US2008040202A1PendingUtilityA1

Generating an Optimized Price Schedule for a Product

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Individually held — no corporate assignee on recordPriority: Oct 6, 2000Filed: Oct 19, 2007Published: Feb 14, 2008
Est. expiryOct 6, 2020(expired)· nominal 20-yr term from priority
Inventors:Joachim Paul WalserVibhu KalyanSrinivas PalamarthyJames M. Crawford, Jr.Mukesh Dalal
G06Q 10/063G06Q 10/06G06Q 10/06315G06Q 10/06375G06Q 30/0202G06Q 30/0205G06Q 30/0206G06Q 30/0283
57
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Claims

Abstract

Generating a price schedule involves generating a graph having paths that include states with values. The graph is generated by determining the values of a successor state from the values of a predecessor state. An optimal path is selected, and a price schedule is determined from the optimal path. Computing an elasticity curve involves having a demand model, values for demand model, and filter sets that restrict the values. Elasticity curves are determined by filtering the values using filter sets, and calculating the elasticity curve using the demand model. A best-fitting elasticity curve is selected. Adjusting a demand forecast value includes estimating an inventory and a demand at a number of locations. An expected number of unrealized sales at each location is calculated. An sales forecast value is determined according to the expected number.

Claims

exact text as granted — not AI-modified
1 . A method for determining a sales forecast, comprising: 
 defining a plurality of locations;    estimating an inventory at each location;    estimating a demand at each location;    calculating an expected number of unrealized sales at each location using a difference between the demand at the location and the inventory at the location; and    determining a sales forecast in response to the expected number.    
     
     
         2 . The method of  claim 1 , wherein: 
 estimating the inventory at each location comprises randomly populating the locations with a plurality of inventory units; and    estimating the demand at each location comprises randomly populating the locations with a plurality of demand units.    
     
     
         3 . The method of  claim 1 , wherein: 
 estimating the inventory at each location comprises calculating a probability of each location receiving a number of inventory units according to a binomial distribution; and    estimating the demand at each location comprises calculating a probability of each location receiving a number of demand units according to the binomial distribution.    
     
     
         4 . The method of  claim 1 , wherein: 
 estimating the inventory at each location comprises calculating a probability of each location receiving a number of inventory units according to an incomplete beta-function; and    estimating the demand at each location comprises calculating a probability of each location receiving a number of demand units according to the incomplete beta-function.    
     
     
         5 . A system for determining a sales forecast, comprising: 
 a database operable to store a plurality of definitions defining a plurality of locations; and    a server coupled to the database and operable to:    estimate an inventory at each location;    estimate a demand at each location;    calculate an expected number of unrealized sales at each location using a difference between the demand at the location and the inventory at the location; and    determine a sales forecast in response to the expected number.    
     
     
         6 . The system of  claim 5 , wherein the server is operable to: 
 estimate the inventory at each location by randomly populating the locations with a plurality of inventory units; and    estimate the demand at each location by randomly populating the locations with a plurality of demand units.    
     
     
         7 . The system of  claim 5 , wherein the server is operable to: 
 estimate the inventory at each location by calculating a probability of each location receiving a number of inventory units according to a binomial distribution; and    estimate the demand at each location by calculating a probability of each location receiving a number of demand units according to the binomial distribution.    
     
     
         8 . The system of  claim 5 , wherein the server is operable to: 
 estimate the inventory at each location by calculating a probability of each location receiving a number of inventory units according to an incomplete beta-function; and    estimate the demand at each location by calculating a probability of each location receiving a number of demand units according to the incomplete beta-function.    
     
     
         9 . Logic for determining a sales forecast, the logic encoded in media and when executed operable to: 
 define a plurality of locations;    estimate an inventory at each location;    estimate a demand at each location;    calculate an expected number of unrealized sales at each location using a difference between the demand at the location and the inventory at the location; and    determine a sales forecast in response to the expected number.    
     
     
         10 . The logic of  claim 9 , further operable to: 
 estimate the inventory at each location by randomly populating the locations with a plurality of inventory units; and    estimate the demand at each location by randomly populating the locations with a plurality of demand units.    
     
     
         11 . The logic of  claim 9 , further operable to: 
 estimate the inventory at each location by calculating a probability of each location receiving a number of inventory units according to a binomial distribution; and    estimate the demand at each location by calculating a probability of each location receiving a number of demand units according to the binomial distribution.    
     
     
         12 . The logic of  claim 9 , further operable to: 
 estimate the inventory at each location by calculating a probability of each location receiving a number of inventory units according to an incomplete beta-function; and    estimate the demand at each location by calculating a probability of each location receiving a number of demand units according to the incomplete beta-function.    
     
     
         13 . A system for determining a sales forecast, comprising: 
 means for defining a plurality of locations;    means for estimating an inventory at each location; means for estimating a demand at each location;    means for calculating an expected number of unrealized sales at each location using a difference between the demand at the location and the inventory at the location; and    means for determining a sales forecast in response to the expected number.    
     
     
         14 . A method for generating a price schedule, comprising: 
 generating a transition graph comprising a plurality of paths, each path comprising a plurality of states, each state having a plurality of values comprising a state value, the transition graph being generated by repeating the following for a plurality of stages until a final stage is reached;    calculating the values of a successor state using the values of a predecessor state; and    quantizing the values of each successor state;    selecting an optimal path according to the state values of the states; and    determining a price schedule from the optimal path.    
     
     
         15 . The method of  claim 14 , wherein the values comprise a price value.  
     
     
         16 . The method of  claim 14 , wherein the values comprise an inventory value.  
     
     
         17 . The method of  claim 14 , wherein selecting the optimal path according to the state values comprises: 
 determining a state at the final stage having an optimal state value; and    determining a path comprising a state of an initial stage and the state having the optimal state value.    
     
     
         18 . The method of  claim 14 , further comprising eliminating a successor state in response to a constraint.  
     
     
         19 . The method of  claim 14 , further comprising: 
 computing an elasticity curve; and    computing an inventory value of each successor state using the elasticity curve.    
     
     
         20 . The method of  claim 14 , wherein: 
 each state has a certainty value; and    selecting the optimal path comprises determining a state at the final stage having a certainty value of a predetermined value.    
     
     
         21 . The method of  claim 14 , further comprising: 
 defining a plurality of locations;    calculating an expected number of unrealized sales at each location; and    adjusting a value of the successor state in response to the expected number.

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