Tank ships sail a large share of their time in ballast conditions, depending on their trading patterns up to half of the time at sea.The aim of this project use case is to test the usage of machine learning and big data approaches based on existing historical ship operation data to improve energy efficiency on ballast trips. Founded on the analysis, guidelines on how to improve the energy efficiency of ships can be made by collecting real-time operational data.The energy needed to propel a vessel is largely dependent on the total weight of it and of the speed it is operated at. Substantial savings in energy consumption and correspondingly to reduced fuel costs as well as to reduced emissions can be achieved by either lowering the speed or optimising the load taken onboard.Ships are normally designed for optimal operation at one single or a few defined load conditions. By analysing off-design conditions (such as partial load, slower speed, and ballast conditions), significant improvements in efficiency can be obtained. Figures achieved by different means range typically from 10 to 40 percent by improving the crew’s methods to load and operate the vessels, increasing resistance and delivered power [1]. Looking at operational regimes of tankers, the crews can only to a limited degree adjust the operational conditions for the loaded voyages when on hire, while when sailing off-hire or in ballast voyages allows for certain flexibility.Building on a grey machine learning model with an underlying hydrodynamic model of the vessel, the data analysis provides a guidance to the mariners on summer ballast conditions that allow for fuel savings. The conditions derived by the model have been demonstrated by the shipping operator in full scale trials. Based on the analysis made, summer ballast conditions imply a reduction in fuel consumption in the range of 10-14% on the feasible trips.