OPTIMIZING MARITIME SUPPLY CHAINS USING ARTIFICIAL INTELLIGENCE AND BIG DATA

Abstract

Maritime transport carries over 80% of global merchandise trade by volume, yet the sector faces mounting pressure from chokepoint disruptions, port congestion, freight rate volatility and decarbonization mandates. This paper examines how the convergence of artificial intelligence (AI) and big data analytics can optimize maritime supply chains across four operational layers: voyage and route optimization, port and terminal operations, predictive asset maintenance, and end-to-end visibility. Drawing on a structured review of recent peer-reviewed literature and industry evidence, the study synthesizes reported performance gains, including vessel estimated-time-of-arrival prediction errors in the range of roughly 0.25–5% depending on the dataset and method, container handling-time reductions of up to 25%, and idle-equipment reductions of 15–20% in digital-twin-enabled terminals. A four-layer conceptual framework is proposed that links Automatic Identification System (AIS) data streams, machine-learning models and digital twin simulations to measurable supply-chain key performance indicators. The framework is then mapped to the Port of Constanța, the largest port on the Black Sea, as an illustrative application from which a staged optimization roadmap is derived, building on the port's current Port Community System programme; a design for the empirical validation of the framework is also specified. The paper analyses the principal barriers to adoption, namely data quality and interoperability, cybersecurity exposure, infrastructure readiness and organizational capability, and outlines a phased implementation pathway for port authorities and shipping operators. The findings indicate that AI and big data are structural enablers of resilient, efficient and lower-emission maritime logistics rather than incremental tools.