Multimodal Deep Learning Approach for Early Warning of Supply Chain Disruptions Using NLP and Anomaly Detection

Abstract

Global supply chains face unprecedented risks of disruption from geopolitical conflicts, pandemic-related closures, and labor shortages. Traditional risk management approaches rely on structured historical data and fail to capture real-time signals from unstructured sources such as news reports and social media. This paper proposes a multimodal deep learning framework that integrates natural language processing with anomaly detection algorithms to enable early warning of supply chain disruptions. The framework processes news articles, social media streams, and operational data through specialized neural network modules. LSTM autoencoders detect temporal anomalies while transformer-based models extract risk signals from multilingual text. Cross-modal fusion through graph neural networks correlates heterogeneous risk factors. Experimental evaluation on real-world datasets demonstrates 92.1% recall and 93.4% precision with a 42-minute average prediction lead time. Case studies validate practical applicability across manufacturing sectors.