AI-Native Supply Chain Resilience: A Multimodal Architecture for Predictive Intelligence, Optimization, and Real-Time Decision-Making

Supply Chain Global Systems are currently in constant states of uncertainty; Disruptions will occur and can rapidly move across all global connected Supply Chains due to their Interconnected Nature. Traditionally, Supply Chain Planning is based upon static forecasting with periodic optimization which does not allow for sufficient responsiveness and scalability. This paper proposes an integrated Architecture for Predictive and Adaptive Supply Chain Resiliency utilizing AI technology in conjunction with multilevel components which incorporate Large Scale Data Engineering, Hybrid Forecasting and Autonomous Optimization. The Framework utilizes Advanced Time Series Models including LSTMs, Temporal Convolutional Network, ARIMA-AI Hybrid Ensembles and Transformer Based Predictor Models for Disruption Sensitive Forecasting. The models utilized for Forecasting are inputted into Adaptive Decision Mechanisms using Reinforcement Learning (i.e. DQN, PPO), Multi-Objective Optimization Engines that balance Service Levels, Operational Costs and Carbon Efficiency. Additionally, Resilience Analytics including Agent-Based Simulation, Monte Carlo Stress Testing and Network Robustness Metrics measure how Disruptions Propagate and how Quickly Systems Can Recover. Finally, The Implementation Layer of the Framework utilizes Cloud Edge Orchestration, Micro-Services Architecture and Real-Time Decision Intelligence Dashboards to Support Human-AI Collaboration. Furthermore, this Paper discusses Governance, Model Transparency and Ethical Deployment Considerations to Ensure Responsible Use of Autonomous Decision Systems. Finally, the Paper discusses potential future Research Directions including Quantum Enhanced Optimization, Digital Twin Ecosystems, Generative Scenario Modeling and Federated Intelligence for Multi-Enterprise Collaboration.