Claims:WE CLAIM
1. A method of dynamically providing recommendations to one or more vendors, the method comprising:
receiving, by a processing circuit, at least one of an area map, vendor
location data associated with the one or more vendors, customer location data associated with one or more potential customers and an inventory of products; and
dynamically providing, by the processing circuit, the recommendations to
the one or more vendors, wherein the recommendations are based on the vendor location data, the customer location data, the inventory of products and at least one of expected waiting period of the one or more vendors in the particular area for the particular time, purchasing history of each of the one or more potential customers in the particular area for the particular time, shelf life of products, travel costs, demand for the products in the particular area for the particular time, or season associated with the particular time, wherein the recommendations comprises at least one of recommended pricing of the products for each of the one or more potential customers.
2. The method as claimed in claim 1, further comprising dynamically determining routing options in the area map, based on the recommendations, the vendor location data, the customer location data, the inventory of products and at least one of travel costs, profit maximization and cost minimization objective, expected waiting period of the one or more vendors in the particular area for the particular time, purchasing history of each of the one or more potential customers in the particular area for the particular time, the shelf life of the products, demand for the products in the particular area for the particular time.
3. The method as claimed in claim 1, wherein the area map comprises at least one of vertical divisions of area, horizontal divisions of area, pictorial view of the expected waiting period of each of the one or more vendors, pictorial view of demographics or pictorial view of the products previously sold in different time ranges.
4. The method as claimed in claim 1, wherein the recommendations comprises at least one of recommended locations associated with the area map, number of vendors assigned to the particular area for the particular time, quantity of the products for the particular area, product mix from the inventory of products for the particular area, expected profit margins from the products or expected waiting period of the one or more vendors.
5. The method as claimed in claim 4, wherein the quantity of the products for the particular area is determined by modelling transportation problem.
6. The method as claimed in claim 4, wherein the number of vendors assigned to the particular area for the particular time is determined using queueing theory.
7. The method as claimed in claim 1, further comprising determining the recommended pricing of the products based on costs for procuring the products, travel costs, vendor salary, profit margin and customer discount, wherein the travel costs comprises at least one of vehicle costs, vehicle maintenance costs or fuel costs.
8. The method as claimed in claim 7, wherein the profit margin is based on at least one of the costs for procuring the products, demand for the products in the particular area for the particular time, or perishable product factor.
9. The method as claimed in claim 1, further comprising enhancing the recommendations and the routing options using machine learning.
10. The method as claimed in claim 1, further comprising managing notifications to the one or more potential customers, wherein the notification comprises at least one of vendor location data, products that are available for sale or prices of the products for each of the one or more potential customers.
11. A system for dynamically providing recommendations to one or more vendors, the system comprising:
a processor;
a memory communicatively coupled to the processor, wherein the memory stores the processor-executable instructions, which, on execution, causes the processor to:
receive at least one of an area map, vendor location data associated with
the one or more vendors, customer location data associated with one or more potential customers and an inventory of products; and
dynamically provide the recommendations to the one or more vendors,
wherein the recommendations are based on the vendor location data, the customer location data, the inventory of products and at least one of expected waiting period of the one or more vendors in the particular area for the particular time, purchasing history of each of the one or more potential customers in the particular area for the particular time, shelf life of products, travel costs, demand for the products in the particular area for the particular time, or season associated with the particular time, wherein the recommendations comprises at least one of recommended pricing of the products for each of the one or more potential customers.
12. The system as claimed in claim 11, wherein the processor is further configured to dynamically determine routing options in the area map, based on the recommendations, the vendor location data, the customer location data, the inventory of products and at least one of travel costs, profit maximization and cost minimization objective, expected waiting period of the one or more vendors in the particular area for the particular time, purchasing history of each of the one or more potential customers in the particular area for the particular time, the shelf life of the products, demand for the products in the particular area for the particular time.
13. The system as claimed in claim 11, wherein the area map comprises at least one of vertical divisions of area, horizontal divisions of area, pictorial view of the expected waiting period of each of the one or more vendors, pictorial view of demographics or pictorial view of the products previously sold in different time ranges.
14. The system as claimed in claim 11, wherein the recommendations comprises at least one of recommended locations associated with the area map, number of vendors assigned to the particular area for the particular time, quantity of the products for the particular area, product mix from the inventory of products for the particular area, expected profit margins from the products or expected waiting period of the one or more vendors.
15. The system as claimed in claim 14, wherein the quantity of the products for the particular area is determined by modelling transportation problem.
16. The system as claimed in claim 14, wherein the number of vendors assigned to the particular area for the particular time is determined using queueing theory.
17. The system as claimed in claim 11, wherein the processor is further configured to determine the recommended pricing of the products based on costs for procuring the products, travel costs, vendor salary, profit margin and customer discount, wherein the travel costs comprises at least one of vehicle costs, vehicle maintenance costs or fuel costs.
18. The system as claimed in claim 17, wherein the profit margin is based on at least one of the costs for procuring the products, demand for the products in the particular area for the particular time, or perishable product factor.
19. The system as claimed in claim 11, wherein the processor is further configured to enhance the recommendations and the routing options using machine learning.
20. The system as claimed in claim 11, wherein the processor is further configured to manage notifications to the one or more potential customers, wherein the notification comprises at least one of vendor location data, products that are available for sale or prices of the products for each of the one or more potential customers.

Dated this 6th day of March, 2017

R Ramya Rao
Of K&S Partners
Agent for the Applicant
, Description:TECHNICAL FIELD
This disclosure relates generally to electronic commerce and more particularly to a system and method for dynamically providing recommendations to one or more vendors.