System and method of end-to-end supply chain segmentation
A system and method are disclosed for identifying supply chain segmentations from an initially non-segmented supply chain is disclosed. Embodiments of identifying supply chain segmentations includes receiving a current state of items in a supply chain network, wherein an inventory of the one or more supply chain entities is used to store one or more items, organizing one or more supply chain entities into one or more customer-product clusters, associating supply chain strategies for customer-product clusters, and transporting items among one or more supply chain entities.
1. A computer-implemented method for identifying supply chain segmentations from an initially non-segmented supply chain by a computer comprising a processor and a memory, comprising:
receiving a current state of items in a supply chain network comprising one or more supply chain entities, wherein an inventory of the one or more supply chain entities is used to store one or more items, and a state of the items comprises a quantity and ordered flow among the inventory of the one or more supply chain entities;
determining one or more customer-product clusters based on the size of the inventory and service expectations of the one or more supply chain entities, wherein the determining the one or more customer-product clusters is based at least in part on a Pareto analysis, wherein each customer-product cluster comprises a customer and product combination and wherein the one or more customer-product clusters are based, at least in part, on a hierarchy, and wherein the hierarchy is adjusted by defining a level of granularity between individual customers and aggregated customers;
generating one or more levers associated with one or more supply chain strategies associated with the one or more customer-product clusters, each of the one or more levers comprising one or more configuration options, wherein the one or more levers are selected from the group consisting of: forecast level, stocking strategy, demand prioritization, production rule, ATP allocation and delivery strategy;
assigning supply chain models to each of the customer-product clusters that meet requirements of the size of the inventory and service expectations of the one or more supply chain entities;
determining an inventory policy based on targets of one or more principal key process indicators that is indicative of a projected service level for an item in the inventory for one or more successive inventory planning periods; and
in response to determining the inventory policy, transporting items via robotic machinery among the one or more supply chain entities to restock the inventory of the one or more items according to the current state of items in the supply chain network.
2. The computer-implemented method of claim 1 , further comprising:
deriving a customer hierarchy comprising one or more levels and one or more priorities.
3. The computer-implemented method of claim 2 , wherein the one or more priorities are based, at least in part, on the supply chain model and a user-selected priority value.
4. The computer-implemented method of claim 3 , wherein the one or more configuration options are based, at least in part, on the one or more supply chain models.
5. The computer-implemented method of claim 4 , further comprising:
configuring supply chain processes with one or more enablers to enforce the supply chain models to one or more of customers, products, and the customer-product clusters.
6. A system of identifying supply chain segmentations from an initially non-segmented supply chain, comprising:
a computer comprising a processor and a memory and configured to:
receive a current state of items in a supply chain network comprising one or more supply chain entities, wherein an inventory of the one or more supply chain entities is used to store one or more items, and a state of the items comprises a quantity and ordered flow among the inventory of the one or more supply chain entities;
determine one or more customer-product clusters based on the size of the inventory and service expectations of the one or more supply chain entities, wherein the determining the one or more customer-product clusters is based at least in part on a Pareto analysis, wherein each customer-product cluster comprises a customer and product combination and wherein the one or more customer-product clusters are based, at least in part, on a hierarchy, and wherein the hierarchy is adjusted by defining a level of granularity between individual customers and aggregated customers;
generate one or more levers associated with one or more supply chain strategies associated with the one or more customer-product clusters, each of the one or more levers comprising one or more configuration options, wherein the one or more levers are selected from the group consisting of: forecast level, stocking strategy, demand prioritization, production rule, ATP allocation and delivery strategy;
assign supply chain models to each of the customer-product clusters that meet requirements of the size of the inventory and service expectations of the one or more supply chain entities; and
determine an inventory policy based on targets of one or more principal key process indicators that is indicative of a projected service level for an item in the inventory for one or more successive inventory planning periods; and
robotic machinery that, in response to determining the inventory policy, transport items among the one or more supply chain entities to restock the inventory of the one or more items according to the current state of items in the supply chain network.
7. The system of claim 6 , wherein the computer is further configured to:
deriving a customer hierarchy comprising one or more levels and one or more priorities.
8. The system of claim 7 , wherein the one or more priorities are based,
at least in part, on the supply chain model and a user-selected priority value.
9. The system of claim 8 , wherein the one or more configuration options are based, at least in part, on the one or more supply chain models.
10. The system of claim 9 , wherein the computer is further configured to:
configure supply chain processes with one or more enablers to enforce the supply chain models to one or more of customers, products, and the customer-product clusters.
11. A non-transitory computer-readable medium embodied with software, the software when executed configured to identify supply chain segmentations from an initially non-segmented supply chain by:
receiving a current state of items in a supply chain network comprising one or more supply chain entities, wherein an inventory of the one or more supply chain entities is used to store one or more items, and a state of the items comprises a quantity and ordered flow among the inventory of the one or more supply chain entities;
determining one or more customer-product clusters based on the size of the inventory and service expectations of the one or more supply chain entities, wherein the determining the one or more customer-product clusters is based at least in part on a Pareto analysis, wherein each customer-product cluster comprises a customer and product combination and wherein the one or more customer-product clusters are based, at least in part, on a hierarchy, and wherein the hierarchy is adjusted by defining a level of granularity between individual customers and aggregated customers;
generating one or more levers associated with one or more supply chain strategies associated with the one or more customer-product clusters, each of the one or more levers comprising one or more configuration options, wherein the one or more levers are selected from the group consisting of: forecast level, stocking strategy, demand prioritization, production rule, ATP allocation and delivery strategy;
assigning supply chain models to each of the customer-product clusters that meet requirements of the size of the inventory and service expectations of the one or more supply chain entities;
determine an inventory policy based, at least in part, on targets of one or more principal key process indicators that is indicative of a projected service level for an item in the inventory for one or more successive inventory planning periods; and
in response to determining the inventory policy, directing robotic machinery to transport items among the one or more supply chain entities to restock the inventory of the one or more items according to the current state of items in the supply chain network.
12. The non-transitory computer-readable medium of claim 11 , wherein the software when executed is further configured to:
derive a customer hierarchy comprising one or more levels and one or more priorities.
13. The non-transitory computer-readable medium of claim 12 , wherein the one or more priorities are based, at least in part, on the supply chain model and a user-selected priority value.
14. The non-transitory computer-readable medium of claim 13 , wherein the one or more configuration options are based, at least in part, on the one or more supply chain models.