The use of ETA in prediction of pick-up of containerized goods at port - Ringborg, Johannes; Svanborg, Erik (My University, 2022)
Increased global trade combined with logistical disturbances due to COVID – 19 as well as Ever Given blockade of the Suez Canal have generated increased pressure on the global supply chain during the recent years. With the global trading predicted to grow during the upcoming period, partly due to increased e-commerce and consumption, the situation within the intermodal transportation chain is more pressured than ever. At the same time, the goods – owners still desire to track their cargo as well as receive precise information regarding the predicted arrival time. This kind of transparency could be difficult to address during the current state of the world, especially within a dynamic environment, such as the maritime domain. This thesis addresses the possibilities as well as difficulties in predicting the arrival time for container vessels, based on the Estimated Time of Arrival (ETA) retrieved from the Automatic Identification System (AIS). The aim is to present a verified process model for import containerised cargo, based on the ETA from the AIS, which could simplify the pick-up process of containers at container terminals. By utilizing a mixed methods approach, through a literature review, interviews and a questionnaire survey, data, information, and opinions were collected from stakeholders within the supply chain. The result showed that there was an overall interest for the subject, and that it was a highly relevant research area due to the ongoing situation within the global supply chain. Three main interfaces were recognized, with different stakeholders interfering with each other alongside the physical movement of containers. By utilizing the AIS and retrieving a reliable ETA, through preferably machine learning, a seamless information exchange regarding the predicted arrival time for container vessel could be possible. If the ETA is reliable enough, it can provide valuable information through all three interfaces, which could simplify different processes for the included stakeholders. It could also serve as a solid foundation for a verified process model, which could simplify the pick-up process for containerised cargo at port container terminals.
Keywords: AIS, ETA Prediction, Maritime trade routes, Maritime logistics, ICT, Supply chain, Containerized transport, Intermodal transport, Machine learning