Supply chain network prescriptions based on artificial intelligence techniques

Abstract

Embodiments provides a method executed by a server computer executing a supply chain network analysis application of a supply chain model. The method includes receiving supply chain network data associated with a supply chain network having one or more supply chain nodes. The method then programmatically executes inferences on the supply chain network data using one or more machine learning models and one or more heuristic algorithms to implement descriptive analytics, diagnostic analytics, and prescriptive analytics to create and store one or more scenario prescriptions that specify one or more changes to the one or more supply chain nodes. The method performs steps for programmatically executing inferences on the supply chain network data that includes extracting one or more data features at a path level, the one or more data features indicating descriptive insights related to one or more paths in the supply chain network. The method includes a step of identifying, using one or more path-level machine learning models, one or more cost drivers of the one or more paths in the supply chain network by computing a feature score of each of the one or more data features at the path level. The method includes creating and storing, using the one or more path-level machine learning models and the feature score of each of the one or more data features, one or more digital representations of the one or more scenario prescriptions. The method includes generating and displaying one or more visualizations of one or more updated network models that implements the one or more scenario prescriptions.