Systems and methods for supply chain management

A method, comprising:

• receiving, by a processor, data from a set of data sources corresponding to a supply chain associated with at least a product, wherein the set of data sources includes a plurality of client systems;
• integrating, by a processor including an integration engine, the data received from the set of data sources, wherein the integration engine includes:
• a neutral sublayer component corresponding to a first set of endpoints used for at least one client system of the plurality of client systems;
• a system sublayer component corresponding to a second set of endpoints configured based on a specific type of client system, wherein the system sublayer component allows the system to reuse connectivity capabilities across the plurality of client systems;
• and
• a client sublayer component configured based on a client environment, wherein the client sublayer component connects the system to a specific client system of the plurality of client systems;
• pre-processing, by the processor, the received data based on the integration of the data from each of the set of data sources;
• generating, by the processor, supply chain data based on the integrated data;
• analyzing, by the processor via an orchestration engine, the generated supply chain data to assess an impact of the generated supply chain data on the supply chain, wherein the processor is configured to analyze the generated supply chain data by:
• performing a feature engineering technique on the integrated data to identify features from the integrated data for modelling a set of prediction machine learning (ML) models for each unit in the supply chain;
• generating a response variable in the modelling based on the feature engineering technique;
• optimizing model parameters by identification of best demand drivers for the modelling of the set of prediction ML models based on the features of the integrated data;
• generating the set of prediction ML models for each unit based on the optimized model parameters, the features of the integrated data, and the response variable;
• determining a best fit model from the set of prediction ML models for each unit in the supply chain;
• and
• applying the determined best fit model on the generated supply chain data to assess the impact of the generated supply chain data on the supply chain;
• based on the analysis and the assessed impact, predicting, by the processor via the orchestration engine, a state associated with a purchase event of the product in the supply chain, wherein the state comprises an identifier of the product and an attribute of the generated supply chain data causing the state;
• and
• generating, by the processor, a resolution flow to be executed in the supply chain for managing the predicted state associated with the purchase event of the product.

A non-transitory computer-readable medium, wherein the readable medium comprises machine-executable instructions that are executable by a processor to:

• receive data from a set of data sources corresponding to a supply chain associated with at least a product, wherein the set of data sources includes a plurality of client systems;
• integrate, via an integration engine, the data received from the set of data sources, wherein the integration engine includes:
• a neutral sublayer component corresponding to a first set of endpoints used for at least one client system of the plurality of client systems;
• a system sublayer component corresponding to a second set of endpoints configured based on a specific type of client system, wherein the system sublayer component allows the system to reuse connectivity capabilities across the plurality of client systems;
• and
• a client sublayer component configured based on a client environment, wherein the client sublayer component connects the system to a specific client system of the plurality of client systems;
• pre-process the received data based on the integration of the data from each of the set of data sources;
• generate supply chain data based on the integrated data;
• analyze, via an orchestration engine, the generated supply chain data to assess an impact of the generated supply chain data on the supply chain, wherein the processor is configured to analyze the generated supply chain data by:
• performing a feature engineering technique on the integrated data to identify features from the integrated data for modelling a set of prediction machine learning (ML) models for each unit in the supply chain;
• generating a response variable in the modelling based on the feature engineering technique;
• optimizing model parameters by identification of best demand drivers for the modelling of the set of prediction ML models based on the features of the integrated data;
• generating the set of prediction ML models for each unit based on the optimized model parameters, the features of the integrated data, and the response variable;
• determining a best fit model from the set of prediction ML models for each unit in the supply chain;
• and
• applying the determined best fit model on the generated supply chain data to assess the impact of the generated supply chain data on the supply chain;
• based on the analysis and the assessed impact, predict, via the orchestration engine, a state associated with a purchase event of the product in the supply chain, wherein the state comprises an identifier of the product and an attribute of the generated supply chain data causing the state;
• and
• generate a resolution flow to be executed in the supply chain for managing the predicted state associated with the purchase event of the product.