	US 12,079,828 B2
	Multi-channel demand planning for inventory planning and control
Omar N. Jabri, Sunnyvale, CA (US); Adam Riggall, Minneapolis, MN (US); Duane Sizemore, Minneapolis, MN (US); Peter Kim, Minneapolis, MN (US); Tikhon Jelvis, Berkeley, CA (US); Yang Liu, Minneapolis, MN (US); Saibal Bhattacharya, San Mateo, CA (US); Sayon Majumdar, Sunnyvale, CA (US); and Zeynep Erkin Baz, San Francisco, CA (US)
Assigned to Target Brands, Inc., Minneapolis, MN (US)
Filed by Target Brands, Inc., Minneapolis, MN (US)
Filed on Nov. 17, 2021, as Appl. No. 17/529,075.
Prior Publication US 2023/0153844 A1, May 18, 2023
Int. Cl. G06Q 30/02 (2023.01); G06Q 10/067 (2023.01); G06Q 10/087 (2023.01); G06Q 30/0202 (2023.01); G06Q 30/0601 (2023.01)

CPC G06Q 30/0202 (2013.01) [G06Q 10/067 (2013.01); G06Q 10/087 (2013.01); G06Q 30/0625 (2013.01)]

20 Claims

1. A system for forecasting demand for each of a plurality of items sold through multiple sales channels of a retail enterprise having a plurality of locations, the system comprising:

at least one processor and at least one memory device, the memory device storing instructions that, when executed by the at least one processor, cause the system to perform:

training a generalized additive model (GAM) using historical sales data of a retail enterprise, the historical sales data including online sales data, to determine an overall item demand forecast for a future time period;

separating the overall item demand forecast into a localized item demand forecast and a non-localized item demand forecast based, at least in part, on an item eligibility for localized item fulfillment and historical localized and non-localized item ordering information;

for the non-localized item demand forecast, determine a portion of non-localized item demand able to be fulfilled from a retail location and a remaining portion of non-localized item demand to be fulfilled from a warehouse;

disaggregating the portion of non-localized item demand able to be fulfilled from a retail location at a location level to attribute sub-portions of the non-localized item demand to each of a plurality of retail locations;

disaggregating the localized item demand forecast into localized item demand at each of the plurality of retail locations;

combining the disaggregated localized item demand and the disaggregated non-localized item demand for each respective one of the plurality of retail locations to determine an overall online item demand for each of the plurality of retail locations;

in response to a request for item demand at an application programming interface (API) exposed by the system from a consumer system, the request identifying at least one item and one or more of the plurality of retail locations, returning, to the consumer system, an overall online item demand forecast for the at least one item at the one or more of the plurality of retail locations;

optimizing a plurality of models, the plurality of models including the GAM, wherein optimizing the plurality of models comprises selectively retraining the plurality of models by retraining a first one or more of the plurality of models at a first time and retraining a second one or more of the plurality of models at a second time that is delayed relative to the first time;

wherein selectively retraining the plurality of models comprises:

determining that the first one or more of the plurality of models are to be retrained at the first time based on an update to demand data for a first one or more items forecasted by the first one or more of the plurality of models; and

determining that the second one or more of the plurality of models are to be retrained at the second time that is delayed relative to the first time based on a lack of an update to demand data for a second one or more items forecasted by the second one or more of the plurality of models.