Predictive Maintenance of Cargo Vessels Using Ai: Implications for Logistics Reliability Using R Programming

Abstract

The marine sector is vital to international trade, and the dependability of cargo ships is necessary to maintain continuous logistics operations. Conventional maintenance methods frequently lead to unforeseen equipment malfunctions, higher operating expenses, and cargo delivery delays. As a result, predictive maintenance based on artificial intelligence (AI) has become a cutting-edge technology that permits early fault identification, real-time monitoring, and optimal maintenance scheduling. The current study, "Predictive Maintenance of Cargo Vessels Using AI: Implications for Logistics Reliability," looks at how AI-based predictive maintenance affects logistics reliability while assessing the impact of operational performance and repair difficulties. The study used a quantitative research approach, and a structured questionnaire with a five-point Likert scale was used to gather primary data from 100 respondents. Respondents with experience in predictive maintenance and cargo vessel operations were chosen using a convenience sample technique. Descriptive statistics, skewness and kurtosis analysis, Cronbach's alpha reliability analysis, correlation analysis, regression analysis, mediation analysis, and structural equation modelling (SEM) were all used in the analysis of the gathered data using R programming. The results showed that all variables had adequate normality, the measuring tool had good to exceptional reliability, and artificial intelligence had a strong positive correlation with logistical reliability. By enhancing operational performance and resolving maintenance issues, AI-based predictive maintenance has a favourable direct and indirect impact on logistics dependability, according to regression and mediation analyses. In order to increase maintenance efficiency, decrease unplanned vessel downtime, and boost logistical reliability, the report recommends that shipping companies invest in AI-driven predictive maintenance solutions, IoT-enabled sensors, worker training, and strong digital infrastructure. The study concludes that AI-enabled predictive maintenance is a successful tactic for boosting operational effectiveness, guaranteeing on-time cargo delivery, cutting maintenance costs, and enhancing the competitiveness and sustainability of the maritime logistics sector.