A good port data management system can undoubtedly improve a port's operations. This, in turn, affects the economic development of the port city or region. Given the fast-changing global environment, forecasting the container throughput of a port is of vital importance. There are many existing time series forecasting methods, which can be used to forecast a port's container throughput. However, there is limited research comparing the performance of different commonly employed methods on the same time series. This study attempts to bridge this gap. This paper first presents several time series forecasting methods, including machine learning-based methods such as Support Vector Regression. Next, these forecasting methods are employed to forecast the port's container throughput using the same set of historical secondary data. Finally, a comparison is made and discussed. Six time series methods were employed for forecasting a port's container throughput and their performances were compared.

Digitalization is gaining its popularity in the maritime logistics sector due to its potential to enhance information sharing and automation. These advantages can significantly improve efficiency and have the potential to replace complex manual tasks. However, the diffusion of digitalization faces certain challenges, which, in turn, has drawn the attention of researchers. Implementing digitalization is a complex process, as it is affected by various enablers and barriers, while research providing a comprehensive overview of digitalization in the maritime logistics sector is limited. This study aims to fill the gap by conducting a literature review that reveals digitalization’s enablers and barriers in the maritime logistics sector and constructs a theoretical framework. It analyzes 117 articles that have made significant contributions to this field. The development of innovative technologies, such as blockchain, digital twins, and autonomous shipping, fosters digitalization in maritime logistics. Conversely, barriers like the lack of awareness about the benefits of digitalization can slow down its progress. In total, this paper identifies 19 enablers of and 10 barriers to digitalization in the maritime logistics sector. These enablers and barriers are classified into three groups–technology, organization, and environment–following the Technology–Organization–Environment (TOE) framework. We develop a theoretical framework accordingly using, as its basis, relevant innovation diffusion theories and studies. This study contributes to the development of effective digitalization strategies for maritime organizations and provides a theoretical foundation for future research.

Container transportation is an important mode of international trade logistics in the world today, and its changes will seriously affect the development of the international market. For example, the COVID-19 pandemic has added a huge drag to global container logistics. Therefore, the accurate forecasting of container throughput can make a significant contribution to stakeholders who want to develop more accurate operational strategies and reduce costs. However, the current research on port container throughput forecasting mainly focuses on proposing more innovative forecasting methods on a single time series, but lacks the comparison of the performance of different basic models in the same time series and different time series. This study uses nine methods to forecast the historical throughput of the world’s top 20 container ports and compares the results within and between methods. The main findings of this study are as follows. First, GRU is a method that can produce more accurate results (0.54–2.27 MAPE and 7.62–112.48 RMSE) with higher probability (85% for MAPE and 75% for RMSE) when constructing container throughput forecasting models. Secondly, NM can be used for rapid and simple container throughput estimation when computing equipment and services are not available. Thirdly, the average accuracy of machine learning forecasting methods is higher than that of traditional methods, but the accuracy of individual machine learning forecasting methods may not be higher than that of the best conventional traditional methods.

Accurate forecasting of port container throughput plays a crucial role in optimising port operations, resource allocation, supply chain management, etc. However, existing studies only focus on the impact of port hinterland economic development on container throughput, ignoring the impact of port foreland. This study proposed a container throughput forecasting model based on deep learning, which considers the impact of port hinterland and foreland on container throughput. Real-world experimental results showed that the proposed model with multiple data sources outperformed other forecasting methods, achieving significantly higher accuracy. The implications of this study are significant for port authorities, logistics companies, and policymakers.

Purpose: This study aims to define Digital Freight Forwarders (DFFs) within the maritime shipping industry and examine how they address the digitalization gaps in compared to traditional freight forwarders. Design/methodology/approach: The research adopts an inductive, qualitative approach, with empirical data collected through a combination of single-case and multiple-case studies in the maritime shipping sector. This methodology facilitates the development of an empirically grounded definition of DFFs, along with the identification of their key operational characteristics and the critical challenges they encounter. Findings: The study identifies two layers of digitalization in DFFs: basic digital functionalities (e.g. online quoting, booking and tracking) and advanced digital integration (e.g. AI-driven analytics, blockchain and application programming interface connectivity). While DFFs enhance service transparency, automation and operational efficiency, they do not entirely replace human expertise due to regulatory constraints, reluctance to data-sharing and integration challenges. The findings further suggest that the adoption of DFFs is not a binary transition but rather a progression along a spectrum of digital maturity, influenced by technological capabilities, stakeholder collaboration and industry-wide trust in digital platforms. Research limitations/implications: This exploratory study is based on qualitative case studies, which may limit the generalizability of its findings across the broader freight forwarding sector. Practical implications: This study provides strategic insights for freight forwarders, industry managers and policymakers on harnessing DFF capabilities while addressing digitalization challenges. It proposes a two-layered digitalization framework to help firms assess digital maturity and strategically adopt AI-driven analytics, automation and blockchain security. Overcoming reluctance to data-sharing requires standardized protocols and regulatory incentives to foster industry-wide collaboration. Policymakers should establish interoperability frameworks to facilitate seamless data exchange, enhance transparency and advance the sector toward a more integrated digital ecosystem. Originality/value: This study bridges a gap in maritime digitalization research by providing one of the first empirically grounded definitions of DFFs alongside a conceptual framework that distinguishes the stages of digitalization. It contributes to the broader discourse on platform-based logistics models, digital transformation in maritime shipping and the future trajectory of freight forwarding in the digital era.