Analysis of Operational Drivers of Unscheduled Downtime in a Container Fleet

Maritime transport, which constitutes a large part of global trade, is a strategic sector in a race against time.Ships engaged in container transport in the maritime industry are operated with the goals of on-time delivery, high operational efficiency, and low downtime.However, due to the nature of maritime operations, unplanned downtime is sometimes unavoidable.These stoppages can occur for a wide variety of reasons, such as technical failures, environmental conditions, operational errors, or maintenance negligence, and consequently lead to significant time losses and damage costs.While there is a significant body of literature on ship malfunctions across diverse areas, such as predictive maintenance, statistical analysis of accident data, and human and organizational factors, these studies often focus on single systems or the single-ship scale, and limited fleetscale downtime assessment based on event records remains limited.While recent studies have demonstrated the technical potential of data-driven/hybrid approaches for main engine AbstractUnplanned downtime in liner shipping undermines voyage reliability and increases costs.This study systematically compiles 397 unplanned downtime events in a container ship fleet between 2017 and 2021, standardizing them according to system/subsystem and time/location attributes.A risk score consisting of frequency × duration × impact components was defined to quantify the severity of downtime on the operation.Pareto analysis was applied to identify a small number of critical causes.Variables driving downtime were extracted using a Classification and Regression Tree (CART) model and supported by Ward's linked hierarchical clustering.The findings show that 89% (352/397) of downtimes were propulsion/ propulsion system-related, representing approximately 1,460 hours of the total 1,767 hours; main engine events predominated in this group with 256 incidents and ~730 hours.Despite their rarity, shaft/propeller/stern-tube failures lead to very long delays per event (≈96 hours on average).CART outputs reveal that the longest downtimes are associated with fuel pump and injection failures (median ≈153.6 hours), while exhaust and lubrication-related failures are also high-impact.Pareto analysis showed that oil mist detector (OMD) related events accounted for 63.1% of the total risk, fuel injection 14.8%, and exhaust 7.9%; these three factors accounted for 85.8% of the risk.The findings suggest prioritizing fleet wide condition-based maintenance packages, targeted spare parts management, exhaust gas trend monitoring, rigorous OMD calibration/validation flows, and role-based training programs across the fuel, exhaust, and lubrication triad.Key indicators should be defined to track annual downtime reductions of ≥10-15% in critical clusters and improvements in diagnostic speed.