Description:TECHNICAL FIELD
The present disclosure relates to optimizing a delivery network through enhanced supplier recommendations. Moreover, the present disclosure relates to a system and a method for optimizing a delivery network through enhanced supplier recommendations.
BACKGROUND
Advancement in the field of delivery network optimization have gained popularity over the years due to continuous improvement in user experience, enhancement in supplier recommendations along with a streamlined delivery process. Furthermore, the administration of delivery networks encompasses various responsibilities, including receiving delivery requests, coordinating with riders, scheduling deliveries, dispatching, route planning, tracking shipments, and performing business analysis. However, as delivery networks expand and the number of delivery requests rises, existing systems face challenges in managing the escalating volume of goods or services to be delivered. Consequently, dispatchers become overwhelmed, deliveries are delayed due to lengthy distances, and route optimization becomes more challenging.
Conventionally, certain systems and methods are used to handle deliveries with reduced cost utilization and reduced overall delivery time. However, such attempts fail due to adapt certain dynamic and multifaceted demands of modern consumers, which is not desirable. Therefore, there exists a technical problem of how to manage and optimize delivery networks to overcome overwhelmed dispatchers, address delays caused by long distances, and enhance route optimization in the face of the growing volume of delivery requests and network expansion.
Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks associated with the conventional systems and methods for optimizing and managing delivery network.
SUMMARY
The aim of the present disclosure is to provide a system and a method for optimizing a delivery network through enhanced supplier recommendations to improve delivery efficiency and customer satisfaction by utilizing hyper-local suppliers and multi-range search techniques. The present disclosure provides a solution to the existing problem of how to how to manage and optimize delivery networks to overcome overwhelmed dispatchers, address delays caused by long distances, and enhance route optimization in the face of the growing volume of delivery requests and network expansion. An objective of the present disclosure is to provide a solution that overcomes at least partially the problems encountered in the prior art and provides an improved system and a method for optimizing and managing delivery network, such as by recommending suppliers.
One or more objectives of the present disclosure are achieved by the solutions provided in the enclosed independent claims. Advantageous implementations of the present disclosure are further defined in the dependent claims.
In one aspect, the present disclosure provides a system for optimizing a delivery network through enhanced supplier recommendations. The system includes a hardware processor configured to receive a first delivery request from a first user device; wherein the first delivery request comprises a list of items and a delivery location, execute a multi-range search module to perform a multi-range search from the delivery location to identify one or more suppliers within each range of a plurality of defined ranges and determine whether to assign one hyper-local supplier or split the list of items among multiple hyper-local suppliers based on the multi-range search and a plurality of search optimization parameters.
The present disclosure provides the system that is configured to optimize delivery networks through intelligent supplier recommendations aided by AI/machine learning techniques. The system is configured to provide an efficient and accurate identification of the one or more suppliers, such as by the multi-range search module within each range of defined ranges based on the delivery location, ensuring a comprehensive evaluation and wide selection of available suppliers. The system is configured to perform multi-range searches around the delivery location to identify hyper-local suppliers, considering various optimization parameters like availability, proximity, reviews, delivery time, costs, inventory levels, and more. The system provides a data-driven approach leveraging an efficient hyperlocal fulfilment, reducing delays and logistics costs. The system provides an enhanced user experience via fast, reliable delivery while optimizing inventory utilization across the supply chain. The system is configured to adapt ranges and search parameters based on the comprehensive multi-range supplier evaluation ensures the optimized supplier distributions and recommendation.
In another aspect, the present disclosure provides a method for optimizing a delivery network through enhanced supplier recommendations. The method includes receiving a first delivery request from a first user device. The first delivery request comprises a list of items and a delivery location. Furthermore, the method includes executing a multi-range search module for performing a multi-range search from the delivery location for identifying one or more suppliers within each range of a plurality of defined ranges and determining whether to assign one hyper-local supplier or split the list of items among multiple hyper-local suppliers based on the multi-range search and a plurality of search optimization parameters.
The method achieves all the advantages and technical effects of the system of the present disclosure.
It has to be noted that all devices, elements, circuitry, units, and means described in the present application could be implemented in the software or hardware elements or any kind of combination thereof. All steps which are performed by the various entities described in the present application as well as the functionalities described to be performed by the various entities are intended to mean that the respective entity is adapted to or configured to perform the respective steps and functionalities. Even if, in the following description of specific embodiments, a specific functionality or step to be performed by external entities is not reflected in the description of a specific detailed element of that entity that performs that specific step or functionality, it should be clear for a skilled person that these methods and functionalities can be implemented in respective software or hardware elements or any kind of combination thereof. will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.
Additional aspects, advantages, features, and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative implementations construed in conjunction with the appended claims that follow.
BRIEF DESCRIPTION OF THE DRAWINGS
The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.
Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:
FIG. 1 is a block diagram of a system for optimizing a delivery network through enhanced supplier recommendations, in accordance with an embodiment of the present disclosure;
FIG. 2 is a flowchart of a method for optimizing a delivery network through enhanced supplier recommendations, in accordance with an embodiment of the present disclosure; and
FIG. 3 is a diagram illustrating an exemplary scenario of optimizing a delivery network through enhanced supplier recommendations, in accordance with an embodiment of the present disclosure.
In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing.

DETAILED DESCRIPTION OF EMBODIMENTS
The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible.
FIG. 1 is a diagram of a system for optimizing a delivery network through enhanced supplier recommendations, in accordance with an embodiment of the present disclosure. With reference to FIG. 1, there is shown a diagram 100 of a system 102 for optimizing the delivery network through enhanced supplier recommendations. The diagram 100 includes the system 102, a communication network 104, a plurality of user devices 106, and a control display 118. Furthermore, the system 102 includes a hardware processor 108, a memory 110, a network interface 112, a multi-range search module 114, and a trained AI model 116.
The hardware processor 108 may refer to one or more individual processors, processing devices, and various elements associated with a processing device that may be shared by other processing devices. Additionally, the one or more individual processors, processing devices, and elements are arranged in various architectures for responding to and processing the instructions that drive the system 102. Examples of the hardware processor 108 may include but are not limited to, a hardware processor, a digital signal processor (DSP), a microprocessor, a microcontroller, a complex instruction set computing (CISC) processor, an application-specific integrated circuit (ASIC) processor, a reduced instruction set (RISC) processor, a very long instruction word (VLIW) processor, a state machine, a data processing unit, a graphics processing unit (GPU), and other processors or control circuitry or a processor forwarding system.
The memory 110 is configured to store the instructions executable by the hardware processor 108. Examples of implementation of the memory 110 may include, but are not limited to, an Electrically Erasable Programmable Read-Only Memory (EEPROM), Dynamic Random-Access Memory (DRAM), Random Access Memory (RAM), Read-Only Memory (ROM), Hard Disk Drive (HDD), Flash memory, a Secure Digital (SD) card, Solid-State Drive (SSD), and/or CPU cache memory.
The communication network 104 includes a medium (e.g., a communication channel) through which the user device (i.e., the user device from the plurality of user devices 106) communicates with the system 102. The communication network 104 may be a wired or wireless communication network. Examples of the communication network 104 may include, but are not limited to, Internet, a Local Area Network (LAN), a wireless personal area network (WPAN), a Wireless Local Area Network (WLAN), a wireless wide area network (WWAN), a cloud network, a Long-Term Evolution (LTE) network, a plain old telephone service (POTS), a Metropolitan Area Network (MAN), and/or the Internet.
The network interface 112 refers to a communication interface to enable communication of the system 102 to any other external device, such as the plurality of user devices 106. Examples of the network interface 112 include but are not limited to, a network interface card, a transceiver, and the like.
The plurality of user devices 106 refers to an electronic computing device operated by a user. The plurality of user devices 106 may be configured to send a delivery request to the system 102. The plurality of user devices 106 includes a first user device 106A, a second user device 106B, up to nth user device 106N. Examples of the plurality of user devices 106 may include but are not limited to a mobile device, a smartphone, a desktop computer, a laptop computer, a Chromebook, a tablet computer, a robotic device, or other user devices.
There is provided the system 102 for optimizing a delivery network through enhanced supplier recommendations. The system 102 utilizes a multi-range search module 114 to identify suppliers within different ranges from the delivery location enhancing the efficiency, cost savings, and user experiences.
The hardware processor 108 is configured to receive a first delivery request from the first user device 106A. Moreover, the first delivery request includes a list of items and a delivery location. The delivery request includes the list of items that the user wants to order and the desired delivery location where the items should be delivered, which could be the user's home address, office address, or any other location where the user (i.e., the first user) want the items to be delivered. For example, the first user device 106A sends the first delivery request that includes the list of items, such as a 1 Kg of rice, 1.5 Kg of sugar, 1 Litre of milk, and 3 packets of potato chips, and the delivery location of his home. As a result, the first delivery request is sent to the system 102 102 to receive and process structured data from the first user device 106A. Therefore, by accepting the first delivery requests in a standardized format (i.e., the list of items and delivery location), the system 102 can efficiently handle and interpret user input, enabling seamless order processing and delivery planning. Additionally, the hardware processor 108 is configured to receive the first delivery request directly from the user devices, which provides a decentralized and scalable architecture thereby allowing the users to initiate orders from various devices and locations.
Furthermore, the hardware processor 108 is configured to execute the multi-range search module 114 to perform a multi-range search from the delivery location to identify one or more suppliers within each range of a plurality of defined ranges. In an implementation, the multi-range search module 114 refers to a module that performs searches for the one or more suppliers within multiple defined ranges from the specified delivery location. For example, the plurality of defined ranges could be 0-2 Kilometers, 2-5 Kilometers, 5-10 Kilometers, and the like. The multi-range search module 114 performs a search within each defined range to identify suppliers that can potentially fulfill the order. In an example, the hardware processor 108 is configured to perform a multi-range search from the delivery location to identify one or more suppliers within 0-2 Kilometers. Similarly, the hardware processor 108 is configured to perform a multi-range search from the delivery location to identify one or more suppliers within 2-5 Kilometers. By searching across multiple ranges, the system 102 is configured to conduct a comprehensive search thereby enhancing the possibilities of finding suitable suppliers to fulfill the order to provide robust and optimized supplier recommendations, improving the overall delivery experience and increasing the likelihood of successful order fulfillment.
Furthermore, the hardware processor 108 is configured to determine whether to assign one hyper-local supplier or split the list of items among multiple hyper-local suppliers based on the multi-range search and a plurality of search optimization parameters. Firstly, the user places an order for a list of items to be delivered to a specific delivery location through a first delivery request. Thereafter, the hardware processor 108 is configured to identify several potential suppliers within different ranges from the delivery location. For example, two suppliers are identified within the range of 2 Km and seven suppliers are identified within the range of 5 Km to 10 Km. In such a case, the hardware processor 108 is configured to determine if all the items mentioned in the list of items are to be assigned to anyone or the identified or split the list of items among the identified suppliers. Moreover, such an assignment is based on the multi-range search and the plurality of search optimization parameters. In an implementation, if one of the identified hyper-local suppliers (i.e., a supplier located very close to the delivery location) can fulfill the entire order and meets the search optimization parameters (e.g., item availability, cost, delivery time), the hardware processor 108 may assign the entire list of items to that single hyper-local supplier. In another implementation, if no single hyper-local supplier can fulfill the demand of an entire list of items or if splitting the order among multiple suppliers is more optimal based on the search optimization parameters (e.g., faster delivery times, better availability, lower costs), the hardware processor 108 may decide to split the list of items among multiple hyper-local suppliers. As a result, the hardware processor 108 is configured to analyze a comprehensive set of potential suppliers identified through the multi-range search, considering their proximity to the delivery location and other relevant factors and further determine the most suitable supplier assignment strategy thereby ensuring efficient and optimized delivery operations even with increasing demand or a growing supplier network.
In accordance with an embodiment, the plurality of search optimization parameters includes the availability of items listed in the list of items, nearest to geo-location, and hyper-local reviews. In an implementation, the plurality of search optimization parameters includes the availability of items listed in the list of items. For example, if someone searches for a particular brand of butter, then, in that case, the hardware processor 108 is configured to prioritize the results that are currently available with the identified suppliers, rather than listing options that are out of stock. In another implementation, the plurality of search optimization parameters includes the nearest to geo-location. The nearest to geo-location refers to the nearest options to the current location or a specified area of the user. In yet another implementation, the plurality of search optimization parameters includes the hyper-local reviews. For example, the ratings and reviews left by other users in the same city or neighborhood are most relevant to the local user experience. By incorporating the plurality of search optimization parameters, the hardware processor 108 is configured to provide more relevant and reliable supplier recommendations to the users.
In accordance with an embodiment, the plurality of search optimization parameters further comprises overall delivery estimation time, current ongoing discounts or offers, cost, and return and refund policies. In an implementation, the plurality of search optimization parameters includes the overall delivery estimation time. For example, when the user is ordering weekly groceries for delivery, then, in that case, the hardware processor 108 is configured to prioritize the supplier that can get the order delivered within 2 hours as compared to the suppliers who can deliver within 5 hours. In another implementation, the plurality of search optimization parameters includes the current ongoing discounts or offers. In yet another implementation, the plurality of search optimization parameters includes the cost. In another implementation, the plurality of search optimization parameters includes the return and refund policies. For example, when the user is ordering weekly groceries for delivery, then, in that case, the hardware processor 108 is configured to prioritize the supplier with generous return policies to the top in case something is unsatisfactory.
In an implementation, the hardware processor 108 is configured to provide product recommendations to users based on the cost price, brand, and hyper-local reviews. For example, if a user searches for a product of a specific brand and other users are searching for the same product of different brand, then, in such a case, the hardware processor 108 is configured to provide the product recommendation of a product having low cost price of the different brand as well. Moreover, such product recommendation offers to the user an alternate option with different brand and different cost price.
In another implementation, the hardware processor 108 is configured to provide product recommendations of the items that are mentioned in the list of items. Moreover, such product recommendations are based on the budget limit set by a user as well as the list of items that are added to the list. For example, a user has set a budget of 1000 INR, then, in that case, the hardware processor 108 is configured to recommend products that are listed in the list of items in such a manner that the total cost price of all the items listed in the list is either less than or equal to 1000 INR.
Alternatively, the hardware processor 108 is configured to identify discounts related to bulk orders and provide the corresponding discounts to each user within the range by considering each delivery request as a combined single delivery request. For example, the first seller offers a 20% discount on butter for a bulk purchase of 20 or more than 20 packets. In such a case, if 20 users that are located within the defined range (e.g., 2 Km, 5 Km, or 10 Km, and the like) are interested in purchasing a single unit of butter, then the hardware processor 108 is configured to combine individual delivery requests into a single delivery request of 20 packets in order to allow each user to get the benefit of such discounts. As a result, the hardware processor 108 is configured to provide recommendations that align with the user's preferences and expectations thereby improving the overall user experience.
In accordance with an embodiment, the hardware processor 108 is further configured to sort hyper-local reviews related to the list of items amongst all reviews related to the list of items. The hyper-local reviews are configured to provide more relevant and accurate feedback about the suppliers and the ability of the corresponding supplier to fulfill the specific order that allows the hardware processor 108 to provide users with more targeted and useful information for making informed decisions.
In accordance with an embodiment, the determination of whether to assign one hyper-local supplier or split the list of items among multiple hyper-local suppliers is further based on the sorting of the hype-local reviews related to the list of items. The hyper-local reviews are used to provide insights into the performance and capabilities of the identified suppliers for specific items, which can inform the optimal supplier recommendation strategy thereby, allowing the hardware processor 108 to make informed decisions about suppliers and improving the overall delivery experience.
In accordance with an embodiment, the system 102 further includes the control display 118 of the one or more suppliers within each range of the plurality of defined ranges on a user interface of the first user device 106A such that a user interface element corresponding to each supplier is interactive to receive a user input. In an implementation, the control display 118 refers to a graphical user interface (GUI) of the one or more suppliers within each range of the plurality of defined ranges. The control display 118 is configured to provide a convenient way to interact and manage the one or more suppliers within different ranges for the users. The user interface (UI) of the control display 118 is designed to be interactive through which each of the suppliers within the defined ranges can engage with that allows the users to provide input, potentially for actions such as selecting a supplier, adjusting settings, or making decisions related to the suppliers. As a result, by allowing the user inputs directly through the control display 118, the hardware processor 108 is configured to make informed decisions regarding the suppliers in real-time, improving the responsiveness of the system 102.
In accordance with an embodiment, the hardware processor 108 is further configured to track the inventory status of the items in the list of items in real-time or near real-time for the one or more hyper-local suppliers and determine whether to assign one hyper-local supplier or split the list of items among multiple hyper-local suppliers based on the inventory status. The tracking of the inventory status in real-time or near real-time is used to ensure that the recommended suppliers have the requested items in stock thereby improving the likelihood of successful order fulfillment. As a result, the hardware processor 108 is configured to assign a single supplier or split the list of items among the one or more identified suppliers based on the availability of the items.
In accordance with an embodiment, the hardware processor 108 is further configured to create a joint cart for a plurality of user devices by dynamic linking of the ID of each user device based on NLP parsing of text chat uploaded by one of the plurality of users. The hardware processor 108 is configured to perform dynamic linking of user device IDs. Moreover, the linking of the IDs of each user device is based on NLP parsing of text chat messages that are uploaded by one of the users. In addition, the NLP parsing includes extraction of the meaningful information from the text chat, such as product names or user intentions related to shopping. In an implementation, the hardware processor 108 is configured to add multiple items in the joint cart by adding the list of items in one go, such as by pasting the names of all the items in one go. The creation of the joint cart can facilitate collaborative shopping or group ordering, allowing multiple users to contribute to a single order, and by leveraging NLP to parse text chat and dynamically link user device IDs, the hardware processor 108 is configured to allow seamless collaboration and order consolidation without requiring manual user input or account linking.
In accordance with an embodiment, the hardware processor 108 is further configured to authenticate and authorize users before linking the ID of each user device. For example, a first user and a second user want to create a joint cart. Then in that case, the hardware processor 108 is configured to confirm the authenticity and authorization of both the users, such as by sending a one-time password or a linking link to the other user. Furthermore, when the second user accepts the request which is sent by the first user through the one-time password or the linking link, a secure channel between both the user devices is established. Furthermore, the authorization allows the hardware processor 108 to define and enforce specific permissions for each user, preventing unauthorized actions before linking device IDs in order to maintain accountability and traceability for the user actions within the collaborative environment.
In accordance with an embodiment, the hardware processor 108 is further configured to utilize a trained AI model 116 to analyze real-time inventory data received from the suppliers to predict future inventory levels and customize search parameters and optimization objectives based on user preferences and behavior patterns. The hardware processor 108 is configured to analyse the incoming real-time inventory data from suppliers in order to make predictions about future inventory levels based on various factors such as past trends, market demand, and supplier behaviour. Additionally, the hardware processor 108 is configured to customize search parameters and optimization based on user preferences and behaviour patterns in order to ensure that the inventory management processes align with the specific needs and preferences of users. As a result, by analysing the real-time inventory data and predicting future levels, the hardware processor 108 is configured to provide informed decision-making regarding inventory management.
Advantageously, the system 102 is configured to optimize delivery networks through intelligent supplier recommendations aided by AI/machine learning techniques. The system 102 is configured to provide an efficient and accurate identification of the one or more suppliers, such as by the multi-range search module 114 within each range of defined ranges based on the delivery location, ensuring a comprehensive evaluation and wide selection of available suppliers. The system 102 is configured to perform multi-range searches around the delivery location to identify hyper-local suppliers, considering various optimization parameters like availability, proximity, reviews, delivery time, costs, inventory levels, and more. The system 102 provides a data-driven approach leveraging efficient hyperlocal fulfilment, reducing delays and logistics costs. The system 102 provides an enhanced user experience via fast, reliable delivery while optimizing inventory utilization across the supply chain. The system 102 is configured to adapt ranges and search parameters based on the comprehensive multi-range supplier evaluation ensuring the optimized supplier distributions and recommendations.
FIG. 2 is a flowchart of a method for optimizing a delivery network through enhanced supplier recommendations, in accordance with an embodiment of the present disclosure. FIG. 2 is described in conjunction with elements from FIG. 1A and FIG. 1B. With reference to FIG. 2, there is shown a flowchart of a method 200 that includes steps 202-to-206. The system 102 (of FIG. 1) is configured to execute the method 200.
There is provided the method 200 that is used for optimizing a delivery network through enhanced supplier recommendations. The method 200 is used to utilize a multi-range search module 114 to identify suppliers within different ranges from the delivery location enhancing the efficiency, cost savings, and user experiences.
At operation 202, the method 200 includes receiving a first delivery request from a first user device. Moreover, the first delivery request includes a list of items and a delivery location. At operation 204, the method 200 includes executing the multi-range search module 114 for performing a multi-range search from the delivery location for identifying one or more suppliers within each range of a plurality of defined ranges and at operation 206, the method 200 includes determining whether to assign one hyper-local supplier or split the list of items among multiple hyper-local suppliers based on the multi-range search and a plurality of search optimization parameters.
Advantageously, the method 200 is used to optimize delivery networks through intelligent supplier recommendations aided by AI/machine learning techniques. The method 200 is used to provide an efficient and accurate identification of the one or more suppliers, such as by the multi-range search module 114 within each range of defined ranges based on the delivery location, ensuring a comprehensive evaluation and wide selection of available suppliers. The method 200 is used to perform multi-range searches around the delivery location to identify hyper-local suppliers, considering various optimization parameters like availability, proximity, reviews, delivery time, costs, inventory levels, and more. The method 200 is used to provide a data-driven approach leveraging efficient hyperlocal fulfilment, reducing delays and logistics costs. The method 200 provides an enhanced user experience via fast, reliable delivery while optimizing inventory utilization across the supply chain. The method 200 is used to adapt ranges and search parameters based on the comprehensive multi-range supplier evaluation ensuring the optimized supplier distributions and recommendations.
The steps 202 to 206 are only illustrative, and other alternatives can also be provided where one or more steps are added, one or more steps are removed, or one or more steps are provided in a different sequence without departing from the scope of the claims herein.
FIG. 3 is a diagram illustrating an exemplary scenario of optimizing a delivery network through enhanced supplier recommendations, in accordance with an embodiment of the present disclosure. With reference to FIG.3, there is shown an exemplary diagram 300 that depicts an optimization of the delivery network through enhanced supplier recommendations.
In an exemplary scenario, the hardware processor 108 is configured to receive a first delivery request from the first user device 106A. Moreover, the first delivery request includes the list of items and a delivery location 302. Additionally, the hardware processor 108 is also configured to create a joint cart for the first user device 106A and the second user device 106B by dynamic linking of ID of each user device based on NLP parsing of text chat uploaded by the first user and the second user. Thereafter, the hardware processor 108 is configured to execute the multi-range search module 114 to perform a multi-range search from the delivery location 302 to identify one or more suppliers within each range of a plurality of defined ranges. For example, a first supplier 304A is identified within a first range 306A, a second supplier 304B, a third supplier 304C, and a fourth supplier 304D are identified within a second range 306B. Similarly, a fifth supplier 304E is identifier within a third range 306C. Furthermore, the hardware processor 108 is configured to determine whether to assign one hyper-local supplier or split the list of items among multiple hyper-local suppliers (i.e., the first supplier 304A, the second supplier 304B, the third supplier 304C, the fourth supplier 304D, and the fifth supplier 304E) based on the multi-range search and a plurality of search optimization parameters. As a result, the comprehensive multi-range supplier evaluation ensures the optimized supplier distributions and supplier recommendations.
Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", and "is" used to describe, and claim the present disclosure are intended to be construed in a non-exclusive manner, namely allowing for items, components, or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural. The word "exemplary" is used herein to mean "serving as an example, instance, or illustration". Any embodiment described as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or to exclude the incorporation of features from other embodiments. The word "optionally" is used herein to mean "is provided in some embodiments and not provided in other embodiments". It is appreciated that certain features of the present disclosure, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable combination or as suitable in any other described embodiment of the disclosure.
, Claims:WE CLAIM
1. A system (102) for optimizing a delivery network through enhanced supplier recommendations, the system (102) comprising:
a hardware processor (108) configured to:
receive a first delivery request from a first user device (106A); wherein the first delivery request comprises a list of items and a delivery location (302);
execute a multi-range search module (114) to perform a multi-range search from the delivery location (302) to identify one or more suppliers within each range of a plurality of defined ranges; and
determine whether to assign one hyper-local supplier or split the list of items among multiple hyper-local suppliers based on the multi-range search and a plurality of search optimization parameters.

2. The system (102) as claimed in claim 1, wherein the plurality of search optimization parameters comprises availability of items listed in the list of items, nearest to geo-location, and hyper-local reviews.
3. The system (102) as claimed in claim 1, wherein the plurality of search optimization parameters further comprises overall delivery estimation time, current ongoing discounts or offers, cost, and return and refund policies.
4. The system (102) as claimed in claim 1, wherein the hardware processor (108) is further configured to sort hyper-local reviews related to the list of items amongst all reviews related to the list of items.
5. The system (102) as claimed in claim 1, wherein the determination of whether to assign one hyper-local supplier or split the list of items among multiple hyper-local suppliers is further based on the sorting of the hype-local reviews related to the list of items.
6. The system (102) as claimed in claim 1, wherein the system (102) further comprises a control display (118) of the one or more suppliers within each range of the plurality of defined ranges on a user interface of the first user device (106A) such that a user interface element corresponding to each supplier is interactive to receive a user input.
7. The system (102) as claimed in claim 1, wherein the hardware processor (108) is further configured to track inventory status of the items in the list of items in real-time or near real-time for the one or more hyper-local suppliers and determine whether to assign one hyper-local supplier or split the list of items among multiple hyper-local suppliers based on the inventory status.
8. The system (102) as claimed in claim 1, wherein the hardware processor (108) is further configured to create a joint cart for a plurality of user devices (106) by dynamic linking of ID of each user device based on NLP parsing of text chat uploaded by one of the plurality of users.
9. The system (102) as claimed in claim 8, wherein the hardware processor (108) is further configured to authenticate and authorize users before linking of ID of each user device.
10. The system (102) as claimed in claim 1, wherein the hardware processor (108) is further configured to utilize a trained AI model (116) to analyze real-time inventory data received from the suppliers to predict future inventory levels and customize search parameters and optimization objectives based on user preferences and behavior patterns.
11. A method (200) for optimizing a delivery network through enhanced supplier recommendations, the method (200) comprising:
receiving a first delivery request from a first user device (106A); wherein the first delivery request comprises a list of items and a delivery location; and
executing a multi-range search module (114) for performing a multi-range search from the delivery location for identifying one or more suppliers within each range of a plurality of defined ranges; and
determining whether to assign one hyper-local supplier or split the list of items among multiple hyper-local suppliers based on the multi-range search and a plurality of search optimization parameters.