LEARNING FROM SUPPLY CHAINS WITH PROBABILISTIC NETWORK MODELS

This paper aims to propose a novel network model, probabilistic Boolean networks (PBN), for supply chain reasoning. We demonstrate how relationships between the variables can be learned from their behaviors and discuss the equivalence between dynamic Bayesian networks (DBN). Compared with the findings of previous investigations, this work emphasizes the advantages of PBN and its roles in leaning DBN in complex temporal settings.