A vessel has to complete its route within a time-frame. When a part of the main engine fails unexpectedly, the ship risks staying off-hire. This can be very damaging to a shipping company, as chartering revenues decrease, while replacing a spare part immediately increases cost. Thus, identification of potential failure allows timely ordering, or even replacement of spare parts before failure.
The main engine, posing the highest risk, consists of various spare parts depending on many parameters. Thus, it is difficult to accurately predict failures. If false alarms occur, the operating costs increase, as ordering of unnecessary parts is not optimal. The multivariate nature of a supply department makes the selection of the port, where the spare part will be delivered, challenging. The price depends on the port, the time frame of order, and the personel replacing the part.
BigDataStack provides an adequate architecture for big data management, thus enhancing the use of analytics and methods for scheduling of orders, preventive maintenance, visualization of the current state and final results. By incorporating these aspects through the DANAOS platform, BigDataStack allows to shipping companies to cherish their data and use them in a difficult decision making process, such as the supply management of a fleet.
The challenges to be met in this field are strictly connected to the needs of ship management, such as:
- Monitoring the main engine of a vessel.
- Identification of malfunction patterns and notification of the supply department.
- Automatic ordering of the appropriate spare part to be delivered at a port on route (upon confirmation).
- Minimization of overall maintenance cost.
- Avoidance of off-hire seasons due to machinery failures and unexpected but compulsory mainntenance.
Focusing on these challenges, BigDataStack will allow:
- Advanced monitoring of key components in the engine room and at office.
- Better organisation of the supply department.
- Minimization of machinery failures that cause the ship to go off-hire.
- Reduction of operating costs, by optimising the requisition process of new spare parts.
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