Proving the potential impact of data sharing among actors in the port call process through data analyses and discrete event simulation

Abstract

About 85 percent of all volume trade goes by marine traffic nowadays which is not expected to get any less soon. In marine operations there are different operations with each having their own characteristic. Different trades for example include containers, tankers, bulk, or passenger vessels. But in the end, all of them have to enter a port, this is the place where many actors come together to service a vessel. Due to the many actors involved in the port call, which could run up to around ten actors, planning a vessel stay can get very tedious. Planning the port call can get especially hard if sequential services are not aligned with each other or if relevant information appears to be inaccurate or even missing.
Major complications in port calls are therefore the lack of information sharing. Often parties have a very poor insight in when a vessel is arriving or departing. In general actors in the port environment have been striving to optimise their own processes not including others affected. Very few research has been done in port operations to see what the effects are of data sharing and collaboration among actors. The question is therefore how information sharing among actors in a port call can affect the situation and to what extent. And in particular what information should be shared and with what interval.
To quantify the effects of data sharing three major components are included in the research approach with the port of Rotterdam as use case. The first part of the research focusses on qualitative aspects exploring actors and the port call event. Through this part of the research a better understanding of the port call process is gained which will be useful for the next steps. Also understanding which actors have a dominant role, benefit, or have a lot of power is important for further steps in the research. After a clear overview of the port call and most important actors a combination of data analysis and modelling is done. In this research a discrete simulation model is used to make an abstraction of the real world and use this for testing. Through a simulation parts of the port call process can be tested under different circumstances or inputs of interest. Outputs will then give an indication how the system will respond to particular changes. To get the model correctly running data from the Port of Rotterdam will be used for a correct parameterisation. Parameters would include statistics of port operations such as the number of vessels, handling time, and speed of the vessel.
After going through the previous mentioned steps results show that vessels can reduce their waiting time at anchorage by 35% and therefore their fuel consumption as well. One of the biggest gains would be realised if captains and terminals would start sharing information with each other about arrival and departure times. Ideally this would be done on an interval smaller than 2 hours. When vessels are aware of delays in the berth they can slow down to arrive just in time at the anchorage, or perhaps they can sail straight to the terminal. This information towards the captain is crucial as it can be used to adjust speed and thereby realise fuel savings. In the most optimal case the waiting time at anchorage could be reduced by 35%. Furthermore throughout the whole process more accurate information is required which will support actors in making a more robust planning and be able to plan farther ahead.
Two things need to be done from here, one is further research to consolidate these outcomes and see effects in other operations such as bulk or on more microscopic level such as inland shipping. Also research with regard to the implementation will be required to get everyone on board such as actors with fewer gains that are required to make this a success. the second is to get stakeholders together and make them realise that cooperation and sharing of data will have tremendous implications not only for the waiting times but also for CO2 emissions and robustness of operations.