A near-miss management system (NMMS) is a tool used for improving safety at sea if adequately implemented. Valuable knowledge to improve safety management might be gained by investigating and analysing reported events. Therefore, it is of the utmost importance to report each observed near-miss event. Because tankers are generally considered dangerous, but at the same time safe due to stringent requirements, near-miss reports and NMMS policy were collected from one oil tanker ship. Data were pre-processed and analysed. Variables used during analysis were near-miss type, risk level, ship position, and onboard location of near-miss occurrence. Analysis of policy and reports revealed that most near misses occurred on the deck area, but higher-risk-level events were reported in the engine room and navigating bridge. Housekeeping, equipment failure, use of personal protective equipment (PPE), and process-/procedure-related events were most common and generally related to lower risk levels. The most frequent corrective actions recorded were implementing safe working practices and PPE. In addition, higher-risk-level events were related to less effective corrective actions. Based on the findings, suggestions for improvements include promoting safe behaviour and adequate PPE usage through additional training, ensuring proper housekeeping, regular maintenance of shipboard equipment and spare parts management, and toolbox meetings and risk assessments that include conclusions of near-miss investigations and analysis.

Safety leadership is critical in high-risk industries such as shipping since inadequate leadership can cause marine accidents, resulting in injuries, fatalities, damage to property and environmental pollution. One of the aspects of good and effective safety leadership is creating good human relations and satisfaction among crewmembers, considered a precondition for effective teamwork. Officers on board ships should establish effective teamwork and implement adequate safety leadership, positively affecting safety culture, increasing safety in general and improving marine environment protection. Safety leadership onboard ships comprises several characteristics, including maintaining safe work performance, carrying out safety training, and encouraging crewmember morale. Therefore, it is essential for all stakeholders in shipping industries that officers onboard ships can identify those characteristics, adapt, and apply them adequately. This paper presents the characteristics and skills that shipboard officers need to apply in order to be excellent leaders and serve with adequate safety leadership abilities. One tool for recognizing those characteristics and skills was a survey carried out among experienced professional seafarers. Analysis of the survey data revealed latent factors, these being transformational and transactional leadership elements affecting safety leadership onboard ships.

It is of the utmost importance to accurately estimate different ships’ weights during their design stages. Additionally, lightship displacement (LD) data are not always easily accessible to shipping stakeholders, while other ships’ dimensions are within hand’s reach (for example, through data from the online Automatic Identification System (AIS)). Therefore, determining lightship displacement might be a difficult task, and it is traditionally performed with the help of mathematical equations developed by shipbuilders. Distinct from the traditional approach, this study offers the possibility of employing machine learning methods to estimate lightship displacement weight as accurately as possible. This paper estimates oil tankers’ lightship displacement using two ships’ dimensions, length overall, and breadth. The dimensions of oil tanker ships were collected from the INTERTANKO Chartering Questionnaire Q88, available online, and, because of similar block coefficients, all tanker sizes were used for estimation. Furthermore, multiple linear regression and extreme gradient boosting (XGBoost) machine learning methods were utilised to estimate lightship displacement. Results show that XGBoost and multiple linear regression machine learning methods provide similar results, and both could be powerful tools for estimating the lightship displacement of all types of ships.