While systematic literature reviews (SLRs) have contributed substantially to developing knowledge in fields such as medicine, they have made limited contributions to developing knowledge in the supply chain management domain. This is due to the ontological and epistemological idiosyncrasies of research in supply chain management, which need to be accounted for when retrieving, selecting, and synthesizing studies in an SLR. Therefore, we propose a new paradigm for SLRs in the supply chain domain that is based on both best practice and the unique attributes of doing supply chain management research. This approach involves exploring existing studies with attention to theoretical boundaries, units of analysis, sources of data, study contexts, definitions and the operationalization of constructs, as well as research methods, with the goal of refining or revising existing theory. This new paradigm will push supply chain management research to the frontier of current methodological standards and build a foundation for improving the contribution of future SLRs in the supply chain and adjacent management disciplines.
We consider the Tramp Ship Routing and Scheduling Problem (TSRSP) in which we plan routes for a fleet of tramp shipping vessels operating on a combined contract and spot market. Earlier research has been fragmented due to variations in the side constraints studied. Hence we present the first unified model that can handle speed optimization, chartering costs, bunker planning, and hull cleaning. The model is solved by column generation, where the columns represent the possible routes of a vessel, while the master problem keeps track of the binding constraints. The pricing problem is solved efficiently using a time–space graph and several dominance rules. Real-life instances with up to 40 vessels, 35 geographic regions, and four months planning horizon can be solved to optimality in less than half an hour. The optimized routes increase earnings by 7% compared to historical schedules. Furthermore, policy-makers can use the model as a simulation of a rational agent behavior.
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.
This white paper shows how small and medium sized companies (SME) involved in supply chains affiliated to the maritime industry and port industrial areas are challenged by the diffusion of technologies and managerial principles associated with Industry 4.0 with a special focus on blockchain technology. Blockchain technology creates potential for added value through transparency and auditability of data flows that arise through system decentralization, where intermediary parties such as a central authority will not store data or verify transactions. Instead of conventional workflows, the technology brings new approaches to collaboration by combining multiple parties with equality of data ownership. In doing so, blockchain technology challenges conventional rules of data ownership.
While attention on blockchain technology has been increasing, most blockchain projects are still under development. However, the technology gained ground in areas such as healthcare, governance, and supply chain management. This white paper focuses on the potentials and challenges of blockchain technology in maritime related supply chains.
Based on a discussion of industry preparedness for Industry 4.0, a taxonomy of blockchain adoption is presented. The taxonomy is based on two dimensions including: (1) the digital complexity of internal activities and (2) the degree of value chain integration between actors in the supply chain. The dimensions encompass four archetypes of behavior on blockchain adoption that are applied in the following analysis.
The potential for blockchain technology is increasingly evident in supply chain logistics and manufacturing that is often located in industrial areas such as ports. By studying blockchain potentials in Danish maritime SMEs, the findings reflect the currently limited insight into blockchain technology from the point of view of business actors. As shown in the study of three Danish supply chains, containers, seafood and recirculated plastics, there are low-hanging potentials to be realized through changes to the current technologies and systems in application.
This chapter presents the latest development in digital platforms for data sharing in Maritime Informatics as discussed in chapter 1—Responding to humanitarian and global concerns with digitally enabled supply chain visibility. Specifically, we use the TradeLens digital data sharing platform as a case study to illustrate the key actors in containerised global transport and the technical set-up (including the utilisation of a hybrid cloud, permissioned blockchain, and data exchange standards), the benefits and challenges for the individual types of actors, and the overall potential and future challenges of the TradeLens platform.
The potential of data sharing platforms is dependent on the wide adoption of the ecosystem. Today, there is a high interest for the TradeLens ecosystem, and many actors have already adopted the platform, due to the vast variety of benefits it provides to all actors in global trade. Regardless, some actors seem to face internal obstacles to adopting the platform, which are either low or high technical advancement. For these actors, a paradigm shift is necessary to move from a reactive to a proactive scheme enabled by a near real-time supply and logistics data network. Finally, we discuss the challenges of network collaboration.
In this study, we investigate the barriers and enablers companies face when they seek to establish a fully decarbonized supply chain from the ground up. While recent research on sustainable supply chain management has advanced our understanding of how existing supply chains can become more sustainable, there is less research on fully decarbonized supply chains that are designed carbon neutral to produce carbon neutral products. This research aims to expand that frontier by investigating the case of the emerging supply chain delivering fossil-neutral e- methanol to the shipping industry.
Reduction of carbon emissions is a societal challenge that demands concerted efforts. The maritime industry is no exception. This paper takes an ecosystem perspective and considers the question of how to enact the green transition of the maritime industry and explore the barriers and enablers of that goal. To this end, we conduct an exploratory case-study to investigate the maritime value chain by focusing on 9 major stakeholders and conducting more than 20 interviews. Our study reveals four continuous enablers and two essential enablers to establishing a functional green maritime ecosystem.
Acknowledgments: I thank Amanda Bille, Anna Land, Attila Marton, Britta Gammelgaard, Carl Marcus Wallenburg, Christian Durach, Christian Hendriksen, Christian Huber, Dane Pflueger, Elise Berlinski, Florian Kock, Frank Fürstenberg, Helen Peck, Ida Schrøder, Ingo Zettler, Jan Mouritsen, Jane Lister, Jennifer Rogan, Johan Lundin, Kamilla Barat, Lisa Paulsen, Lorraine van Halewijn, Marin Jovanovic, Michael Herburger, Paola Trevisan, Philip Beske-Janssen, Regina Brogna, Stefano Ponte, and Thomas Johnsen for their helpful suggestions and comments. I also thank Journal of Supply Chain Management’s editorial team, especially Mark Pagell and the anonymous associate editor, for their exceptionally constructive and thoughtful guidance. In particular, I thank Günther Metzger and Jean François Bianco, who have been very inspirational throughout my thought process. This research resulted from a collaboration between Copenhagen Business School and Nordakademie.
Purpose: The purpose of the paper is to identify the multiple types of data that can be collected and analyzed by practitioners across the cold chain, the ICT infrastructure required to enable data capture and how to utilize the data for decision making in cold chain logistics. Design/methodology/approach: Content analysis based literature review of 38 selected research articles, published between 2000 and 2016, was used to create an overview of data capture, technologies used for collection and sharing of data, and decision making that can be supported by the data, across the cold chain and for different types of perishable food products. Findings: There is a need to understand how continuous monitoring of conditions such as temperature, humidity, and vibration can be translated to support real-time assessment of quality, determination of actual remaining shelf life of products and use of those for decision making in cold chains. Firms across the cold chain need to adopt appropriate technologies suited to the specific contexts to capture data across the cold chain. Analysis of such data over longer periods can also unearth patterns of product deterioration under different transportation conditions, which can lead to redesigning the transportation network to minimize quality loss or to take precautions to avoid the adverse transportation conditions. Research limitations/implications: The findings need to be validated through further empirical research and modeling. There are opportunities to identify all relevant parameters to capture product condition as well as transaction data across the cold chain processes for fish, meat and dairy products. Such data can then be used for supply chain (SC) planning and pricing products in the retail stores based on product conditions and traceability information. Addressing some of the above research gaps will call for multi-disciplinary research involving food science and engineering, information technologies, computer science and logistics and SC management scholars. Practical implications: The findings of this research can be beneficial for multiple players involved in the cold chain like food processing companies, logistics service providers, ports and wholesalers and retailers to understand how data can be effectively used for better decision making in cold chain and to invest in the specific technologies, which will suit the purpose. To ensure adoption of data analytics across the cold chain, it is also important to identify the player in the cold chain, which will drive and coordinate the effort. Originality/value: This paper is one of the earliest to recognize the need for a comprehensive assessment for adoption and application of data analytics in cold chain management and provides directions for future research.
Most regulatory tools for low-carbon transition are jurisdiction-specific, respecting the principle of national sovereignty. Although possibly locally successful, they typically capture only scope 1 and scope 2 emissions. Value chains-related (scope 3) emissions remain largely unregulated. This is problematic, as global value chains are commonly organized across multiple jurisdictions with different climate policy ambitions. Products are often produced at different location than where they are consumed, and production-related emissions are transferred with the products. These emissions embedded in imported products amount to large volumes (e.g. in the EU estimated to about 30% of member state’s national emissions). This chapter gathers the scientific evidence on upstream scope 3 emissions and discusses the available regulatory toolbox for reducing those. Both private and public regulatory tools are represented as well as soft and hard regulatory tools, and modifications between those categories. The interactions between the various types of regulation are discussed with the aim to identify possible synergies and conflicts. The chapter takes the EU as its starting point and draws in examples from other jurisdictions where relevant.