Development of a Ship Performance Monitoring System and Data Analysis of Spliethoff Vessels

Development of a Ship Performance Monitoring System and Data Analysis of Spliethoff Vessels

Grutterink, Roeland (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Marine and Transport Technology)

Frouws, J.W. (mentor)
Van de Voorde, E.E.M. (graduation committee)
de Koning Gans, H.J. (graduation committee)
van Zijl, Karel (graduation committee)

Delft University of Technology

Marine Technology | Ship Design, Production and Operations

2017-08-23

Spliethoff is a ship-operating company with around 120 cargo vessels under management.
The vessels of Spliethoff are generating a lot of operational data as they sail around the globe. To make better decisions based on this data, a system is required that takes operational data and translates it to useful information, accessible in the offce and on the ship through online dashboards. Ultimately, better informed decision making should lead to cost minimisation in the areas of fuel consumption and maintenance. In this research a Ship Performance Monitoring System (SPM System) for operational data of Spliethoff's vessels was developed, after which data analysis was performed with the goal to minimise
operational cost of the vessels.
The SPM System was developed using a Rapid Prototyping methodology. By conducting decision support analysis, data requirements analysis and user requirements analysis, Key Performance Indicators (KPIs) for the system were established and coupled to endusers.
In general, one can state that when developing new information systems, end-user involvement is key. If end-users are not involved from the start, interest will be low, resulting in limited added value.
Using the SPM System, data analysis was performed targeting a number of key questions with regards to fuel efficiency, maintenance and ship operation.
Firstly, from the data analysis it can be concluded that for a Spliethoff S-Type vessel the most fuel efficient speed is 14 knots, both in laden and unladen condition. It is recommended to implement this speed for all voyages where the schedule allows it. Accurate implementation of this speed optimum could result in bunker cost savings of up to 10% over a voyage.
Regarding engine efficiency, the specific fuel consumption (SFC) of the main engines of all three vessels is worse than the specifications given by the engine manufacturer. This is according to expectation, given the fact that manufacturers specifications are attained at different conditions. The development of SFC over time showed that the main engine
maintenance schedule currently employed is working effectively, so it is advised to continue in the same manner.
Hull and propeller fouling negatively in uence fuel consumption and speed of a ship. In the data analysis different trends are found regarding fouling. Therefore is is recommended to prolong the measuring period to at least a year before drawing any conclusions. If after a year a fuel consumption increase in the region of 5% is measured, it is advised to increase hull cleaning frequency to once every year. Regardless of any measured fouling increase, it is recommended to inspect the hull of all vessels at least twice a year to increase knowledge of fouling.
A ship's crew is of large in uence on the operational cost of a ship. Consequently,
more intensive collaboration between office and ship on the vessel's operation is suggested, using data in a supporting role. Training crew for correct use of onboard decision support systems and giving feedback on performance will build awareness among crew and increase fuel efficiency.
The SPM System can be improved by installing thrust sensors on the vessels. Implementing a newer correction method for wave added resistance will also enhance the system significantly. Thirdly, it is recommended to reduce dependence on manually entered data to a minimum, due to the inherent inaccuracy of manual input. Finally, matching data quality of all different data sources will increase the precision of the system. In general, the biggest challenge for Spliethoff is to create a company culture where decision making
is data-driven. This will yield the biggest benefits.

Data
Analysis
System
Development

http://resolver.tudelft.nl/uuid:8a82e046-321b-4ec6-ab72-346816a0de7a

2022-08-23

Student theses

master thesis

© 2017 Roeland Grutterink