Transformative AI Models for Predictive Resource Allocation and Risk Mitigation in Global Supply Chain Networks

In this research, Integrated Transformative AI Models for Predictive Resource Allocation and Risk Mitigation in Global supply Chain Networks are considered. In order to tackle this problem, this work proposed the use of machine learning techniques such as Long Short-Term Memory (LSTM), XGBoost, and Reinforcement Learning (RL), which can enhance the efficiency in resource allocation, lower expenses, and reinforce the systems against disturbances. The AI models were surpassing traditional approaches across vital metrics including demand prediction, decision latency, and risk detection to name some, according to the simulation. The carbon footprints in logistics were coped up effectively through an AI-powered optimization system which also contributed to environmental sustainability. This is an example of how AI plays a role to provide significant efficiency, scalability and sustainability in the supply chain leading to a competitive edge in an increasingly complex and volatile global marketplace through AI. This paper provides a solid framework for AI utilization within supply chain management, demonstrating its ground-breaking impacts on operational efficiency and risk management.