According to Clarksons Research, spares and maintenance costs in the shipping industry have been steadily on the rise and can constitute approximately 18% of a vessel’s Operating Expenses (OPEX).
Costs due to unscheduled repairs for equipment failures and spare part unavailability can adversely impact total maintenance expenditure. Specifically, it is observed that breakdown repairs can be significantly higher than planned ones due to increased acquisition, service and forwarding needs. In addition, all maintenance cost elements can further deteriorate as the vessel ages.
Within a volatile tramp market, where rates tend to fluctuate wildly across all segments, i.e. tanker, dry and container fleets, the need to set maintenance costs under tight control is indisputable as it is an urgent matter for ship owners. The current legacy approach implemented by Technical departments in rightsizing such costs is a combination of a preventive and a corrective maintenance system. Crew on board performs inspections and actions, such as cleaning of main components, based on specific running hours or calendar intervals. In case an equipment fails, it is repaired or replaced provided sufficient stock exists onboard. Periodically repeated maintenance actions, such as cleaning of scavenging area and lubricating of main components, keep equipment in decent working condition but an adequate prevention of machinery failures cannot be guaranteed.
The proper processing of the data which results from monitoring systems and after factoring various parameters, e.g. if a spare was sourced via a maker or parallel market supplier, the vessel’s age and prior maintenance history, the onboard crew’s experience etc., can yield a much more accurate assessment of a component’s condition. Such a framework would contribute in the prediction of potential equipment failure, would prevent it through dynamic adjustments of the maintenance plan and the subsequent reevaluation of the P-F curves (Potential Failure-Functional Failure graph).
From an organizational perspective, adhering to a robust predictive maintenance framework is a cross functional effort necessitating active participation from critical front-line departments such as Technical, Purchasing and Safety & Quality. Collaboration would be enabled via a structured process that entails stakeholders across departments reaching consensus on a maintenance forecast for the coming 12 months, performing strategic sourcing initiatives to secure spare part supply and service engineer availability and booking forwarding capacity to ensure timely delivery in a cost efficient way. The set-up would be further completed through the monitoring of specific KPIs such as forecast accuracy, volume discounts achieved, breakdown ratio etc.
Properly identifying the equipment that will partake in such a bulk ordering process is always a challenge for shipping companies, in particular for those whose sister-vessel overlap is small. The Planned Maintenance System (PMS) houses important information on equipment history such as frequency of work orders, defects, downtimes. In order to be able to efficiently handle this very granular data, an aggregation of equipment into larger categories or “forecast groups” is needed. PMS contains the full function hierarchy of components per vessel with additional details such as the maker and various interdependencies which can further help in the segmentation effort. For example, an easy rule of thumb commonly is: all vessels carrying Diesel Generators built by the same manufacturer are sister vessels and belong to the same group.
The aforementioned predictive maintenance approach can yield significant benefits for the shipping companies that choose to deploy it. We expect that the first phase alone, which doesn’t necessitate any advanced analytics or telemetry technologies, can lead to an approximate 15% reduction in acquisition costs through volume discounts from vendors. Deploying phase 2 can further bring down the entire supply chain costs by an approximate 10%. Whereas phase 3 can additionally reduce maintenance costs by 7%. Furthermore, this predictive maintenance framework can be transformative not just in terms of costs, but also in the way maintenance is perceived and performed by the office and the crew onboard leading to less firefighting and stress, less rescheduling, better upkeep of working hours and enhanced resiliency across the entire organization.
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