System and method for predicting and managing the risks in a supply chain network

This disclosure relates to predicting and managing supply chain network risks. In one embodiment, a processor-implemented method obtains identifiers for supply chain contributors and parameters; and a query. The method performs a natural language processing algorithm on the query to extract text components, which it analyzes to identify supply chain component clusters and risk identifiers. It also includes executing a machine learning technique for learning of the risk identifiers and generating co-occurrence rules between the risk identifiers, as well as associated rule support and rule confidence parameters. It further includes sorting the co-occurrence rules to generate a prioritized rules list, and generating a risk prediction model for the supply chain using the prioritized rules list, using a classifier algorithm. The method further includes training the risk prediction model using a machine learning techniques for incremental learning, and generating a supply chain element modification using the trained risk prediction model.