Our research highlights the current state and trends of artificial intelligence (AI) adoption in Denmark’s chartering, particularly in the dry bulk and tanker segments. Companies in the dry bulk sector are leading AI adoption, with the tanker segment closely following and adoption rates in our sample appear higher than national averages reported by consultancies. Most firms are in either the experimental phase or transitioning toward more integrated AI systems, often opting for hybrid models that allow them to maintain internal control over key processes. Factors such as company size and maturity also influence the pace and approach to AI adoption.AI is seen as a tool to enhance rather than replace jobs in the early stages of shipping operations, especially in pre-fixture activities. However, there is greater potential for automation and job substitution in the post-fixture phase, particularly in tasks such as contract (CP) management.
On the supply side, the market for maritime AI and software solutions is highly competitive and fragmented, with many providers offering diverse products. Recent consolidation trends reflect different strategies: some companies, like are specializing in core offerings, while others, like are diversifying into both SaaS and pure software models. These consolidations are not only intensifying competition but also fostering partnerships between rivals—a dynamic known as coopetition. Interestingly, some shipping firms are entering the software market themselves, signaling innovation in business models. Machine learning (ML) technologies are primarily used in pre-fixture tools (like email management and tracking), while generative AI is increasingly applied in post-fixture functions, particularly contract management.
The introduction of Marine Non-Indigenous Species (NIS) poses a significant threat to global marine biodiversity and ecosystems. To mitigate this risk, the Ballast Water Management Convention (BWMC) was adopted by the UN International Maritime Organisation (IMO), setting strict criteria for discharges of ballast water. However, the BWMC permits exemptions for shipping routes operating within a geographical area, known as a Same-Risk-Area (SRA). An SRA can be established in areas where a risk assessment (RA) can conclude that the spread of NIS via ballast water is low relative to the predicted natural dispersal. Despite the BWMC's requirement for RAs to be based on modelling of the natural dispersal of NIS, no standard procedures have been established. This paper presents a methodology utilizing biophysical modelling and marine connectivity analyses to conduct SRA RA and delineation. Focusing on the Kattegat and Øresund connecting the North Sea and Baltic Sea, we examine two SRA candidates spanning Danish and Swedish waters. We provide an example on how to conduct an RA including an RA summary, and addressing findings, challenges, and prospects. Our study aims to advance the development and adoption of consistent, transparent, and scientifically robust SRA assessments for effective ballast water management.
This article examines the theoretical and practical implications of artificial intelligence (AI) integration in supply chain management (SCM). AI has developed dramatically in recent years, embodied by the newest generation of large language models (LLM) that exhibit human-like capabilities in various domains. However, SCM as a discipline seems unprepared for this potential revolution, as existing perspectives do not capture the potential for disruption offered by AI tools. Moreover, AI integration in SCM is not only a technical but also a social process, influenced by human sensemaking and interpretation of AI systems. This article offers a novel theoretical lens called the AI Integration (AII) framework, which considers two key dimensions: the level of AI integration across the supply chain and the role of AI in decision-making. It also incorporates human meaning-making as an overlaying factor that shapes AI integration and disruption dynamics. The article demonstrates that different ways of integrating AI will lead to different kinds of disruptions, both in theory and practice. It also discusses the implications of AI integration for SCM theorizing and practice, highlighting the need for cross-disciplinary collaboration and sociotechnical perspectives.