How to Drive Business Intelligence With AIS Vessel Tracking Data

in Trends, Maritime Knowledge by

Navigation in itself is a multi-century old phenomenon, which has been there since mankind discovered what they could do with a piece of wood. However, modern ship navigation has experienced a lot of changes, and subsequent ‘rebirths’, over the last couple of decades.

One such year of rebirth was 1952: For the very first time, vessel routing services got introduced into the industry. 1952 is when vessels were retrofitted with a prototype that would later evolve into the Automatic Identification System (AIS) in the late 90s, something that ushered a new era in maritime navigation so to speak.

AIS data, when clubbed together, gives us all-around insights into the vessel involved, its speed, position, ship dimensions, as well as its draft, helping us identify when the ship was loaded or unloaded with its designated cargo. However, the last point is an application of various data points obtained via AIS, and not available via raw data obtained from the systems onboard.

AIS, as stated above, was originally meant for ensuring navigational safety, but has quickly proved to be a vital source of business intelligence for maritime personnel.

Business Intelligence:

Business Intelligence, combines software and other services, transforming generated data from different systems into vital insights that might reveal hidden patterns and analytical findings. The generated data reveal hidden patterns capable enough to aid in any key strategic and tactical business decisions for a company in its future course of action.

In the 2020s, business intelligence has been entirely transformed with the help of Artificial Intelligence (AI) and Machine Learning (ML) algorithms that keep churning out valuable insights based on the way it has been programmed.

Business Intelligence and AIS:

FleetMon continues to pioneer how ship owners, charterers, and commodity traders make key decisions, thanks to our AIS data mining.

At the heart of this, lies vital insights that get derived from assessing vessel movements during the entirety of its course. When combined with other key data points obtained from various sources, AIS and Business Intelligence now go hand in hand for any player in the maritime industry, whether big or small.

Managing commercial risks in the high seas, as well as the economic viability of a particular route for a company, understanding an imbalance in supply and demand, maritime business intelligence covers it all, thanks to AIS data-mining centres.

FleetMon’s historic vessel data enables various stakeholders in the maritime world to answer basic questions that form the foundation of their business.

Analyzing the route of a Panamax engaged in the grain trade between China and Australia, we can answer a few important questions :

  • In the last 18 months, how many sailings did my fleet do between Brisbane and Qingdao?
  • When compared to Argentine loadings, how is this trade thriving?
  • What is the vessel turnaround time at ports and average waiting time?

On combining AIS data with freight rates, pricing of the carried cargo, weather patterns, and other factors involved in the supply chain on land, we see the entire picture that might significantly impact the profit margin of the targeted business. This used to be guesswork before AIS and data science changed the entire way maritime businesses operate.

Using various tools from FleetMon, a whole host of questions can be easily answered, thanks to our data miners:

  • List of ports and terminals that face acute congestion, and the time of the congestion.
  • Comparing the operational efficiency between various companies.
  • Availability of the targeted berth, comparing both real-time and historic data in the process.

A few examples, where FleetMon’s extensive databases has been leveraged:

  • Economists from the Kiel Institute for the World Economy (IfW Kiel) used FleetMon’s API services to create an AI-based leading indicator for international trade based on real-time data from global container shipping.
  • Researchers at ETH Zurich used data from FleetMon to provide new insights on the emission reduction potential of shore-side electricity.
  • Our customers, which includes some of the biggest players in the Offshore and Maritime industries, regularly use FleetMon Explorer for real-time vessel tracking to monitor their fleets, as well as the performance of their competitors to chalk out effective strategies for fleet operations.

Visit our new Maritime Innovation Lab to gain insights on the latest research & development projects at FleetMon.

Predicting ETA and Next Port of Call Using AIS:

Working on a scalable AI-based approach, FleetMon predicts the next port of call of a vessel using our huge historical AIS data. Naturally, this method is only applied to scenarios when the AIS destination entry of a vessel is not interpretable.

Using an extremely efficient AI solution based on Markov models that are ideal for massively parallel prediction tasks with high accuracy, we succeeded in finding a port prediction model that has a high degree of accuracy.

Based on the next port of call, ETA predictions can be easily done after taking AIS data into account and combining it with data points from other sources like port congestion, time spent on berths, etc.

Vessel Route Forecast and Fuel Optimisation Using AIS:

Ship routing service is perhaps one of the most widespread applications of raw AIS data obtained over a vast period. The prediction of an ideal route, that would cause no delay in your shipment reaching its designated port, avoiding storms and abnormal weather patterns along the way is what ship managers, ship owners, and manning agencies look for.

Read about our research project MERMAID to learn more about FleetMon’s research on vessel route forecasting.

Thanks to rapid advancements in data and computer science, as well as our AIS stations that have been collecting data on shipping routes for over a decade, choosing the ideal route requires nothing more than a few clicks on your desktop.

Conditions for ideal route selection:

  1. As we mentioned above, the ideal route is free from any powerful underwater currents, high waves, storms, or any other natural factors that might cause harm to the ship, its crew, and the cargo on board.
  2. The route should be fuel-efficient, enabling the company to cut down on its carbon footprint and operational costs, and as a result, contribute to both company profits as well as IMO’s 2050 goal.
Route Planning: Workings

In the past, a route analyst used to draw up drafts of potentially viable routes that a vessel can take while sailing from port A to port B on the route planner. Apart from route recommendations, the planner also gets recommendations on the optimum speed that the vessel needs to follow, as slow steaming ensured reduced fuel usage during the voyage.

However, modern route planning systems have come a long way from its initial days, as it has added a whole host of new features, including weather forecast systems and uses various models to depict potentially viable routes. These models are heavily based on historical AIS data provided by vessel tracking companies like FleetMon. AI and ML algorithms play a big role in modern route planners and form a key part in chalking out the ideal route.

Knowing if your ship is loaded

AIS comes in handy in cases when there’s homogenous cargo involved, like crude oil, iron ore, grain, coal, etc. We can predict if the ship is loaded or unloaded upon comparing data from the vessel specifications (e.g., draught) to its keel depth below the waterline.

This method has proven to be extremely handy in cases where AIS spoofing takes place and was recently used to identify tankers that load crude cargo from UN-sanctioned states.

Since raw data received from AIS is not enough to draw business insights, FleetMon combines big data, ambitious data analysts, and intelligent algorithms to add value to our clients’ businesses.