Skip to content
Log in
#artificial intelligence Open access

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

M. A. Shakila Banu Pazila Sara. R. S A.S. Minhaj Begum
Aug 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

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.

DOI
View source

Similar papers

#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17
#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

Various recent Artificial Intelligence (AI) system failures, some of which have made the global headlines, have highlighted issues in these systems. These failures have resulted in calls for more ethical AI systems that better take into account their effects on various stakeholders. However, implementing AI ethics into practice is still an on-going challenge. High-level guidelines for doing so exist, devised by governments and private organizations alike, but lack practicality for developers. To address this issue, in this paper, we present a method for implementing AI ethics. The method, ECCOLA, has been iteratively developed using a cyclical action design research approach. The method aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 64 citations · ⚡6
#artificial intelligence May 2023

Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets

This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.

Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al. · 59 citations · ⚡8
#artificial intelligence Jun 2019

Ethically Aligned Design of Autonomous Systems: Industry viewpoint and an empirical study

Progress in the field of artificial intelligence has been accelerating rapidly in the past two decades. Various autonomous systems from purely digital ones to autonomous vehicles are being developed and deployed out on the field. As these systems exert a growing impact on society, ethics in relation to artificial intelligence and autonomous systems have recently seen growing attention among the academia. However, the current literature on the topic has focused almost exclusively on theory and more specifically on conceptualization in the area. To widen the body of knowledge in the area, we conduct an empirical study on the current state of practice in artificial intelligence ethics. We do so by means of a multiple case study of five case companies, the results of which indicate a gap between research and practice in the area. Based on our findings we propose ways to tackle the gap.

Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al. · 56 citations · ⚡6
#artificial intelligence Conference Open access Jun 2018

The Key Concepts of Ethics of Artificial Intelligence

The growing influence and decision-making capacities of Autonomous systems and Artificial Intelligence in our lives force us to consider the values embedded in these systems. But how ethics should be implemented into these systems? In this study, the solution is seen on philosophical conceptualization as a framework to form practical implementation model for ethics of AI. To take the first steps on conceptualization main concepts used on the field needs to be identified. A keyword based Systematic Mapping Study (SMS) on the keywords used in AI and ethics was conducted to help in identifying, defying and comparing main concepts used in current AI ethics discourse. Out of 1062 papers retrieved SMS discovered 37 re-occurring keywords in 83 academic papers. We suggest that the focus on finding keywords is the first step in guiding and providing direction for future research in the AI ethics field.

Ville Vakkuri, P. Abrahamsson · 39 citations · ⚡2
#artificial intelligence Oct 2019

AI Ethics in Industry: A Research Framework

Artificial Intelligence (AI) systems exert a growing influence on our society. As they become more ubiquitous, their potential negative impacts also become evident through various real-world incidents. Following such early incidents, academic and public discussion on AI ethics has highlighted the need for implementing ethics in AI system development. However, little currently exists in the way of frameworks for understanding the practical implementation of AI ethics. In this paper, we discuss a research framework for implementing AI ethics in industrial settings. The framework presents a starting point for empirical studies into AI ethics but is still being developed further based on its practical utilization.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 27 citations · ⚡3

Related blog posts