Models to support capacity planning in ship-from-store operations
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.
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, wherein each region includes one or more zip code and at least one of a plurality of retail store locations within a retail enterprise;
receiving, at the computing system, second data including node data for each of the retail store locations, wherein the node data includes: a physical location, a throughput configuration defining physical packing station equipment currently configured at the node, and one or more constraint value selected from a packing station equipment value corresponding to a limit on number of packing stations to be allocated to a respective node, a limit on number of packages able to be handled at a respective node, one or more type of package able to be handled at a respective node, and a limit on package size able to be handled at a respective node;
receiving, at the computing system, shipping data, the shipping data including a shipping cost associated with delivery of the forecasted demand for each of the plurality of regions from one or more of the plurality of nodes; and
providing the first data, the second data, and the shipping data to the decision model, the decision model being configured to execute an optimization process to generate output data, the output data including an assignment of the forecasted demand to one or more nodes among the plurality of nodes based on the shipping cost data and the node data while minimizing one or more of a distance, a shipping cost, and a packing station equipment cost;
wherein the output data defines an optimal configuration for each node of the plurality of nodes, the optimal configuration including one or more of an optimized throughput value corresponding to the forecasted demand to be handled by the node, a quantity of packing station equipment assigned to the node, and a type of packing station equipment assigned to the node.
2 . The method of claim 1 , wherein the optimization process comprises segmenting the node data into a plurality of segments based on defined regions within an overall geographical region in which each respective node is located.
3 . The method of claim 1 , 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.
4 . The method of claim 1 , 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.
5 . The method of claim 1 , wherein the second data further includes a packing station cost corresponding to a cost of adding one or more packing stations at the node and 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 the optimization process further comprises 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 optimization process further comprises optimizing a supply chain objective selected from one or more of: minimizing a shipping distance, minimizing a shipping cost, and minimizing a combination of a shipping cost and an equipment cost.
10 . The method of claim 1 , wherein the output data further comprises two or more of: a total cost to achieve the optimal configuration, an estimated shipping cost total for the optimal configuration, and an estimated shipping cost total for a current configuration.
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, wherein each region includes one or more zip code and at least one of a plurality of retail store locations within a retail enterprise;
receiving second data from a second data source, the second data source including node data for each of the retail store locations, wherein the node data includes: a physical location, a throughput configuration defining physical packing station equipment currently configured at the node, and one or more constraint value selected from a packing station equipment value corresponding to a limit on number of packing stations to be allocated to a respective node, a limit on number of packages able to be handled at a respective node, one or more type of package able to be handled at a respective node, and a limit on package size able to be handled at a respective node;
receiving shipping cost data from a shipping cost management system, the shipping data including a shipping cost associated with delivery of the forecasted demand for each of the plurality of regions from one or more of the plurality of nodes; and
providing the first data, the second data, and the shipping data to the decision model, the decision model being configured to execute an optimization process to generate output data, the output data including an assignment of the forecasted demand to one or more nodes among the plurality of nodes based on the shipping data and the node data while minimizing one or more of a distance, a shipping cost, and a packing station equipment cost;
wherein the output data defines an optimal configuration for each node of the plurality of nodes, the optimal configuration including one or more of an optimized throughput value corresponding to the forecasted demand to be handled by the node, a quantity of packing station equipment assigned to the node, and a type of packing station equipment assigned to the node.
13 . The system of claim 12 , wherein the optimization process comprises segmenting the node data into a plurality of segments based on defined regions within an overall geographical region in which each respective node is located.
14 . The system of claim 12 , wherein the 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.
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 12 , 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.
18 . The system of claim 12 , wherein the output data further comprises two or more of: a total cost to achieve the optimal configuration, an estimated shipping cost total for the optimal configuration, and an estimated shipping cost total for a current configuration.
19 . The system of claim 12 , wherein the optimization process further comprises optimizing a supply chain objective selected from one or more of: minimizing a shipping distance, minimizing a shipping cost, and minimizing a combination of a shipping cost and an equipment cost.
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, wherein each region includes one or more zip code and at least one of a plurality of retail store locations within a retail enterprise;
receiving, at the computing system, second data including node data, for each of the retail store locations, wherein the node data includes: a physical location, a throughput configuration defining physical packing station equipment currently configured at the node, and one or more constraint value selected from a packing station equipment value corresponding to a limit on number of packing stations to be allocated to a respective node, a limit on number of packages able to be handled at a respective node, one or more type of package able to be handled at a respective node, and a limit on package size able to be handled at a respective node;
receiving, at the computing system, shipping data, the shipping data including a shipping cost associated with delivery of the forecasted demand for each of the plurality of regions from one or more of the plurality of nodes;
segmenting the node data into a plurality of geographical segments; and
providing the first data, the second data, and the shipping data to the decision model, the decision model being configured to, for each segment of the plurality of geographical segments, execute an optimization process to generate output data, the output data including an assignment of the forecasted demand to one or more nodes among the plurality of nodes based on the shipping data and the node data while minimizing one or more of a distance, a shipping cost, and a packing station equipment cost;
wherein the output data defines an optimal configuration for each node of the plurality of nodes across the plurality of geographical segments, the optimal configuration including one or more of an optimized throughput value corresponding to the forecasted demand to be handled by the node, a quantity of packing station equipment assigned to the node, and a type of packing station equipment assigned to the node.