Generating an Optimized Price Schedule for a Product
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.
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.