WO2021181352A1

WO2021181352A1 - System and method for pre-dispatched return to origin prediction of a package in logistics

Info

Publication number: WO2021181352A1
Application number: PCT/IB2021/052076
Authority: WO
Inventors: Vipul M YADAV; Bharat A. KAROTRA; Paresh Bhupat PARMAR; Nikul Jaysukhbhai DODIYA; Zaiba Umer SARANG
Original assignee: Ithink Logistic Quick Services Llp
Priority date: 2020-03-13
Filing date: 2021-03-12
Publication date: 2021-09-16

Abstract

A system for pre-dispatched return to origin prediction of a package is disclosed. The system includes a return to origin factor collection subsystem to collect a plurality of pre-dispatched return to origin (RTO) factors associated with a package delivery in the logistics; a pre-dispatch return to origin score prediction subsystem to assign a pre-dispatch return to origin (RTO) score corresponding to each of a plurality of collected pre-dispatched return to origin factors, to compute a variation of an assigned pre-dispatch return to origin (RTO) score based on one or more predefined conditions, to predict a pre-dispatch return to origin score based on a computed variation of the assigned pre-dispatch return to origin (RTO) score using a return to origin prediction technique.

Description

SYSTEM AND METHOD FOR PRE-DISPA TCHED RETURN TO ORIGIN PREDICTION OF A PACKAGE IN LOGISTICS

This International Application claims priority from a Patent application filed in India having Patent Application No. 202021010931, filed on March 13, 2020, and titled “SYSTEM AND METHOD FOR PRE-DISPATCHED RETURN TO ORIGIN PREDICTION OF A PACKAGE IN LOGISTICS”.

BACKGROUND

Embodiments of the present disclosure relate to a logistics risk prediction and more particularly to a, system and method for pre-dispatched return to origin prediction of a package in logistics.

Logistics services field is important to people and businesses across the world. Providing managed and organised delivery services by the logistics partners is a challenging task with many facets. While the logistics services field has grown and matured over the years, delivery and shipping inefficiencies still cause substantial problems for shipping senders, shipping recipients, and shipping carriers alike. In particular, it becomes difficult for the shipping senders and/or recipients to accurately estimate transit time for a given shipment or package. Also, the risk prediction associated with the logistics becomes difficult. Generally, the risk associated with the logistics is related to return to origin (RTO) of the package. The RTO refers as a package has failed to deliver to an end consumer after more than one attempts by the logistics partners. There are various reasons for which the package doesn't reach to the end consumer and returns back to e-seller. Various systems are available which helps in estimating the RTO percentage of the package.

Conventionally, the system available for estimating the RTO percentage of the package includes identifying the one or more reasons which leads to failure of the package in reaching the customer end and instead returning to the e-seller. However, such conventional system are unable to help the e-seller to know about the future RTO and help to minimize the RTO ratio by identifying contribution of one or more significant factors for the RTO of the package. Also, such conventional system is unbale to predict the package getting return to the seller or to the origin within a predefined interval of time. Moreover, such conventional system in unable to generate an automated action/communication channel to inform the seller as a result, there is a significant loss to the e-seller business.

Hence, there is a need for an improved system and a method for pre-dispatched return to origin prediction of a package in logistics in order to address the aforementioned issues.

BRIEF DESCRIPTION

In accordance with an embodiment of the present disclosure, a system for pre dispatched return to origin prediction of a package in logistics is disclosed. The system includes a return to origin factor collection subsystem configured to collect a plurality of pre-dispatched return to origin (RTO) factors associated with a package delivery in the logistics. The system also includes a pre-dispatch return to origin score prediction subsystem operatively coupled to the return to origin factor collection subsystem. The pre-dispatch return to origin score prediction subsystem is configured to assign a pre- dispatch return to origin (RTO) score corresponding to each of a plurality of collected pre-dispatched return to origin factors. The pre-dispatch return to origin score prediction subsystem is also configured to compute a variation of an assigned pre dispatch return to origin (RTO) score based on one or more predefined conditions. The pre-dispatch return to origin score prediction subsystem is also configured to predict a pre-dispatch return to origin score based on a computed variation of the assigned pre-dispatch return to origin (RTO) score using a return to origin prediction technique.

In accordance with another embodiment of the present disclosure, a method for pre- dispatched return to origin prediction of a package in logistics is disclosed. The method includes collecting, by a return to origin factor collection subsystem, a plurality of pre-dispatched return to origin (RTO) factors associated with a package delivery in the logistics. The method also includes assigning, by a pre-dispatch return to origin score prediction subsystem, a pre-dispatch return to origin (RTO) score corresponding to each of a plurality of collected pre-dispatched return to origin factors. The method also includes computing, by the pre-dispatch return to origin score prediction subsystem, a variation of an assigned pre-dispatch return to origin (RTO) score based on one or more predefined conditions. The method also includes predicting, by the pre-dispatch return to origin score prediction subsystem, a pre dispatch return to origin score based on a computed variation of the assigned pre dispatch return to origin (RTO) score using a return to origin prediction technique.

To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures. BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:

FIG. 1 is a block diagram of a system for pre-dispatched return to origin prediction of a package in logistics in accordance with an embodiment of the present disclosure. FIG. 2 illustrates a schematic representation of an exemplary embodiment of a system for pre-dispatched return to origin prediction of a package in logistics in accordance with an embodiment of the present disclosure.

FIG. 3 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure. FIG. 4 is a flow chart representing the steps involved in a method for pre-dispatched return to origin prediction of a package in logistics of FIG. 1 in accordance with the embodiment of the present disclosure.

Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.

DETAILED DESCRIPTION

For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.

The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or sub-systems orelements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.

In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.

Embodiments of the present disclosure relate to a system for pre-dispatched return to origin prediction of a package in logistics. The system includes a return to origin factor collection subsystem configured to collect a plurality of pre-dispatched return to origin (RTO) factors associated with a package delivery in the logistics. The system also includes a pre-dispatch return to origin score prediction subsystem operatively coupled to the return to origin factor collection subsystem. The pre-dispatch return to origin score prediction subsystem is configured to assign a pre-dispatch return to origin (RTO) score corresponding to each of a plurality of collected pre-dispatched return to origin factors. The pre-dispatch return to origin score prediction subsystem (120) is also configured to compute a variation of an assigned pre-dispatch return to origin (RTO) score based on one or more predefined conditions. The pre-dispatch return to origin score prediction subsystem (120) is also configured to predict a pre-dispatch return to origin score based on a computed variation of the assigned pre-dispatch return to origin (RTO) score using a return to origin prediction technique.

FIG. 1 is a block diagram of a system (100) for pre-dispatched return to origin prediction of a package in logistics in accordance with an embodiment of the present disclosure. The system (100) includes a return to origin factor collection subsystem (110) configured to collect a plurality of pre-dispatched return to origin (RTO) factors associated with a package delivery in the logistics. As used herein, the term ‘return to origin (RTO)’ is defined as a non-deliverability of a package to an end-consumer by a logistics partner and return of the package to a seller’s address. Similarly, the term ‘pre-dispatched return to origin’ is defined as indicating a chance of the return to origin of the package before the package gets picked up from the seller’s address. In one embodiment, the pre-dispatched return to origin (RTO) factors may include at least one of an incorrect or an incomplete address identification using an artificial intelligence technique, identification of mismatch of address to pin code mapping, identification of duplicate orders associated with a customer from one or more e- commerce platforms, logistics performance measurement for a predefined pin code, blacklisted customer identification or a combination thereof.

The system (100) also includes a pre-dispatch return to origin score prediction subsystem (120) operatively coupled to the return to origin factor collection subsystem (110). The pre-dispatch return to origin score prediction subsystem (120) is configured to assign a pre-dispatch return to origin (RTO) score corresponding to each of a plurality of collected pre-dispatched return to origin factors. In one embodiment, the pre-dispatch RTO score corresponding to the each of the plurality of collected pre- dispatched RTO factors may be assigned based on an initial performance measurement for a predefined situation.

The pre-dispatch return to origin score prediction subsystem (120) is also configured to compute a variation of an assigned pre-dispatch return to origin (RTO) score based on one or more predefined conditions. The variation of the assigned pre-dispatch return to origin (RTO) score depends upon preference of each of the plurality of pre dispatched RTO factors for a predefined situation. The pre-dispatch RTO score dynamically changes based on weightage of the each of the plurality of pre-dispatched RTO factors. The pre -dispatch return to origin score prediction subsystem is also configured to predict a pre-dispatch return to origin score based on a computed variation of the assigned pre-dispatch return to origin (RTO) score using a return to origin prediction technique.

In one embodiment, the one or more predefined conditions to compute the variation of the assigned pre-dispatch return to origin score (RTO) associated with the incorrect and incomplete address identification using the artificial intelligence technique includes determination of an accuracy level of an address pattern associated with a customer. The accuracy level of the address pattern is determined by identification of a missing value in the address pattern of the customer. Once, the missing value is identified, the RTO prediction technique predicts the address correction level such as an accurate level and inaccurate level and updates the pre-dispatch RTO score.

In another embodiment, the one or more predefined conditions to compute the variation of the assigned pre-dispatch return to origin score (RTO) associated with an identification of mismatch of the address to pin code mapping may include detection of an incorrect pin code in the address associated with the customer using a map GeoSystem and the return to origin prediction technique. In such embodiment, the incorrect pin code in the address is detected based on mapping of address geocode with pin code geocode. Each geocode location corresponding to an address is stored in a database. Also, each geocode location corresponding to a pin code is also stored in the database. Once, a customer’s address is received in real-time for delivery of the package, the received address geocode, and the pin code geocode is mapped with prestored address geocode and the prestored pin code geocode. In yet another embodiment, the one or more predefined conditions to compute the variation of the assigned pre-dispatch return to origin score (RTO) associated with logistics performance measurement for a predefined pin code may include prediction of the logistics performance for a predefined interval of time upon comparing a real time performance of the logistics partners with historical performance of the logistics partners. In such embodiment, the predefined interval of time may include a period of a month. The RTO prediction technique compares a measured level of the performance of the logistics partners in the real-time with the historical performance of the logistics partners and changes the performance score factor such as the pre-dispatch return to origin score (RTO).

In one embodiment, the one or more predefined conditions to compute the variation of the assigned pre-dispatch return to origin score (RTO) associated with blacklisted customer identification may include identification of one or more blacklisted customers upon comparison of a real-time blacklisted data of one or more customers with prestored historical data of blacklisted one or more customers. In some embodiment, the historical data of the blacklisted one or more customers may be created based on maximum number of the RTO associated with the one or more customers. In such embodiment, the one or more customers with the maximum number of the return package may be marked or flagged as the blacklisted customers.

In a specific embodiment, the system (100) further includes a pre-dispatch return to origin notification subsystem (not shown in FIG. 1) operatively coupled to the pre dispatch return to origin score prediction subsystem (120). The pre-dispatch return to origin notification subsystem is configured to identify one or more customer’s intent and one or more logistics partner’s intent received through a plurality of communication channels. In one embodiment, the plurality of communication channels may include at least one of a short message service (SMS), an email, a web notification, an automated call generation or a combination thereof. The identification of the one or more customer’ s intent and one or more logistics partner’ s intent through the plurality of communication channels helps the seller in obtaining feedback from the one or more customers and deciding where to reattempt the delivery of the package or return the package. In one embodiment, the intent of the one or more customers are understood analyzed using an artificial intelligence context understanding technique. The pre-dispatch return to origin notification subsystem is also configured to notify one or more sellers a chance of the package to return to origin (RTO) based on an identified intent of the one or more customers and an identified intent of the one or more logistics partners. In some embodiment, the one or more sellers may be notified through at least one of voice calls, a text message, a push notification, the email and the like. The notification sent to the one or more sellers helps in alerting the one or more sellers and reducing the chance of the RTO of the package associated with the one or more customers.

FIG. 2 illustrates a schematic representation of an exemplary embodiment of a system (100) for pre-dispatched return to origin prediction of a package in logistics in accordance with an embodiment of the present disclosure. The system (100) for pre dispatched return to origin (RTO) of the package helps an electronic-commerce business platform especially an e-seller (105) of the electronic commerce platform. The system (100) helps the e-seller (105) in obtaining a prior information about a chance of the return to origin (RTO) of the package. For example, let us assume that the e-seller (105) ‘A’ wants to deliver a package to a customer (108) ‘B’ via a logistics partner (115). In such a scenario, the system (100) helps in calculating and predicting the RTO percentage of the package to inform the e-seller (105) before the package is picked up from the seller’s end. For prediction of the RTO, a plurality of pre dispatched return to origin (RTO) factors associated with the package delivery in the logistics is collected by a return to origin factor collection subsystem (110). For example, the plurality of pre-dispatched RTO factors may include at least one of an incorrect address identification, address to pin code mapping, logistics performance measurement for a predefined pin code, blacklisted customer identification or a combination thereof.

Now, once the plurality of pre-dispatched factors are collected, each of the plurality of factors are considered in detail for prediction of the pre-dispatched RTO prediction. In a shipment life cycle, a pre-dispatch return to origin (RTO) score corresponding to the each of the plurality of collected pre-dispatched return to origin factors is assigned via a pre-dispatch return to origin score prediction subsystem (120). The pre-dispatch RTO score is assigned based on an initial performance measurement of the each of the plurality of pre-dispatched RTO factors for a predefined situation. Here, the assigned pre-dispatch score for each of the plurality of pre-dispatched RTO factors is stored in a database (125). Once, the assigned pre -dispatch score varies dynamically, such score is updated in the database (125) in real-time.

Again, based on one or more predefined conditions, a variation of an assigned pre dispatch RTO score is also computed by the pre-dispatch RTO score prediction subsystem (120). Here, the pre-dispatch RTO score dynamically changes based on weightage of the each of the plurality of pre-dispatched RTO factors. The pre-dispatch return to origin score prediction subsystem (120) also predicts a pre-dispatch return to origin score based on a computed variation of the assigned pre-dispatch return to origin (RTO) score using a return to origin prediction technique.

For example, the assigned pre-dispatch RTO score associated with the address pattern of the customer (108) is varied when there is some change in the address pattern of the customer (108). For example, the change in the address pattern is computed upon determination of an accuracy level of the address pattern associated with the customer. Here, the accuracy level of the address pattern is determined by identification of a missing value in the address pattern of the customer. For example, the missing value may include an area name or a locality name missing from the address of the customer. Once, the missing value is identified, the RTO prediction technique predicts the address correction level such as an accurate level and inaccurate level and updates the pre-dispatch RTO score. For example, the RTO prediction technique may include an artificial intelligence technique such as a neural network technique or a machine learning technique.

Similarly, suppose if the address geocode and a corresponding pin code geocode is mismatched, then in such a case also, the assigned score associated with the address to pin code mapping is varied. The address to pin code mapping is detected using Map Geosystem and the artificial intelligence or machine learning technique. Again, the logistics performance management also plays a key role in the prediction of the RTO of the package. Here, the assigned pre-dispatch RTO score associated with the logistics performance management varies based on a result of the logistics performance measurement for a predefined pin code within a predefined interval of time. For example, if the logistics partner ‘C’ (115) has an insufficient performance in month of ‘December’ for an area with the pin code ‘712232’ and an extraordinary successful performance in the month of ‘November’ for the same area with the pin code ‘712232’. Then, in such a scenario, prediction of the logistics performance for the predefined interval of time is done by comparing a real-time performance of the logistics partners (115) with historical performance of the logistics partners (115). As a result, a performance score factor changes for the RTO prediction accordingly.

Also, another condition of the one or more predefined conditions for the pre-dispatch RTO prediction of the package includes the blacklisted customer identification. The assigned pre-dispatch RTO score associated with the blacklisted customer identification varies depending upon the customer (108) who holds a highest number of the RTO. The pre-dispatch RTO prediction is based on comparison of a real-time blacklisted data of one or more customers with prestored historical data of one or more blackli ted customers stored in a database (125).

Further, once, the pre-dispatch RTO prediction is completed, the customer’ s intent and the logistics partner’s intent which is received through a plurality of communication channels, are identified by a pre -dispatch return to origin (RTO) notification subsystem (130). Here, the plurality of communication channels may include a web message. Also, the pre-dispatch RTO notification subsystem notifies the seller a chance of the package to get into the return to origin (RTO) in real-time based on an identified intent of the customer and an identified intent of the logistics partner. The notification sent to the seller (105) helps in alerting the seller (105) and reducing the chance of the RTO of the package associated with the customer (108).

FIG. 3 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure. The server (200) includes processor(s) (230), and memory (210) operatively coupled to the bus (220).

The processor(s) (230), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof. The memory (210) includes a plurality of subsystems stored in the form of executable program which instructs the processor (230) to perform the method steps illustrated in FIG. 1. The memory (210) is substantially similar to a system (100) of FIG.l. The memory (210) has following subsystems: a return to origin factor collection subsystem (110), and a pre-dispatch return to origin score prediction subsystem (120).

The return to origin factor collection subsystem (110) is configured to collect a plurality of pre-dispatched return to origin (RTO) factors associated with a package delivery in the logistics. The pre -dispatch return to origin score prediction subsystem (120) is configured to assign a pre-dispatch return to origin (RTO) score corresponding to each of a plurality of collected pre-dispatched return to origin factors. The pre dispatch return to origin score prediction subsystem (120) is also configured to compute a variation of an assigned pre-dispatch return to origin (RTO) score based on one or more predefined conditions. The pre-dispatch return to origin score prediction subsystem (120) is also configured to predict a pre-dispatch return to origin score based on a computed variation of the assigned pre-dispatch return to origin (RTO) score using a return to origin prediction technique.

The bus (220) as used herein refers to be internal memory channels or computer network that is used to connect computer components and transfer data between them. The bus (220) includes a serial bus or a parallel bus, wherein the serial bus tran mit data in bit-serial format and the parallel bus transmit data across multiple wires. The bus (220) as used herein, may include but not limited to, a system bus, an internal bus, an external bus, an expansion bus, a frontside bus, a backside bus and the like.

FIG. 4 is a flow chart representing the steps involved in a method (300) for pre- dispatched return to origin prediction of a package in logistics of FIG. 1 in accordance with the embodiment of the present disclosure. The method (300) includes collecting, by a return to origin factor collection subsystem, a plurality of pre-dispatched return to origin (RTO) factors associated with a package delivery in the logistics in step 310. In one embodiment, collecting the plurality of pre-dispatched return to origin (RTO) factors associated with the package delivery may include collecting the plurality of pre-dispatched RTO factors which may include but not limited to, an incorrect address identification, address to pin code mapping, logistics performance measurement for a predefined pin code, blacklisted customer identification and the like. The method (300) also includes assigning, by a pre-dispatch return to origin score prediction subsystem, a pre-dispatch return to origin (RTO) score corresponding to each of a plurality of collected pre-dispatched return to origin factors in step 320. In one embodiment, assigning the pre-dispatch RTO score may include assigning the pre dispatch RTO score corresponding to the each of the plurality of collected pre dispatched RTO factors based on an initial performance measurement of the each of the plurality of collected pre-dispatched RTO factors for a predefined situation.

The method (300) also includes computing, by the pre-dispatch return to origin score prediction subsystem, a variation of an assigned pre-dispatch return to origin (RTO) score based on one or more predefined conditions in step 330. In one embodiment, computing the variation of the assigned pre-dispatch RTO score may include computing the variation of the assigned pre-dispatch RTO score based on preference of each of the plurality of pre-dispatched RTO factors for a predefined situation. The pre-dispatch RTO score dynamically changes based on weightage of the each of the plurality of pre-dispatched RTO factors.

In some embodiment, computing the variation of the assigned pre-dispatch return to origin (RTO) score based on the one or more predefined conditions may include computing the variation of the assigned pre -dispatch return to origin score (RTO) associated with the incorrect an the incomplete address identification which includes determination of an accuracy level of an address pattern associated with a customer.

In another embodiment, computing the variation of the assigned pre-dispatch return to origin (RTO) score based on the one or more predefined conditions may include computing the variation of the assigned pre -dispatch return to origin score (RTO) associated with an identification of mismatch in address to pin code mapping may include detection of an incorrect pin code in the address associated with the customer using a map GeoSystem and the return to origin prediction technique. In such embodiment, the incorrect pin code in the address is detected based on mapping of address geocode with pin code geocode.

In yet another embodiment, computing the variation of the assigned pre-dispatch return to origin (RTO) score based on the one or more predefined conditions may include computing the variation of the assigned pre-dispatch return to origin score (RTO) associated with logistics performance measurement for a predefined pin code may include prediction of the logistics performance for a predefined interval of time upon comparing a real-time performance of the logistics partners with historical performance of the logistics partners. In one embodiment, computing the variation of the assigned pre -dispatch return to origin (RTO) score based on the one or more predefined conditions may include computing the variation of the assigned pre -dispatch return to origin score (RTO) associated with blacklisted customer identification may include identification of one or more blacklisted customers upon comparison of a real-time blacklisted data of one or more customers with prestored historical data of blacklisted one or more customers .

The method (300) also includes predicting, by the pre-dispatch return to origin score prediction subsystem, a pre-dispatch return to origin score based on a computed variation of the assigned pre-dispatch return to origin (RTO) score using a return to origin prediction technique in step 340. In a specific embodiment, the method (300) further includes identifying, by a pre dispatch return to origin notification subsystem, one or more customer’s intent and one or more logistics partner’s intent received through a plurality of communication channels. In such embodiment, the plurality of communication channels may include at least one of a short message service (SMS), an email, a web notification or a combination thereof. In one embodiment, the method further includes notifying, by the pre-dispatch return to origin notification subsystem, one or more sellers a chance of the package to return to origin (RTO) based on an identified intent of the one or more customers and an identified intent of the one or more logistics partners.

Various embodiments of the present disclosure helps in preventing and reducing the RTO of the package by identifying the one or more customers intent in advance.

Moreover, the present disclosed system predicts the RTO of the package by using artificial intelligence driven systematisation approaches and as a result helps in reducing revenue loss and customer satisfaction. Furthermore, the present disclosed system by identifying the one or more customers intent saves a lot of time of each sellers in deciding whether to dispatch the package or not.

It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof.

While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.

The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.

Claims

WE CLAIM:

1. A system (100) for pre-dispatched return to origin prediction of a package in logistics comprising: a return to origin factor collection subsystem (110) configured to collect a plurality of pre-dispatched return to origin (RTO) factors associated with a package delivery in the logistics; a pre-dispatch return to origin score prediction subsystem (120) operatively coupled to the return to origin factor collection subsystem (110), wherein the pre dispatch return to origin score prediction subsystem (120) is configured to: assign a pre -dispatch return to origin (RTO) score corresponding to each of a plurality of collected pre-dispatched return to origin factors; compute a variation of an assigned pre-dispatch return to origin (RTO) score based on one or more predefined conditions; and predict a pre-dispatch return to origin score based on a computed variation of the assigned pre-dispatch return to origin (RTO) score using a return to origin (RTO) prediction technique.

2. The system (100) as claimed in claim 1, wherein the plurality of pre- dispatched return to origin (RTO) factors comprises at least one of an incorrect an incomplete address identification, identification of mismatch of address to pin code mapping, identification of duplicate orders associated with a customer from one or more e-commerce platforms, logistics performance measurement for a predefined pin code, blacklisted customer identification or a combination thereof.

3. The system (100) as claimed in claim 1, wherein the one or more predefined conditions to compute the variation of the assigned pre-dispatch return to origin score (RTO) associated with an incorrect address identification comprises determination of an accuracy level of an address pattern associated with a customer.

4. The system (100) as claimed in claim 1, wherein the one or more predefined conditions to compute the variation of the assigned pre-dispatch return to origin score (RTO) associated with address to pin code mapping comprises detection of an incorrect pin code in the address associated with the customer using a map GeoSystem and the return to origin prediction technique.

5. The system (100) as claimed in claim 1, wherein the one or more predefined conditions to compute the variation of the assigned pre-dispatch return to origin score (RTO) associated with logistics performance measurement for a predefined pin code comprises prediction of the logistics performance for a predefined interval of time upon comparing a real-time performance of the logistics partners with historical performance of the logistics partners.

6. The system (100) as claimed in claim 1, wherein the one or more predefined conditions to compute the variation of the assigned pre-dispatch return to origin score (RTO) associated with blacklisted customer identification comprises identification of one or more blacklisted customers upon comparison of a real-time blacklisted data of one or more customers with prestored historical data of blacklisted one or more customers.

7. The system (100) as claimed in claim 1, further comprising a pre-dispatch return to origin notification subsystem (130) operatively coupled to the pre dispatch return to origin score prediction subsystem (120), wherein the pre-dispatch return to origin notification subsystem (130) is configured to: identify one or more customer’s intent and one or more logistics partner’s intent received through a plurality of communication channels; notify one or more sellers a chance of the package to return to origin (RTO) based on an identified intent of the one or more customers and an identified intent of the one or more logistics partners.

8. A method (300) comprising: collecting, by a return to origin factor collection subsystem, a plurality of pre-dispatched return to origin (RTO) factors associated with a package delivery in the logistics (310); assigning, by a pre-dispatch return to origin score prediction subsystem, a pre-dispatch return to origin (RTO) score corresponding to each of a plurality of collected pre-dispatched return to origin factors (320); computing, by the pre-dispatch return to origin score prediction subsystem, a variation of an assigned pre-dispatch return to origin (RTO) score based on one or more predefined conditions (330); and predicting, by the pre-dispatch return to origin score prediction subsystem, a pre-dispatch return to origin score based on a computed variation of the assigned pre-dispatch return to origin (RTO) score using a return to origin prediction technique (340).

9. The method (300) as claimed in claim 8, further comprising identifying, by a pre-dispatch return to origin notification subsystem, one or more customer’s intent and one or more logistics partner’s intent received through a plurality of communication channels.

10. The method (300) as claimed in claim 9, further comprising notifying, by the pre-dispatch return to origin notification subsystem, one or more sellers a chance of the package to return to origin (RTO) based on an identified intent of the one or more customers and an identified intent of the one or more logistics partners.