The increasing complexity of supply chain (SC) networks and the associated risks have captured global attention, leading to the emergence of the concept of supply chain resilience (SCRES). Over the past two decades, SCRES has been a focal point of research, explored through various perspectives, approaches, and tools. Among these, the discrete event simulation (DES) technique stands out for its effectiveness in modeling SCRES. While DES models offer multiple advantages and have been widely used in the literature, they lack the capability to measure a crucial element of SCRES: the cumulative learning of a SC network as it experiences risk events over time. The absence of this attribute renders attempts to operationalize SCRES incomplete. This research aims to address this methodological gap by proposing-from a theoretical standpoint-the integration of artificial intelligence (AI) algorithms into DES models. The research delves into several categories of AI algorithms that can learn from successive iterations of DES models. Based on this exploratory analysis, it is suggested that neural networks, particularly backpropagation, Kolmogorov-Arnold, and reinforcement learning algorithms, are the most suitable to address this gap in the literature. Additionally, a novel definition of SCRES is proposed, emphasizing the importance of learning within supply chain networks.
