<![CDATA[Purpose - This study aims to develop an IoT–Decision Support System (DSS)-based Smart Fleet Management System (SFMS) to improve the operational efficiency of small- to medium-scale fishing vessels through real-time monitoring and data-driven decision-making. Design/methods/approach – The proposed system integrates IoT sensors (GPS, fuel flow, temperature) with an ESP32 microcontroller for real-time data acquisition and transmission via MQTT to a cloud platform. A hybrid DSS combining linear regression and fuzzy logic is developed to analyze vessel performance and generate operational recommendations. Field validation was conducted on three fishing vessels, with 1,200 telemetry samples for regression modeling, 300 decision samples for DSS evaluation, and 4,320 data packets for communication analysis. Findings - The implementation of SFMS resulted in significant improvements in operational performance. Fuel consumption decreased by 20.0% (from 50.7 L/h to 40.6 L/h), idle operational time was reduced by 28.9% (from 3.8 to 2.7 hours/day), and the Operational Efficiency Index (OEI) improved by 22.7% (from 0.110 to 0.135 kn/L). The DSS achieved an accuracy of 92.7% in decision recommendations, while system reliability reached 99.2% uptime with low latency and acceptable packet loss (1.48%). Research implications/limitations – Although effective, the study is limited by a small number of vessels and the lack of synchronized environmental data, suggesting the need for broader validation. Originality/value – This study presents a cost-effective and scalable IoT–DSS framework tailored for small-scale fisheries, supporting sustainable operations and maritime digital transformation.]]>
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