Method and system to streamline return decision and optimize costs

A system comprising:

• an input/output interface, to receive an input data, wherein the input data includes one or more returned items, a Stock Keeping-Unit (SKU) of each one or more returned items, store numbers, vicinity stores, a regional delivery center, T−1 sales, and T−1 returns;
• a memory in communication with the one or more hardware processors, wherein the one or more hardware processors are configured to execute programmed instructions stored in the memory to:
• pre-process the received plurality of input data to remove outliers and sort values of the plurality of inputs based on a predefined format to generate a pre-processed input data and creates a data set to input to a reinforcement learning (RL) model using details such as a data, a store, an item SKU and a corresponding warehouse, a value of sales and returns to obtain outcomes that is a sales dataset being transformed for return's decision application;
• forecast an actual sale and an actual return from the pre-processed input data using a predefined forecasting technique;
• compute a Stock-Transfer-Return (STR) data using the pre-processed input data, wherein steps for computing STR comprises:
• determining sales demand for each of the one or more returned items for the store number and the item SKU from the forecasted actual sale and T−1 sale, wherein based on the outcome of the determined sales demand of the each of the one or more returned items is (1) re-stocked in the store, or (2) transferred to the vicinity store, or (3) returned to the regional distributor center (DC);
• and
• determining sales demand for each of the one or more returned items in the vicinity store associated with the store number and the item SKU from the forecasted actual sale and T−1 sale, wherein based on the outcome of the determined sales demand in the vicinity store the item is (1) transferred to the vicinity store, or (2) returned to the regional distributor center (DC);
• create an environment model for the STR to train a Reinforcement Learning (RL) agent and a model agent reward, wherein the environmental model is created using an OpenAI gym base class and initialized using the input data and set of functions are customized to implement the OpenAI gym base class for a supply chain simulation and reinforcement learning, wherein the environment model aids to model classical actors in the supply chain simulation and a return decision class interacts with the RL agent in episodic timesteps through each of the functions, among the set of functions, being available only as abstract class in a OpenAI gym tool kit,
• wherein the RL model learns optima in a user dataset through exploration and exploitation of a reward function, thereby reinforcing positive outcomes and penalizing negative outcomes during RL training, and defining rewards strategy allows the RL agent to learn an exploratory search space and propagate intermediate q-value rewards for unknown outcomes to take an informed decision;
• and
• train the RL agent with the computed STR data using the OpenAI gym tool kit to generate a return decision of the one or more returned items to (1) re-stock in the store, or (2) transfer to a vicinity store, or (3) return to the regional distributor center.

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, via an input/output interface, an input data, wherein the input data includes one or more returned items, a Stock Keeping-Unit (SKU) of each one or more returned items, store numbers, vicinity stores, a regional delivery center, T−1 sales, and T−1 returns;
• pre-processing the received input data to remove outliers and sort values of a plurality of inputs based on a predefined format to generate a pre-processed input data and creates a data set to input to a reinforcement learning (RL) model using details such as a data, a store, an item SKU and a corresponding warehouse, a value of sales and returns to obtain outcomes that is a sales dataset being transformed for return's decision application;
• forecasting an actual sale and an actual return from the pre-processed input data using a predefined forecasting technique;
• computing, via the one or more hardware processors, a Stock-Transfer-Return (STR) data using the pre-processed input data, wherein steps for computing STR comprises;
• determining sales demand for each of the one or more returned items for the store number and the item SKU from the forecasted actual sale and T −1 sale, wherein based on the outcome of the determined sales demand of the one or more returned items is (1) re-stocked in the store, or (2) transferred to the vicinity store, or (3) returned to the regional distributor center (DC);
• and
• determining sales demand for each of the one or more returned items in the vicinity store associated with the store number and the item SKU from the forecasted actual sale and T−1 sale, wherein based on the outcome of the determined sales demand in the vicinity store the item is (1) transferred to the vicinity store, or (2) returned to the regional distributor center (DC);
• creating an environment model for the STR data to train a Reinforcement Learning (RL) agent and a model agent reward, wherein the environmental model is created using an OpenAI gym base class and initialized using the input data and set of functions are customized to implement the OpenAI gym base class for a supply chain simulation and reinforcement learning, wherein the environment model aids to model classical actors in the supply chain simulation and a return decision class interacts with the RL agent in episodic timesteps through each of the functions, among the set of functions, being available only as abstract class in a OpenAI gym tool kit,
• wherein the RL model learns optima in a user dataset through exploration and exploitation of a reward function, thereby reinforcing positive outcomes and penalizing negative outcomes during RL training, and defining rewards strategy allows the RL agent to learn an exploratory search space and propagate intermediate q-value rewards for unknown outcomes to take an informed decision;
• and
• training the RL agent with the computed STR data using the OpenAI gym tool kit to generate a return decision of the one or more returned items to (1) re-stock in the store, or (2) transfer to a vicinity store, or (3) return to the regional distributor center.