The changing face of fleet performance

Performance monitoring and data analytics

Real-time data monitoring is transforming how fleets operate, replacing static reporting methods with dynamic performance tracking. CMB.TECH has adopted an analytical approach that involves monitoring vessel efficiency on a passage-by-passage basis, rather than relying solely on monthly or quarterly reports.

“For performance monitoring to be meaningful, it has to be as close to real-time as possible. Monitoring on a passage-by-passage basis allows us to track vessel efficiency more accurately than traditional monthly or quarterly assessments,” Mr Yayla explains. By analysing data in near real-time, the company can flag underperforming vessels, investigate speed degradation, and make immediate adjustments.

A key element in fleet performance is the transition from manual data reporting to automated validation. Noon reports, while still widely used, often contain discrepancies due to manual entry errors. By incorporating sensor-based performance tracking, CMB.TECH has reduced reliance on manually entered data.

“To improve reporting accuracy and reduce the workload on board, we have moved towards automated data validation reports. These reports are generated automatically, not only by noon time but also at every status change of the vessel. This ensures that data inconsistencies are caught early, reducing discrepancies,” Mr Yayla says. Rather than simply compiling performance data, the system actively verifies it against expected parameters, creating a more reliable foundation for operational decisions.

“We assess performance based on both theoretical and historical baselines”

This level of monitoring is crucial in tracking hull and engine efficiency. “We assess performance based on both theoretical and historical baselines that we generate using both empirical and data-driven methodologies. If a vessel’s efficiency declines beyond acceptable thresholds, we investigate the root cause — whether it’s hull fouling, engine wear, or operational inefficiencies,” he explains. By taking a proactive stance, the company avoids unnecessary fuel wastage and ensures compliance with increasingly stringent environmental regulations.

Key takeaways

One of the recurring challenges in fleet performance management is striking a balance between efficiency and compliance. The introduction of the Carbon Intensity Indicator (CII), EU ETS, and now Fuel EU Maritime has placed additional regulatory burdens on shipowners, requiring more comprehensive fleet monitoring strategies.

“The industry is still adjusting to these regulations, and while IMO-level measures don’t yet impose financial penalties, compliance is becoming a key factor in chartering decisions,” Mr Yayla explains. Charterers are already evaluating vessels based on their CII ratings, and this trend is expected to intensify as enforcement mechanisms become stricter.

The ability to forecast compliance trends has become a necessity. Instead of waiting for annual performance assessments, fleet managers must now project a vessel’s compliance trajectory over multiple years. “Rather than reacting to poor CII ratings at the end of the year, we model projected ratings to ensure that vessels remain compliant in the long term,” he says. This proactive approach prevents regulatory shortfalls and avoids last-minute corrective actions that could disrupt operations.

Looking ahead, Mr Yayla sees artificial intelligence playing (AI) an increasing role in fleet optimisation. While AI has not yet reached the stage of fully autonomous decision-making in commercial shipping, it has already transformed vessel modelling and predictive analytics that pave the way for holistic voyage optimisation. Machine learning algorithms, combined with sensor data, allow for more accurate vessel performance simulations. Additionally, recent developments in computational power along with AI technologies facilitate the use of advanced algorithms and methodologies to execute multi-objective optimisation, which is the necessary approach in the maritime domain due to the subtle trade-off between bunker costs and time.

“In vessel modelling, we use a combination of traditional naval architecture formulas and machine learning-based ‘black box’ modelling. This allows us to account for real-world variables more effectively,” he explains. Instead of relying solely on theoretical performance calculations, AI-driven models incorporate live operational data to refine predictive accuracy.

However, Mr Yayla remains sceptical about fully autonomous fleet management, arguing that the human element remains irreplaceable. “AI will enhance decision-making, but it won’t replace the need for human oversight. The shipping industry is too dynamic, with too many variables, to rely solely on automation,” he says. While predictive analytics can provide valuable recommendations, ultimate decision-making will remain with experienced fleet operators.

Looking forward, he emphasises that operational efficiency is not solely dependent on technology but also on how well shipowners integrate digital tools into their decision-making processes. “Technology is a tool, but it needs to be paired with the right expertise. Fleet managers who can combine data insights with practical experience will always have an advantage,” he says.

“Regulations will continue to evolve, and the best strategy is to remain ahead of the curve — both in terms of compliance and operational efficiency,” he concludes.