Artificial Intelligence in Maritime Fleet Management: Enhancing Operational Efficiency and Cost Reduction

Abstract

The article explores the potential applications of artificial intelligence (AI) in maritime fleet management, focusing on improving operational efficiency and reducing costs. An analysis of key technological solutions is presented, including predictive maintenance, intelligent routing systems, crew performance monitoring tools, and energy consumption optimization. It is demonstrated that machine learning algorithms processing vast datasets, such as Automatic Identification System (AIS) data, weather information, and vessel sensor readings, can predict emergency situations and schedule maintenance based on actual equipment wear. The study examines case studies from Maersk, Shell, Wärtsilä, and other companies, highlighting fuel savings of up to 15%, reductions in unplanned maintenance events, and improvements in environmental sustainability. Special attention is given to decision-support systems that integrate diverse data sources into a unified information platform, enabling comprehensive analysis and timely decision-making. The implementation of AI technologies can enhance not only safety levels but also the profitability of maritime transport by optimizing cargo flows and reducing fuel and maintenance costs. The article concludes with practical recommendations for shipping operators transitioning to a "digital" fleet and outlines promising directions for further research. The information presented will be of interest to professionals and researchers in maritime logistics, digital transformation, and operational management who aim to integrate advanced AI-driven models with systems analysis to develop innovative strategies for improving efficiency and reducing costs in maritime fleet management amid global industry dynamics.