Estimated Time of Arrival Module

Accuratly predict the ETA of any vessel

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.

They trust us

Designed for Ports and Shipping

Use cases

Best smart port models

Ports

Ports can minimize vessel turnaround time and predict how long it will take a ship to come at berth
Smart Port Data AI

Terminal Operators

Terminal Operators can optimize cargo operations, and port call processes by better allocating resources
ETA in shipping estimated time of arrival of delivery

Shippers

Shippers can track their vessels along the entire route and optimize vessel routes
containers port

Beneficial Cargo Owners (BCOs)

Beneficial Cargo Owners (BCOs) can track their cargo, which is especially important for interoperable routes

Sinay ETA module product leaflet

Resources

ETA Module Leaflet

Discover how our ETA Module is revolutionizing port and shipping efficiency. Download our Product Leaflet

Use case for Ports

Why Is ETA Important for Ports?

Port authorities need precise times to reduce congestion, improve safety, and better allocate resources. Accurately monitoring the time of arrival of the vessels coming to the port will help to ensure an efficient supply chain and enhance the port’s activity, capacity, and competitiveness. 
When the exact ETA is known, all port operations become more efficient.

Benefits

Benefits for ports

Optimize time at berth
& Better allocate resources
Avoid berth conflicts 
& improve maritime safety. 
Prevent port congestion
and reduce air pollution.

Intuitive and easy-to-use

How it works

The port ETA Dashboard

Overview of all incoming vessels and their announced and predicted ETAs in horizontal rows.

Announced VS Predicted ETA

The announced ETA is given by the ship’s captain, and the predicted ETA is calculated by Sinay’s advanced AI algorithms.

01.

02.

vessels time of arrival prediction

03.

04.

Trust Index

To evaluate the accuracy of the outcome, our algorithms provide a Trust Index calculated thanks to multiple parameters.

Voyage Details

access all the voyage details in the panel on the right, or even use the map view to see the vessel’s route.

Resources

Estimated Time of Arrival Whitepaper

Discover how our module is revolutionizing the Estimated Time of Arrival Prediction thanks to AI and Machine Learning. Download our Whitepaper.

Take the step to enhance your activities

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. 

Sinay ETA module

Use case for Shipping

Why Is ETA Important for Shipping?

Planning and scheduling vessels must be improved due to an increase in globalization and intense global port congestion.
As the world population increases, demand, ship size, and vessel traffic also increase.

For instance, a vessel may arrive on time to a port, but if other vessels are late, then an on-time vessel still can wait days before unloading. This costs shipping companies and BCOs time and money.

However, the ETA module can be implemented so that the ETA of any ship may be calculated in just four steps.

Focus on logistics data to make an effective decision

Discover Our Modules

Choose the needed logistic module, either to predict ETA, choose the best route plan, predict consumption, or know important statistics. Increase value chain efficiency, save time and money, and reduce your environmental footprint. Each module lets you solve specific key pain points thanks to the IoT connected sensors.

Logistics Efficiency

Advanced search for maritime routes with filters and save your routes to use them in our others modules.

Predict the best maritime route to optimize fleet rounds & fuel consumption.

Predict the best maritime route to optimize fleet rounds & fuel consumption.

Select the relevant data regarding your activity and visualize all the events in your defined zone to better understand the marine environment.

Visualize your fleet statistics on charts and maps, compare vessels and export your key indicators.

Featured Content About ETA

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.