Maritime Data Learning
Maritime companies collect an increasing amount of data every day. This is undoubtedly an important input for data-driven business models and innovative decision support systems. Making the best use of available data is therefore a crucial task for forward-looking management and gaining a competitive advantage. It helps to capture knowledge, reduce costs and increase profits.
Machine Learning in Maritime LogisticsMachine Learning (ML) is an application of artificial intelligence. Based on the evaluation of large amounts of data, ML can be used to optimize or automate processes and to support decisions in complex tasks. Exemplary applications in maritime logistics are the improvement of operational processes and maintenance forecasts.
The analysis of all tasks on board, their durations and the qualifications of involved crew is the prerequisite for the assessment of crew demands. Furthermore, analyses of the gathered data provide insights into the work processes on board and give valuable support for business decisions.
High cost pressure in merchant shipping and increasing availability requirements on the part of charterers require reliable maintenance of ships and their systems. This challenge can be met with optimized maintenance planning and more efficient maintenance processes. At present, time-based, preventive maintenance processes are predominantly carried out. Condition-based maintenance has so far mainly been used for ship propulsion. Significant potential cost savings could be realized in maintenance and spare parts procurement. To this end, the CML has developed a methodology for condition-based maintenance (CBM) of other ship systems.
SCEDAS is a decision support system which assists the planner to calculate detailed work schedules for every seaman.
Nowadays, after sales services are more and more important. They satisfy customer needs, such as the excellent availability of spare parts, offer services across individual departments and induce a high customer loyalty. This results in the outcome that today’s service champions earn up to 50 percent of profits in the aftermarket.
Procurement of vessel spare parts causes about ten percent of vessel operating costs. An optimized supply chain, the analysis of procurement data and applied forecasting are the basis of effective procurement. All models can be customized individually to gain best possible compatibility. This flyer describes all important proceedings detailed.
Crewing has among other things to deal with fix operating costs and increasing demands by new regulations. Therefore, it is a key success factor to manage crew resources effectively and to increase the company`s competitiveness. Various client requirements and human resource needs are involved in this tool and allow to determine the necessary personnel demand for a ship`s managers fleet.
The CSO creates an optimized personnel deployment plan to keep the Manning costs low and the satisfaction of the crew high. The planning parameters can be set up in detail in order to ensure a comprehensive analysis for the best possible result. The CSO generates an output, which can be implemented in an existing system.
Fleet Management Systems (FMS) support, optimize and monitor business processes. They are helpful to reduce the use of resources, such as fleet management running costs, and responds quickly and flexibly to changing market requirements. Hereby, shipping companies have the opportunity to acquire a cross-functional integrated software solution or to integrate individual modular components in their current system network.
Fraunhofer Center for Maritime Logistics and Services