​​Big Data Analytics as an Enabler of Trade Facilitation​

Abstract

This thesis investigates the role of big data analytics (BDA) in supporting maritime trade facilitation through a Systematic Literature Review (SLR). Against the backdrop of rising global pressures for efficient, sustainable, and resilient supply chains, the review systematically examines how BDA has been applied in port operations and customs procedures. The study maps available tools and applications, evaluates reported benefits, identifies adoption barriers, and assesses the maturity of decision-support frameworks. A structured SLR process was implemented, analysing 55 peer-reviewed studies published between 2015 and 2025. To ensure synthesis beyond descriptive reporting, findings were clustered into three thematic domains: efficiency (e.g., port optimisation, customs automation, forecasting), sustainability (e.g., emissions reduction, energy efficiency), and resilience (e.g., disruption detection, vulnerability analysis). Evidence confirms strong contributions of BDA to operational efficiency, but integration into sustainability and resilience strategies remains limited. Common barriers include weak data interoperability, regulatory restrictions, and organisational inertia. The review also identifies a shortage of empirically validated decision-support models. The contribution of this thesis lies in offering a structured and replicable synthesis of the academic evidence base, alongside a typology of BDA tools and applications relevant to maritime trade. By aligning closely with the goals of an SLR, the study not only consolidates fragmented research but also exposes critical gaps and future research priorities. It provides actionable insights for stakeholders such as port authorities, customs agencies, and policy-makers, especially in relation to scaling real-world implementations and fostering cross- sectoral collaboration.