Home / Current Issue / Paper 1722787
Data-Driven Performance Monitoring in Maritime Logistics: A Framework for Improving Operational Efficiency in Developing-Economy Ports
Subject area: Management and Commerce · Area of research: Maritime Logistics Management
Abstract
This review examines how data-driven performance monitoring can strengthen operational efficiency in developing-economy ports. It addresses persistent challenges including vessel congestion, prolonged cargo dwell time, equipment downtime, fragmented documentation, weak hinterland connectivity, unreliable electricity, limited digital integration, and institutional coordination failures. The study adopts a structured review approach, synthesising pre-2020 scholarly literature on maritime logistics, port performance measurement, digital technologies, governance, energy resilience, and analytical decision support. The analysis evaluates operational processes, performance indicators, enabling technologies, implementation barriers, and the conditions required for an integrated monitoring framework. The findings show that port efficiency cannot be assessed through cargo throughput alone. Effective monitoring requires a balanced portfolio of indicators covering vessel turnaround, berth productivity, crane performance, equipment utilisation, yard occupancy, customs clearance, gate processing, service reliability, safety, environmental performance, and stakeholder satisfaction. The review further establishes that terminal operating systems, port community systems, automatic identification systems, sensors, predictive analytics, simulation tools, blockchain, and real-time dashboards can improve visibility and decision quality when supported by accurate data, interoperable platforms, skilled personnel, resilient power systems, and clear governance arrangements. The study concludes that sustainable performance improvement depends on linking data collection, analytics, accountability, corrective action, and continuous learning within a phased implementation model. It recommends prioritising data standards, process mapping, staff development, renewable and backup power solutions, cybersecurity, inter-agency collaboration, and measurable pilot projects before advancing toward predictive and prescriptive analytics. The proposed framework provides a practical basis for improving reliability, competitiveness, resilience, and resource utilisation across maritime logistics systems in developing economies while preserving institutional adaptability and financial feasibility under local conditions.
Keywords
Maritime logistics; port performance monitoring; operational efficiency; developing economies; digital port technologies; predictive analytics.
References
[1] Acciaro, M., Vanelslander, T., Sys, C., Ferrari, C., Roumboutsos, A., Giuliano, G., Lam, J.S.L. and Kapros, S. (2014) ‘Environmental sustainability in seaports: A framework for successful innovation’, Maritime Policy & Management, 41(5), pp. 480–500. DOI: 10.1080/03088839.2014.932926.
[2] Brooks, M.R. and Pallis, A.A. (2008) ‘Assessing port governance models: Process and performance components’, Maritime Policy & Management, 35(4), pp. 411–432. DOI: 10.1080/03088830802215060.
[3] de Langen, P.W. (2006) ‘Stakeholders, conflicting interests and governance in port clusters’, Research in Transportation Economics, 17, pp. 457–477. DOI: 10.1016/S0739-8859(06)17020-1.
[4] Gumede, S. and Chasomeris, M. (2015) ‘Maritime port pricing and governance in South Africa: Trends and stakeholder comments’, Journal of Economic and Financial Sciences, 8(1), article 83. DOI: 10.4102/jef.v8i1.83.
[5] Ng, A.K.Y. and Pallis, A.A. (2010) ‘Port governance reforms in diversified institutional frameworks: Generic solutions, implementation asymmetries’, Environment and Planning A, 42(9), pp. 2147–2167. DOI: 10.1068/a42514.
[6] Notteboom, T.E., de Langen, P.W. and Jacobs, W. (2013) ‘Institutional plasticity and path dependence in seaports: Interactions between institutions, port governance reforms and port authority routines’, Journal of Transport Geography, 27, pp. 26–35. DOI: 10.1016/j.jtrangeo.2012.05.002.
[7] Rachinger, M., Rauter, R., Müller, C., Vorraber, W. and Schirgi, E. (2019) ‘Digitalization and its influence on business model innovation’, Journal of Manufacturing Technology Management, 30(8), pp. 1143–1160. DOI: 10.1108/JMTM-01-2018-0020.
[8] Sunday, E.A. and Omoegun, G.O. (2018) ‘Integrating solar power solutions in small-scale manufacturing industries in Nigeria’, International Journal of Scientific Research in Science, Engineering and Technology, 4(8), pp. 832–853. No verifiable standard DOI located. Publisher record.
[9] Sunday, E.A. and Omoegun, G.O. (2019) ‘Optimizing electrical load distribution for hybrid solar installations in developing economies’, International Journal of Scientific Research in Mechanical and Materials Engineering, 3(6), pp. 27–47. No verifiable standard DOI located. Publisher record.
[10] Venkatesh, V., Morris, M.G., Davis, G.B. and Davis, F.D. (2003) ‘User acceptance of information technology: Toward a unified view’, MIS Quarterly, 27(3), pp. 425–478. DOI: 10.2307/30036540.
How to cite this paper
@article{1722787,
author = {Ayokunle Olamide Ijagbemi, Ogochukwu T. izuchukwu, Dominic Feboh, Stanley Nwakamma, Marudi Oyefuga},
title = {Data-Driven Performance Monitoring in Maritime Logistics: A Framework for Improving Operational Efficiency in Developing-Economy Ports},
journal = {Iconic Research And Engineering Journals},
year = {2019},
volume = {3},
number = {6},
pages = {630-649},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722787.pdf},
abstract = {This review examines how data-driven performance monitoring can strengthen operational efficiency in developing-economy ports. It addresses persistent challenges including vessel congestion, prolonged cargo dwell time, equipment downtime, fragmented documentation, weak hinterland connectivity, unreliable electricity, limited digital integration, and institutional coordination failures. The study adopts a structured review approach, synthesising pre-2020 scholarly literature on maritime logistics, port performance measurement, digital technologies, governance, energy resilience, and analytical decision support. The analysis evaluates operational processes, performance indicators, enabling technologies, implementation barriers, and the conditions required for an integrated monitoring framework.
The findings show that port efficiency cannot be assessed through cargo throughput alone. Effective monitoring requires a balanced portfolio of indicators covering vessel turnaround, berth productivity, crane performance, equipment utilisation, yard occupancy, customs clearance, gate processing, service reliability, safety, environmental performance, and stakeholder satisfaction. The review further establishes that terminal operating systems, port community systems, automatic identification systems, sensors, predictive analytics, simulation tools, blockchain, and real-time dashboards can improve visibility and decision quality when supported by accurate data, interoperable platforms, skilled personnel, resilient power systems, and clear governance arrangements.
The study concludes that sustainable performance improvement depends on linking data collection, analytics, accountability, corrective action, and continuous learning within a phased implementation model. It recommends prioritising data standards, process mapping, staff development, renewable and backup power solutions, cybersecurity, inter-agency collaboration, and measurable pilot projects before advancing toward predictive and prescriptive analytics. The proposed framework provides a practical basis for improving reliability, competitiveness, resilience, and resource utilisation across maritime logistics systems in developing economies while preserving institutional adaptability and financial feasibility under local conditions.},
keywords = {Maritime logistics; port performance monitoring; operational efficiency; developing economies; digital port technologies; predictive analytics.},
month = {December},
doi = {https://doi.org/10.64388/IREV3I6-1722787}
}