Abstract
Resilience engineering focuses on enhancing the ability of complex systems, organisations and processes to adapt to and recover from disruptions and unexpected events while maintaining their essential functions. It relies on the detection of early precursor of system failure states, on flexibility and controllability of processes, protective barriers construction and minimization of recovery time. Additionally, each process of the system should be characterised by learnability due to constant feedback and competently built management. It has been widely recognized the pivotal role of AI algorithms, which can analyse big data collected from sensors, historical records, and external sources to identify patterns, detect anomalies, and make predictive assessments. This paper critically explores the possibilities of applying text mining and Natural Language Processing techniques for entity extraction to construct an organisational resilience model more efficiently. Accordingly, visualization techniques are used to understand data patterns and trends and identify any areas for improvement (EDA – Exploratory Data Analysis). The textual analyses were based on accident reports obtained from Genoa port companies over 10 years. The data-driven decision-making enables proactive risk mitigation, early identification of potential failures, and optimization of safety protocols, and, in perspective, optimising the learning capacity of the port resilience system.
