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.
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.
Supply chain researchers are confronted with a dizzying array of research questions, many of which are not mutually independent. This research was motivated by the need to map the landscape of research themes, identify potential overlapping areas and interactions, and provide guidelines on areas of focus for researchers to pursue. We conducted a three-phase research study, beginning with an open-ended collection of opinions on research themes collected from 102 supply chain management (SCM) researchers, followed by an evaluation of a consolidated list of themes by 141 SCM researchers. These results were then reviewed by 10 SCM scholars. Potential interactions and areas of overlap were identified, classified, and integrated into a compelling set of ideas for future research in the field of SCM. We believe these ideas provide a forward-looking view on those themes that will become important, as well as those that researchers believe should be focused on. While areas of research deemed to become most important include big data and analytics, the most under-researched areas include efforts that target the “people dimension” of SCM, ethical issues and internal integration. The themes are discussed in the context of current developments that the authors believe will provide a valuable foundation for future research.
Purpose This study aims to explore how operational resilience can be achieved within supply ecosystems in the delicate yet harsh natural environments of the Arctic. Design/methodology/approach An in-depth, multiple qualitative case study of offshore supply operations in Arctic oil and gas field projects is conducted. Data from semi-structured interviews, personal observations and archival materials are analyzed through institutional work and logics approaches. Findings The findings suggest that achieving social-ecological resilience depends on the interaction between social and natural (irreversible) systems, which are shaped and influenced by various institutional dynamics. Different resilience solutions were detected. Research limitations/implications This study develops a comprehensive understanding of how social-ecological resilience emerges in supply ecosystems through institutional dynamics. The study's empirical basis is limited to offshore oil and gas projects in the Arctic. However, due to anticipated future growth of Arctic economic activities, other types of supply ecosystems may benefit from the study's results.Originality/value This research contributes with empirical knowledge about how social-ecological resilience is created through institutional interaction within supply ecosystems to prevent disruptions of both social and ecological ecosystems under the harsh natural conditions of the Arctic.
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.
Manufacturing companies who ship goods globally often rely on external Logistics Service Providers (LSPs) to manage the containerization and transportation of their freight. Those LSPs are usually required to follow rules when deciding how to mix the goods in the containers, which complicates the planning task. In this paper, we study such a freight containerization problem with a specific type of cargo mixing requirements recurrently faced by an international LSP. We show that this problem can be formulated as a Multi-Class Constrained Variable Size Bin Packing Problem: given a set of items that all have a size and a fixed number of classes for which they can take certain values, the objective is to pack the items in a minimum-cost set of bins while ensuring that the size capacity and maximum number of distinct values per class are not exceeded in any of the bins. We propose two adapted and one novel greedy heuristics, as well as an Adaptive Large Neighborhood Search (ALNS) metaheuristic, to find feasible solutions to the problem. We also provide a pattern-based formulation that is used to obtain lower bounds using a Column Generation approach. Using three extensive datasets, including a novel one with up to 1000 items and 5 classes reflecting real industrial cases, we show that the novel greedy heuristic outperforms the adaptations of the existing ones and that our ALNS yields significantly better solutions than a commercial solver within a mandatory 5-minute time limit. Practical insights are given about the solutions for the industrial benchmark.
As a young researcher—admittedly a long time ago—I was struggling with capturing the connections of the many published articles on logistics management. Apparently, many articles dealt with the same topics, yet they were not the same. There seemed to be different viewpoints on what was a scientific contribution and what kinds of methods were allowed. Why did everyone not agree with these questions? Without an understanding of the different research traditions, it was difficult to compare results, validate them and create a meaningful discussion of the various contributions.
Within the Nordic research community, which consists of economists, engineers and management researchers, there were several answers to these questions. In this community, I learnt about systems from engineers. Then, it became clear that there are various philosophies of science that do not have the same understanding of what a theory is and what appropriate methods are. I was searching the philosophy of science literature for a systems perspective and the research implications of such a perspective. To my surprise, I did not find it here, but luckily, I came across a book about methodologies in business research where the systems approach was one of three approaches. I also learnt that, depending on the methodology, the term theory has a different meaning depending on the philosophy of science it originates from. That made a lot of sense to me. Understanding this better, I published my research on this topic in “Schools in Logistics Research” in 2004 (Gammelgaard, 2004). This rather old article is still my daily research guide.
Purpose
The purpose of this paper is to investigate the impact of cloud computing (CC) on supply chain management (SCM).
Design/methodology/approach
The paper is conceptual and based on a literature review and conceptual analysis.
Findings
Today, digital technology is the primary enabler of supply chain (SC) competitiveness. CC capabilities support competitive SC challenges through structural flexibility and responsiveness. An Internet platform based on CC and a digital ecosystem can serve as “information cross-docking” between SC stakeholders. In this way, the SC model is transformed from a traditional, linear model to a platform model with the simultaneous cooperation of all partners. Platform-based SCs will be a milestone in the evolution of SCM – here conceptualised as Supply Chain 3.0.
Research limitations/implications
Currently, SCs managed holistically in cyberspace are rare in practice, and therefore empirical evidence on how digital technologies impact SC competitiveness is required in future research.
Practical implications
This research generates insights that can help managers understand and develop the next generation of SCM with the use of CC, a modern and commonly available Information and Communication Technologies (ICT) tool.
Originality/value
The paper presents a conceptual basis of how CC enables structural flexibility of SCs through easy, real-time resource and capacity reconfiguration. CC not only reduces cost and increases flexibility but also offers an effective solution for disruptive new business models with the potential to revolutionise current SCM thinking.
The interest in sustainability in the maritime industry has been on the rise. Attention has shifted from how to develop and comply with environmental regulation and labour standards to a more integrated view on sustainable maritime transport that aims at incorporating sustainability in maritime firm strategies. The liner shipping industry, which has been at the forefront, plays a crucial role in global supply chains, with its commitment to sustainable maritime container transport gaining recognition. In particular, procurement relationships stand out as an area where sustainability can exert the most significant impact. Ocean transport is among the most widely outsourced services globally both by shippers and by freight forwarders. Unlike bulk transport, container ocean transport is always outsourced, as shippers do not use their own vessels. Yet, the selection criteria that logistics firms use regarding sustainability when choosing ocean transport service providers and the role of sustainability in value creation among shippers/freight forwarders and ocean transport providers have been scarcely explored.
This article delves into value creation via quality improvement and sustainability practices in ocean freight transport. Employing a case study of an ocean carrier, alongside interviews and survey data, it explores how liner shipping companies can leverage high-quality and sustainable operations to enhance service for their clients and create logistics value. A novel aspect of this study is the application of sustainable supplier management concepts to maritime logistics, highlighting how shippers’ sustainability requirements in sourcing ocean freight services shape procurement relationships and how shipping companies can employ sustainable procurement strategies for value creation.
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.