Scheidweiler, TinaTinaScheidweilerJahn, CarlosCarlosJahn2019-11-142019-11-142019-09-26Hamburg International Conference of Logistics (HICL): 341-368 (2019)http://hdl.handle.net/11420/3787Purpose: As maritime digitalization progresses, great opportunities for maritime transport arise: The introduction of the AIS opened up a number of possibilities and perspectives for increasing efficiency, automation and cost reduction using business analytics and machine learning in the supply chain and maritime sector. Methodology: Various analysis and forecast techniques of machine learning as well as interactive visualizations are presented for the automated analysis of ship movement patterns, risk assessments of encounter situations of two or more ships as well as anomaly detections or performance indicators to quickly extract key figures of certain ships, routes or areas. Findings: In addition to a comprehensive representation of relevant potentials and business analytics areas of AIS data, the feasibility and associated accuracy of the data mining and machine learning methods used are described. In addition, limitations will be shown and perspectives especially on autonomous surface ships will be discussed. Originality: At present, there is no information platform that bundles the areas described in the previous sections in a central source. Previous work has either been limited to the visualization of historical and current ship movements or deals with narrowly limited individual questions of isolated applications.en2019341368epubli GmbHhttps://creativecommons.org/licenses/by-sa/4.0/BusinessAnalyticsMaritimeTrafficWirtschaftHandel, Kommunikation, VerkehrConference Paperurn:nbn:de:gbv:830-882.05492210.15480/882.2503https://www.epubli.de/shop/buch/Digital-Transformation-in-Maritime-and-City-Logistics-Christian-M-Ringle-Wolfgang-Kersten-Carlos-Jahn-9783750249493/9209710.15480/882.2503Other