IoT and Big Data Analytics for Smart Port Management Efficiency

Authors

  • Evan Putra Naliska Senior High School of 10 Depok

DOI:

https://doi.org/10.55123/ijisit.v2i1.113

Keywords:

Smart Ports, Internet of Things, Big Data Analytics, Port Management Efficiency, Digital Transformation

Abstract

 The rapid digitalization of global port systems has transformed seaports into complex socio-technical infrastructures where operational efficiency, managerial capability, and sustainability are increasingly shaped by data-driven technologies. This study examines the role of the Internet of Things (IoT), Big Data analytics, and artificial intelligence (AI) in enhancing port management efficiency through a qualitative literature-based analysis. Drawing on selected peer-reviewed studies in port management, sustainability, and technology management, the research synthesizes how digital technologies influence cargo handling efficiency, stakeholder coordination, resilience, and sustainability performance. The findings indicate that IoT-enabled real-time monitoring and Big Data–supported decision-making significantly improve operational effectiveness, while AI-based systems strengthen predictive planning and adaptive capacity. Importantly, the results highlight that technological effectiveness is strongly mediated by managerial digital literacy and organizational readiness, underscoring the human and educational dimensions of smart port transformation. By linking digital infrastructure to competency development and socio-economic outcomes, this research addresses gaps in prior studies that often focus narrowly on technical or infrastructural aspects. The study contributes to maritime management literature by positioning digital technologies as strategic enablers of sustainable and competitive port development, particularly relevant for emerging maritime economies navigating accelerated digital transformation.

Downloads

Download data is not yet available.

References

[1] V. Caldeirinha, J. A. Felício, T. Pinho, and R. Rodrigues, “Fuzzy-Set QCA on Performance and Sustainability Determinants of Ports Supporting Floating Offshore Wind Farms,” Sustainability, vol. 16, no. 7, p. 2947, 2024, doi: 10.3390/su16072947.

[2] H. Paridaens and T. Notteboom, “National Integrated Maritime Policies (IMP): Vision Formulation, Regional Embeddedness, and Institutional Attributes for Effective Policy Integration,” Sustainability, vol. 13, no. 17, p. 9557, 2021, doi: 10.3390/su13179557.

[3] P. Caldas, M. I. Pedro, and R. C. Marques, “An Assessment of Container Seaport Efficiency Determinants,” Sustainability, vol. 16, no. 11, p. 4427, 2024, doi: 10.3390/su16114427.

[4] K. Zhou, X. Yuan, Z. Guo, J. Wu, and R. Li, “Research on Sustainable Port: Evaluation of Green Port Policies on China’s Coasts,” Sustainability, vol. 16, no. 10, p. 4017, 2024, doi: 10.3390/su16104017.

[5] Y.-H. Liao and H.-S. Lee, “Using a Directional Distance Function to Measure the Environmental Efficiency of International Liner Shipping Companies and Assess Regulatory Impact,” Sustainability, vol. 15, no. 4, p. 3821, 2023, doi: 10.3390/su15043821.

[6] S.-K. Kim, S. Choi, and C. Kim, “The Framework for Measuring Port Resilience in Korean Port Case,” Sustainability, vol. 13, no. 21, p. 11883, 2021, doi: 10.3390/su132111883.

[7] G.-Y. Chae, S.-H. An, and C.-Y. Lee, “Demand Forecasting for Liquified Natural Gas Bunkering by Country and Region Using Meta-Analysis and Artificial Intelligence,” Sustainability, vol. 13, no. 16, p. 9058, 2021, doi: 10.3390/su13169058.

[8] B. Kim, G. Kim, and M.-H. Kang, “Study on Comparing the Performance of Fully Automated Container Terminals During the COVID-19 Pandemic,” Sustainability, vol. 14, no. 15, p. 9415, 2022, doi: 10.3390/su14159415.

[9] A. Bilal, L. Xiao-ping, Z. Nanli, R. Sharma, and A. Jahanger, “Green Technology Innovation, Globalization, and CO₂ Emissions: Recent Insights From the OBOR Economies,” Sustainability, vol. 14, no. 1, p. 236, 2021, doi: 10.3390/su14010236.

[10] H. Buddha, L. Shuib, N. Idris, and C. I. Eke, “Technology-Assisted Language Learning Systems: A Systematic Literature Review,” IEEE Access, vol. 12, pp. 27645–27668, 2024, doi: 10.1109/ACCESS.2024.3366663.

[11] P. Ciancarini, R. Giancarlo, and G. Grimaudo, “Digital Transformation in the Public Administrations: A Guided Tour for Computer Scientists,” IEEE Access, vol. 12, pp. 20890–20915, 2024, doi: 10.1109/ACCESS.2024.3363075.

[12] A. D. Elbouzidi, A. Artiba, R. Pellerin, S. Lamouri, E. T. Valencia, and M.-J. Bélanger, “The Role of AI in Warehouse Digital Twins: Literature Review,” Applied Sciences, vol. 13, no. 11, p. 6746, 2023, doi: 10.3390/app13116746.

[13] M. Favaretto, E. De Clercq, A. L. Caplan, and B. S. Elger, “United in Big Data? Exploring Scholars’ Opinions on Academic-Industry Partnership and the Use of Corporate Data in Digital Behavioral Research,” PLOS ONE, vol. 18, no. 2, p. e0280542, 2023, doi: 10.1371/journal.pone.0280542.

[14] Y. Shi, T. Ramayah, L. Hongmei, Y. Zhang, and W. Wang, “Analysing the Current Status, Hotspots, and Future Trends of Technology Management: Using the WoS and Scopus Database,” Heliyon, vol. 9, no. 9, p. e19922, 2023, doi: 10.1016/j.heliyon.2023.e19922.

[15] N. A. Ahmad, S. M. Drus, and H. Kasim, “Factors That Influence the Adoption of Enterprise Architecture by Public Sector Organizations: An Empirical Study,” IEEE Access, vol. 8, pp. 113162–113181, 2020, doi: 10.1109/ACCESS.2020.2996584.

Downloads

Published

2025-06-30

How to Cite

Evan Putra Naliska. (2025). IoT and Big Data Analytics for Smart Port Management Efficiency. IJISIT: International Journal of Computer Science and Information Technology, 2(1), 68–74. https://doi.org/10.55123/ijisit.v2i1.113

Issue

Section

Articles