Mathematical Optimization and Decision Support

marFM - Innovating Maritime Radio Communication 

In shipping, a lot depends on radio communication. Especially in emergencies, communication between officers on board and those in charge in coordination centers on shore by means of VHF equipment is of crucial importance. However, there are often sources of interference of various kinds (engine noise, environmental conditions, dialects, etc.) that can severely impair the quality of radio communication and make the necessary exchange of information more difficult. 

To solve this problem, the CML has developed the marFM (short for „maritime VHF radio“) speech recognition software. marFM is therefore of interest to search and rescue services, commercial shipping companies and, in particular, Vessel Traffic Service Centers and Remote Control Centers. Learn more about marFM in the linked information.

Portmodel - Visualization of Innovative Solutions for Terminals 

The Fraunhofer CML‘s novel port model visualizes digital processes. Quay and gantry cranes demonstrate automated container handling and transport from the truck via the terminal on board the ship and vice versa. In this real-digital environment, new applications can be illustrated and discussed before they find their use in the port. 

Trucks arriving uncoordinated at terminal gates, damaged or dirty containers that have to be replaced unexpectedly - in the maritime transport chain, friction losses and ineffi ciencies can lead to avoidable costs and reduced productivity. In many cases, a targeted analysis of available information reveals weaknesses and potential for optimization. We at the Fraunhofer CML accept this challenge and develop solutions for your practice.

Maritime Data Check

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 Logistics

Machine 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.

SCEDAS® Timekeeper

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.

Intelligent Algorithms for Condition-Based Maintenance (CBM)

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.

Cost-Efficient Procurement with Supply Chain Optimization (SCO)

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.

Decision Support in Crewing

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.

Long-term Crew Scheduling with Crew Scheduling Optimizer (CSO)

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.


Decision Support for Selecting Fleet Management Systems

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.