Search results

This paper summarizes and synthesizes existing research while critically assessing findings for future studies to advance the scholarship of maritime logistics and digital transformation with big data.

A bibliometric analysis was conducted on 159 journal articles from the Scopus database with search keywords “maritime*” and “big data.” This analysis helps identify research gaps by identifying themes via keyword co-occurrence, co-citation and bibliographic coupling analysis. The Theory-Context-Characteristics-Methodology (TCCM) framework was applied to understand the findings of bibliometric analysis and provide a research agenda.

The analyses identified emerging themes of the scholarship of maritime logistics and digital transformation with big data and their relationships to identify research clusters. Future research directions were provided by examining existing research's theory, context, characteristics and method.

This research is grounded in bibliometric analysis and the TCCM framework to understand the scholarly evolution, giving managers and academics retrospective and prospective insights.

To explore the benefits and challenges of WBL in MET in Thailand.

A qualitative approach was used, involving interviews and surveys with participants from MET.

WBL offers practical experience, improved employability, networking opportunities, and real-world problem-solving skills, industry knowledge, professional development, and better learning outcomes, but faces challenges like language barriers, environmental issues, limited placement availability, time limits and task management, safety concerns, limited resources, and assessment issues.

Provides insights into WBL in the Thailand maritime sector and highlights areas for improvement to enhance student preparedness for industry roles.

Maritime transportation plays an important role in facilitating both the global and regional merchandise trade, where accurate trend prediction is crucial in assisting decision-making in the industry. This paper aims to conduct a macro-level study to predict world vessel supply and demand.

The automatic autoregressive integrated moving average (ARIMA) is used for the univariate vessel supply and demand time-series forecasting based on the data records from 1980 to 2021.

For the future projection of the demand side, the predicted outcomes for total vessel demand and world dry cargo vessel demand until 2030 indicate upward trends. For the supply side, the predominant upward trends for world total vessel supply, oil tanker vessel supply, container vessel supply and other types of vessel supply are captured. The world bulk carrier vessel supply prediction results indicate an initial upward trend, followed by a slight decline, while the forecasted world general cargo vessel supply values remain relatively stable. By comparing the predicted percentage change rates, there is a gradual convergence between demand and supply change rates in the near future. We also find that the impact of the COVID-19 pandemic on the time-series prediction results is not statistically significant.

The results can provide policy implications in strategic planning and operation to various stakeholders in the shipping industry for vessel building, scrapping and deployment.

This study aims to explore the influence of dynamic knowledge management capabilities on organizational innovation and supply chain resilience in maritime shipping companies. Furthermore, this study investigates the moderating role of growth-oriented strategies and the mediating role of organizational innovation.

Data were collected from 76 maritime shipping companies in South Korea. The research hypotheses were tested using structural equation modeling.

This study demonstrates that effective dynamic knowledge management capabilities in maritime shipping companies significantly enhance organizational innovation and boost the resilience of their supply chains. Organizational innovation positively mediates the relationship between dynamic knowledge management and supply chain resilience. Moreover, a company’s growth-oriented strategy positively moderates the relationship between dynamic knowledge management and organizational innovation.

Based on the existing literature, this study develops the concept of dynamic knowledge management and validates its impact on organizational innovation and the resilience of maritime supply chains. Furthermore, unlike previous studies, this study focuses specifically on maritime supply chains. Through a survey of 76 maritime companies in South Korea, the study validates relevant hypotheses and draws conclusions. This contributes to expanding and enriching existing research while offering meaningful insights for relevant enterprises.

This paper's purpose is twofold. First, based on a case study, it aims to comprehend the consequences of COVID-19 on the demand and supply shocks of the freight transportation system in Mexico. Second, it seeks to provide an integrated perspective of four transportation modes, which would help prepare public policies for future global pandemics.

Analyzing the impact of the COVID-19 pandemic on the freight transportation system, which affects national and global economies, is essential to drawing valuable insights for the future. To facilitate international comparative analysis, conducting case studies at a country level was deemed necessary. As a result, a case study was conducted in Mexico using an integrated approach involving four transportation modes.

To manage disruptions in freight flow during uncertain conditions, a comprehensive perspective on the four modes of transportation and data-driven decision-making is crucial. Under this context, three initiatives can be identified: 1) establishing a National Center for Intelligence in Logistics to improve data-driven governance; 2) appointing the “Integrated Transportation Corridor Management Manager” (ITCMM) function to coordinate multiple authorities with different acting in critical freight transport corridors, and 3) creation of a digital tool based on millions of GPS data to monitor freight flows, allowing for collective intelligence among logistics actors.

This research's limitations are related to using non-standardized databases to gather information on four transportation modes. However, this limitation is also an interesting discovery. Mexico is becoming a strategic logistics hub between North America and Latin America, especially under the “Nearshoring” trend. Unfortunately, the lack of an integrated public policy in logistics and transportation reduces Mexico's capacity to deal with disruptions and its economic competitiveness.

This research has identified practices that could be crucial in improving public policies to optimize shipping routes and reduce wait times while minimizing disruptions caused by unforeseen events. A concrete example is the digital platform called “eraclitux,” a computer tool similar to an Enterprise Resource Planning (ERP) system companies use. This tool can enable a “Control Tower” that monitors freight flow in transportation corridors under the supervision of “Integrated Transportation Corridor Management Managers.” The tool can make reactive and predictive decisions that help to enhance the logistics value provided by transportation infrastructure.

The importance of a well-coordinated and integrated public policy for freight transportation was identified to ensure better performance during disruptions. Delays in the flow of goods can significantly impact the supply of essential items such as food and medicine, ultimately affecting the population's quality of life.

Numerous studies have been conducted to determine the extent of vulnerability and the impact of COVID-19 on freight transportation. However, most of these studies assume a developed market context or a single-mode transportation approach, which only applies to some situations. To gain a comprehensive understanding of how pandemics-induced demand and supply shocks affected freight transportation in developing countries such as Mexico, this paper offers insights from a four-transportation mode perspective. Mexico is facing a challenging Nearshoring trend in manufacturing, making it a significant logistics node between North and South America.

Aiming to resolve cross-cultural paradoxes in combining artificial intelligence (AI) with human intelligence (HI) for international humanitarian logistics, this paper aims to adopt an unorthodox Yin–Yang dialectic approach to address how AI–HI interactions can be interpreted as a sophisticated cross-cultural knowledge creation (KC) system that enables more effective decision-making for providing humanitarian relief across borders.

This paper is conceptual and pragmatic in nature, whereas its structure design follows the requirements of a real impact study.

Based on experimental information and logical reasoning, the authors first identify three critical cross-cultural challenges in AI–HI collaboration: paradoxes of building a cross-cultural KC system, paradoxes of integrative AI and HI in moral judgement and paradoxes of processing moral-related information with emotions in AI–HI collaboration. Then applying the Yin–Yang dialectic to interpret Klir’s epistemological frame (1993), the authors propose an unconventional stratified system of cross-cultural KC for understanding integrative AI–HI decision-making for humanitarian logistics across cultures.

This paper aids not only in deeply understanding complex issues stemming from human emotions and cultural cognitions in the context of cross-border humanitarian logistics, but also equips culturally-diverse stakeholders to effectively navigate these challenges and their potential ramifications. It enhances the decision-making process and optimizes the synergy between AI and HI for cross-cultural humanitarian logistics.

The originality lies in the use of a cognitive methodology of the Yin–Yang dialectic to metaphorize the dynamic genesis of integrative AI-HI KC for international humanitarian logistics. Based on system science and knowledge management, this paper applies game theory, multi-objective optimization and Markov decision process to operationalize the conceptual framework in the context of cross-cultural humanitarian logistics.

In the existing era, international trade is boosted by maritime freight movement. The academicians and Government are concerned about environmental contamination caused by maritime goods that transit global growth and development. Digital technologies like blockchain help the maritime freight business to stay competitive in the digital age. This study aims to illuminate blockchain technology (BCT) adoption aspects to alleviate early industry adoption restrictions.

This study adopts a two-stage approach comprising of structural equation modeling (SEM) with artificial neural networks (ANN) to analyze critical factors influencing the adoption of BCT in the sustainable maritime freight industry.

The SEM findings from this study illustrate that social, organizational, technological and infrastructual and institutional factors affect BCT execution. Furthermore, the ANN technique uses the SEM data to determine that sustainability enabled digital freight training (S3), initial investment cost (O5) and trust over digital technology (G1) are the most essential blockchain deployment factors.

The hybrid approach aims to help decision-makers and policymakers examine their organizational blockchain adoption goals to construct sustainable, efficient and effective maritime freight transportation.

This paper reviews and analyses renewable energy options, namely underground thermal, solar, wind and marine wave energy, in seaport cargo terminal operations.

Four renewable energy options that are deployed or tested in different ports around the world are qualitatively examined for their overall implementation potential and characteristics, and their cost and benefits. An application to the port of Singapore is discussed.

Geophysical conditions are key criteria in assessing renewable energy options. In the case of Singapore, solar power is the only suitable renewable energy option.

Being a capital-intensive establishment with high intensities of cargo operations, seaports usually involve a high level of energy consumption. The study of renewable energy options contributes to seaport sustainability.

A key recommendation is to implement a smart energy management system that enables the mixed use of renewable energy to match energy demand and supply optimally and achieve higher energy efficiency.

The use of renewable energy as an eco-friendlier energy source is underway in various ports. However, there is almost no literature that analyses and compares various renewable energy options potentially suitable for cargo terminal operations in ports. This paper narrows the knowledge gaps.

To achieve the Sustainable Development Goals (SDGs) in the era of Logistics 4.0, machine learning (ML) techniques and simulations have emerged as highly optimized tools. This study examines the operational dynamics of a supply chain (SC) in Vietnam as a case study utilizing an ML simulation approach.

A robust fuel consumption estimation model is constructed by leveraging multiple linear regression (MLR) and artificial neural network (ANN). Subsequently, the proposed model is seamlessly integrated into a cutting-edge SC simulation framework.

This paper provides valuable insights and actionable recommendations, empowering SC practitioners to optimize operational efficiencies and fostering an avenue for further scholarly investigations and advancements in this field.

This study introduces a novel approach assessing sustainable SC performance by utilizing both traditional regression and ML models to estimate transportation costs, which are then inputted into the discrete event simulation (DES) model.