AI-Driven Predictive Logistics Orchestration for Offshore Energy Infrastructure: A Framework for Strengthening Critical Energy Supply Chains in the United States (Published)
Offshore energy supply chains constitute a strategic pillar of United States national security by ensuring reliable energy production, supporting economic stability, and sustaining the resilience of critical infrastructure. Despite their strategic importance, contemporary offshore logistics systems remain predominantly reactive, fragmented, and functionally siloed, limiting their ability to anticipate disruptions arising from adverse weather, vessel delays, equipment failures, inventory shortages, and contractor performance variability. This fragmented decision environment constrains operational resilience and increases the likelihood of costly production interruptions that may adversely affect national energy reliability. To address this challenge, this study proposes an Artificial Intelligence (AI)-driven Predictive Logistics Orchestration Framework designed to transform offshore logistics from reactive response to proactive decision-making. Adopting a design science research methodology, the framework integrates seven heterogeneous operational data streams: offshore production data, Automatic Identification System (AIS)-based vessel movements, aviation logistics, meteorological intelligence, inventory availability, equipment reliability indicators, and contractor performance metrics. A hybrid analytical architecture combines a Long Short-Term Memory (LSTM) neural network for multivariate time-series forecasting with a Reinforcement Learning (RL)-based orchestration engine that dynamically optimizes vessel routing, maintenance scheduling, inventory allocation, and contractor deployment under evolving operational conditions. The framework was validated retrospectively using historical operational data from offshore energy facilities in the U.S. Gulf of Mexico. Experimental results demonstrate that the proposed framework substantially outperforms conventional logistics planning approaches, achieving forecasting improvements exceeding 60% across key operational variables, extending disruption prediction lead time by more than 300%, reducing unplanned production downtime by approximately 29%, decreasing inventory stock-out events by over 56%, and improving average logistics response time by nearly 40%. Scenario-based evaluations further demonstrate the framework’s capability to anticipate high-impact events, including hurricanes, vessel failures, and simultaneous equipment degradation with inventory shortages, while recommending coordinated corrective actions that enhance operational continuity. These findings indicate that predictive logistics orchestration offers a significant advancement beyond isolated predictive analytics by integrating diverse operational intelligence into a unified, adaptive decision-support capability. The proposed framework contributes to supply chain resilience theory while providing a practical roadmap for policymakers, offshore operators, and infrastructure managers seeking to strengthen U.S. energy security, modernize critical infrastructure logistics, improve operational reliability, and enhance national preparedness against increasingly complex environmental, technological, and geopolitical disruptions.
Keywords: Artificial Intelligence, critical infrastructure protection, offshore energy infrastructure, predictive logistics, supply chain resilience