Signal Maritime and Signal Ocean are the two primary divisions of the Signal Group. Signal Maritime is our ship management division, and it now manages an Aframax fleet of around 30 vessels. Signal Ocean Platform is a SaaS (Software as a Service) platform for commercial shipping.
In this post, I will discuss how Signal Maritime uses Signal Ocean shipping technology in its day-to-day chartering company to make better commercial decisions and manage its tanker pool more effectively and economically. The areas of focus are spot freight market fixing and geography fleet deployment strategy.
The Signal Ocean chartering platform assists chartering desks on all three sides of the fixing triangle – shipbrokers, charterers, and commercial operators – while giving each customer their own unique picture of the market based on their own data. Chartering teams can manage the timing of fixing in volatile markets, the best deployment of a vessel or fleet, and other economic choices to the best of their abilities.
Commercial shipping’s challenges and the significance of big data
There are almost 50 thousand commercial ships at sea. Every few minutes, tracking signals from the Automatic Identification System (AIS) are recorded. As you might expect, there’s already a lot of information on where ships are going, where they’ve been, how fast they’re traveling, and where they’re going. Then, on the business side, shipping experts get hundreds of emails every day with commercial information including market trends, fixtures, position lists, and lineups. The sheer volume of shipping data makes it difficult for a human to process it on a regular basis.
To handle and share this sort of data, the shipping sector traditionally relied on inefficient methods such as email, telex, and talking on the phone. Not only is there a lack of organization in the material available, but it also does not arrive in the same manner. There is a lot of live information in addition to written material such as emails. What isn’t in an e-mail is frequently disseminated via unstructured channels like ICE, WhatsApp, Skype, and the phone. In a highly volatile market like the spot market, the Signal Ocean chartering platform analyses this commercial data and gives insights in a more organized fashion, allowing commercial desks to know what is occurring “now” (now can be a time span of an hour, a few minutes, or a few hours).
Signal Maritime’s commercial shipping platform, Signal Ocean
Spot chartering, particularly in the tanker industry, is a very unpredictable market. You could fix a vessel for $10K per day one day, and the next week, you could fix the same vessel for twice as much for the same journey. As a result, it is critical to recognize when a cargo has the potential to pay more or less. The technology is used by the Signal Ocean chartering platform to analyze competition for every potential and real cargo we observe on the market. This implies having real-time information on how many vessels can compete for the cargo, the hardness of the other locations and their itineraries, and the possible revenues from the enterprise.
What has worked though is the capacity to examine competition in real time, such as how many vessels can meet the laycan, how many have the correct itinerary, the right location, and if they have already visited that port.
The table below shows an example of a prospective cargo as well as the boats that could fulfill the laycan and were commercially available at the time. We estimate that one vessel will earn $1.5K and another would earn $10.6K if it opens in the Black Sea. If the vessel with the greater time charter equivalent (TCE) is interested in the cargo, she will almost certainly be more competitive.
Fixture timing at Signal Ocean Platform
Signal Maritime is fascinated with ship counts. The approach has aided in the development of a strong, up-to-date baseline of what it means to have an excess vs an undersupply of boats in the markets we are interested in.
The blue line in the graph below represents the market, while the red line represents the vessel supply for that market. Everyday users track their whereabouts. User examines how many boats are available during the average fixing window for that market (e.g. Aframax in the Med). Are users exaggerating? Is the number of users on the low side? Do users anticipate a surge? Users change their scheduling based on these shipping information, and when feasible, in order to repair boats at the best moment.
Benchmarking and strategic fleet deployment of Signal Ocean Platform
A corporation may use the Signal Ocean Platform to make strategic decisions about fleet deployment, find best trade routes, and predict trip profitability.
Assume that a new vessel has been added to the pool. She arrives in China the next week, and so we must locate her the greatest job possible. To begin repairing the ship, we examine the travels dataset. The graph below depicts an aggregated view of all journeys that have taken place since the beginning of 2020. Each line denotes a loading region, whereas each column denotes a discharge area. If we look at the top line, in the second box, we can see that 29 trips have loaded in the Arabian Gulf and discharged in Australia or New Zealand since the beginning of the year. So, what are our options for a vessel in China? We’d concentrate on the most profitable journeys for vessels operating in that region.
A challenging difficulty for any commercial operator to address is how to account for the market’s flexibility, with journeys of varying lengths ending up in various discharge zones. The idea of “position value” was created to solve this issue. This dynamic model, which combines several of the Signal Ocean chartering platform’s main functions and data, allows users to estimate an approximate, real-time TCE offset for any loading region in the world when compared to a reference point (e.g. Gibraltar). Users may use this model to calculate that having an Aframax available in Japan on a given day is roughly $0.7 million worse than having the same vessel available in the Baltic.
The predicted relative profit for a journey departing from a port is shown by its position value. We can observe how the value of locations varies by area. These figures are utilized in Signal’s daily calculations to determine the best rate to travel East vs the best rate to reposition to the United States versus staying in the local market.
Last but not least, decision-making requires easy access to data such as voyages and how individuals have traded, where they trade, what their idle time is, and what their triangulation time is. Using a benchmarking tool may assist you in determining what makes sense, determining who is the best in that market, understanding their approach, and, as a consequence, forming your own.
The ability to determine how much a vessel makes for each journey using a dynamic analytical computation and then averaging that by the commercial operator has helped us evaluate where we stand in comparison to our competitors. Analyzing our competitors, making decisions based on predicting signals, and closely cooperating through the Signal Ocean platform’s collaboration capabilities have enabled us maintain a permanent position at the top of that distribution for the past few years.
A live webinar with the Baltic Exchange was also held to showcase this case study.