MOSES (autoMated vessels and supply chain Optimisation for Sustainable short SEa Shipping) is an H2020 funded project, which aims to significantly enhance the SSS component of the European container supply chain by a constellation of innovations including innovative vessels and the optimisation of logistics operations.
MOSES is an H2020 funded project, which aims to significantly enhance the SSS component of the European container supply chain by a constellation of innovations including innovative vessels and the optimisation of logistics operations:
For the SSS leg, an innovative, hybrid electric feeder vessel that will prevail from different vessel concepts that will be designed to match dominant SSS business cases and will increase the utilization rate of small ports.
For DSS ports, the adoption of an autonomous vessel manoeuvring and docking scheme (MOSES AutoDock) that will provide operational independency from the availability of port services.
A digital collaboration and matchmaking platform (MOSES platform) aiming to match demand and supply of cargo volumes by logistics stakeholders (shippers, forwarders, shipping lines, ports) using Machine Learning (ML) and data driven-based analysis (availability of mode, cargo volumes, delivery times) to maximize SSS traffic.
MOSES will be validated by pilot demonstrations in relevant testing environments (TRL5), supported by concrete business cases. A sustainability framework will be developed within the project for evaluating the performance and viability of the proposed innovations. This evaluation will also lead to concrete policy recommendations regarding SSS in Europe.
The MOSES consortium consists of 17 partners from 7 EU countries, united in a common vision to enhance the sustainable short-sea shipping through the achievement of beyond state-of-the-art applied know-how and technological developments. The project started July 2020 and will have a duration of three years.
Contact
Gerco Hagesteijn
Senior Project Manager Ships
HORIZON EUROPE
MOSES project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 861678.