup.6)... + (F.sub.f4 .times. (1 + g).sup.N)/(1
+ K).sup.N+4) Where: F.sub.fx = Forecast revenue, expense or
capital requirements for year .times. after valuation date (from
advanced finance system) N = Number of years in CAP (from prior
calculation) K = Cost of capital - % per year (from prior
calculation) g = Forecast growth rate during CAP - % per year (from
advanced finance system)
After the capitalized value of every component and sub-component of
value is complete, the results are stored in the component of value
definition table (156) in the application database (50).
[0162] Every component capitalization bot contains the information
shown in Table 29.
TABLE-US-00029 TABLE 29 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (day, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
or Enterprise ID 6. Component of Value (Revenue, Expense or Capital
Change) 7. Sub Component of Value
[0163] After the component capitalization bots are initialized by
the software in block 345 processing passes to block 346. In block
346 the component capitalization bots activate in accordance with
the frequency specified by the user (20) in the system settings
table (140). After being activated, the bots retrieve information
for each component and sub-component of value from the advanced
finance system table (147) and the component of value definition
table (156) as required to calculate the capitalized value of each
component. The resulting values are then saved in the component of
value definition table (156) in the application database (50)
before processing advances to a block 347.
[0164] The software in block 347 checks the bot date table (149)
and deactivates any valuation bots with creation dates before the
current system date. The software in block 347 then retrieves the
information from the system settings table (140), the metadata
mapping table (141), the element of value definition table (155),
the component of value definition table (156) as required to
initialize valuation bots for each enterprise, element and
sub-element of value in the organization before processing advances
to a block 348.
[0165] Bots are independent components of the application that have
specific tasks to perform. In the case of valuation bots, their
task is to calculate the contribution of every enterprise, element
of value and sub-element of value in the organization using the
overall procedure outlined in Table 7. The first step in completing
the calculation in accordance with the procedure outlined in Table
7, is determining the relative contribution of each enterprise and
element of value by using a series of predictive models to find the
best fit relationship between:
[0166] 1. the enterprise contribution vectors and the organization
components of value;
[0167] 2. the element of value vectors and the enterprise
components of value; and
[0168] 3. the sub-element of value vectors and the element of value
they correspond to.
The system of the present invention uses 9 different types of
predictive models to determine relative contribution: neural
network; CART; projection pursuit regression; generalized additive
model (GAM), redundant regression network; boosted Naive Bayes
Regression; MARS; linear regression; and stepwise regression to
determine relative contribution. The model having the smallest
amount of error as measured by applying the mean squared error
algorithm to the test data is the best fit model. The "relative
contribution algorithm" used for completing the analysis varies
with the model that was selected as the "best-fit". For example, if
the "best-fit" model is a neural net model, then the portion of
revenue attributable to each input vector is determined by the
formula shown in Table 30.
TABLE-US-00030 TABLE 30 ( k = 1 k = m j = 1 j = n .times. O k / j =
1 j = n I ik ) / k = 1 k = m j = 1 j = n I jk .times. O k
##EQU00002## Where I.sub.jk = Absolute value of the input weight
from input node j to hidden node k O.sub.k = Absolute value of
output weight from hidden node k m = number of hidden nodes n =
number of input nodes
After the relative contribution of each enterprise, element of
value and sub-element of value is determined, the results of this
analysis are combined with the previously calculated information
regarding element life and capitalized component value to complete
the valuation of each: enterprise contribution, element of value
and sub-element using the approach shown in Table 31.
TABLE-US-00031 TABLE 31 Element Gross Value Percentage Life/CAP Net
Value Revenue value = $120 M 20% 80% Value = $19.2 M Expense value
= ($80 M) 10% 100% Value = ($8.0) M Capital value = ($5 M) 5% 80%
Value = ($0.2) M Total value = $35 M Net value for this element:
Value = $11.0 M
The resulting values are stored in the element of value definition
table (155) for each element and sub-element of value of each
enterprise in the organization.
[0169] Every valuation bot contains the information shown in Table
32.
TABLE-US-00032 TABLE 32 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (day, hour, minute,
second) 3. Mapping information 4. Storage location 5. Enterprise
Contribution, Element of Value or Sub-Element of Value 6.
Organization, Enteprise or Element of Value ID
[0170] After the valuation bots are initialized by the software in
block 347 processing passes to block 348. In block 348 the
valuation bots activate in accordance with the frequency specified
by the user (20) in the system settings table (140). After being
activated, the bots retrieve information from the element of value
definition table (155) and the component of value definition table
(156) as required to complete the valuation. The resulting values
are then saved in the element of value definition table (155) in
the application database (50) before processing advances to a block
349.
[0171] The software in block 349 checks the bot date table (149)
and deactivates any residual bots with creation dates before the
current system date. The software in block 349 then retrieves the
information from the system settings table (140), the metadata
mapping table (141) and the element of value definition table (155)
as required to initialize residual bots for each enterprise in the
organization.
[0172] Bots are independent components of the application that have
specific tasks to perform. In the case of residual bots, their task
is to retrieve data from the as required from the element of value
definition table (155) and the component of value definition table
(156) and then calculate the residual going concern value for the
organization and each enterprise in the organization in accordance
with the formula shown in Table 33.
TABLE-US-00033 TABLE 33 Residual Going Concern Value = Total
Current-Operation Value - .SIGMA. Financial Asset Values - .SIGMA.
Elements of value
[0173] Every residual bot contains the information shown in Table
34.
TABLE-US-00034 TABLE 34 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (day, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
or Enterprise ID
[0174] After the residual bots are initialized by the software in
block 348 processing passes to block 349. In block 349 the residual
bots activate in accordance with the frequency specified by the
user (20) in the system settings table (140). After being
activated, the bots retrieve information from the element of value
definition table (155) and the component of value definition table
(156) as required to complete the residual calculation for the
organization or enterprise. After the calculation is complete, the
resulting values are then saved in the element of value definition
table (155) in the application database (50) before processing
advances to a block 402.
Analyze Market Sentiment
[0175] The flow diagram in FIG. 7 details the processing that is
completed by the portion of the application software (400) that
analyzes the market sentiment for the enterprises in the
organization. Processing begins in a software block 402.
[0176] The software in block 402 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a new calculation or a structure change. If the
calculation is not a new calculation or a structure change, then
processing advances to a software block 409. Alternatively, if the
calculation is new or a structure change, then processing advances
to a software block 404.
[0177] The software in block 404 checks the bot date table (149)
and deactivates any sentiment calculation bots with creation dates
before the current system date. The software in block 404 then
retrieves the information from the system settings table (140), the
metadata mapping table (141), the external database table (146),
the element of value definition table (155), the component of value
definition table (156) and the real option value table (162) as
required to initialize sentiment calculation bots for each
enterprise in the organization.
[0178] Bots are independent components of the application that have
specific tasks to perform. In the case of sentiment calculation
bots, their task is to retrieve data as required from: the external
database table (146), the element of value definition table (155),
the component of value definition table (156) and the real option
value table (162) then calculate the sentiment for each enterprise
in the organization in accordance with the formula shown in Table
35.
TABLE-US-00035 TABLE 35 Sentiment = Total Market Value - Total
Current-Operation Value - .SIGMA. Real Option Values
[0179] Every sentiment calculation bot contains the information
shown in Table 36.
TABLE-US-00036 TABLE 36 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (day, hour, minute,
second) 3. Mapping information 4. Storage location 5. Enterprise
ID
[0180] After the sentiment calculation bots are initialized by the
software in block 404 processing passes to block 405. In block 405
the sentiment calculation bots activate in accordance with the
frequency specified by the user (20) in the system settings table
(140). After being activated, the bots retrieve information from
the external database table (146), the element of value definition
table (155), the component of value definition table (156) and the
real option value table (162) as required to complete the sentiment
calculation for each enterprise. After the calculation is complete,
the resulting values are then saved in the enterprise sentiment
table (166) in the application database (50) before processing
advances to a block 409.
[0181] The software in block 409 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a new calculation or a structure change. If the
calculation is not a new calculation or a structure change, then
processing advances to a software block 412. Alternatively, if the
calculation is new or a structure change, then processing advances
to a software block 410.
[0182] The software in block 410 checks the bot date table (149)
and deactivates any sentiment factor bots with creation dates
before the current system date. The software in block 410 then
retrieves the information from the system settings table (140), the
metadata mapping table (141), the external database table (146),
the element of value definition table (155), the component of value
definition table (156) and the real option value table (162) as
required to initialize sentiment factor bots for each enterprise in
the organization.
[0183] Bots are independent components of the application that have
specific tasks to perform. In the case of sentiment factor bots,
their primary task is to calculate sentiment related attributes
including cumulative total value, the period to period rate of
change in value, the rolling average value, a series of time lagged
values as well as pre-specified combinations of variables called
composite variables. The bots also use attribute derivation
algorithms such as the AQ program to create combinations of the
variables that weren't pre-specified for combination. While the AQ
program is used in the preferred embodiment of the present
invention, other attribute derivation algorithms such as the LINUS
algorithms, may be used to the same effect. The newly calculated
sentiment factors are stored in the sentiment factor table (169)
before processing advances to a block 411.
[0184] Every sentiment factor bot contains the information shown in
Table 37.
TABLE-US-00037 TABLE 37 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (day, hour, minute,
second) 3. Mapping information 4. Storage location 5. Enterprise
ID
[0185] After the sentiment factor bots are initialized by the
software in block 410 processing passes to block 411. In block 411
the sentiment factor bots activate in accordance with the frequency
specified by the user (20) in the system settings table (140).
After being activated, the bots retrieve information from the
external database table (146), the element of value definition
table (155), the component of value definition table (156) and the
real option value table (162) as required to generate the sentiment
factors for each enterprise. After the calculation is complete, the
resulting values are then saved in the sentiment factors table
(169) in the application database (50) before processing advances
to a block 412.
[0186] The software in block 412 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a new calculation or a structure change. If the
calculation is not a new calculation or a structure change, then
processing advances to a software block 502. Alternatively, if the
calculation is new or a structure change, then processing advances
to a software block 413.
[0187] The software in block 413 checks the bot date table (149)
and deactivates any sentiment analysis bots with creation dates
before the current system date. The software in block 413 then
retrieves the information from the system settings table (140), the
metadata mapping table (141), the external database table (146),
the element of value definition table (155), the component of value
definition table (156), the real option value table (162), the
enterprise sentiment table (166) and the sentiment factors table
(169) as required to initialize sentiment analysis bots for each
enterprise in the organization.
[0188] Bots are independent components of the application that have
specific tasks to perform. In the case of sentiment analysis bots,
their primary task is determine the relationship between sentiment
factors and the calculated sentiment for each enterprise in the
organization. A series of predictive model bots are initialized at
this stage because it is impossible to know in advance which
predictive model type will produce the "best" predictive model for
the data from each commercial enterprise. The series for each model
includes 9 predictive model bot types: neural network; CART;
projection pursuit regression; generalized additive model (GAM),
redundant regression network; boosted Naive Bayes Regression; MARS;
linear regression; and stepwise regression.
[0189] Every sentiment analysis bot contains the information shown
in Table 38.
TABLE-US-00038 TABLE 38 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (day, hour, minute,
second) 3. Mapping information 4. Storage location 5. Enterprise
ID
[0190] After the sentiment analysis bots are initialized by the
software in block 413 processing passes to block 414. In block 411
the sentiment analysis bots activate in accordance with the
frequency specified by the user (20) in the system settings table
(140). After being activated, the bots retrieve information from
the system settings table (140), the metadata mapping table (141),
the enterprise sentiment table (166) and the sentiment factors
table (169) and randomly partition sentiment factors for each
enterprise into a training set and a test set. The software in
block 414 uses "bootstrapping" where the different training data
sets are created by re-sampling with replacement from the original
training set, so data records may occur more than once. The same
sets of data will be used to train and then test each predictive
model bot. When the predictive model bots complete their training
and testing, the resulting sets of "best fit" factors are then
saved in the sentiment factors table (169) in the application
database (50) before processing advances to a block 415.
[0191] The software in block 415 combines the results from the
sentiment analysis from each bot type to determine the best set of
sentiment factors for each enterprise. The models having the
smallest amount of error as measured by applying the mean squared
error algorithm to the test data are given preference in
determining the best set of variables. As a result of this
processing the best set of variables contain the sentiment factors
that correlate most strongly with changes in the components of
value. The best set of variables will hereinafter be referred to as
the "sentiment drivers". The software in block 415 saves an
indicator in each item record identifying the sentiment factors
that are "sentiment drivers" before processing advances to block
502.
Display and Print Results
[0192] The flow diagram in FIG. 8 details the processing that is
completed by the portion of the application software (500) that
creates and displays financial management reports, optionally
prints financial management reports and optionally trades company
equity securities. The financial management reports use the Value
Map.RTM. report format to summarize information about the
categories of business value for the organization and each
enterprise in the organization. If there are prior valuations, then
a Value Creation report will be created to highlight changes in the
categories of business value during the period between the prior
valuation and the current valuation date.
[0193] System processing in this portion of the application
software (900) begins in a block 502. The software in block 502
checks the system settings table (140) in the application database
(50) to determine if the current calculation is a new calculation
or a structure change. If the calculation is not a new calculation
or a structure change, then processing advances to a software block
505. Alternatively, if the calculation is new or a structure
change, then processing advances to a software block 504.
[0194] The software in block 504 checks the bot date table (149)
and deactivates any report bots with creation dates before the
current system date. The software in block 504 then retrieves the
information from the system settings table (140) and the report
table (164) as required to determine the format (Value Map.RTM.
& Value Creation format and/or traditional: balance sheet,
income & cash flow statement format) and type of report (text
or graphical) bots that need to be created for the organization,
each enterprise in the organization and the sub-elements of value
before processing advances to block 505.
[0195] Bots are independent components of the application that have
specific tasks to perform. In the case of report bots, their
primary tasks are to: retrieve data from the system settings table
(140), the basic finance system table (143), the advanced finance
system table (147), the element of value definition table (155),
the component of value definition table (156) and the real option
value table (162), calculate market equity using the formula shown
in Table 39 and generate the reports in the specified formats for
the specified time period(s).
TABLE-US-00039 TABLE 39 Market Equity = (Current Operation Value) +
(.SIGMA. Real Option Values) - (.SIGMA. Short Term Liabilities) -
(.SIGMA. Contingent & Long Term Liabilities) - (Book Value of
Equity) *calculated in accordance with GAAP
[0196] Every report bot contains the information shown in Table
40.
TABLE-US-00040 TABLE 40 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (day, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization,
Enterprise or Element of ValueID 6. Report Format (text or
graphical) 7. Report Type (Value Map .RTM./Value Creation format or
traditional format)
[0197] The general format of the Value Map.RTM. Reports is shown in
Table 41 and Table 42.
TABLE-US-00041 TABLE 41 Value Map .TM. Report XYZ Corporation
ASSETS 12/31/19XX 12/31/XXXX Current Operation: Financial Assets
Cash and Marketable Securities: $7,871,230 $15,097,057 Accounts
Receivable $39,881,200 $42,234,410 Inventory $19,801,140
$21,566,540 Property, Plant & Equipment $22,800,000 $21,221,190
Prepaid Expenses $2,071,440 $1,795,890 Subtotal Current Operation
Assets: $92,425,010 $101,915,087 Cash Generating "Soft" Assets
Brandnames $17,000,000 $12,000,000 Customer Base $62,000,000
$49,500,000 Employees $10,750,000 $8,250,000 Strategic Alliances
$33,250,000 $33,500,000 Vendors $11,500,000 $9,750,000 General
Going Concern Value $31,250,000 $31,750,000 Subtotal Cash
Generating Assets $165,750,000 $144,750,000 Subtotal Current
Operation $258,175,010 $246,665,087 Real Options: GUI Market Option
$12,500,000 $10,000,000 IPX Market Option $17,000,000 $12,500,000
Subtotal Enterprise Options $29,500,000 $22,500,000 Industry Growth
Options: $80,000,000 $60,000,000 Subtotal Real Options $109,500,000
$82,500,000 Total Assets & Options $367,675,010 $329,165,087
Market Sentiment $27,123,116 $18,273,698 Total Market Value
$394,798,126 $347,438,785 Copyright, Jeff S. Eder 1999, All Rights
Reserved
TABLE-US-00042 TABLE 42 Value Map .TM. Report XYZ Corporation
LIABILITIES & SHAREHOLDER EQUITY Liabilities: Accounts Payable
$15,895,585 $18,879,949 Salaries Payable $8,766,995 $10,468,305
Short Term Debt, Notes Payable $20,189,900 $11,506,130 Taxes
Payable $12,430,120 $9,099,880 Subtotal Short Term Liabilities
$57,282,600 $49,954,264 Contingent Liabilities $5,100,000
$4,800,000 Long Term Debt $17,800,000 $20,916,650 Total Liabilities
$80,182,600 $75,670,914 Shareholder's Equity: Stock $2,000,000
$2,000,000 Market Equity $27,123,116 $18,273,698 Retained Earnings
$15,342,410 $29,044,173 Future Earnings $270,150,000 $222,450,000
Total Shareholder's Equity $314,615,526 $271,767,871 Total
Liabilities & Shareholder Equity $394,798,126 $347,438,785
Copyright, Jeff S. Eder 1999, All Rights Reserved
After the report bots are initialized by the software in block 504
processing passes to a block 505. In block 505 the bots activate in
accordance with the frequency specified by the user (20) in the
system settings table (140). After being activated, the bots
retrieve information for the organization, enterprise or element of
value from the element of value definition table (155), the
component of value definition table (156) and the real option value
table (1) as required to complete the report in accordance with the
pre-specified format. The resulting reports are then saved in the
report table (164) in the application database (50). The software
in block 505 creates and displays all Value Map.RTM. reports and
Value Creation Statement reports the user (20) requests using the
report selection and display data window (705) in the general
format shown in Table 41. Graphical reports such as those in a
Hyperbolic Tree format that have been saved over time can be
displayed like a "movie" shows the evolution of value over time.
The software in block 505 also prompts the user (20) using the
report selection and display data window (705) to select reports
for printing. After the user's input regarding reports to print has
been stored in the reports table (164), processing advances to
block 507. If the user doesn't provide any input, then only the
default reports specified by the user (20) in the system settings
table (140) will be produced for storage.
[0198] The software in block 507 checks the reports tables (164) to
determine if any reports have been designated for printing. If
reports have been designated for printing, then processing advances
to a block 506. The software in block 506 sends the designated
reports to the printer (118). After the reports have been sent to
the printer (118), processing advances to a software block 509.
Alternatively, if no reports were designated for printing then
processing advances directly from block 507 to block 509.
[0199] The software in block 509 checks the system settings table
(140) in the application database (50) to determine if trading in
enterprise equity is authorized. If trading in enterprise equity is
not authorized, then processing advances to a software block 507.
Alternatively, if trading in enterprise equity is authorized, then
processing advances to a software block 510.
[0200] The software in block 510 retrieves information from the
system settings table (140) and the advanced finance system table
(147) that is required to calculate the minimum amount of cash that
will be available for investment in enterprise equity during the
next 12 month period. The system settings table (140) contains the
minimum amount of cash and available securities that the user (20)
indicated was required for enterprise operation while the advanced
finance system table (147) contains a forecast of the cash balance
for the enterprise for each period during the next 12 months. After
the amount of available cash for each enterprise is calculated and
stored in the equity purchase table (165), processing advances to a
software block 511.
[0201] The software in block 511 checks the equity purchase table
(165) and enterprise sentiment table (166) to see if there is
negative sentiment in any enterprise with available cash. If there
are no enterprises with negative sentiment and available cash, then
processing advances a software block 602. Alternatively, if there
are enterprises with available cash and negative sentiment, then
processing advances to a software block 512.
[0202] The software in block 512, retrieves the current enterprise
equity price from the external database table (146), calculates the
number of shares that can be purchased using the available cash and
then generates a purchase order for the number of shares that can
be purchased. The software in block 512 then prompts the user (20)
via the purchase shares and confirm data window (706) to confirm
the purchase. Once the user (20) confirms the equity purchase, the
software in block 512 retrieves the on-line equity account
information from the system settings table (140) and transmits and
confirms the order to purchase the shares with the on-line broker
via the network (45). The details of equity purchase transaction
and confirmation are saved in the equity purchase table (156)
before processing advances to block 602.
Generate and Analyze Value Improvements
[0203] The flow diagram in FIG. 9 details the processing that is
completed by the portion of the application software (600) that
generates and analyzes value improvements. Processing in this
portion of the application starts in software block 602.
[0204] The software in block 602 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a new calculation or a structure change. If the
calculation is not a new calculation or a structure change, then
processing advances to a software block 606. Alternatively, if the
calculation is new or a structure change, then processing advances
to a software block 603.
[0205] The software in block 603 checks the bot date table (149)
and deactivates any improvement bots with creation dates before the
current system date. The software in block 603 then retrieves the
information from the system settings table (140), the soft asset
system table (148), the element of value definition table (155) and
the component of value definition table (156) as required to
initialize improvement bots before processing advances to a block
604.
[0206] Bots are independent components of the application that have
specific tasks to perform. In the case of improvement bots, their
primary task is to analyze and prioritize potential changes to
value drivers for each enterprise in the organization. The analysis
of value driver changes closely mirrors the calculation of profit
improvement that was completed in the related U.S. Pat. No.
5,615,109 a "Method of and System for Generating Feasible, Profit
Maximizing Requisition Sets". The capital efficiency of the
potential improvements identified by the improvement bots is
evaluated in accordance with the formula shown in Table 43.
TABLE-US-00043 TABLE 43 Capital Change (+) Capital Change (-)
Capital efficiency Revenue .DELTA. - Expense .DELTA. Capital
.DELTA. ##EQU00003## Revenue.DELTA. - Expense.DELTA. -Capital
.DELTA. Where: Revenue .DELTA. = revenue impact of 1% change in
value driver Expense .DELTA. = expense impact of 1% change in value
driver Capital .DELTA. = capital impact of 1% change in value
driver
The software in block 604 generates a list of potential
improvements for each element of value defined and measured by the
system of the present invention.
[0207] Every improvement bot contains the information shown in
Table 44.
TABLE-US-00044 TABLE 44 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (day, hour, minute,
second) 3. Mapping information 4. Storage location 5. Element of
ValueID 6. Soft Asset System 7. Value Driver
[0208] After the improvement bots are initialized by the software
in block 603 processing passes to a block 604. In block 604 the
bots activate in accordance with the frequency specified by the
user (20) in the system settings table (140). After being
activated, the bots retrieve information for the element of value
from the system settings table (140), the soft asset system table
(148), the element of value definition table (155) and the
component of value definition table (156) as required to complete
the analyses in accordance with the formula shown in Table 40. The
soft asset management system that corresponds to the element of
value being analyzed may also have generated a list of potential
improvements. If it has generated a list, these improvements are
analyzed in the same manner that the improvements generated by the
system of the present invention are analyzed. The resulting list of
prioritized improvements are then saved in the value driver change
table (167) in the application database (50) before processing
advances to a block 605.
[0209] The software in block 605 prepares a list of the potential
value improvements in capital efficiency order and prompts the user
(20) via a value driver and structure change window (707) to modify
and/or select the improvements and/or structure changes that should
be included in the revised forecast. If the user (20) chooses not
to enter any selections, then the software in block 605 will select
the potential improvements that produce the most benefit within the
constraints imposed by the available cash. The information
regarding the improvement selections made by the user (20) or the
system are stored in the value driver change table (167) in the
application database (50). In a similar fashion, if the user made
any changes to the structure, the information regarding the new
change is stored in the system settings table (140) before
processing advances to a software block 606.
[0210] The software in block 606 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a structure change. If the calculation is new or a
structure change, then processing advances to software block 204
and the processing described above is repeated. Alternatively, if
the calculation is not a structure change, then processing advances
to a software block 610.
[0211] The software in block 610 retrieves information from the
system settings table (140), the element of value definition table
(155), the component of value definition table (156) and the value
driver change table (167) as required to define and initialize a
probabilistic simulation model. The preferred embodiment of the
probabilistic simulation model is a Markov Chain Monte Carlo model,
however, other simulation models can be used with similar results.
The information defining the model is then stored in the simulation
table (168) before the software in block 610 iterates the model as
required to ensure the convergence of the frequency distribution of
the output variables. After the simulation calculations have been
completed, the software in block 610 saves the resulting
information in the simulation table (168) before displaying the
results of the simulation to the user (20) via a Value Mentor.TM.
Reports data window (708) that uses a summary Value Map.TM. report
format to display the mid point and the range of estimated future
values for the various elements of each enterprise and the changes
in value drivers, user-specified or system generated, that drove
the future value estimate. The user (20) is prompted to indicate
when the examination of the displayed report is complete and to
indicate if any reports should be printed. If the user (20) doesn't
provide any information regarding reports to display or print, then
no reports are displayed or printed at this point and system
processing continues. The information entered by the user (20) is
entered in to the report table (164) before processing advances to
a block 611.
[0212] The software in block 611 checks the reports tables (164) to
determine if any additional reports have been designated for
printing. If additional reports have been designated for printing,
then processing advances to a block 612 which prepares and sends
the designated reports to the printer (118). After the reports have
been sent to the printer (118), processing advances to a software
block 614. If the software in block 611 determines that no
additional reports have been designated for printing, then
processing advances directly to block 614.
[0213] The software in block 614 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a continuous calculation. If the calculation is a
continuous calculation, then processing advances to software block
204 where the processing described previously is repeated
continuously. Alternatively, if the calculation is not continuous,
then processing advances to a software block 615 where processing
stops.
[0214] Thus, the reader will see that the system and method
described above transforms extracted transaction data, corporate
information and information from the internet into detailed
valuations for an organization, the enterprises in the organization
and for specific elements of value within the 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.
[0215] 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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