US 12,240,856 B2
System and method for inventory planning in a supply chain network
Thierry Moisan, Montréal (CA); Eric Prescott-Gagnon, Montréal (CA); and Yossiri Adulyasak, Montréal (CA)
Assigned to Blue Yonder Group, Inc., Scottsdale, AZ (US)
Filed by Blue Yonder Group, Inc., Scottsdale, AZ (US)
Filed on Aug. 11, 2023, as Appl. No. 18/233,142.
Application 18/233,142 is a continuation of application No. 17/562,509, filed on Dec. 27, 2021, granted, now 11,763,226.
Application 17/562,509 is a continuation of application No. 15/585,594, filed on May 3, 2017, granted, now 11,216,760, issued on Jan. 4, 2022.
Claims priority of provisional application 62/336,224, filed on May 13, 2016.
Prior Publication US 2023/0385729 A1, Nov. 30, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. C07D 487/04 (2006.01); A61K 31/519 (2006.01); A61K 31/5377 (2006.01); A61K 45/06 (2006.01); A61P 35/00 (2006.01); C07D 471/04 (2006.01); C07D 473/34 (2006.01); G01N 33/574 (2006.01); G06Q 10/0631 (2023.01); G06Q 10/087 (2023.01)
CPC C07D 487/04 (2013.01) [A61K 31/519 (2013.01); A61K 31/5377 (2013.01); A61K 45/06 (2013.01); A61P 35/00 (2018.01); C07D 471/04 (2013.01); C07D 473/34 (2013.01); G01N 33/57492 (2013.01); G06Q 10/06315 (2013.01); G06Q 10/087 (2013.01); G01N 2800/52 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for searching alternate inventory policies from an initial inventory policy of an inventory of one or more supply chain entities to a target inventory policy by a computer, the computer comprising a processor and memory, comprising:
receiving, by the computer, a current state of items in a supply chain network comprising the one or more supply chain entities, wherein the current state of items in the supply chain network is based at least in part on received item data from automated machinery;
determining, by the computer, an initial inventory policy based, at least in part, on a target service level and a discrete demand distribution that is indicative of a projected demand for an item in the inventory for one or more successive periods;
determining, by the computer, a transition matrix comprising a set of states of different inventory levels and probabilities of transitioning between states;
solving, by the computer, the transition matrix;
determining, by the computer, a solution probability vector comprising a probability of being in a state of the set of states;
in response to determining the solution probability vector, performing, by the computer, one or more of:
calculating a cost associated with each possible inventory state;
calculating an inventory fill rate by calculating an expected backlog; and
calculating a proportion of time an inventory is not in a stockout state;
performing, by the computer, a local search by incrementing or decrementing a reorder point and a target quantity until one or more of the calculated cost, the calculated inventory fill rate and the calculated proportion of time show no further improvement;
in response to the local search not showing improvement, selecting, by the computer, a provisional inventory policy; and
based on the selected provisional inventory policy, causing, by the computer, items to be transported among the one or more supply chain entities to restock the inventory according to the current state of items in the supply chain network.