Neural Networks in Predicting Global Supply Chain Disruptions

Abstract

Predicting disruptions in global supply chains is critical for maintaining efficiency and minimizing financial losses. Neural networks have emerged as a powerful tool for forecasting potential disruptions by analyzing vast amounts of data from various sources. This article explores the use of neural network models in predicting supply chain disruptions, including factors such as natural disasters, political instability, and transportation bottlenecks. We examine the advantages of using deep learning algorithms, such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, in supply chain forecasting, along with the challenges involved in integrating neural networks into existing supply chain management systems.