Systems and methods for dynamic demand sensing and forecast adjustment

Abstract

System and method relating to demand forecasting and readjusting forecasts based on forecast error.

What is claimed is: 
     
         1 . A computer-implemented method comprising:
 using a first forecast model for providing first forecast data for a first forecast window, the first forecast data for one or more store locations;   transmitting the first forecast data to a user;   receiving first sales data for the one or more store locations for a second time interval, the second time interval subsequent a first time interval;   determining an error in the first forecast data based on the first forecast data and the first sales data;   removing the error from the first forecast data for forming first adjusted forecast data, the first adjusted forecast data for the one or more store locations; and   transmitting the first adjusted forecast data to the user.   
     
     
         2 . The method of  claim 1  including:
 collecting historical data for the first time interval; 
 generating first feature data based on the historical data; and 
 processing the first feature data by a processor for training a machine learning algorithm for forming the first forecast model. 
 
     
     
         3 . The method of  claim 1  wherein determining the error in the first forecast data based on the first forecast data and the first sales data includes determining the error between the first forecast data for each of the one or more store locations and the first sales data for each of the one or more store locations corresponding thereto. 
     
     
         4 . The method of  claim 1  wherein determining the error in the first forecast data based on the first forecast data and the first sales data includes determining a percentage error between the first forecast data for each of the one or more store locations and the first sales data for each of the one or more store locations and an average percentage error thereof. 
     
     
         5 . The method of  claim 4  wherein removing the error from the first forecast data for forming the first adjusted forecast data includes removing the average percentage error from the first forecast data for each of the one or more store locations, the first adjusted forecast data includes first adjusted forecast data for each of the one or more store locations. 
     
     
         6 . The method of  claim 1  further including,
 collecting second data for a third time interval, the second data including at least historical data and the first sales data; 
 generating second feature data based on the second data; 
 processing second feature data by a processor for training a machine learning algorithm for forming a second forecast model; 
 using the second forecast model for providing second forecast data for a second forecast window, the second forecast data including second forecast data for the one or more store locations; and 
 transmitting the second forecast data to the user. 
 
     
     
         7 . The method of  claim 1  further includes,
 receiving second sales data, the second sales data for the one or more store locations during a fourth time interval subsequent the second time interval; 
 determining an error in the first forecast data based on the first forecast data and the second sales data; 
 removing an error from the first forecast data for forming third forecast data; and 
 transmitting the third forecast data to the user. 
 
     
     
         8 . A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to:
 use a first forecast model for providing first forecast data for a first forecast window, the first forecast data for one or more store locations;   transmit the first forecast data to a user;   receive first sales data for the one or more store locations for a second time interval, the second time interval subsequent a first time interval;   determine an error in the first forecast data based on the first forecast data and the first sales data;   remove the error from the first forecast data for forming first adjusted forecast data, the first adjusted forecast data for the one or more store locations; and   transmit the first adjusted forecast data to the user.   
     
     
         9 . The non-transitory computer-readable storage medium of  claim 8 , wherein the computer-readable storage medium further includes instructions that when executed by a computer, cause the computer to:
 collect historical data for the first time interval;   generate first feature data based on the historical data; and   process the first feature data by a processor for training a machine learning algorithm for forming the first forecast model.   
     
     
         10 . The non-transitory computer-readable storage medium of  claim 8 , wherein determining the error in the first forecast data based on the first forecast data and the first sales data includes determining the error between the first forecast data for each of the one or more store locations and the first sales data for each of the one or more store locations corresponding thereto. 
     
     
         11 . The non-transitory computer-readable storage medium of  claim 8 , wherein determining the error in the first forecast data based on the first forecast data and the first sales data includes determining a percentage error between the first forecast data for each of the one or more store locations and the first sales data for each of the one or more store locations and an average percentage error thereof. 
     
     
         12 . The non-transitory computer-readable storage medium of  claim 11 , wherein removing the error from the first forecast data for forming the first adjusted forecast data includes removing the average percentage error from the first forecast data for each of the one or more store locations, the first adjusted forecast data includes first adjusted forecast data for each of the one or more store locations. 
     
     
         13 . The non-transitory computer-readable storage medium of  claim 8 , wherein the computer-readable storage medium further includes instructions that when executed by a computer, cause the computer to:
 collect second data for a third time interval, the second data including at least historical data and the first sales data;   generate second feature data based on the second data;   process second feature data by a processor for training a machine learning algorithm for forming a second forecast model;   use the second forecast model for providing second forecast data for a second forecast window, the second forecast data include second forecast data for the one or more store locations; and   transmit the second forecast data to the user.   
     
     
         14 . The non-transitory computer-readable storage medium of  claim 8 , wherein the computer-readable storage medium further includes instructions that when executed by a computer, cause the computer to:
 receive second sales data, the second sales data for the one or more store locations during a fourth time interval subsequent the second time interval;   determine an error in the first forecast data based on the first forecast data and the second sales data;   remove an error from the first forecast data for forming third forecast data; and   transmit the third forecast data to the user.   
     
     
         15 . A computing apparatus comprising:
 a processor; and   a memory storing instructions that, when executed by the processor, configure the apparatus to:   use a first forecast model for providing first forecast data for a first forecast window, the first forecast data for one or more store locations;   transmit the first forecast data to a user;   receive first sales data for the one or more store locations for a second time interval, the second time interval subsequent a first time interval;   determine an error in the first forecast data based on the first forecast data and the first sales data;   remove the error from the first forecast data for forming first adjusted forecast data, the first adjusted forecast data for the one or more store locations; and   transmit the first adjusted forecast data to the user.   
     
     
         16 . The computing apparatus of  claim 15  further configured to:
 collect historical data for the first time interval; 
 generate first feature data based on the historical data; and 
 process the first feature data by the processor for training a machine learning algorithm for forming the first forecast model. 
 
     
     
         17 . The computing apparatus of  claim 15  wherein determining the error in the first forecast data based on the first forecast data and the first sales data includes determining the error between the first forecast data for each of the one or more store locations and the first sales data for each of the one or more store locations corresponding thereto. 
     
     
         18 . The computing apparatus of  claim 15  wherein determining the error in the first forecast data based on the first forecast data and the first sales data includes determining a percentage error between the first forecast data for each of the one or more store locations and the first sales data for each of the one or more store locations and an average percentage error thereof. 
     
     
         19 . The computing apparatus of  claim 18  wherein removing the error from the first forecast data for forming the first adjusted forecast data includes removing the average percentage error from the first forecast data for each of the one or more store locations, the first adjusted forecast data includes first adjusted forecast data for each of the one or more store locations. 
     
     
         20 . The computing apparatus of  claim 15  further configured to:
 collect second data for a third time interval, the second data including at least historical data and the first sales data; 
 generate second feature data based on the second data; 
 process second feature data by the processor for training a machine learning algorithm for forming a second forecast model; 
 use the second forecast model for providing second forecast data for a second forecast window, the second forecast data include second forecast data for the one or more store locations; and 
 transmit the second forecast data to the user. 
 
     
     
         21 . The computing apparatus of  claim 15  further configured to:
 receive second sales data, the second sales data for the one or more store locations during a fourth time interval subsequent the second time interval; 
 determine an error in the first forecast data based on the first forecast data and the second sales data; 
 remove an error from the first forecast data for forming third forecast data; and 
 transmit the third forecast data to the user.