Supply Chain Disruption

Supply chain disruption research focuses on developing methods to predict, mitigate, and respond to disruptions that impact the flow of goods and services. Current research emphasizes data-driven approaches, employing machine learning algorithms like tree-based models, neural networks, and Hawkes processes, often integrated with simulation frameworks (e.g., system dynamics and discrete event simulation) to model complex scenarios and test resilience strategies. These advancements aim to improve forecasting accuracy, enable faster responses to disruptions, and ultimately enhance supply chain resilience, leading to reduced costs and improved operational efficiency across various industries. The ultimate goal is to develop more agile and robust supply chains capable of withstanding unforeseen events.