Global maritime transportation facilitates more than 80% of world trade; therefore, the reliability of ships is inextricably linked to global supply chains. Inefficient inspection and maintenance practices can trigger failures, which in turn increase operating costs. These failures can account for up to 35% of total expenses and cause downtime that cascades through logistic networks. Consequently, unplanned equipment and machinery failures that occur on a vessel can disrupt the ship’s overall operation and lead to delays, higher transport costs, and wider societal impacts, most notably supply shortages and elevated environmental risks. To avoid these domino effects, ship operators must understand the most common failure mechanisms and their underlying causes. Thus, systematic risk identification is a starting point for any strategy that aims to mitigate operational downtime. Existing literature on risk identification in ship inspection and maintenance operations largely focuses on technical and engineering solutions. In order to address this gap, this research adopts the socio-technical system (STS) approach to identify improvement opportunities for inspection and maintenance activities, ultimately mitigating operational downtime. A two-stage Failure Mode Effects and Criticality Analysis (FMECA) is conducted to determine the fundamental failure mechanism in oil tankers and the most critical system, based on industry inspection reports. The Functional Resonance Analysis Method (FRAM) is used to deep dive into that failure. Semi-structured expert interviews and operational data serve as a means of identifying performance variability scenarios across human, organizational, and environmental contexts, within the ship operational process. Finally, the integration of the FMECA and FRAM assists in evaluating suggested control measures based on their effect on ship availability. Corrosion in the steam supply subsystem arises as the leading operational downtime driver. Personnel competence, equipment availability, inspection areas accessibility, and time constraints are the key factors that create performance variability in the operational process. Hinging upon the FRAM models, which qualitatively visualize the propagation of these variability scenarios, control measures are developed based on a Hierarchy of Controls (HoC) As Low As Reasonably Practicable (ALARP) framework. Next steps should begin with a pilot on a single ship subsystem that is highly critical, and a high-resolution failure and downtime dataset. Virtual tests are advised to be conducted before the actual deployment of the identified control measures.