System and method for providing warehousing service
Systems and methods for providing warehousing services that utilize a machine learning model are provided. The system trains a machine learning model with training data comprising item attributes and transaction locations extracted from past transactions for each item category to identify a plurality of transaction zones where each item category has a highest probability for selling. Subsequently, the system receives a warehouse request to warehouse inventory in a remote location. At least one transaction zone is determined based on item attributes of the inventory by applying the trained machine learning model. Based on the determined at least one transaction zone, the system determines one or more warehouse spaces that satisfy a spacing requirement for the inventory and causes presentation of the warehouse recommendation. The warehouse recommendation can indicate the one or more warehouse spaces.
1 . A method comprising:
training, by a training hardware module of a network system, a machine learning model with training data comprising item attributes and transaction locations extracted from past transactions for each item category to identify a plurality of transaction zones;
performing a risk and validation assessment by evaluating ratings and reviews of a fulfillment partner and length of time on the network system;
onboarding, by an onboarding engine of the network system, warehouse space of the fulfillment partner;
receiving, by a warehousing engine of the networking system, a warehouse request to warehouse inventory in a remote location;
in response to receiving the warehouse request, triggering an analysis hardware module of the network system to determine one or more transaction zones based on item attributes of the inventory using the trained machine learning model;
based on the determined one or more transaction zones, determining, by a recommendation hardware module of the network system, one or more warehouse spaces that satisfy a spacing requirement for the inventory; and
causing presentation, by the recommendation hardware module, of a warehouse recommendation on a client device, the warehouse recommendation indicating the one or more warehouse spaces.
2 . The method of claim 1 , wherein onboarding the warehouse space comprises:
causing presentation of an onboarding user interface to the fulfillment partner;
receiving, via the onboarding user interface, size information for the warehouse space;
identifying location information for the fulfillment partner; and
persisting the warehouse space of the fulfillment partner with the size information and the location information to data storage.
3 . The method of claim 1 , wherein training the machine learning model comprises calculating a probability of an item selling in a transaction zone of the plurality of transaction zones.
4 . The method of claim 1 , further comprising:
monitoring a fulfillment service performed by the fulfillment partner; and
updating the risk and validation assessment of the fulfillment partner based on the monitoring.
5 . The method of claim 1 , wherein determining the one or more transaction zones comprises determining a combination of an item of the inventory and the one or more transaction zones where the item is recommended to be warehoused, wherein the warehouse recommendation indicates the item to be warehoused.
6 . The method of claim 1 , wherein determining the one or more transaction zones comprises predicting item sale probabilities for at least some of the plurality of transaction zones based on the inventory, the determined one or more transaction zones comprising a transaction zone having one or more of the highest item sale probabilities for an item of the inventory.
7 . The method of claim 1 , further comprising:
determining the spacing requirement to warehouse one or more items of the inventory, wherein the one or more warehouse spaces within the one or more transaction zones satisfy the spacing requirement.
8 . The method of claim 1 , further comprising:
determining, by the recommendation hardware module, a number of items to warehouse at the one or more transaction zones.
9 . The method of claim 1 , further comprising:
monitoring sales of items of the inventory at a warehouse location; and
based on the monitoring, providing a notification to move more of items of the inventory to the warehouse location in response to the inventory reaching a low threshold.
10 . A machine-storage medium comprising instructions which, when executed by one or more hardware processors of a machine, cause the machine to perform operations comprising:
training, by a training hardware module of a network system, a machine learning model with training data comprising item attributes and transaction locations extracted from past transactions for each item category to identify a plurality of transaction zones;
performing a risk and validation assessment by evaluating ratings and reviews of a fulfillment partner and length of time on the network system;
onboarding, by an onboarding engine of the network system, warehouse space of the fulfillment partner;
receiving, by a warehousing engine of the networking system, a warehouse request to warehouse inventory in a remote location;
in response to receiving the warehouse request, triggering an analysis hardware module of the network system to determine one or more transaction zones based on item attributes of the inventory using the trained machine learning model;
based on the determined one or more transaction zones, determining, by a recommendation hardware module of the network system, one or more warehouse spaces that satisfy a spacing requirement for the inventory; and
causing presentation, by the recommendation hardware module, of a warehouse recommendation on a client device, the warehouse recommendation indicating the one or more warehouse spaces.
11 . The machine-storage medium of claim 10 , wherein onboarding the warehouse space comprises:
causing presentation of an onboarding user interface to the fulfillment partner;
receiving, via the onboarding user interface, size information for the warehouse space;
identifying location information for the fulfillment partner; and
persisting the warehouse space of the fulfillment partner with the size information and the location information to data storage.
12 . The machine-storage medium of claim 10 , wherein training the machine learning model comprises calculating a probability of an item selling in a transaction zone of the plurality of transaction zones.
13 . The machine-storage medium of claim 10 , wherein the operations further comprise:
monitoring a fulfillment service performed by the fulfillment partner; and
updating the risk and validation assessment of the fulfillment partner based on the monitoring.
14 . The machine-storage medium of claim 10 , wherein determining the one or more transaction zones comprises determining a combination of an item of the inventory and the one or more transaction zones where the item is recommended to be warehoused, wherein the warehouse recommendation indicates the item to be warehoused.
15 . The machine-storage medium of claim 10 , wherein determining the one or more transaction zones comprises predicting item sale probabilities for at least some of the plurality of transaction zones based on the inventory, the determined one or more transaction zones comprising a transaction zone having one or more of the highest item sale probabilities for an item of the inventory.
16 . The machine-storage medium of claim 10 , wherein the operations further comprise:
determining the spacing requirement to warehouse one or more items of the inventory, wherein the one or more warehouse spaces within the one or more transaction zones satisfy the spacing requirement.
17 . The machine-storage medium of claim 10 , wherein the operations further comprise:
determining, by the recommendation hardware module, a number of items to warehouse at the one or more transaction zones.
18 . The machine-storage medium of claim 10 , wherein the operations further comprise:
monitoring sales of items of the inventory at a warehouse location; and
based on the monitoring, providing a notification to move more of items of the inventory to the warehouse location in response to the inventory reaching a low threshold.
19 . A system comprising:
one or more hardware processors; and
a memory storing instructions that, when executed by the one or more hardware processors, cause the one or more hardware processors to perform operations comprising:
training, by a training hardware module of a network system, a machine learning model with training data comprising item attributes and transaction locations extracted from past transactions for each item category to identify a plurality of transaction zones;
performing a risk and validation assessment by evaluating ratings and reviews of a fulfillment partner and length of time on the network system;
onboarding, by an onboarding engine of the network system, warehouse space of the fulfillment partner;
receiving, by a warehousing engine of the networking system, a warehouse request to warehouse inventory in a remote location;
in response to receiving the warehouse request, triggering an analysis hardware module of the network system to determine one or more transaction zones based on item attributes of the inventory using the trained machine learning model;
based on the determined one or more transaction zones, determining, by a recommendation hardware module of the network system, one or more warehouse spaces that satisfy a spacing requirement for the inventory; and
causing presentation, by the recommendation hardware module, of a warehouse recommendation on a client device, the warehouse recommendation indicating the one or more warehouse spaces.