Artificial Intelligence in Network Analytics for Supply Chain Optimization: Forecasting Demand and Preventing Disruptions

The current supply chain operates in a turbulent, unpredictable environment characterized by volatility, uncertainty, complexity, and ambiguity (VUCA), and thus requires a higher level of analytical skills than conventional statistical techniques. The objective of this article is to merge artificial intelligence into supply chain network analytics, focusing primarily on demand prediction and disruption reduction. The article is based on present-day documentation and technological implementations, which makes it clear how the machine learning algorithms used, namely Long Short-Term Memory (LSTM) networks and Random Forests, respectively, succeed in better forecasting and offer predictive risk management. The article proposes a model of AI-assisting network analytics and investigates consequences for resilience and operational efficiency