The shipping industry has been quite successful in reducing the number of major accidents in the past. In order to continue this development in the future, innovative leading risk indicators can make a significant contribution. If designed properly, they enable a forward-looking identification and assessment of existing risks for ship and crew, which in turn allows the implementation of mitigating measures before adverse events occur. Right now, the opportunity for developing such leading risk indicators is positively influenced by the ongoing digital transformation in the maritime industry. With an increasing amount of data from ship operation becoming available, these can be exploited in innovative risk management solutions. By combining the idea of leading risk indicators with data and algorithm-based risk management methods, this paper firstly establishes a development framework for designing maritime risk models based on safety-related data collected onboard. Secondly, the development framework is applied in a proof of concept where an innovative machine learning-based approach is used to calculate a leading maritime risk indicator. Overall, findings confirm that a data- and algorithm-based approach can be used to determine a leading risk indicator per ship, even though the achieved model performance is not yet regarded as satisfactory and further research is planned.
Due to imbalances in the global transport of containerised goods, liner shipping companies go to great lengths to match the regional supply and demand for empty containers by transporting equipment from surplus to deficit regions. Making accurate forecasts of regional empty container availability could support liner companies and other involved actors by making better relocation decisions, thus avoiding unnecessary transport costs of empty equipment. Previously proposed container availability prediction models are limited to the application in individual regions and typically characterized by a high degree of temporal aggregation. Against this background, this paper introduces two novel approaches based on machine learning and probabilistic techniques to predict the future weekly availability of empty containers for more than 280 locations worldwide. The machine learning and probabilistic prediction models are built by analysing a unique data set of more than 100 million events from past container journeys. These events represent different stages during the transport process of a container. Both models use a two-step forecast logic. First, the expected future location of a container is predicted. Second, the expected timestamp for arriving at that location is estimated. The machine learning model uses artificial neural networks and mixture density networks to forecast the movements of containers. The models are quantitatively assessed and compared to the actual availability of containers and two more conventional forecasting approaches. The results indicate that the probabilistic prediction approach can keep up with conventional approaches while the neural network approach significantly outperforms the other approaches concerning every evaluation metric.
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