	US 12,346,921 B2
	Systems and methods for dynamic demand sensing and forecast adjustment
Ali Khanafer, Ottawa (CA); Behrouz Haji Soleimani, Ottawa (CA); Sebastien Ouellet, Ottawa (CA); Christopher Wang, Ottawa (CA); Chantal Bisson-Krol, Ottawa (CA); and Zhen Lin, Ottawa (CA)
Filed by Kinaxis Inc., Ottawa (CA)
Filed on Aug. 25, 2023, as Appl. No. 18/456,111.
Application 18/456,111 is a continuation in part of application No. 18/071,802, filed on Nov. 30, 2022.
Application 18/071,802 is a continuation of application No. 16/837,182, filed on Apr. 1, 2020, granted, now 11,537,825, issued on Dec. 27, 2022.
Application 16/837,182 is a continuation in part of application No. 16/599,143, filed on Oct. 11, 2019, granted, now 11,526,899, issued on Dec. 13, 2022.
Claims priority of provisional application 63/518,713, filed on Aug. 10, 2023.
Prior Publication US 2023/0401592 A1, Dec. 14, 2023
Int. Cl. G06Q 30/02 (2023.01); G06F 18/20 (2023.01); G06F 18/21 (2023.01); G06F 18/211 (2023.01); G06F 18/22 (2023.01); G06F 18/25 (2023.01); G06N 20/00 (2019.01); G06Q 30/0204 (2023.01)

CPC G06Q 30/0205 (2013.01) [G06F 18/211 (2023.01); G06F 18/217 (2023.01); G06F 18/22 (2023.01); G06F 18/251 (2023.01); G06F 18/285 (2023.01); G06N 20/00 (2019.01)]

18 Claims

1. A computer-implemented method, comprising:

receiving, by a processor, a first forecast request;

training, by the processor, a plurality of machine learning forecast models on a first portion of a data set;

validating, by the processor, a machine learning forecast model on a second portion of the data set; and

retraining, by the processor, the machine learning forecast model on a sum of the first portion and the second portion of the data set, the data set comprising processed historical data;

forecasting, by the processor, a forecast based on the first forecast request;

receiving, by the processor, a subsequent forecast request;

selecting, by the processor, a machine learning forecast model when:

i) the data set has been updated by a new class of relevant signal data since a previous forecast request; or

ii) the data set has been updated by an amount of new relevant signal data beyond a first threshold since the previous forecast request; or

iii) the machine learning forecast model has degraded;

and

retraining, by the processor, a previously-selected machine learning forecast model when a time interval between successive forecast requests is greater than a second threshold;

using one of the machine learning forecast model selected by the processor and the previously-selected machine learning 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.