Solving Global Challenges in Maritime Logistics

Uncertainty in the arrival times of a vessel causes many problems; longer waiting times, higher costs, port congestion, etc. There have long been attempts to tackle the challenges posed by data management in maritime logistics. These problems unfortunately still occur globally every day. Studies have identified that better predictability and information sharing is required to boost operational efficiency.

As a response to global challenge in maritime logistics, Awake.AI has launched Prediction API to improve the competitiveness in maritime logistics, boost the efficiency of port operators’ routine activities, support the development of automation, help to anticipate exceptional circumstances, and reduce environmental emissions.


Operational Efficieny and Savings

Awake.AI’s ETA prediction API provides global predictions on vessel voyages. The API can be called for individual vessels to obtain machine learning – optimized predictions on where the vessel is headed currently, arrival times for current and even future port calls, and remaining travel times for vessels not currently under way.

The API can also be called by destination, providing schedule predictions on all vessels currently detected to be headed to the selected port. The prediction service has global coverage, enabling automated monitoring and visibility of future arrivals up to several weeks ahead for oceangoing vessels.

See more use case examples below: 




Tangible business benefits

Assisted by Awake.AI’s ETA prediction API you’ll create accurate plans and use predictions to warn you if plans will be disrupted. Operate with accuracy by having resources at the right place at the right time and adjust your plans on-the-fly with the help of the predictions.

With the increasing focus on environmental issues in maritime logistics you’ll be able to reach emission goals with reduced vessel turnaround times.

Achieve concrete savings by utilizing the industry’s best machine learning powered predictions.

Download's ETA Prediction Brochure



Custom machine learning models are trained for each customer destination to optimize prediction accuracy. These enable reducing variation in prediction results (less than 5% prediction error) caused for example by location-specific changes in vessel speed.


Global coverage is provided for predicting current vessel destinations and arrival times, and remaining travel times for vessels not currently under way. Predictions can be queried per vessel or per port.


Full swagger style API documentations are available and kept up-to-date with each release. Our support will assist you with API integration and accurate service uptime status and planned maintenance information is always available at our status site.

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