Computer implemented method and system for computing and evaluating demand information
AssigneeDemantra Ltd.
InventorsMichael Aronowich, Sofia Mariamova, Ofra Natan, Henry Harel
Life of this patent
Estimating the term…
Abstract
Computer implemented method and system for improving demand forecasting by estimating the hidden demand at an occurrence of a sellout using a single parameter probability distribution with a parameter assuming a forecasted mean demand value derived from a statistical seasonal causal time series forecasting model of count data on a new data set of sales values excluding truncated sales values at occurrences of sellouts. The present invention also provides for new more accurate performance evaluation techniques together with new performance metrics for evaluating an actual draw and for comparing a recommended draw to an actual draw.
Claims16
A computer-implemented method for estimating the hidden demand for a perishable consumer item at an outlet at an occurrence of a sellout for use with a demand forecast tree having at least one node with a time series of actual sales values associated therewith representing the actual sales of the perishable consumer item at the outlet over an observation period, the observation period containing at least one occurrence of a sellout, the method comprising:
- determining a subset of sales values of the time series of actual sales values over the observation period for the perishable consumer item at the outlet, the subset of sales values excluding the actual sales value(s) at the at least one occurrence of a sellout, the occurrence of the sellout being determined by comparing a sales value of the time series of sales values against a corresponding draw quantity of a time series of draw quantities;
- applying a statistical seasonal causal time series forecasting model of count data on the subset of sales values to determine a forecasted mean demand value for the perishable consumer item at the outlet at the occurrence of the sellout;
- and
- estimating the hidden demand at the occurrence of the sellout using a single parameter probability distribution conditioned on the forecasted mean demand value;
- wherein the forecasted mean demand value is calculated from the subset of actual sales values excluding the actual sales value(s) at the at least one occurrence of the sellout;
- and
- wherein the single parameter probability distribution is conditioned on the forecasted mean demand value;
- and
- whereby the hidden demand for the item is estimated using the formula
- H
- =
- λ
-
- (
- 1
- +
- f
-
- (
- D
- )
- 1
- -
- F
-
- (
- D
- )
- )
- -
- D
- where the parameter λ is the forecasted mean demand value, H is the hidden demand, f(·) is the single parameter probability distribution function, and F(·) is the cumulative distribution function of the single parameter probability distribution, and D is the draw of the perishable consumer item leading up to the occurrence of the sellout.
A computer-implemented system for estimating the hidden demand for a perishable consumer item at an outlet at an occurrence of a sellout for use with a demand forecast tree having at least one node with a time series of actual-sales values associated therewith representing the actual sales of the perishable consumer item at the outlet over an observation period, the observation period containing at least one occurrence of a sellout, the system comprising:
- a database server for storing time series of actual sales values over an observation period;
- a forecast engine server for computing demand forecast information for the demand forecast tree;
- and
- a processor for executing the steps of:
- determining a subset of sales values of the time series of actual sales values over the observation period for the perishable consumer item at the outlet, the new subset of sales values excluding the actual sales value(s) at the at least one occurrence of a sellout, the occurrence of the sellout being determined by comparing a sales value of the time series of sales values against a corresponding draw quantity of a time series of draw quantities;
- applying a statistical seasonal causal time series forecasting model of count data on the subset of sales values to determine a forecasted mean demand value for the perishable consumer item at the outlet at the occurrence of the sellout;
- and
- estimating the hidden demand at the occurrence of the sellout using a single parameter probability distribution conditioned on the forecasted mean demand value;
- wherein the forecasted mean demand value is calculated from the subset of actual sales values excluding the actual sales value(s) at the at least one occurrence of the sellout;
- and
- wherein the single parameter probability distribution is conditioned on the forecasted mean demand value;
- and
- whereby the hidden demand for the item is estimated using the formula
- H
- =
- λ
-
- (
- 1
- +
- f
-
- (
- D
- )
- 1
- -
- F
-
- (
- D
- )
- )
- -
- D
- where the parameter λ is the forecasted mean demand value, H is the hidden demand, f(.) is the single parameter probability distribution function, and F(.) is the cumulative distribution function of the single parameter probability distribution, and D is the draw of the perishable consumer item leading up to the occurrence of the sellout.
Dependencies are inferred from the claim text.