Analyzing technological trends in maritime supply chains: A multi-method patent analysis

This study presents a comprehensive patent-based analysis of the maritime supply chain innovation landscape, integrating social network analysis (SNA), natural language processing (NLP), life cycle assessment, and multi-criteria decision-making (MCDM) methods. A dataset of over 19,000 patents was collected and cleaned to reveal collaborative structures and thematic domains. Community detection identified five principal innovation hubs, highlighting influential actors and collaborative networks. Subsequent clustering using keyword-driven NLP resulted in 19 distinct technological groups, each reflecting specific areas of maritime logistics innovation, including integrated logistics management, autonomous port vehicles, UAV-based monitoring, optical sensing, and alternative fuels. Life cycle analysis evaluated the emergence, growth, maturity, and decline of each cluster, providing insights into the evolution and strategic relevance of maritime technologies. Finally, TOPSIS was employed to prioritize clusters based on technological impact, breadth, collaboration intensity, and market relevance, identifying high-potential innovation domains for both research and industrial investment. The integrated framework not only maps the structure and evolution of maritime supply chain innovations but also supports data-driven strategic decision-making in this critical sector.