Increase shipping efficiency using ship data analytics and AI to assist ship operations

Rapport, 2023

Various energy efficiency measures (EEMs) have been used in the shipping market, but their potential to reduce fuel consumption and air emissions are not fully recognized partly due to uncertain ship performance models used in those EEMs. The project report investigates the feasibility of shipping EEMs that can be improved by implementing data analytics and AI through the demonstration of their integration into the IMO Just-In-Time (JIT) arrival guidance. What big data analytics can help to improve and promote EEMs in shipping through, 1) improving ship performance models in these EEMs, 2) developing intelligent decision support for individual vessels, 3) accurate evaluation of fuel and environmental benefits from these measures, etc. This report also investigates the requirements and willingness of different stakeholders to use data analysis for those EEMs from seafarers’ perspectives to find obstacles/requirements for the implementation of these EEMs. From a social perspective, by studying the capability, willingness, and barriers to use AI to assist ship operations, this project will look for AI integrated solutions to help smoothen implementation and utilization of the EEMs without introducing extra workload/burdens to seafarers and assist decision-making processes to reduce pressure for ship masters onboard.