receiving, via one or more hardware processors, an input request from one or more entities;

querying, via the one or more hardware processors, a parts repository based on the input request to identify a plurality of parts;

applying, via the one or more hardware processors, a Jaccard similarity for each pair of parts from the plurality of parts by calculating a similarity score between each set of parts from the plurality of parts to obtain one or more combinations of a set of similar parts;

identifying, via the one or more hardware processors, based on the similarity score, at least one corresponding supplier from a suppliers repository and tagging the at least one corresponding supplier to each of the one or more combinations of the set of similar parts;

performing, via the one or ore hardware processors, a comparison between the similarity score and a threshold;

identifying, via the one or more hardware processors, based on the comparison, one or more target suppliers from the one or more combinations of the set of similar parts and the at least one supplier;

determining, via the one or more hardware processors, a sourcing cost for each set of similar parts corresponding to the one or more identified target suppliers based on one or more pre-defined scenarios;

optimizing, via the one or more hardware processors, a total sourcing cost derived from the sourcing cost for each set of similar parts based on one or more constraints;

predicting, via the one or more hardware processors, a target cost for subsequent time instances using a Neural Network AutoRegressive with eXogenous input (NNARX) model, wherein the NNARX model comprises of three layers, wherein a first input layer comprises neurons representing historical values of the cost, current and historical values for external driver variables, a final output layer comprises a neuron representing the target cost, and a middle layer comprises neurons configured to compute nodal weights of the NNARX model with a rectified linear activation function, wherein each node of the middle layer is connected to one or more nodes of the input layer and to a node of the output layer, wherein the input request is divided into a training set and a testing set, wherein the NNARX model is used on the testing set to compute predicted cost and a forecast error for the testing data and wherein the NNARX model is used with the forecast error to predict the target cost for the subsequent time instances;

performing, via the one or more hardware processors, a comparison of (i) the target cost predicted for the subsequent time instances and (ii) the optimized sourcing cost for each of the one or more pre-defined scenarios; and

identifying, via the one or more hardware processors, a focal scenario from the one or more pre-defined scenarios based on the comparison, wherein the focal scenario serves as an output; and

iteratively performing, via the one or more hardware processors, until the optimized total sourcing cost of the focal scenario under the target cost with a tolerance value by: