Research-Thesis: AI-Enabled Supply Chain Management in Maritime Logistics

Abstract

Maritime logistics represent the backbone of global supply chains through the utilization of containerized vessels and processes taking place in ports and container terminals. Even though the maritime logistics industry uses a variety of advanced digital tools, information remains scattered throughout various systems and participants. As a result, vessel arrivals, port preparations, yard management, containers' availability, and other types of logistical information cannot always be effectively combined. Therefore, the following research question was formulated for this thesis: How can artificial intelligence (AI) technologies optimize the supply chain in maritime logistics? The current work will examine information fragmentation, lack of real-time visibility, yard logistics, ship-port coordination, and preconditions for using artificial intelligence in maritime logistics operations.

A qualitative and exploratory approach will be used in carrying out the research. First, literature will be reviewed on maritime logistics, port information systems, IoT, AIS, blockchain technology, big data analytics, machine learning, and artificial intelligence. Then, five interviews were conducted with maritime logistics professionals – a ship captain, yard dispatcher, IT manager, freight forwarder, and port operations manager. Interviewees represented different perspectives related to maritime logistics and provided valuable insights into issues examined in the literature.

Research results show that digital solutions have already been implemented in many aspects of maritime logistics; however, their usage does not guarantee operational connectivity. All interviewees noted that the information existed, but its completeness, timeliness, and accessibility for relevant parties remained problematic. Ship-side operations require a better combination of vessel arrival times and port preparations. The yard management requires relevant information on asset locations, dwell time, congestion, and events in yards. Freight forwarders benefit from the timely visibility of disruptions and expected peaks in arrivals. Meanwhile, port management needs to combine the information on vessels, warehousing, customs operations, and container handling.

It is concluded that artificial intelligence can provide help in optimizing maritime logistics only as a decision-supporting mechanism. Artificial intelligence can predict vessel arrivals, match the vessel arrivals with port preparations, assist in managing yard operations and container handling, enable timely detection of disruptions, and help with freight-planning decisions. Nonetheless, artificial intelligence cannot create value without reliable information, integration of systems, and cooperation between stakeholders.