Vessel selection has always been an extremely time-consuming and complex task for charterers. Access to real-time data and accurate fuel performance modelling to help them choose the most effective ship for their needs has historically been challenging at best, and highly limited at worst, argues Pelle Sommansson, Chief Product & AI Officer, ZeroNorth.
his is especially true in tramp shipping, where vessels do not follow a fixed schedule or route, making access to long-term information even more difficult. Charterers end up selecting vessels based on the limited operational data on offer, such as selected speeds and good weather, rather than the reality of how a vessel will perform when faced with the thousands of variables and potential scenarios experienced at sea.
Whether a charterer is selecting a vessel for a single trip or longer period charters, the ship they choose has a huge impact on their bottom line, on compliance, on ESG performance and the overall efficacy of their operations. It pays to get it right, and – with much upheaval and regulatory change on the horizon – the implications of making the wrong choice are more damaging than ever. The incoming EEXI and CII regulations in January will raise the stakes, with CII rating projections set to be one of the key metrics that charterers will use to assess which vessel to choose.
A poor CII rating will have a significant impact on the commercial attractiveness of a vessel, as charterers will be hesitant to charter them due to higher fuel costs, insurance premiums and port fees. Instead, charterers will want to select the best scoring, most fuel-efficient vessel to keep costs down. This creates a somewhat difficult situation for ship owners and operators, as charterers also have a major influence over the CII rating of the vessel they charter depending on the voyage route, amount of cargo and speed. Ship owners and operators will need to continuously and proactively assess the CII rating of their entire fleet to ensure no vessel turns into a dead asset.
It has never been more critical for charterers and ship owners alike to have access to real-time, accurate data on a vessel’s performance. Digitalisation can help bridge this gap, with sophisticated algorithms able to tap into millions of data points, connect the information, evaluate recommendations, and consider commercial outcomes together with emissions reduction potential.
Machine learning can enable charterers to simulate a vessel’s fuel consumption, with machine learning crunching millions of data points including weather, vessel characteristics, and operational and historical performance data, even if a user themselves might not have all the information needed.
Advanced fuel models are a great example of how AI-driven technology can process all this information and translate it into reliable predictions on fuel consumption for any vessel in the global fleet. Today, these models provide charterers with unprecedented insight into a ship’s projected CO2 emissions and CII rating during the voyage, enabling them to make smarter, more informed pre-chartering decisions. The same data modelling can also be used to inform operational decisions and ensure vessels are kept in a good condition, score well on CII and remain financially viable assets for ship owners.
Charterers now have access to more accurate and dynamic fuel consumption and cost predictions by modelling the complex conditions a vessel will experience while at sea, enhancing decision making. Owners are also armed with the real-time insights they need to assess the impact a charterer’s voyage will have on its vessel CII rating. Working from the same data set helps build greater transparency and mutual trust between ship owners and charterers, can help reduce fuel costs, and, ultimately, have a positive impact on making global trade green.
The views presented hereabove are only those of the author and do not necessarily reflect those of SAFETY4SEA and are for information sharing and discussion purposes only.