Method of and system for defining and measuring the real options of a commercial enterprise

• US 20050144106A1
• Filed: 05/20/2003
• Published: 06/30/2005
• Est. Priority Date: 12/10/1997
• Status: Abandoned Application

• Current Assignee: Jeffrey Eder
• Original Assignee: Jeffrey Eder
• Inventors: Eder, Jeff Scott
• Application Number:
  US10/441,385
• Publication Number:
  US 20050144106A1
• Time in Patent Office: Days
• US Class Current: 705/36.R
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

An automated system (100) and methods for defining and measuring the real options of a commercial enterprise on a specified valuation date. The real options are evaluated on the basis of the relative strength of the elements of value of the enterprise. The performance of the elements of value are first summarized using composite variables. The elements strength of the cause change in enterprise stock price are then determined. The relative strength of the causal elements of value for the enterprise vis a vis its competitors are then calculated. The relative ranking of the enterprise causal elements of value is then used in determining the discount rate to be used in real option valuation. The real options are then valued.

Claims

1-75. -75. (canceled)

76. A system for valuing tangible elements of value, intangible elements of value, real options and combinations thereof for a business, comprising:

(a) processing means for processing data;

(b) storage means for storing data;

(c) first means for obtaining data related to the value of the business enterprise, the business enterprise having one or more tangible or intangible elements of value contributing to the value of the business enterprise, one or more real options contributing to the value of the business and the value of the business enterprise including a revenue component, an expense component and a capital component;

(d) second means for calculating, for each one of the tangible or intangible elements of value, a vector characterizing performance of the tangible or intangible element of value of the business enterprise;

(e) third means for calculating the real option category of value, the revenue, expense and capital components of the value of the business enterprise;

(f) fourth means for determining, for each one of the tangible or intangible elements of value, a percentage of the real option category contributed by the tangible or intangible element of value, a percentage of the revenue component contributed by the tangible or intangible element of value, a percentage of expense component contributed by the tangible or intangible element of value and a percentage of the capital component contributed by the tangible or intangible element of value;

(g) fifth means for calculating a value for each of the tangible or intangible elements of value of the business enterprise based on the revenue, expense and capital components of value and the real option category of value of the business enterprise and the percentages of the revenue, expense, capital and real option category contributed by the tangible or intangible elements of value; and

(h) sixth means for displaying the values.

77. The system of claim 76 wherein the said sixth means for displaying the values further comprises a paper document or an electronic display.

78. A data processing system as claimed in claim 76, wherein said second means further comprises

(a) means for combining composite variables, transaction averages, time lagged transaction ratios, time lagged transaction trends, time lagged transaction averages, time lagged transaction data, transaction patterns, geospatial measures, relative rankings, link counts, frequencies, time periods, average time periods, cumulative time periods, rolling average time period, cumulative total values, period to period rates of change to calculate the vector.

79. A data processing system as claimed in claim 76, wherein said third means further comprises:

(a) means for determining the discount rate to be used in real option valuation as a function of the element of value profile of the business and the real option.

80. A data processing system as claimed in claim 76, wherein said third means further comprises:

(b) means for determining the real option value using algorithms selected from the group consisting of binomial, black scholes, dynamic programming and multinomial.

81. A data processing system as claimed in claim 76, wherein said fourth means further comprises:

(a) means for using output from a predictive model to determine the percentage of the revenue component contributed by the tangible or intangible element of value, the percentage of the expense component contributed by the tangible or intangible element of value, and the percentage of the capital component contributed by the tangible or intangible element of value.

82. A data processing system as claimed in claim 76, wherein said fourth means further comprises:

(b) means for using output from a predictive model trained using a genetic algorithm to determine the percentage of the revenue component contributed by the tangible or intangible element of value, the percentage of the expense component contributed by the tangible or intangible element of value, and the percentage of the capital component contributed by the tangible or intangible element of value.

83. A data processing system as claimed in claim 76 further comprising:

(i) means for using the vectors to evaluate the impact of the tangible or intangible elements of value on the value of the business enterprise.

84. A data processing system as claimed in claim 76 further comprising:

(i) seventh means for user modification of, for each one of the tangible and intangible elements of value, selected one or ones of the value drivers that drive the value of the business enterprise; and

(j) eighth means for calculating a value for each of the tangible or intangible elements of value of the business enterprise based on the value of the business enterprise and the percentage of the value contributed by the tangible or intangible elements of value after user modification.

(k) ninth means for displaying the new value.

85. The system of claim 84 wherein the said ninth means for displaying the new value further comprises a paper document or an electronic display.

86. A computer readable medium having sequences of instructions stored therein, which when executed cause the processor in a computer to perform a performance information method, comprising:

aggregating enterprise related data,

identifying tangible indicators of element impact on one or more aspects of enterprise financial performance using at least a portion of said data,

developing solid measures of element impact on one or more aspects of enterprise financial performance using one or more of said indicators, and

producing enterprise performance management information using at least one of the measures.

87. The computer readable medium of claim 86 where the method further comprises making the enterprise performance management information available for review and use via a paper document or electronic display.

88. The computer readable medium of claim 86 where an enterprise is a single product, a group of products, a division or a company.

89. The computer readable medium of claim 86 where data is aggregated using xml and a common schema

90. The computer readable medium of claim 86 wherein enterprise related data is aggregated from the group consisting of 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, enterprise resource planning systems (ERP), material requirement planning systems (MRP), quality control systems, sales management systems, human resource systems, accounts receivable systems, accounts payable systems, capital asset systems, inventory systems, invoicing systems, payroll systems, purchasing systems, web site systems, the Internet, external databases, user input and combinations thereof.

91. The computer readable medium of claim 86 wherein the elements are selected from the group consisting of alliances, brands, channels, customers, customer relationships, employees, intellectual property, partnerships, processes, production equipment, vendors, vendor relationships and combinations thereof.

92. The computer readable medium of claim 86 where the tangible indicators of element performance are selected from the group consisting of composite variables, transaction averages, time lagged transaction ratios, time lagged transaction trends, time lagged transaction averages, time lagged transaction data, transaction patterns, geospatial measures, relative strength rankings, link counts, frequencies, time periods, average time periods, cumulative time periods, rolling average time period, cumulative total values, period to period rates of change and combinations thereof.

93. The computer readable medium of claim 86 where a series of models is used to select tangible indicators of element impact.

94. The computer readable medium of claim 93 wherein the series of models are developed in an automated fashion.

95. The computer readable medium of claim 94 where the series of models further comprises predictive models to select candidates and causal models to finalize the selection.

96. The computer readable medium of claim 95 where predictive models are selected from the group consisting of neural networks;

regression models, generalized autoregressive conditional heteroskedasticity, generalized additive models;

multivariate adaptive regression splines, rough-set analysis;

Bayes models, support vector method, multivalent models and combinations thereof.

97. The computer readable medium of claim 95 where causal models are selected from the group consisting of Bayes, minimum message length and path analysis.

98. The computer readable medium of claim 86 where the solid measures are selected from the group consisting of value drivers, mathematical equations that combine two or more value drivers, logical combinations of two or more value drivers, vectors and combinations thereof.

99. The computer readable medium of claim 98 where value drivers are tangible indicators that are causal to change in one or more aspects of financial performance.

100. The computer readable medium of claim 98 where the choice of measures is at least in part a function of the level of interaction between elements.

101. The computer readable medium of claim 86 wherein the one or more aspects of enterprise financial performance are selected from the group consisting of revenue, expense, capital change, current operation value, real option value, market sentiment value and business value.

102. The computer readable medium of claim 86 wherein the performance management information is selected from the group consisting of element valuations, lists of changes that will optimize one more aspects of enterprise financial performance, management reports and combinations thereof.

103. The method of claim 102 where the element valuations quantify the impact of an element on one or more aspects of enterprise financial performance net of any impact on other elements.

104. The computer readable medium of claim 102 where calculating element valuations further comprises:

initializing and training predictive models that use concrete measures of element impact as inputs for one or more select aspects of enterprise financial performance;

using the weights from the best fit predictive models to identify net relative contributions by element of value to each of the one or more select aspects of enterprise financial performance;

combining the net relative contributions with the value of the select aspects of enterprise financial performance to determine a value of the element.

105. The computer readable medium of claim 104 where the predictive models are trained using a genetic algorithm.

106. The computer readable medium of claim 104 where select aspects of enterprise financial performance are chosen from the group consisting of revenue, expense, capital change, market value and combinations thereof.

107. The computer readable medium of claim 102 where creating lists of changes that will optimize one or more aspects of enterprise financial performance further comprises:

initializing and training optimization models that use the concrete measures of element impact as inputs for one or more select aspects of enterprise financial performance; and

reporting the changes identified by the models

108. The computer readable medium of claim 107 where optimization models are genetic algorithms, multi criteria optimization models or Monte Carlo simulation models.

109. The computer readable medium of claim 107 where Monte Carlo simulation models are used to identify changes that will optimize one aspect of enterprise financial performance.

110. Measures of element impact on enterprise financial performance that are derived from tangible indicators of element performance and support the development of useful enterprise performance management information.

111. The measures of claim 110 that are confirmable.

112. The measures of claim 110 that are selected from the group consisting of value drivers, composite variables, vectors and combinations thereof.

113. The measures of claim 112 where value drivers are causal tangible indicators of element performance.

114. The measures of claim 112 where composite variables are equations that combine one or more value drivers, logical combinations of value drivers and combinations thereof.

115. The measures of claim 110 where the elements are selected from the group consisting of alliances, brands, channels, customers, customer relationships, employees, intellectual property, partnerships, processes, production equipment, vendors and vendor relationships.

116. The measures of claim 110 that quantify net element impact on one or more aspects of enterprise financial performance.

117. The concrete measures of claim 116 where the one or more aspects of enterprise financial performance are selected from the group consisting of revenue, expense, capital change, current operation value, real option value, market sentiment value and business value.

118. The measures of claim 110 where the enterprise performance management information is selected from the group consisting of element contributions, element valuations, lists of changes that will optimize one more aspects of enterprise financial performance, management reports and combinations thereof.

119. The measures of claim 110 where the tangible indicators of element performance are selected from the group consisting of composite variables, transaction ratios, transaction trends, transaction averages, time lagged transaction ratios, time lagged transaction trends, time lagged transaction averages, time lagged transaction data, patterns, geospatial measures, relative strength rankings, link counts, frequencies, time periods, average time periods, cumulative time periods, rolling average time periods, cumulative total values, period to period rates of change and combinations thereof.

120. Network models that quantify a net contribution of each of one or more elements of value of an enterprise to one or more aspects of financial performance.

121. The models of claim 120 where the aspects of enterprise financial performance are selected from the group consisting of revenue, expense, capital change, current operation value, market sentiment value, market value and combinations thereof.

122. The network models of claim 120 being further comprised of:

input nodes, hidden nodes and output nodes with each input node representing an element value driver or an element of value, each hidden node representing the inter-relationship between each element other elements and an aspect of financial performance and each output node representing an aspect of financial performance; and

relationships between said nodes, each said relationship being directional and being characterized by a degree of influence from one node to another;

said degree of influence being dependent upon the impact of the element or element value driver represented by said node and its interrelationship with other elements.

123. The models of claim 120 where the weights from the network models are used to quantify the net contribution of each element to each aspect of financial performance.

124. The models of claim 120 where the net contributions of each element by aspect are combined with aspect valuations to determine the value of each element.

125. The models of claim 120 that supports enterprise optimization analyses.

126. The models of claim 120 where the elements are selected from the group consisting of alliances, brands, channels, customers, customer relationships, employees, intellectual property, partnerships, processes, production equipment, vendors, vendor relationships and combinations thereof.

127. The models of claim 120 where an enterprise is a single product, a group of products, a division or a company.

128. The models of claim 120 where development is completed in an automated fashion.

129. The models of claim 120 where the inputs for each element of value are composite variables or vectors.

130. A financial performance method, comprising:

integrating data from a plurality of enterprise related data sources, and

calculating a net relative contribution for each of one or more elements of value to each of one or more aspects of enterprise financial performance using at least a portion of said data.

131. The method of claim 130 where the net relative contribution is the relative direct contribution of an element to an aspect of enterprise financial performance net of any contribution to other elements of value.

132. The method of claim 130 where the method further comprises:

calculating the value of each element of value using the relative contributions, and

displaying the element values using a paper document or electronic display.

133. The method of claim 130 where the method further comprises:

identifying a list of changes to the elements of value that will optimize one or more aspects of enterprise financial performance, and

displaying the list of changes using a paper document or electronic display.

134. The method of claim 130 where the data is integrated in accordance with a common xml schema.

135. The method of claim 134 where the xml schema includes a data dictionary.

136. The method of claim 135 where the data dictionary defines standard data attributes from the group consisting of account numbers, components of value, currencies, elements of value, enterprise designations, time periods, units of measure and combinations thereof.

137. The method of claim 135 where the xml schema includes an xml metadata standard.

138. The method of claim 130 where enterprise related data sources are from the group consisting of 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, enterprise resource planning systems (ERP), material requirement planning systems (MRP), quality control systems, sales management systems, human resource systems, accounts receivable systems, accounts payable systems, capital asset systems, inventory systems, invoicing systems, payroll systems, purchasing systems, web site systems, the Internet, external databases, user input and combinations thereof.

139. The method of claim 130 where the elements are selected from the group consisting of alliances, brands, channels, customers, customer relationships, employees, intellectual property, partnerships, processes, production equipment, vendors, vendor relationships and combinations thereof.

140. The method of claim 130 where the aspects of enterprise financial performance are selected from the group consisting of revenue, expense, capital change, current operation value, market sentiment value, market value and combinations thereof.

141. The method of claim 130 where calculating a net relative contribution of each of one or more elements of value to each of one or more aspects of enterprise financial performance further comprises:

creating one or more tangible measures of element impact,

using a series of models to select causal tangible indicators of element impact,

identifying a level of interaction between elements of value,

identifying a concrete measure for each element of value as a function of the level of interaction between elements of value,

initializing and training predictive models that use concrete measures of element impact as inputs for one or more aspects of enterprise financial performance; and

using the weights from the best fit predictive models to identify net relative contributions by element of value to each of the one or more aspects of enterprise financial performance.

142. The method of claim 141 where the tangible indicators of element impact are selected from the group consisting of composite variables, transaction ratios, transaction trends, transaction averages, time lagged transaction ratios, time lagged transaction trends, time lagged transaction averages, time lagged transaction data, patterns, geospatial measures, relative strength rankings, link counts, frequencies, time periods, average time periods, cumulative time periods, rolling average time periods, cumulative total values, period to period rates of change and combinations thereof.

143. The method of claim 141 where the series of models used to select causal tangible indicators further comprises predictive models to select candidates and causal models to finalize the selection.

144. The method of claim 143 where predictive models are selected from the group consisting of neural networks;

regression models, generalized autoregressive conditional heteroskedasticity, generalized additive models;

multivariate adaptive regression splines, rough-set analysis;

Bayes models, support vector method, multivalent models and combinations thereof.

145. The method of claim 143 where causal models are selected from the group consisting of Bayes, minimum message length and path analysis.

146. The method of claim 141 where the models are trained using a genetic algorithm.

147. The method of claim 141 where aspects of enterprise financial performance are selected from the group consisting of revenue, expense, capital change, market value and combinations thereof.

148. The method of claim 141 wherein the models are developed in an automated fashion by learning from the data.

149. The method of claim 133 where the changes are changes in element value drivers.

150. The method of claim 130 where an enterprise is a single product, a group of products, a division or a company.