With the increasing availability of large datasets and improvements in prediction algorithms, machine-learning-based techniques, particularly deep learning algorithms, are becoming increasingly popular. However, deep-learning algorithms have not been widely applied to predict container freight rates. In this paper, we compare a long short-term memory (LSTM) method and a seasonal autoregressive integrated moving average (SARIMA) method for forecasting the comprehensive and route-based Shanghai Containerized Freight Index (SCFI). The research findings indicate that the LSTM deep learning models outperformed SARIMA models in most of the datasets. For South America and the east coast of the U.S. routes, LSTM could reduce forecasting errors by as much as 85% compared to SARIMA. The SARIMA models performed better than LSTM in predicting freight movements on the west and east Japan routes. The study contributes to the literature in four ways. First, it presents insights for improving forecasting accuracy. Second, it helps relevant parties understand the trends of container freight markets for wiser decision-making. Third, it helps relevant stakeholders understand overall container shipping market trends. Lastly, it can help hedge against the volatility of freight rates.

A cross section panel model is applied to estimate the effect that the Herfindahl-Hirschman Index (HHI) has on container freight rates for a sample of six major container liner shipping routes during 2009 to 2011. The estimated coefficient of HHI is non-positive and statistically insignificant, indicating that higher concentration level does not lead to high price and the container liner shipping market is contestable for the period under consideration. The suggestion that efficiency can be achieved without actual competition in a contestable market is highly significant for policy makers

Purpose: This paper aims to retrieve key components of blockchain applications in supply chain areas. It applies natural language processing methods to generate useful insights from academic literature. Design/methodology/approach: It first applies a text mining method to retrieve information from scientific journal papers on the related topics. The text information is then analyzed through machine learning (ML) models to identify the important implications from the existing literature. Findings: The research findings are three-fold. While challenges are of concern, the focus should be given to the design and implementation of blockchain in the supply chain field. Integration with internet of things is considered to be of higher importance. Blockchain plays a crucial role in food sustainability. Research limitations/implications: The research findings offer insights for both policymakers and business managers on blockchain implementation in the supply chain. Practical implications: This paper exemplifies the model as situated in the interface of human-based and machine-learned analysis, potentially offering an interesting and relevant avenue for blockchain and supply chain management researchers. Originality/value: To the best of the knowledge, the research is the very first attempt to apply ML algorithms to analyzing the full contents of blockchain-related research, in the supply chain sector, thereby providing new insights and complementing existing literature.

Shipping, like most industries, is undergoing a digital transformation process which influences existing business models and operational practices, in a multifaceted way. Today, the shipping business context has been changing to incorporate further social demands, environmental, innovation and sustainability priorities, into fundamental shipping strategies, while taking advantage of technological advancements. In the era of Industry 4.0, which constitutes a recent evolution of advanced communications and information technologies and further promotes sustainable, human-centric, and resilient business development strategies, shipping and port entities need to embrace a broader perspective and a deeper understanding of various elemental technologies, namely: Artificial Intelligence, Blockchain, Cloud Computing, Big Data, and Physical Internet, in addition to core maritime logistics matters. This chapter proposes a descriptive framework of shipping digitalization and port automation, while providing a review of related technologies and business approaches, also international initiatives, for automation in global ports. Hence the chapter offers insights for business practitioners to steer through the current challenging global environment, also for policy makers to gain a more informed understanding of maritime logistics developments, towards necessary coordination and oversight mechanisms implementation.