Evaluation of just in time strategy regarding carbon intensity indicator

This paper proposes an energy efficiency strategy based on data-driven artificial neural network (ANN) methodology. It is aimed to reduce fuel consumption and ship-sourced emissions emitted into the atmosphere as well as increase the energy efficiency level of the ship. In this regard, a noon report data belonging to a bulk carrier that cruises open seas is obtained. Using average daily distance and speed as input parameters and fuel consumption as an output parameter, various ANN configurations are created to determine fuel consumption based on the arranged optimal ship speed identified by eliminating anchorage periods as possible. After defining the best structure, a new dataset with optimal speeds is constituted. As a result of this procedure, annual fuel consumption is decreased by 397 tons which equals 6.21% of annual total consumption. Accordingly, significant contributions regarding emission reduction and fuel expenses are revealed. Lastly, the impact of just in time approach on ship energy efficiency management is examined. It is observed that it provides influential advantages in increasing the carbon intensity indicator degree of the ship, especially for the near future. It is determined that additional precautions are required for long-term operations.