[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” the disclosure of which is also incorporated herein by reference.
[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 accceptance 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
[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 lenth 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 thoght 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.
[0023] 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.
[0024] 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.
[0025] 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.
[0026] 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.
[0027] 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.
[0028] 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 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
[0029] 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 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) Curent 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 − IN − Enterprise element of value (GGCV) PE − PEQ − OPA − OA − VVCC Contribution: Real options/Contingent Liabilities Real option algorithms + allocation of industry real options based on relative industry position
[0030] 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 enteprise value as shown in Table 3.
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 − Element of Value: (GCV) PE − PEQ − OPA − OA − ΣEV 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 + ΣReal option Values)
[0031] 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.
[0032] 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 4 Enterprise Market Value = Σ Market value of enterprise equity + Σ Market value of company debt
[0033] 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 TABLE 5 System Value Categories Three Horizons Current Operation Assets Short Term Elements of Value Growth Real Options Options
[0034] 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.
[0035] 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.
[0036] 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 evaluted 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).
[0037] 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.
[0038] 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:
[0039]
[0040]
[0041]
[0042]
[0043]
[0044]
[0045]
[0046]
[0047]
[0048]
[0049] All extracted information is stored in a file or table (hereinafter, table) within an application database (
[0050] As shown in
[0051] The database-server personal computer (
[0052] The application-server personal computer (
[0053] The user-interface personal computer (
[0054] The application software (
[0055] User input is initially saved to the client database (
[0056] The computers (
[0057] Using the system described above, the value of the organiztion, 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%
Discount rate = 35%
[0058] 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 6 Value of curr nt-op ration = (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
[0059] 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).
[0060] 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 (
[0061] 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.
[0062] The components and sub-components of current-operation value will be used in calculating the value of: enteprise 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”.
[0063] 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 7 Percent- Element Gross Value age 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
[0064] 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
[0065] 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),
[0066] 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,
[0067] 3. determine the appopriate cost of capital and value the organization and enteprise real options;
[0068] 4. determine the appopriate cost of capital, value and allocate the industry real options to each organization or enterprise on the basis of relative element strength;
[0069] 5. determine the expected life of each element of value and sub-element of value;
[0070] 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;
[0071] 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,
[0072] 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);
[0073] The third stage of processing (block
[0074] The flow diagrams in
[0075] 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.
[0076] 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 8 Account Type: Debit Impact: Credit Impact: Asset Increase Decrease Revenue Decrease Increase Expense Increase Decrease Liability Decrease Increase Equity Decrease Increase
[0077] 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.
[0078] 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 9 Subsystem Detailed Information Accounts Vendor, Item(s), Transaction Date, Amount Payable Owed, Due Date, Account Number Accounts Customer, Transaction Date, Product Sold, Receivable Quantity, Price, Amount Due, Terms, Due Date, Account Number Capital Asset ID, Asset Type, Date of Purchase, Assets Purchase Price, 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
[0079] 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.
[0080] 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.
[0081] 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 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
[0082] 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 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
[0083] 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 12 Human R s urc 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
[0084] 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 (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.
[0085] The system of the present invention uses different “bot” types to process each distinct data type from external databases (
[0086] Advanced financial systems may also use information from external databases (
[0087] 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 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
[0088] System processing of the information from the different databases and the internet (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
[0089] The organization and enterprise checklists are used by a “rules” engine (such as the one available from Neuron Data) in block
[0090] The software in block
[0091] After the storage of system setting data is complete, processing advances to a software block TABLE 16 Account Number 01 - 800 - 901 - 677- 003 Segment Organi- Enterprise Department Account Sub- zation account Subgroup Products Workstation Marketing Labor P.R. Position 5 4 3 2 1
[0092] As part of the metadata mapping process, any database fields that are not mapped to pre-specified fields are defined by the user (
[0093] The software in block
[0094] The software in block 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)
[0095] After the software in block
[0096] The software in block
[0097] The software in block
[0098] After the software in block
[0099] The software in block
[0100] The software in block
[0101] After the software in block
[0102] The software in block
[0103] The software in block
[0104] After the software in block
[0105] The software in block
[0106] The software in block
[0107] After the software in block
[0108] The software in block
[0109] The software in block
[0110] After the software in block
[0111] The software in block
[0112] The software in block
[0113] The software in block
[0114] 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 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
[0115] In block
[0116] The software in block 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
[0117] In block
[0118] The software in block
[0119] The software in block
[0120] The software in block
[0121] The software in block
[0122] 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 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
[0123] In block
[0124] The software in block
[0125] The software in block
[0126] The software in block
[0127] The software in block
[0128] The software in block
[0129] The software in block
[0130] The software in block
[0131] The flow diagrams in
[0132] 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),
[0133] 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,
[0134] 3. determine the appopriate cost of capital and value the organization and enteprise real options;
[0135] 4. determine the appopriate cost of capital, value and allocate the industry real options to each organization or enterprise on the basis of relative element strength;
[0136] 5. determine the expected life of each element of value and sub-element of value;
[0137] 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;
[0138] 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,
[0139] 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);
[0140] Processing in this portion of the application begins in software block
[0141] The software in block
[0142] The software in block
[0143] 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 (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
[0144] When bots in block
[0145] The software in block
[0146] 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 Naïve Bayes Regression; MARS; linear regression; and stepwise regression. The software in block 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
[0147] Every predictive model bot contains the information shown in Table 23.
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
[0148] After predictive model bots for each level in the organization are initialized, the bots activate in accordance with the frequency specified by the user (
[0149] The software in block
[0150] The software in block
[0151] 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 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
[0152] After the induction bots are initialized by the software in block
[0153] The software in block
[0154] The software in block
[0155] The software in block
[0156] 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 (
[0157] Option bots contain the information shown in Table 25.
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)
[0158] After the option bots are initialized by the software in block
[0159] The software in block
[0160] The software in block
[0161] The software in block
[0162] 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 seperately before calculating the cash flow. The software in block
[0163] Every cash flow bot contains the information shown in Table 26.
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
[0164] After the cash flow bots are initialized by the software in block
[0165] The software in block
[0166] The software in block
[0167] 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 (
[0168] Every element life bot contains the information shown in Table 27.
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)
[0169] After the element life bots are initialized by the software in block
[0170] The software in block
[0171] The software in block
[0172] 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 28 Value = F (F (F
[0173] 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 (
[0174] Every component capitalization bot contains the information shown in Table 29.
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
[0175] After the component capitalization bots are initialized by the software in block
[0176] The software in block
[0177] 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:
[0178] 1. the enterprise contribution vectors and the organization components of value;
[0179] 2. the element of value vectors and the enterprise components of value; and
[0180] 3. the sub-element of value vectors and the element of value they correspond to.
[0181] 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 Naïve 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 30
[0182] 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 31 Percent- Element Gross Value age Lif/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
[0183] The resulting values are stored in the element of value definition table (
[0184] Every valuation bot contains the information shown in Table 32.
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
[0185] After the valuation bots are initialized by the software in block
[0186] The software in block
[0187] 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 (TABLE 33 Residual Going Concern Value = Total Current-Operation Value − Σ Financial Asset Values − Σ Elements of value
[0188] Every residual bot contains the information shown in Table 34.
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
[0189] After the residual bots are initialized by the software in block
[0190] The flow diagram in
[0191] The software in block
[0192] The software in block
[0193] 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 (TABLE 35 Sentiment = Total Market Value − Total Current-Operation Value − Σ Real Option Values
[0194] Every sentiment calculation bot contains the information shown in Table 36.
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
[0195] After the sentiment calculation bots are initialized by the software in block
[0196] The software in block
[0197] The software in block
[0198] 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 (
[0199] Every sentiment factor bot contains the information shown in Table 37.
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
[0200] After the sentiment factor bots are initialized by the software in block
[0201] The software in block
[0202] The software in block
[0203] 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 Naïve Bayes Regression; MARS; linear regression; and stepwise regression.
[0204] Every sentiment analysis bot contains the information shown in Table 38.
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
[0205] After the sentiment analysis bots are initialized by the software in block
[0206] The software in block
[0207] The flow diagram in
[0208] System processing in this portion of the application software (
[0209] The software in block
[0210] 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 (TABLE 39 Market Equity = (Current Operation Value) + (Σ Real Option Values) − (Σ Short Term Liabilities) − (Σ Contingent & Long Term Liabilities) − (Book Value of Equity)
[0211] Every report bot contains the information shown in Table 40.
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 ®/Value Creation format or traditional format)
[0212] The general format of the Value Map® Reports is shown in Table 41 and Table 42.
TABLE 41 Value Map ™ 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
[0213]
TABLE 42 Value Map ™ 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 $20,189,900 $11,506,130 Payable Taxes Payable $12,430,120 $9,099,880 Subtotal Short Term $57,282,600 $49,954,264 Liabilities 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 & $394,798,126 $347,438,785 Shareholder Equity
[0214] After the report bots are initialized by the software in block
[0215] The software in block
[0216] The software in block
[0217] The software in block
[0218] The software in block
[0219] The software in block
[0220] The flow diagram in
[0221] The software in block
[0222] The software in block
[0223] 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 43 Capital Change (+) Capital Change (−) Capital RevenueΔ − ExpenseΔ − efficiency Capital Δ Capital Δ Where: Revenue Δ = revenue impact of 1% change in value driver Expense Δ = expense impact of 1% change in value driver Capital Δ = capital impact of 1% change in value driver
[0224] The software in block
[0225] Every improvement bot contains the information shown in Table 44.
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
[0226] After the improvement bots are initialized by the software in block
[0227] The software in block
[0228] The software in block
[0229] The software in block
[0230] The software in block
[0231] The software in block
[0232] 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.
[0233] 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.