Method and system for managing item returns

A system comprising:

• at least one memory storing programmed instructions;
• one or more Input/Output (I/O) interfaces;
• and
• one or more hardware processors operatively coupled to the at least one memory, wherein the one or more hardware processors are configured by the programmed instructions to:
• receive a historical transaction data associated with each of a plurality of invoices pertaining to shoppers and a contextual information, wherein the historical transaction data comprises an online transaction data, a customer profile, an online return data, and the contextual information comprises a plurality of local events, wherein the plurality of local events include sports events, cultural events received from generative Artificial Intelligence (AI),
• wherein each of the plurality of invoices is associated with at least one group from among a plurality of groups, wherein each of the plurality of groups is formed based on a level of hierarchy, and
• wherein the level of hierarchy comprises a department, a category, a class, and a subclass;
• generate a return spread for each of the plurality of groups pertaining to each of the plurality of invoices based on the historical transaction data, wherein a value associated with each column of the return spread pertaining to each of a plurality of items associated with each of the plurality of groups is one of a) a zero and b) a one based on a return status associated with each of a plurality of purchased items, wherein zero value indicates one of a) an item is not returned and b) the item is not purchased, wherein the one value indicates the item is returned previously and, wherein each of the plurality of purchased items comprises a plurality of attributes and, wherein each of the plurality of attribute value is one of (i) a qualitative value or (ii) a quantitative value;
• simultaneously generate a dynamic similarity spread for each of the plurality of groups pertaining to each of the plurality of invoices based on the historical transaction data, wherein each of a plurality of column associated with the similarity spread pertains to each of the plurality of items associated with each of the plurality of groups, wherein each of the plurality of columns associated with the similarity spread is updated with a number of similar items associated with each of the plurality of purchased items from among the plurality of items by measuring a similarity between each of the plurality of purchased items pertaining to each of the plurality of groups using one of (i) a distance matrix (ii) a correlation matrix and (iii) an attribute matching percentage matrix;
• simultaneously generate a dynamic linkage spread for each of the plurality of groups pertaining to each of the plurality of invoices based on the historical transaction data by applying PCoA (Principal Coordinate Analysis) on an associated distance matrix;
• dynamically adjust a cut-off value for multivariate distance in a similarity matrix or adjust a cut-off value for linkage strength in a principal component of the PCoA to identify similar items corresponding to an item of an invoice, and a cut-off value of propensity for return to decide an item to be returned is adjusted by estimating and minimizing a gap between a predicted return spread and an actual return spread;
• train a multivariate multiple binary Machine Learning (ML) model for predicting a plurality of potential return items based on the historical transaction data by mapping the return spread associated with each of the plurality of groups with one of a) the dynamic similarity spread and b) the dynamic linkage spread associated with each of the plurality of groups and the customer profile and contextual information, wherein the return spread is related with the dynamic similarity spread or the dynamic linkage spread along with the contextual information through machine learning models for capturing intrinsic mechanism that result returns by training the multivariate multiple binary ML model using the actual return spread, the dynamic similarity spread, or the dynamic linkage spread and the customer profile, and the captured return mechanism is leveraged to pre-empt the returns online in the form of the return spread at the time of ordering or scanning the items in real time.to obtain the predicted return spread for a subgroup of an invoice;
• and
• finetune the trained multivariate multiple binary ML model by modifying inputs associated with one of a) similarity spread and d) linkage spread until an optimal wilks lambda criterion is obtained, wherein the inputs comprises the distance matrix, the correlation matrix, the attribute matching percentage matrix and a linkage strength obtained from PCoA;
• use the finetuned multivariate multiple binary ML model to predict the plurality of potential return items from among the plurality of purchased items for a new shopper, during inferencing stage, wherein the prediction of the plurality of potential return items is used as input to optimize operations associated with the plurality of potential return items across value chain, wherein the plurality of potential return items are updated in a central database which is linked with a plurality of applications associated with the value chain, wherein the finetuned multivariate multiple binary ML model is applied with real time information received from different sources for the group to predict the plurality of potential return items, and during prediction, an attribute value mismatch score and upcoming events are received from the generative AI based on the context;
• and
• communicate the predicted plurality of potential return items to an associated application from the plurality of applications linked with the central database to enable automation of emails to shoppers with instructions on returning items after they receive their order, inclusion of return labels in a packing box to ensure that potential items will be returned, or an advanced planning for providing of incentives or rewards for completing returns in a store and considering additional purchase made in the store during a return by a shopper.

One or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• receiving, a historical transaction data associated with each of a plurality of invoices pertaining to shoppers and a contextual information, wherein the historical transaction data comprises an online transaction data, a customer profile, an online return data, and the contextual information comprises a plurality of local events, wherein the plurality of local events include sports events, cultural events received from generative Artificial Intelligence (AI),
• wherein each of the plurality of invoices is associated with at least one group from among a plurality of groups, wherein each of the plurality of groups is formed based on a level of hierarchy, and
• wherein the level of hierarchy comprises a department, a category, a class, and a subclass;
• generating, a return spread for each of the plurality of groups pertaining to each of the plurality of invoices based on the historical transaction data, wherein a value associated with each column of the return spread pertaining to each of a plurality of items associated with each of the plurality of groups is one of a) a zero and b) a one based on a return status associated with each of a plurality of purchased items, wherein zero value indicates one of a) an item is not returned and b) the item is not purchased, wherein the one value indicates the item is returned previously and, wherein each of the plurality of purchased items comprises a plurality of attributes and, wherein each of the plurality of attribute value is one of (i) a qualitative value or (ii) a quantitative value;
• simultaneously generating, a dynamic similarity spread for each of the plurality of groups pertaining to each of the plurality of invoices based on the historical transaction data, wherein each of a plurality of column associated with the similarity spread pertains to each of the plurality of items associated with each of the plurality of groups, wherein each of the plurality of columns associated with the similarity spread is updated with a number of similar items associated with each of the plurality of purchased items from among the plurality of items by measuring a similarity between each of the plurality of purchased items pertaining to each of the plurality of groups using one of (i) a distance matrix (ii) a correlation matrix and (iii) an attribute matching percentage matrix;
• simultaneously generating, a dynamic linkage spread for each of the plurality of groups pertaining to each of the plurality of invoices based on the historical transaction data by applying PCoA (Principal Coordinate Analysis) on an associated distance matrix;
• dynamically adjusting a cut-off value for multivariate distance in a similarity matrix or adjusting a cut-off value for linkage strength in a principal component of the PCoA to identify similar items corresponding to an item of an invoice, and a cut-off value of propensity for return to decide an item to be returned is adjusted by estimating and minimizing a gap between a predicted return spread and an actual return spread;
• training, a multivariate multiple binary Machine Learning (ML) model for predicting a plurality of potential return items based on the historical transaction data by mapping the return spread associated with each of the plurality of groups with one of a) the dynamic similarity spread and b) the dynamic linkage spread associated with each of the plurality of groups and the customer profile and contextual information, wherein the return spread is related with the dynamic similarity spread or the dynamic linkage spread along with the contextual information through machine learning models for capturing intrinsic mechanism that result returns by training the multivariate multiple binary ML model using the actual return spread, the dynamic similarity spread, or the dynamic linkage spread and the customer profile, and the captured return mechanism is leveraged to pre-empt the returns online in the form of the return spread at the time of ordering or scanning the items in real time.to obtain the predicted return spread for a subgroup of an invoice;
• finetuning, the trained multivariate multiple binary ML model by modifying inputs associated with one of a) similarity spread and d) linkage spread until an optimal wilks lambda criterion is obtained, wherein the inputs comprise the distance matrix, the correlation matrix, the attribute matching percentage matrix and a linkage strength obtained from PCoA;
• using the finetuned multivariate multiple binary ML model to predict the plurality of potential return items from among the plurality of purchased items for a new shopper, during inferencing stage, wherein the prediction of the plurality of potential return items is used as input to optimize operations associated with the plurality of potential return items across value chain, wherein the plurality of potential return items are updated in a central database which is linked with a plurality of applications associated with the value chain, wherein the finetuned multivariate multiple binary ML model is applied with real time information received from different sources for the group to predict the plurality of potential return items, and during prediction, an attribute value mismatch score and upcoming events are received from the generative AI based on the context;
• and
• communicating the predicted plurality of potential return items to an associated application from the plurality of applications linked with the central database to enable automation of emails to shoppers with instructions on returning items after they receive their order, inclusion of return labels in a packing box to ensure that potential items will be returned, or an advanced planning for providing of incentives or rewards for completing returns in a store and considering additional purchase made in the store during a return by a shopper.