Models to support capacity planning in ship-from-store operations

Patent metadata

Field	Value
Publication number	US-2024362584-A1
Application number	US-202418646453-A
Country	US
Kind code	A1
Filing date	Apr 25, 2024
Priority date	Apr 25, 2023
Publication date	Oct 31, 2024
Grant date	—

Abstract

Methods and systems for optimizing a ship-from-shore process are provided. A decision model receives forecasted demand and node data representing a plurality of possible shipping node configurations. The decision model generates output data that includes an assignment of the forecasted demand to nodes among the plurality of nodes based on the shipping cost data and the node data while optimizing a supply chain objective subject to a plurality of constraints, the plurality of constraints including a fulfillment of the forecasted demand within a predetermined delivery service level, and identifies an optimal configuration for each shipping node. In some aspects, the decision model segments the node data to improve efficiency of analysis.

First claim

1 . A method of optimizing a ship-from-store process at a decision model implemented on a computing system, the method comprising:
receiving, at the computing system, first data including a forecasted demand for each of a plurality of regions; receiving, at the computing system, second data including node data, the node data including, for each node of a plurality of nodes: a location, a throughput configuration, and one or more constraint values; receiving, at the computing system, shipping cost data, the shipping cost data including a shipping cost associated with delivery of the forecasted demand from one or more of the plurality of nodes; and providing the first data, the second data, and the shipping cost data to the decision model, the decision model being configured to generate output data including an assignment of the forecasted demand to nodes among the plurality of nodes based on the shipping cost data and the node data while optimizing a supply chain objective subject to a plurality of constraints, the plurality of constraints including a fulfillment of the forecasted demand within a predetermined delivery service level; wherein the output data defines an optimal configuration for each node of the plurality of nodes, the optimal configuration including an optimized throughput value.

2 . The method of claim 1 , wherein the one or more constraint values includes a packing station equipment value.

3 . The method of claim 2 , wherein the packing station equipment value includes a current number of packing stations at the node, the second data including a packing station cost corresponding to a cost of adding one or more packing stations at the node.

4 . The method of claim 1 , wherein the one or more constraint values includes a limit on number of packages able to be handled at the node.

5 . The method of claim 1 , wherein the second data includes a plurality of data entries associated with a node from among the plurality of nodes, the plurality of entries corresponding to different configurations of numbers of packing stations at the node.

6 . The method of claim 1 , wherein the plurality of regions comprises a plurality of zip codes, and wherein the first data comprises aggregated demand for each zip code.

7 . The method of claim 6 , wherein the first data includes a plurality of entries, each entry corresponding to a zip code and including aggregated demand for the zip code and a location corresponding to a centroid of the zip code.

8 . The method of claim 1 , wherein optimizing, using the node data, the supply chain objective subject to the plurality of constraints further comprising allocating demand of at least one bulky item to fewer than all nodes of the plurality of nodes.

9 . The method of claim 1 , wherein the supply chain objective is one or more of the following: minimizing a distance, minimizing a shipping cost, or minimizing a combination of a shipping cost and an equipment cost.

10 . The method of claim 1 , wherein the optimal configuration for each node of the plurality of nodes includes one or more of a quantity of equipment, a type of equipment, a quantity of labor, or a type of labor.

11 . The method of claim 1 , further comprising optimizing, using the node data, the supply chain objective subject to a plurality of constraints for a plurality of scenarios, each of the scenarios including one or more of an altered demand, an altered shipping rate, or an altered throughput rate.

12 . A system for automatically determining ship-from-store equipment configurations in a distributed enterprise supply chain, the system comprising:
a computing system comprising a processor and a memory, the memory storing instructions which, when executed by the computing system, cause the system to perform:
receiving first data from a first data source including a forecasted demand for each of a plurality of regions;
receiving second data from a second data source, the second data source including node data, the node data including, for each node of a plurality of nodes: a location, a throughput configuration, and one or more constraint values;
receiving shipping cost data from a shipping cost management system, the shipping cost data including a shipping cost associated with delivery of the forecasted demand from one or more of the plurality of nodes; and
providing the first data, the second data, and the shipping cost data to the decision model, the decision model being configured to generate output data including an assignment of the forecasted demand to nodes among the plurality of nodes based on the shipping cost data and the node data while optimizing a supply chain objective subject to a plurality of constraints, the plurality of constraints including a fulfillment of the forecasted demand within a predetermined delivery service level;
wherein the output data defines an optimal configuration for each node of the plurality of nodes, the optimal configuration including an optimized throughput value.

13 . The system of claim 12 , wherein the optimal configuration for each node of the plurality of nodes includes one or more of a quantity of equipment, a type of equipment, a quantity of labor, or a type of labor.

14 . The system of claim 12 , wherein the supply chain objective is one or more of the following: minimizing a distance, minimizing a shipping cost, or minimizing a combination of a shipping cost and an equipment cost.

15 . The system of claim 12 , further comprising optimizing, using the node data, the supply chain objective subject to a plurality of constraints for a plurality of scenarios, each of the scenarios including one or more of an altered demand, an altered shipping rate, or an altered throughput rate.

16 . The system of claim 12 , wherein the plurality of regions comprises a plurality of zip codes, and wherein the first data comprises aggregated demand for each zip code; and
wherein the first data includes a plurality of entries, each entry corresponding to a zip code and including aggregated demand for the zip code and a location corresponding to a centroid of the zip code.

17 . The system of claim 10 , wherein the one or more constraint values includes a packing station equipment value, and wherein the one or more constraint values includes a limit on number of packages able to be handled at the node.

18 . The system of claim 10 , wherein the decision model further comprises a mixed-integer linear optimization program configured to identify a minimized value of as defined over a problem space of a set of delivery locations having a demand value assigned thereto, a set of nodes, and a set of possible node configurations.

19 . The system of claim 18 , wherein the first data and the second data is segmented into a plurality of optimization segments, in which the set of nodes and set of delivery locations are separated into a plurality of regional sets of nodes and delivery locations, wherein the decision model determines an optimal configuration for each of the plurality of optimization segments without requiring analysis of all node to delivery location combinations included in the first data and the second data.

20 . A method of optimizing a ship-from-store process at a decision model implemented on a computing system, the method comprising:
receiving, at the computing system, first data including a forecasted demand for each of a plurality of regions; receiving, at the computing system, second data including node data, the node data including, for each node of a

Assignees

• Target Brands Inc

Inventors

• Herasymova Daria
• Holschuh Anna
• Tallichetty Chandrashekar

Classifications

• G06Q10/06315

  Needs-based resource requirements planning or analysis · CPC title

• G06Q10/087 Primary

  Inventory or stock management, e.g. order filling, procurement or balancing against orders · CPC title