Port State Control (PSC) inspections conducted under the Paris Memorandum of Understanding (MoU) agreement have become a crucial tool for maritime administrations in European Union countries to ensure compliance with international maritime safety standards by ships entering their ports. This paper analyses all PSC inspections conducted in 10 major European ports belonging to the Paris MoU between 2012 and 2019. For its study, a multivariate HJ-Biplot statistical analysis is carried out, which facilitates the interpretation and understanding of the underlying relationships in a multivariate data set by representing a synthesis of the data on a factorial plane, with an interpretation that is very intuitive and accessible for readers from various fields. Applying this method with ship characteristics as explanatory variables, several classifications were derived. These classifications align with the annual performance lists published by the Paris MoU and the International Association of Classification Societies list, suggesting that this method could serve as a reliable classification approach. It provides maritime authorities with an additional indicator of a ship’s risk profile, aiding in the prioritising of inspections. The method also effectively categorises ports and types of ships used for cargo transport, offering insights into the specific maritime traffic each port experiences. Furthermore, this study identifies characteristics associated with substandard ships, which is a primary objective of PSC inspections. Beyond revealing these traits, this research underscores the existence of several readily applicable techniques to enhance maritime safety and reduce the risk of ocean pollution.

Port state control inspections implemented under the Paris Memorandum of Understanding (MoU) have become known as one of the best instruments for maritime administrations in European Union (EU) Member States to ensure that the ships docked in their ports comply with all maritime safety requirements. This paper focuses on the analysis of all inspections made between 2013 and 2018 in the top ten EU ports incorporated in the Paris MoU (17,880 inspections). The methodology consists of a multivariate statistical information system (STATIS) analysis using the inspected ship’s characteristics as explanatory variables. The variables used describe both the inspected ships (classification society, flag, age and gross tonnage) and the inspection (type of inspection and number of deficiencies), yielding a dataset with more than 600,000 elements in the data matrix. The most important results are that the classifications obtained match the performance lists published annually by the Paris MoU and the classification societies. Therefore, the approach is a potentially valid classification method and would then be useful to maritime authorities as an additional indicator of a ship’s risk profile to decide inspection priorities and as a tool to measure the evolution in the risk profile of the flag over time.

Port state control inspections implemented under the Paris Memorandum of Understanding (MoU) have become known as one of the best instruments for maritime administrations in European Union (EU) Member States to ensure that the ships docked in their ports comply with all maritime safety requirements. This paper focuses on the analysis of all inspections made between 2013 and 2018 in the top ten EU ports incorporated in the Paris MoU (17,880 inspections). The methodology consists of a multivariate statistical information system (STATIS) analysis using the inspected ship’s characteristics as explanatory variables. The variables used describe both the inspected ships (classification society, flag, age and gross tonnage) and the inspection (type of inspection and number of deficiencies), yielding a dataset with more than 600,000 elements in the data matrix. The most important results are that the classifications obtained match the performance lists published annually by the Paris MoU and the classification societies. Therefore, the approach is a potentially valid classification method and would then be useful to maritime authorities as an additional indicator of a ship’s risk profile to decide inspection priorities and as a tool to measure the evolution in the risk profile of flag over time.

The first pandemic of the 21st Century was declared at the beginning of the year 2020 due to the spread of the COVID-19 virus. Its effects devastated the world economy and greatly affected maritime transport, one of the precursors of globalisation. This paper studies the effects of the pandemic on this type of transport, using data from 23,803 Paris Memorandum of Understanding Port State Control (PSC) inspections conducted in the top 10 major European ports. Comparisons have been made between Pre-COVID (2013–2019) and COVID (2020–2021) years, by way of multivariate methodologies: CO-X-STATIS, X-STATIS, and correspondence tables. The results were striking and indicate a clear change in the conduct of inspections during the COVID period, both quantitatively and qualitatively, showing a drastic reduction in the number of inspections and a change in type, with exhaustive inspections assuming a secondary role. Another notable result came from the use of the same methodology to study the different countries of registry and their evolution within PSC inspections during the Pre-COVID and COVID periods, where different behaviours were identified based on a ship’s flag. These results can help us to determine important supervisory objectives for each country’s maritime administration and their inspectors, to indicate weaknesses in the inspection routines caused by the pandemic, and to attempt corrections to improve maritime safety.

This study analyzes the results of Port State Control (PSC) inspections carried out under the Paris Memorandum of Understanding between 2018 and 2022. Through a correspondence analysis, the most frequent deficiencies were identified according to the type of ship being inspected. The study sample included 186,255 inspections obtained from the THETIS platform. The results revealed significant heterogeneity in deficiency profiles across ship types, highlighting specific patterns associated with each category. Container ships, oil tankers and bulk carriers, for instance, exhibited distinctive deficiency profiles. The study emphasizes the necessity for a tailored approach to PSC inspections, with the objective of optimizing resources through the utilization of risk zone indicators for the inspector. The identification of specific risk indicators would not only facilitate the work of inspectors but also enable the earlier detection of potential problems and more effective intervention. The study provides a solid foundation for future research and decision-making on PSC inspections, with the aim of enhancing maritime safety and pollution prevention.

This literature review examines the relationship between the number and type of deficiencies identified during Port State Control (PSC) inspections and a ship’s overall risk. The main objective is to synthesise the current academic evidence, detailing the analytical methodologies employed and highlighting key research contributions. The selection of literature has focused on peer-reviewed articles and relevant doctoral theses addressing detention risk prediction, accident risk and ship risk profiling. The findings indicate a consistent correlation between PSC deficiencies and ship risk, although the nature and strength of this correlation may vary depending on the type of risk considered and the specific deficiencies. A methodological evolution is observed in the field, from descriptive statistical analyses and regressions towards more complex predictive models, such as Machine Learning (ML) and Bayesian Networks (BNs). This transition reflects a search for greater accuracy in risk assessment, going beyond simple numerical correlation to improve the selection of ships for inspection. Multivariate statistical techniques, on the other hand, focus on the identification of risk patterns and the evaluation of the PSC system. The conclusions underline the importance of deficiencies as indicators of risk, the need for differentiated inspection approaches and the persistent challenges related to data quality and model interpretability.