Research

An accurate ETA of incoming vessels in the port of Rotterdam is beneficial to the entire chain of processes. Currently, ETA’s are not always known among the various chain partners, or ETAs are not precise enough. A suitable ETA predictor is therefore highly desirable. This research examined whether it is feasible to develop a reliable ETA predictor that would benefit the Port of Rotterdam Authority as well as the companies in the port.

When it comes to the arrival at Maascenter, (the offshore navigation point where, for example, the pilot embarks the ship) an ETA predictor should be able to predict the ETA with an accuracy of part of the day. A higher accuracy is required for the prediction of the arrival times at the terminal quay.

Opportunities

• Better planning – A reliable ETA predictor for incoming ships provides a careful planning of activities related to the arrival of the vessels, from the arrival at Maascenter until arrival and stay at the terminal quay. Better connection of various loading and unloading processes will be possible.
• Intensified utilization of assets – Unnecessary waiting times are being limited, the quay occupation is being intensified and other assets are also being better utilized.
• Greater cooperation among companies – All chain partners can plan their actions based on objective data. The companies involved in the processes can respond better, cooperation has increased
• Transparency – Insight into the ETA for all parties in the chain results in greater collaboration and increases trust in the system and between chain partners.

Challenges

• Data security.
• What data are needed – Which data can lead to a good ETA predictor? What is the predictive value of the various data, and how does this work for different arrival routes?
• New ways of working – To generate success, working processes need to change. For example: first come, first serve. Can that be discarded when planning is based on accurate ETA’s?
• Not all parties currently involved, will benefit – There are parties in the chain that do not benefit from an accurate ETA predictor. And there are even parties (forwarders and other intermediaries) whose business interests are harmed by the effects of an accurate ETA predictor. Such parties are hardly interested in collaboration, sharing data and facilitating processes. How to cope with conflicting interests of some of the chain partners?

Partners

Erasmus University Rotterdam, Hermess, Intertransis, SmartPort, TKI-Dinalog, TNO, Vanad Group, VOPAK.