Estimated time of arrival module
Predicting the ETA of a ship
Predict the arrival time of your vessel with pinpoint accuracy.
Your data-driven estimated time of arrival predictor.
Thanks to Artificial Intelligence and data-driven algorithms, our Estimated Time of Arrival module accurately predicts the Estimated Time of Arrival of any vessel.
Our machine-learning software combines the data obtained from a ship’s AIS (Automatic Identification System), with historic voyage data and metocean data to precisely calculate the next port of call of a ship and its arrival time.
Benefits
Benefits for shipping industries
Better predict waiting times
and reduce fuel consumption.
Increases efficiency and transparency
in the global supply chain.
Positively contribute
to decarbonization.
Benefits for ports
Optimize time at berth
& Better allocate resources
Avoid berth conflicts
& improve maritime safety.
Prevent port congestion
and reduce air pollution
Calculate estimated time of arrival with just a few clicks
Let us guide you through the steps.
Find your vessel with the MMSI/IMO
Use your current position or set a custom departure port.
Define your departure time
Enter your destination and predict the ETA of your vessel at the arrival port
Find your vessel with the MMSI/IMO
Use your current position or set a custom departure port.
Define your departure time
Enter your destination and predict the ETA of your vessel at the arrival port
Easily integrate the predicted time of arrival to your IT system
Enhance your supply chain with a more accurate eta
Our ETA Predictions can be easily connected to any IT system thanks to an API integration. The new system can benefit from real-time estimated time of arrival predictions bringing in transparency in maritime traffic patterns.
FAQ
The calculated information available in the estimated time of arrival Module are:
- The estimated time of arrival of a vessel in a port
- The route time
- Statistics of the ship on this route
Our machine-learning software combines the data obtained from a ship’s AIS (Automatic Identification System), with historic voyage data and metocean data to precisely calculate the next port of call of a ship and its arrival time.
The estimated time of arrival Module is a “plug-and-play” solution.
Thanks to 4 Steps, the results are delivered within a span of a few seconds.
Featured Content about ETA
Port Efficiency and Vessel Turnaround Time
For ship management organizations, owners, brokers, and suppliers; port efficiency, accessibility, and proximity are decision-making factors-all of which have time as the dimension.
The Butterfly Effect: Understanding Port Congestion in Asia
“A port can only be as efficient as the people who work in it”
-UNCTAD Secretary-General Mukhisa Kituyi.
Vessel Route Forecast: Predicting ETA and Increasing Fuel Efficiency.
1952 was a big year for the shipping industry. If you go forward and ask ‘why?’, you might find it interesting to know that ’52 was the year when vessel routing services began their very first implementation in the industry.
Let's solve your time of arrival challenges
Estimated time of arrival prediction:
A complete guide.
How does ETA prediction work?
The AIS data traditionally obtained from a ship’s sensors is wholly based on the information that is provided by the crew on board. When we base the results on the thoroughness of the crew’s reporting, the accuracy of the ETA and the next port of call can be misreported due to human error, something that has been frequently happening in the industry for decades.
Sinay with its distinctive data-based approach aims to transform the way ship owners and charterers predict a ship’s ETA. Backed by AI algorithms, Sinay’s ETA module combines AIS data with historic shipping patterns, improving accuracy and facilitating a more transparent workplace for all the stakeholders concerned.
Why should you use ETA prediction?
For starters, ETA prediction with advanced AI algorithms predict results with pinpoint accuracy. Shipping companies can improve their logistics chain when it comes to delivering freight within a stipulated time in the due process. What’s more is, all stakeholders get to have vital insights about future fleet positions, which can then be leveraged for framing future cargo handling plans.
Besides shipping companies and charterers, the tool would be of particular interest to companies that carry out port-based operations. Some examples are companies engaged in: classification societies, paint, spare parts, etc. Port authorities could find this tool to be of particular interest when it comes to traffic management.
