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

Аннотация

The article explores the potential applications CITATION of artificial intelligence (AI) in maritime fleet Anatoly Matveev. (2025). artificial intelligence in maritime fleet management, focusing on improving operational management: enhancing operational efficiency and cost reduction. The American Journal of Engineering and Technology, 7(03), 133-140. efficiency and reducing costs. An analysis of key https://doi.org/10.37547/tajet/Volume07Issue03-13 technological solutions is presented, including COPYRIGHT predictive maintenance, intelligent routing systems, © 2025 Original content from this work may be used under the terms crew performance monitoring tools, and energy of the creative commons attributes 4.0 License. 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 The American Journal of Engineering and Technology 133 https://www.theamericanjournals.com/index.php/tajet The American Journal of Engineering and Technology and reducing costs in maritime fleet management of sustainable development. Similarly, Mollaoglu M., amid global industry dynamics. Altay B. C., and Balin A. [2] conduct a bibliometric analysis of optimization solutions in maritime transport, Keywords: artificial intelligence, maritime fleet hypothesizing that the application of systemic management, predictive maintenance, route optimization approaches can significantly enhance both optimization, cost reduction, operational efficiency, operational efficiency and environmental safety. maritime logistics, maritime safety. Additional contributions to this area are made by Fan A. et al. and Kuroda M., Sugimoto Y., [8] who conduct Introduction: Maritime transport is one of the key empirical studies on the impact of vessel technical components of global logistics, accounting for up to parameters—such as speed, trim, and weather 90% of international trade volume [1]. However, the conditions—on operational performance. Meanwhile, industry faces several critical challenges. First, Zis T. P. V., Psaraftis H. N., and Ding L. [13] systematize operational risks and the high cost of accidents remain existing routing methods with a focus on meteorological significant concerns. Adverse weather conditions, factors. heavy vessel traffic, limited visibility, and human The second group of studies focuses on the application factors frequently lead to incidents that pose threats of artificial intelligence methods and AIS data analysis to to human life and the environment. Second, increasing enhance safety and optimize navigation processes. Tu E. demands for environmental sustainability and et al. [3] provide a comprehensive review of the use of emission reduction place additional pressure on the Automatic Identification System (AIS) data, industry, as maritime transport has a substantial demonstrating the potential of intelligent algorithms in impact on marine ecosystems. Lastly, fuel costs processing and analyzing navigational information. represent one of the largest expense categories, with Chen X., Ma D., Liu R. W. [4] expand this field by price fluctuations directly affecting the overall budget exploring the application of artificial intelligence in of shipping companies [2]. maritime transport to improve predictive accuracy and Artificial intelligence and big data are becoming minimize the risk of accidents. The methodology of integral to the modern maritime industry, enhancing these studies is based on a combination of machine both safety and cost efficiency through route learning techniques, big data processing, and risk optimization, predictive maintenance, and intelligent scenario modeling, supporting the hypothesis that AI fleet management. The emergence of solutions such as integration can