Digital Twins in the Context of Seaports and Terminal Facilities

Neugebauer, Julian; Heilig, Leonard; Voß, Stefan Article — Published Version Digital Twins in the Context of Seaports and Terminal Facilities Flexible Services and Manufacturing Journal Provided in Cooperation with: Springer Nature Suggested Citation: Neugebauer, Julian; Heilig, Leonard; Voß, Stefan (2024) : Digital Twins in the Context of Seaports and Terminal Facilities, Flexible Services and Manufacturing Journal, ISSN 1936-6590, Springer US, New York, NY, Vol. 36, Iss. 3, pp. 821-917, https://doi.org/10.1007/s10696-023-09515-9 This Version is available at: https://hdl.handle.net/10419/315265 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Digital Twins in the Context of Seaports and Terminal Facilities Julian Neugebauer 1 Leonard Heilig 1 Stefan Voß 1 Accepted: 25 September 2023 / Published online: 13 January 2024 The Author(s) 2024 Abstract Increasing freight volumes and challenging environments in seaports and container terminals worldwide require streamlined and reliable operations. Digital twins are seen as important drivers of the digitalization in seaports by providing a basis for higher transparency, control and data-driven decision making. In this context, however, the concept is rarely studied, and implementation issues are not comprehensively discussed. The paper presents an exploratory study of digital twins in seaports based on a literature review and case studies. The analysis reveals a standardization deficit for digital twin implementations, an inflationary and improper use of the term digital twin, and fields of research that need to be explored further. The application of optimization methods and the integration of simulation-based optimization in the field of seaports and container terminals is examined, due to its relevance for digital twins. Important lessons learned can be taken from the most advanced implementations, integrating simulations and emulations with optimization methods. An in-depth examination of multiple case studies and discussions with global port leaders yields valuable perspectives on the varied levels of digital twin implementations being applied today, including insights into the most advanced implementations currently being used in ports and container terminals. As a result of the analyses conducted, various research directions and a research agenda are presented. Keywords Digital twin · Maritime logistics · Seaports · Container terminal · Use cases · Literature review Julian Neugebauer [email protected] 1 Institute of Information Systems, University of Hamburg, Von-Melle-Park 5, 20146 Hamburg, Germany 123 Flexible Services and Manufacturing Journal (2024) 36:821–917 https://doi.org/10.1007/s10696-023-09515-9(0123456789().,-volV)(0123456789().,-volV) 1 Introduction Besides integrating new and improved infrastructure and equipment, digital transformation is seen as a key component for planning and improving operational processes in seaports (Karas 2020; Heilig et al. 2017a). The need for further optimization measures has grown steadily in parallel with the worldwide increase in container throughput (The World Bank 2020). After decades of streamlining port processes and operations, the potential of further improvements are seen especially in automation and the utilization of data to facilitate (near) real time and data-driven decision making (Heilig et al. 2017b). The digital twin is considered a key concept of Industry 4.0 (Lasi et al. 2014) and strives to meet the increasing practical requirements. It enables physical systems to be mapped in the digital world. Digital twins combine multiple technologies and differ in the scope of the physical entity being digitized. From the representation of individual parts or components to the mapping of entire seaports or cities, digital twins can be used for creating digital models of objects and their processes as well as environments (Fuller et al. 2020). 1 In recent years, digital twins have become increasingly important in the port industry (Port Technology 2021) and in research (Madusanka et al. 2023). By increasing efficiency and helping to restructure logistical processes, digital twins are seen as a part of future ports and the maritime sector in general (Lind et al. 2020). They can be viewed as a tool for connecting a variety of research methods and areas, such as simulation or machine learning (ML) and technologies, thus facilitating the combination of the two. Offering a common platform for analyzing data from multiple sources allows researchers as well as practitioners to gain a deeper understanding of the system and its behavior and to identify patterns and trends that may not be immediately apparent from a single data source. Combined with comprehensive models, the behavior of the system under various conditions can then be simulated and analyzed to explore and test new designs or configurations and to identify as well as solve potential problems before they occur in the real world. Solving problem before they occur with a digital twin is similar to prescriptive analytics and is enabled by automated data exchange in (near) real time as part of the digital twin (Bertsimas and Kallus 2020; Tian et al. 2023). Prescriptive analytics is focused on the optimization of processes, while digital twins focus on the optimization of complete physical objects with the underlying processes. Both fields are worth discussing, however, due to length limitations, prescriptive analytics will not be considered further below. As for the basic theories about digital twins, much has already been contributed to the body of knowledge (Jones et al. 2020; Barricelli et al. 2019), even to the extent of questioning whether definitions of digital twins are too broad (VanDerHorn and Mahadevan 2021). Nonetheless, focusing on a single technology is not part of these definitions. However, this is exactly what is usually described in the context of digital twins for seaports in the literature. Digital twins have many functions, which makes it 1 Due to the scope of the paper only digital twins in ports and terminal operations are discussed. For other applications in industry and manufacturing the reader is referred to Jiang et al. (2021); Qi and Tao (2018); Tao et al. (2019a). 123 822 J. Neugebauer et al. difficult to develop a unified understanding. As a result, there is neither a common definition nor a clear analysis of this research area in the context of seaports. This paper analyzes how far the current research on digital twins as well as practical applications have progressed in the field of seaports and container terminals. This is done as part of an exploratory study to help identify opportunities for using digital twins to improve container terminal and port operations, understand the potential and limitations of digital twins, identify challenges and barriers to their implementation or use and generate new ideas and concepts for their research and application. As such, this study can provide information about the development based on the current state and use of digital twins in container terminals. A dual focus is presented on the research area explored in the literature review and the application area examined in the literature, as well as through several case studies. The exploratory research focuses on four main areas: assessing underlying methods, technologies and approaches for creating and maintaining digital twins and their components and improving their accuracy and functionality; understanding the potential and limitations of digital twins in different contexts; identifying best practices for implementing and using digital twins in container terminals; and evaluating the impact and effectiveness of digital twins. For this approach, it is important to start with a brief introduction to the topic. Based on the available research, a clear definition is provided forming the basis for the following literature analysis and being the starting point for identifying problems and exploring them. These problems are later analyzed through interviews and by discussing real-world case studies. For this purpose, various international seaports are considered and interviews are conducted with decision makers from the port sector. The result of the examination of the practical applications is an overview of the state of the art with respect to digital twins in seaports. This yields new insights into hurdles that must be overcome and opportunities that arise from implementing digital twins. Furthermore, a contribution is made to the theoretical but also practical understanding of digital twins and an overview is given of current hotspot studies to support port operations, management and research. To this end, a detailed overview is provided using a structured approach, followed by an extensive analysis in terms of decision support. The result is an analysis of discrepancies and research gaps. The remainder of the paper is organized as follows. Starting with an introduction to the concept of digital twins in Sect. 2, this section elaborates on how research and practical implementations in the field of digital twins are interrelated. Afterwards, a literature review and major research contributions based on the methods used and the technologies employed for digital twins are presented in Sect. 3. Based on this, use cases are combined with a series of interviews involving port leaders from around the world to provide a practical perspective and discuss key findings. Through these assessments, the current scientific and practical state as well as the technologies and methods used are described in Sect. 4. The paper concludes by identifying potential areas for future research and development of digital twins in ports, with a particular focus on container terminals. It also offers practical insights and reflections for the utilization of digital twins in these fields and shows how different research areas and methods could foster the advancement of digital twins. 123 Digital Twins in Ports and Container Terminals 823 2 Background It is crucial to establish what a digital twin actually is. Although often also referred to as a “cyber-physical system”(CPS), a digital twin according to VanDerHorn and Mahadevan (2021) can be described as “virtual representation of a physical system (and its associated environment and processes) that is updated through the exchange of information between the physical and virtual system.”The definition is derived from a review of many definitions in the literature and represents the current state of research. In this context, a digital twin differs from a generic CPS by a higher degree of integration, synchronization and intelligence. It also represents a one-to-one relationship with the physical system (Tao et al. 2019b). In addition, digital twins enable a continuous feedback loop between the physical and virtual systems. They provide various functions such as simulation, optimization, prediction, diagnosis or control (Grieves and Vickers 2017; Boschert and Rosen 2016). The digital twin definition originates from Michael Grieves in 2002, who was the first to describe the idea of “dual systems in nature”in a way that the physical version and the digital or information version exist with each thing or system as a digital twin (Grieves 2019; Jones et al. 2020). The use of this definition by NASA in 2012 for describing the simulation model of complex parts manifested the term digital twin and led to the widespread adoption of the concept (Tao et al. 2019b). Over the course of the last decade the definitions for digital twins developed and led to misconceptions regarding the scope of digital twins, resulting in the common distinction of digital support systems, digital shadows and digital twins. As shown in Fig. 1, the flow of information between the physical and the digital object of a digital twin is fully integrated and automated in all directions, which in turn describes the characteristics of a digital twin. Thus, all three domains, the physical, the digital and the information domain are bilaterally connected. Each digital twin, therefore, contains a model of unspecified complexity that can imply a representative behavior of the twinned physical system or operation (Fuller et al. 2020). Complexity in the context of a mathematical model implemented in a digital Fig. 1 Differentiating the digital twin levels based on Fuller et al. (2020) 123 824 J. Neugebauer et al. twin refers to the level of detail and, e.g., the number of variables and equations included in the model. A digital twin is only realized when the underlying processes and changes from the operation automatically change the twin in (near) real time as well as when the twins’behavior and learning are automatically adopted in the physical operation. However, such systems with intermediate steps, such as a person reviewing the learnings of the digital twin and then automatically applying the results, can still be classified as a digital twin. Digital twins satisfying those definitions are referred to as strict digital twins in the following, in particular to illustrate the state of implementation. A simple example would be optimized route planning for an autonomous vehicle based on (near) real-time position monitoring and simulation of all other vehicles at a container terminal based on a model of the operations. Another example would be a model of a quay crane that tracks the crane’s movements in (near) real time and can also simulate the crane’s operation for many future shifts, so that wear and tear can be predicted and thus any parts that may be needed can be ordered in advance automatically. In contrast, a digital shadow is described as one-way integration, assuming the implied model. Since the digital shadow of the strict definition is often referred to as a digital twin in practice, it can also be considered a digital twin in a broader sense. An example is the integration of (near) real-time data that is enriched by the model, visualized in 3D and then manually analyzed. Practically, the model of a vehicle transporting containers could show, based on (near) real-time data, that continuing operation at the same speed could lead to overheating, whereupon the technical staff would tell the driver to slow down. Based on these definitions, the digital support system could be, e.g., a 3D model or a technical documentation file or just a static database record which is manually maintained. This means that there is no automated data flow or update between the digital model or system and the physical object or process. Digital twins generally form the basis for a (near) real-time and highly effective digital representation of objects or systems for further use in the context of optimization approaches, simulation or for other technological applications (Zheng et al. 2019). This is particularly advantageous because previous applications, such as (near) real-time analysis or simulation, were not technically feasible, due to the high computing requirements, or could not be sufficiently distinguished in research. In addition, for understanding the overarching concept of a digital twin, it is important to further differentiate the types of digital twins. Fundamentally, a distinction can be made according to the development as described by Grieves and Vickers (2017) or the type of thing that is digitally twinned. The types of digital twin modeling start with component or part twinning, where only a specific object is twinned, extending to product or asset twinning, which can include a combination of materials and parts, to the highest-level twinning, where entire systems or processes are represented by the digital twin. In terms of observation level, system or process twins can also be found in seaports and potentially in applications such as port community systems (PCS). Both of these systems consist of a variety of components, including infrastructure and superstructure, as well as various participants and their processes. 123 Digital Twins in Ports and Container Terminals 825 As seaports face high demands, many are undergoing digital transformation, although the extent of digitization varies. 2 Some key digital technologies that are being utilized in this process relate to the internet of things (IoT), big data, simulation and cloud computing, among others. The list of applicable technologies and concepts includes countless others, resulting in use cases such as port monitoring or smart maintenance of the terminal (Terziev et al. 2020). All of these support digitalization and continuous improvement, but are costly regarding the implementation time and the finances needed (PavlićSkender et al. 2020). Furthermore, without having a meaningful human and machine interface the benefits of the digitalization in seaports are minor. Properly implemented, however, the benefits of reducing costs and creating new revenue opportunities outweigh the difficulties that must be overcome in implementation (Min 2022). In addition, simulations in seaports based on the digitized systems can help to evaluate, improve and even generate more data to help replicate complex systems and advance them in the long term (Zhou et al. 2018). This will provide resources for automation and thus for further development in the seaport sector (Li et al. 2021c). The use of advanced IoT technologies and methodologies such as simulation, optimization, and mathematical modeling are essential components in the creation of digital twins. These tools allow for the construction of virtual representations of physical systems or processes, enabling the analysis and improvement of their performance in a virtual environment. While the combination of these technologies and methods is crucial for the creation of digital twins, it is important to note that digital twins represent more than just the sum of their parts. The combination and integration of the components of a digital twin are shown in Fig. 2, highlighting the importance of connecting technologies with scientific methods. A digital twin provides a holistic approach to understanding and improving complex systems and processes, by offering a comprehensive view that incorporates multiple technologies and methods. While the primary focus of digital twins is on improving processes and efficiency, they can also be useful for understanding and optimizing complex systems like ports. In fact, the position of vehicles, containers and ships can have an impact on the model contained within a digital twin of a port, making it a valuable tool for managing and improving the various processes and tasks that take place within this specialized area. In the context of seaports and container terminals, digital twins provide a framework to address specific challenges that are central to seaport or terminal operations and overall efficiency. These problems include, e.g., optimizing vehicle allocation, quay crane allocation, berth allocation, container storage, and traffic management. Research in this area is, e.g., focused on developing algorithms and models to efficiently address these challenges. Due to the large amount of work in these areas, only a few papers are described or cited below, mostly focusing on providing an overview. One of the most important areas of research is the 2 Digitization is the process of transforming analog data into digital formats, digitalization is the process of using technologies to improve business processes, and digital transformation is the process of creating new business processes as well as new models and value propositions based on digital technologies (Heilig et al. 2017a; Vrana and Singh 2021). All three have to be applied to facilitate the creation of a strict digital twin. 123 826 J. Neugebauer et al. optimization of vehicle allocation and scheduling within the terminal as, e.g., described by Chen et al. (2021). Efficiently allocating terminal vehicles such as automated guided vehicles (AGVs) and straddle carriers to different tasks is critical to reducing waiting times, minimizing congestion, and streamlining overall operations. To achieve this, researchers are exploring dynamic allocation strategies that take into account factors such as container location, priority, and driving distances (Böse et al. 2000; Gao et al. 2023). In addition, efficient allocation of berths is important for terminal and port operations. Optimal berth allocation ensures smooth vessel traffic and minimizes waiting times for ships. For these problems various constraints and parameters such as vessel types, handling requirements, and berth availability are taken into account (Bierwirth and Meisel 2015). Another important aspect of managing seaport terminals is the allocation of quay cranes to handle ships, as is discussed, e.g., by Yang et al. (2023). Researchers are working to develop advanced algorithms that optimize the allocation of quay cranes based on factors such as vessel size, cargo volume, and berth availability. By streamlining the use of quay cranes, terminals can significantly increase productivity and reduce vessel turnaround times. Related to this, container storage is an important area of research in seaport terminal management. Effective allocation of storage space within the terminal area is critical to reducing retrieval times, avoiding congestion, and maximizing space utilization (Chen and Lu 2012). All of these issues are tangential to digital twins and are addressed in many of the subsequently analyzed scientific papers. Knowledge of these challenges in seaports and container terminals serves to follow the literature analysis and exploration of practical use cases. The research on digital twins in the context of ports and terminals in particular will be examined in the next section. The literature review will also examine the extent to which the contemplated strict definition of a digital twin has been followed by research and industry in the area of seaports. 3 Literature review After defining digital twins in the field of seaports, a set of keywords was established as search terms for the literature search. The keywords were selected based on a sample of important works in the field and have been improved after the initial Fig. 2 Digital twin connecting applications and methods 123 Digital Twins in Ports and Container Terminals 827 searches were completed. The search was conducted using Scopus, Web of Science, IEEE Xplore and included the first 50 Google Scholar results. Afterwards, the papers were filtered and additions were made, either manually or through conducted forward and backward searches. In general, the search was limited to recent papers (2000– 2022) as the term digital twins has not been used in the past. In addition, only works in English were considered. 3 The process of searching can be summarized using the flowchart shown in Fig. 3. The literature review resulted in a set of 141 papers. Although different structured and semi-structured approaches to find all related literature were carefully applied, the completeness of the selected literature cannot be fully assured. 4 Nonetheless, the literature analyzed should provide a solid overview of the research domain and support the subsequent conclusions. After conducting the literature review, the papers were grouped according to the scientific methods and technologies used. In addition, the degree of understanding of the digital twin in the individual papers was analyzed. The result is a comprehensive overview of the entire research area of digital twins in seaports and terminals, which is visualized in Table 2. Due to the table’s extensive length it is placed in Appendix A. The table is organized into four general sections, starting with the reference and a brief summary of the research scope of each paper on the left. After that, the methods used and the technologies are classified. These are selected based on the respective discussion of technologies or methods in the obtained papers. The third section lists the functions of each digital twin mentioned in the paper. Each of these sections either contains a (·) indicating usage or they contain a (−), indicating that the methods are mentioned as example only. A blank cell indicates that nothing related to the column is mentioned or included in the analyzed paper. The fourth and right part of Table 2shows the classification of the digital twins based on the strict definition discussed in Sect. 2. It can be shown that less than a quarter of all analyzed papers propose a strict digital twin (labeled “Twin”in Table 2). As a result of the research, it is shown that the majority of works presents the digital twin as digital shadow, support system or defines a digital twin in the broader sense. More than a quarter of all analyzed papers do not contain a digital twin, shadow or support system in general, but rather discuss specific parts or methods of one. On the one hand, this could indicate the need for a clear definition and further research. On the other hand, this has been expected as research is often rather theoretical in nature and practical implementations and automatic data flows back to the physical systems cannot always be realized. This approach advocates for an exploratory research of the field of digital twins in container terminals and seaports, where a variety of research methodologies, approaches and general principles are employed in the mentioned literature with the aim of gaining a deeper understanding of the subject matter. Not only are established technologies and their practical applications evaluated, but the potential for future 3 To document this process and make the exclusions as transparent as possible, all search results and exemplary queries, separated by individual sources and keywords, are available as RIS files at: https:// github.com/Newgefarmer/digital_twins. 4 For a comprehensive discussion of structured literature reviews, which allows for better classification of the findings presented in this paper, see Pahl and Voß (2022). 123 828 J. Neugebauer et al. also at the tactical and strategic levels. In summary, there is still a lot of potential in the research areas of simulation and optimization methods. 3.1.3 Implementation and application The broadest area within the identified literature discusses the implementation or application of digital twins as well as parts of it (marked with a dot in column 3 of Table 2). Although it is strictly speaking not a scientific research area, it shows the applied concepts and is, therefore, part of this exploratory research. From papers applying 5G as an enabling technology to assessing various impacts of digital twins, there is a wide variety of works. It should be noted that the following is an overview of most, but not all, of the work on applied digital twins, listed in Table 2. Agostinelli et al. (2022a) and Cumo (2021) both apply digital twins and IoT in the context of maintenance and energy management, respectively. In the former, the solar and wind energy potential of the Port of Anzio (Italy) is analyzed based on data collected over the last 40 years. Energy consumption for lighting and power supply to parts of the port is planned to be improved as a result of the forecasts for energy generation. The results suggest lower GHG emissions due to a smarter use of renewable energy. Since the energy production data has already been collected, maintenance can also be improved. For example, a decrease in energy production under the same conditions could indicate the need to clean the solar panels. The approach of Cumo (2021) is similar and shows comparable results for the Ventotene Island Port (Italy) with greater focus on Building Information Modeling (BIM). In both papers, important parts of the port infrastructure are modeled in 3D and partially connected to data from IoT devices to enable transparency and more precise control. However, both papers only consider a digital shadow, since no automatic data flow back to the real system is implemented. This is in contrast to the literature dealing with the implementation of simulation-based digital twins. Gao et al. (2022b) focus on the automated yard and present special benefits of a digital twin application with respect to the simulation-based optimization. The application is divided into the control of machinery, display interfaces, programmable controllers and transmission lines. These are combined as “control and execution modules.”Hereby, it is shown how an implementation can be facilitated with the digital twin bridging the gap between the optimization results and actual operations in the terminal. Meanwhile, Hofmann and Branding (2019) also apply a simulation model with a dispatching algorithm based on the concept of starvation avoidance. The digital twin is used to evaluate different results of the dispatching algorithm in combination with (near) real time data from IoT devices. However, no automated application of the optimization results is performed. There is a paucity of literature on the topics discussed, especially when compared to literature dealing with a more general application of digital twins in the supply chain. Based on Busse et al. (2021), who present a framework for a universal digital supply chain twin, a commonly used perspective regarding the digital twin is its function as part of a general data space to connect a variety of actors. This can either be as part of a PCS, as part of a national single window (NSW), or regarding a general (industrial or maritime) data space. A PCS is generally described as an 123 Digital Twins in Ports and Container Terminals 835 interorganizational system supporting the exchange of port-related information and documents needed to efficiently manage port operations and procedures (Heilig and Voß 2017; Simoni et al. 2022). Data in the PCS can be incorporated into the digital twin and its model and vice versa. The integration of a digital twin is proposed to support collaboration among stakeholders with technical standards and a similar objective, as these are necessary for the implementation of the digital twin. It makes it easier for users to utilize business-related information collected through the platform. The same holds true for the NSW, which can provide a more standardized access for even more users not included in the PCS, e.g., governments (Rødseth and Berre 2018). The Data Space (DS) as described in various forms 9 is even more generalized as the PCS or NSW. It can contain all data mentioned above and data beyond that. Examples include automatic identification system (AIS) data provided by the port authority (Sarabia-Jacome et al. 2020) or information for the tracking and monitoring of containers throughout the supply chain (Rajput and Singh 2018). Therefore, the DS either supports the digital twin by providing even more data as input to each model or by sharing the twin itself within the DS. This would not be possible with a PCS or NSW, as these are used more as data hubs for interorganizational processes. Frequently mentioned problems concern data management and access rights control as well as finding the right management organization to which all stakeholders can entrust their data. None of the articles discuss standards, although it can be assumed that this must be a challenge for implementation. The results of implementing such a general DS are beneficial, as the example of the Port of Valencia (Spain) indicates. Sarabia-Jacome et al. (2019) indicate an improvement in interoperability and describe the generation of new port-wide KPIs that indicate the operational and overall efficiency of the seaport. Therefore, this supports better decision making regarding the included stakeholders of the DS. In most papers that mention a PCS, NSW or DS, only digital shadows are described or no other digital twin levels are discussed. Although the literature referenced in this section shows how the underlying technologies of digital twins are used, none of the papers describe an inclusion of the digital twin or how it could be made available to other stakeholders. It can, therefore, be concluded that further exploration is needed in this area. It is likely to be of interest, e.g., to make the simulation components of the digital twin available to other players in the seaport. This can be used, for instance, to simulate the estimated time of arrival of a truck at a container terminal based on traffic models provided by a port authority. The control of components via the digital twin of a container terminal, e.g., by third parties performing maintenance work, could also be analyzed. A more generalized view on digital twin implementations and applications in the context of smart ports and in Industry 4.0 is considered in many of the analyzed papers. Dalaklis et al. (2021) describe a smart port as an integration of intelligent trafficflow, information systems and digital infrastructures. They conduct a literature review and identify digital technologies used in ports around the world. As one of 9 The interested reader is referred to, e.g., Osório et al. (2019); Rajput and Singh (2018); Rødseth and Berre (2018); Sarabia-Jacome et al. (2019,2020) and Schislyaeva (2021) 123 836 J. Neugebauer et al. these technologies, the digital twin is characterized as a simulation of the port’s characteristics, changing several variables and testing them quickly and effectively. The results of the literature review are compared with a case study at the Port of Gothenburg, Sweden. The case study shows that from the use of sensors to monitor emissions caused by onboard power generation to automatic gates for trucks, the overall performance of the port is suggested to improve. However, it is clear from the overview of used technologies that the definition of a smart port is very vague and can encompass a wide range of technologies and levels of digitalization. Wang et al. (2021) focuses more on the combined application of the various technologies mentioning the digital twin not only as part of some port’s smart projects, but as a way of combining the technologies making up a smart port. This is done by developing a framework that divides the focus of managing the smart port processes into the physical-, data-, model-, serviceand application layers. Results include recommendations for standardization and a suggested focus on platform building. Furthermore, using the digital twin as a connecting construct is suggested. Especially the concept of having the digital twin as a connection, representing all states and allowing to simulate different situations at the port, can be found in Pang et al. (2021) and Pita Costa et al. (2021), albeit to varying degrees. Adding to these learnings with the focus on application and implementation of digital twins, Ibrion et al. (2019) discuss the possible risks of digital twins in the maritime domain. This is done in the context of learning from risks regarding the implementation of digital twins in aviation by focusing on problematic use cases. Results of this analysis include problems concerning sensor reliability and data quality, showing the importance of correct measurements, the risk of making decisions on the basis of wrong measurements and the importance of better or correct modeling, simulation and verification. Other risks considered by Ilin et al. (2022) include hacking or data manipulation, albeit with a greater focus on blockchain-based systems, showing that despite the existing risks, the potentials of digitalization are outweighed. This conclusion is also evident from the analysis of the North Sea Route as a whole, whose most important development step is suggested to be digitization according to Ilin et al. (2022). Interestingly, few authors discuss the risk associated with digital twins in global ports. There is a need to transfer research on digital twin risk to other areas. Besides Ilin et al. (2022), many other authors show a similar general need for digitalization and digital transformation, with the digital twin being a vital part of it. 10 In addition, in most cases, the described digital twins have a (near) real-time connectivity and a strong focus on visualization. The digitalization efforts, including the implementation of digital twins, are also included in the future development, e.g., for the Turkish Seaports (Özkanli and DeniZhan 2020) or the Port of Hamburg (Tesse et al. 2021). Altogether, most papers show a considerable need for implementing and applying digital twins in the seaport domain, albeit most often in the more general context of digital transformation as also discussed by Yang et al. (2023). Sanchez-Gonzalez et al. (2022) describe “quick-win applications”and include container tracking, virtual reality (VR) applications for maintenance or 10 The interested reader is referred to, e.g., Heilig et al. (2017a); Koroleva et al. (2019); Min (2022); PavlićSkender et al. (2020); Sanchez-Gonzalez et al. (2022); Wattanakul et al. (2022). 123 Digital Twins in Ports and Container Terminals 837 training, route optimization via ML as well as multiple applications of data analytics, especially concerning energy usage and emissions. All of these can be part of a digital twin application and contribute to the possible benefits for global seaports. Since they are the enabling part of digital twins these technologies get discussed further in the next section. 3.2 Research and use of digital twin enabling technologies Digital twins can incorporate a variety of technologies. Especially for the realization of strict digital twins in modern seaports, it has to be discussed which technologies can be of use in which respect. Therefore, the next section analyzes the different possibilities of applicable technologies, starting with the acquisition of data, the different possibilities of usage and visualization and ending with the storage and processing of the data. As the various areas covered would provide sufficient depth in their own right, the next section only considers an overview and the impact of the technologies on the digital twin. Moreover, the ambition is not to provide a complete list of all potentially useful technologies, but rather those that are most common. 3.2.1 Sensors, actuators and connectivity In general, mobile wireless connectivity is the first step in digitizing the port, as in most cases this needs to be established in order to receive data from infrastructure and moving objects, like vehicles, ships and cranes. To make this possible, different technologies can be used. WiFi is particularly interesting for terminals, while wireless communications and especially LTE and 5G are useful for all port stakeholders (Heilig and Voß 2017). In all cases, a high bandwidth network and low latencies are required to transmit the amounts of data needed to create a digital twin with higher accuracy. Transfers are often made over cellular as a backup when connection quality is poor. For larger areas, WiFi might be less practical, showing the advantage of fast cellular connections like 5G. For automation and for control, e.g., through a video transmission, 5G brings the necessary bandwidth and stability (Agarwala and Guduru 2021). Also the control of ships or the collection of sensor data from ships will only be possible with fast wireless connections, as stated in the Hamburg 5G trial (Rost et al. 2018). The use cases described above can be extended to those in terminals where remote crane operations and remote inspections as well as automated trucks can be realized and controlled based on 5G networking (Sun 2021). All in all, it is mobile connectivity that enables (near) real-time integration of physical systems and their digital twins. Sensors and many IoT devices are connected in the same way. The position of vehicles determined by GNSS, which includes GPS, Beidou, Galileo, etc., can also be transmitted like this. Positioning as part of the possible sensors directly shows what distinguishes the port and even more the terminal environment compared to digital twins used in other industrial applications, since the positioning gives the context information for the digital twin. In addition to GNSS, other methods of position determination can be used. Examples include laser, lidar or beacon-based 123 838 J. Neugebauer et al. systems, as well as differential GPS, and many others that can be improved by triangulation or trilateration where required (Balaji and Chaudhry 2018). Which other sensors can be deployed depends on the respective use cases and objectives. If tire pressures of vehicles (e.g., straddle carriers, reachstackers, rubbertyred gantry cranes) are to be transmitted, pressure sensors and temperature sensors must be used. For the monitoring of buildings and infra-/superstructure (e.g., quay cranes), ultrasonic sensors, strain gauges or various other sensors are helpful (Yang et al. 2018). The selected sensor should be considered according to the resulting detection capacities, implementation costs and accuracy. At the end of the digital twin cycle, actuators can be used to automatically adopt decisions made by the digital twin. These can be applied for controlling specific elements such as locks or traffic lights (Rost et al. 2018). Actuators thus provide means of (near) real-time interaction with the physical world through the digital twin. The connection and integration of the various collected information with the Internet consequently define the IoT (Min 2022). Depending on the number and capabilities of the sensors used, different levels of detail of the digital twin as well as automation can be realized. However, the data often needs to be aggregated and filtered before transferring it to the model of the physical object. In combination with data from the terminal operating system (TOS), the supervisory control and data acquisition (SCADA) 11 , the equipment control systems (ECS) for automated equipment or integrating the enterprise resource planning (ERP) system and others, the information from, e.g., vehicles, cranes and ships can be visualized and used to create models and simulate the various physical systems. Although different combinations of IoT-enabled devices need to be implemented depending on the purpose of the digital twin, there is no analysis in the literature of which systems and devices need to be implemented for which purpose of the digital twin. Comparisons of different implementations and combinations have not yet been conducted, indicating a potential future development in this particular area with respect to the start of digital twin integration. 3.2.2 Virtual reality, augmented reality and visualization Visualizations are important as an interface between users and digital systems. Because the complexity of digital twins can be substantial, dashboards and other 2D visualizations can have problems displaying all layers of the twin. Therefore, 3D visualizations are included in many digital twin realizations. Although visualization is not a necessary part of implementing the digital twin, it can often be beneficial. For instance, 3D visualization can be helpful for truckor vessel stowage planning, since all dimensions of the goods can be viewed (Aro et al. 2020). Generally, for the planning and monitoring of all operations, it can be a supporting tool that gives a better overview of the usually huge area that has to be managed in the port. For example, Wang et al. (2022) use these visualizations to show a machine room or container with (near) real-time data overlaid for temperature, pressure, and other IoT 11 SCADA describes a computer system with which technical processes in automated production can be monitored and controlled. 123 Digital Twins in Ports and Container Terminals 839 data. The general complexity and size of ports is certainly the reason why different forms of 3D visualization can be found in almost all use cases of the digital twin discussed in the analyzed literature. In addition, the visualizations can serve as a method for interpreting the results of simulations performed with other systems. VR systems are just another use of this type of visualization, although potentially even more intuitive for the user. The benefits resulting from an implementation will be especially high in the areas of maintenance and training (Sanchez-Gonzalez et al. 2022). AR technology, which creates another layer on top of physical objects, requires a different approach, as the recognition of the object or at least another layer than the previously mentioned visualization has to be implemented (PavlićSkender et al. 2020). This could be a reason why the analyzed literature mentions the visualization but does not discuss its implementation. However, this can be more helpful, as the analysis of Tam and Jones (2019) shows, because the overlaid information can be presented in an even more user-friendly way without having to create a complete separate visualization of the physical object. An example is navigation of a ship, where waypoints and markers on the sea are overlaid. Another example is visual confirmation of correct alignment for the operator of a container gantry crane, enabling faster operation. Since there are no publications yet that analyze AR systems to support a digital twin of a container terminal, the potential impact has yet to be discussed, even if the combined application of AR and digital twins is already happening in industry (Ericsson 2020). Certainly, it would be helpful for staff monitoring port operations to look out of the window and see the overlaid KPIs of the equipment working on the terminal. But the size of most modern container terminals, or ports in general, exceeds the areas visible from one position, which could lead to the conclusion that AR systems are simply not capable of visualizing or supporting the entirety in a helpful way. These potentially very different outcomes of an AR-based implementation suggest a need for further exploration and thus point to potential areas of further development. BIM is a completely different approach to not only visualizing but also modeling. Not only is an object (usually a building or infrastructure) digitized with a 3D model, but a variety of information can be included in the model. The Port of Anzio (Italy) uses this technique in combination with information from a geographic information system (GIS). Areas for the installation of solar panels can thus be identified and energy production modeled in combination with the geographical position of the solar panels (Agostinelli et al. 2022a). Combining GIS and BIM is encountered several times in the seaport sector, but mostly in relation to the integration of this data into the overall visualization or as a further basis for the digital twin. 12 Therefore, these data models can be a good starting point to make the construction of the digital twin more accessible, even if a model of the functions of the physical objects still needs to be created independently. Although the various types of visualization are not absolutely necessary for a digital twin, they help to prepare resulting additional information in a comprehensible way. Therefore, and due to the intuitive operation, visualization options are an 12 The interested reader is referred to, e.g., Cumo (2021); Yao et al. (2021a); Doleski et al. (2022); Wang et al. (2021). 123 840 J. Neugebauer et al. important part of digital twin considerations, especially in the context of seaports and container terminals, with the typically large areas that need to be monitored. 3.2.3 Big data and cloud computing No matter what system has been discussed in the above sections, required data must be processed, transformed, analyzed and provided again. Therefore, it is to be expected that most of the work analyzed is related to data lakes, cloud computing or big data in one form or another. Heilig et al. (2020) describe cloud computing as well as big data technologies as a basis for scalable data mining. Data has existed in all phases of port development, but with the ever increasing amount of data generated by IoT devices and the processing it requires, the term big data has taken hold. While many technologies must be linked to enable big data, typically only the resulting insights are of interest, which has shaped the term big data analytics (Henríquez et al. 2022). Compared to the technical domain regarding cloud computing, the actual outcomes and impacts are more often discussed in the literature on digital twins in the context of seaports. Cloud computing makes data accessible from any device, including handhelds and the resulting computing power supports decision making, simulations and enables digital twins, much like big data or any other advanced data storage (Brunetti et al. 2020). In summary, the broad application of big data in almost all of the literature analyzed emphasizes the importance of data processing, transformation, analysis and provision for various systems. The main focus is on the insights gained, hence the term big data analytics. In addition, cloud computing plays a key role in advanced data storage and decision making in various areas of digital twins for seaports and container terminals. 4 Case studies of digital twins in the seaport context In this section, digital twins in the context of seaports are considered both from the perspective of the selected literature and from a practical perspective. For the latter, interviews with 16 questions as a guideline were conducted with decision makers from container terminals and port authorities around the world as well as with researchers involved in practical projects. An unstructured interview style was chosen because it allows to represent the different levels of implementation and to take into account the exploratory nature of the areas studied. The aim is not to provide a complete list of all projects, but to provide an overview and look in particular at advanced digital twins in ports. In the following, an outline of practical projects is given, sorted by the digital twin implementation level. This is combined and put into perspective regarding the existing literature. 4.1 Digital twin use cases in seaports around the world About one third of the literature contains a use case showcasing the functions of the digital twin studied. It varies whether these use cases refer to a real use case, if several use cases are analyzed or if a fictional use case is considered. In this section, 123 Digital Twins in Ports and Container Terminals 841 the focus is on analyzing the various levels of digital twin implementation and their related implications. It is important to note that the term “level”is used appropriately, as only a portion of the selected papers discussing use cases propose a strict digital twin, while none outlines the implementation process. Furthermore, a comparison between theoretical work and practical projects or implementations is strived for. By comparing the two, the paper can provide a more complete picture of the state of digital twins in seaports and container terminals, and identify areas where additional research or development is needed. To further illustrate the feasibility, limitations, and real-world impact of the proposed solution, Table 1is created, also using the information from the conducted interviews. It shows the global ports being analyzed as part of the exploratory analysis and their progress on the implementation of digital twins and parties involved in research as well as the development of the digital twin (PA = Port Authorities; INS = Institutions and Universities; CT = Container Terminal Operators; BBT = Break Bulk Terminal Operators). For each item in the last column, an interview was conducted in addition to the analyzed literature (two separate interviews in Hamburg). Moreover, Table 1shows how different the levels of digital twins in seaports across the world are. For example, the largest seaports in Asia, Europe and North America have implemented a strict digital twin. Smaller but still large ports and terminals are working on or planning the integration and small ports have only implemented digital shadows or are still working on basic digitization. In addition, almost all of the digital twins in seaports are described and developed by or in collaboration with a university or research institute. This could be due to the research character especially regarding the modeling of digital twins, but also due to the approach of a literature analysis. A broad range of use cases in different ports applying a variety of technologies and connecting different parts and processes exists. As can be seen from Fig. 4, the range of applied technologies is broader in the practical domain. The majority of interviewed decision makers from the port domain named nine or more technologies which were or will soon be applied in their digital twin implementation. All incorporate IoT or sensor data and a system to process and analyze collected data, for which big data technologies are used. In addition, every digital twin implementation addressed in the interviews includes a 3D visualization, while this is an aspect not often considered in literature. Due to the similar shape of the technologies discussed and shown on the right side of Fig. 4, it can be assumed that the literature has a roughly similar view on the implementation of digital twins in the field of seaports and terminals. However, the generalized view of practical implementations is clear, in contrast to the more technology-specific view in the literature. Functions incorporated into the digital twin implementation include simulation, business intelligence and monitoring at nearly all ports surveyed. These are frequently addressed in the literature, although less often in combination with the multitude of other functions. The only topic that occurs more frequently is advanced optimization methods. This more holistic view of practical implementations might result from the fact that half of the surveyed ports have implemented strict digital twins, based on the definition of Sect. 2. The other half have implemented at least a 123 842 J. Neugebauer et al. Table 1 Classification of digital twin use cases in the analyzed ports Port of Mio. gross tons/year Described parts Parties included Summary Level of digital twin Sources (excl. interview) Interview Antwerp-Bruges, Belgium 289.3 Infrastructure, Seaside PA Geographical digital twin with AIS, BIM, GIS, radar, power and weather data, traffic management and automated drones. Predictions are currently being implemented Shadow (Twin in Future) PavlićSkender et al. (2020)  Anzio, Italy n.a. Infrastructure (Energy Systems) INS Evaluation of solar and wind potential with BIM and GIS data Shadow Agostinelli et al. (2022a) Barcelona, Spain 67.7 Terminal INS Adoption of Industry 4.0 technologies still limited. Improvement of overall port competitiveness as scope –Henríquez et al. (2022) Bremen, Germany 59,7 Terminal BBT Digital twin of a steel coil terminal with focus on positioning and automated booking in the TOS Shadow  Genoa, Italy 51.4 Terminal, Infrastructure INS, PA, CT Business process re-engineering with simulation-based validation. Energy management and forecasting Shadow Battilani et al. (2022), Damiani et al. (2019) Gothenburg, Sweden 39.0 Port Community CT, PA, INS Digitalization with a focus on the port community, describing features such as intelligent trafficflow and containerand vessel management –Dalaklis et al. (2021) Hamburg, Germany 128.7 Terminal, Infrastructure, Seaside CT, PA, INS Digital twin testbed of the port infrastructure including bridges, streets, traffic, etc. One container terminal is currently building a digital twin Digital Twin Ali-Tolppa and Kajo (2020), Harnischmacher et al. (2021), Özkanli and DeniZhan (2020), PavlićSkender et al. (2020), Rost et al. (2018), Tesse et al. (2021)  123 Digital Twins in Ports and Container Terminals 843 Table 1 continued Port of Mio. gross tons/year Described parts Parties included Summary Level of digital twin Sources (excl. interview) Interview Livorno, Italy 29.6 Terminal, Infrastructure, Seaside PA, INS Currently forecasting, monitoring and control tools implemented. Aim is to build a digital twin for ports, routes, environment, energy production and pollution Shadow (Twin in Future) Cavalli et al. (2021), Pagano et al. (2022)  Los Angeles + Long Beach, USA 256.2 Infrastructure, Seaside INS Implementation of a risk assessment tool and decision support system –Lennon et al. (2012), PavlićSkender et al. (2020) Multiple, Thailand n.a. Seaside INS Adoption of Industry 4.0 technologies still limited, simulations of port capacity are already created for a future digital twin. The scope is the implementation of autonomous equipment Shadow PavlićSkender et al. (2020), Wattanakul et al. (2022) Multiple, Turkey n.a. Terminal, Infrastructure INS Adoption of Industry 4.0 technologies only planned. Currently a guideline for future implementations of the technologies is being defined –Özkanli and DeniZhan (2020) Rotterdam, Netherlands 468.7 Terminal, Infrastructure, Seaside INS, CT, PA Digital twin of the complete container terminal processes and containers shipped with focus also on the port community Digital Twin Henríquez et al. (2022), Özkanli and DeniZhan (2020), PavlićSkender et al. (2020), Simoni et al. (2022), Taylor et al. (2021) Shanghai (Application also at Dalian), China 514.0 Terminal INS, CT Digital twin of the complete container terminal processes Digital Twin Li et al. (2021c), PavlićSkender et al. (2020), Yao et al. (2021a)  123 844 J. Neugebauer et al. realized from the outset. Projects such as PortML or TIC4.0 can be seen as pioneers in this regard. Other hurdles highlighted by interviewees were finding personnel with technical skills, including simulation component development and operational knowledge for the complex port or terminal processes. Furthermore, the methodology for developing a digital twin is not yet clear or even in place, as it is still very new and each digital twin is as different as the physical objects and processes. This means that the scope and size of the investments required to implement a digital twin are not entirely clear at the outset. The need to create an understanding of the novel digital twins and their technical implementation reinforces this problem. Decision makers may underestimate the financial outlay and technical requirements for creating the digital twin. The problem of finding good business cases for implementing a digital twin is similar. Discussing these hurdles might give the impression that the implementation is not worthwhile, making it equally important to look at practical strategies for implementing digital twins. During the interviews, it was suggested that a selection of initial use cases should be made during the start of the implementation to address issues related to communications, protocols, data exchange and security levels that need to be properly addressed from the beginning. Additionally, optimization methods should be considered separately at the operational and strategic levels. Operationally, optimization approaches could be considered, e.g., in terms of increasing twin movements, strategically, general processes and strategies can be considered, which could lead to various deeper and long-term changes. The digital twin could further help with optimization approaches by showing the current state as well as suggesting changes and trying out different approaches before making mediumto long-term decisions. This improved information flow and better understandability was cited by all respondents as a strategic goal for implementing the respective digital twins. A general framework, if one exists at all, could be helpful for port decision makers in implementing and applying digital twins in seaports and terminal facilities, but specific working methods may also provide support. These topics are discussed further in the next section. 5 Discussion and future research agenda The analysis aims to explore what the digital twin looks like in global ports. Literature in this regard is still scarce, but already shows the potential of the implementation and research domain, although the main focus varies depending on the particular scope as can be seen in Fig. 6, depicting a word cloud generated from all texts cited in Table 1. The terms “system,”“data”and “port”are used most frequently in the literature analyzed, but the wide use of the terms “model,”“service,”“information”and “operation”also indicates the focus on developing digital twins in the seaport domain. However, the deep implementation of models of seaports and the (near) real 123 Digital Twins in Ports and Container Terminals 851 time integration of the results obtained by the digital twin is almost absent from the literature so far. This is due to the fact that most research is rather theoretical and practical implementations are rarely published. However, the collaboration between institutions and ports shows that interdisciplinary research is already making an important contribution towards this area. It is important to keep in mind that digital twins are primarily to be understood as an application area and that, e.g., modeling or optimization methods within the digital twin are tangible research areas. The use cases considered show how far some seaports have come with the implementation of strict digital twins, but this is mainly the case with the largest ports. In contrast, the implementation of strict digital twins in smaller ports has not been described in literature. In general, the location of the port has a great impact on how advanced the implemented system and digital twin are, as it can be assumed that smaller ports have fewer resources available for developments. Since the field of research under consideration is quite new, especially in terms of implementation in seaports and terminals, there are still some areas that need to be explored. These areas include the actual benefits of implementing digital twins in seaports, which have yet to be quantified. Further, the development of a more general implementation framework needs to be explored, since currently all twins are very different and there is not a general guide for implementing such a system. This framework would describe the basic architecture needed based on the different goals of the implementation. Furthermore, once implementations are becoming broader, the sharing of digital twins and their models must be discussed as well as problems resulting from false models or decisions autonomously made on the basis of these. The implementation process itself is another area that should be examined more closely, as the way of working on such an implementation may involve workshops, the adoption of SCRUM or other project management methodologies such as the V-model, which are mentioned in the interviews. Determining a best possible approach, particularly in the port space or, comparing these approaches for implementing digital twins, may be an area for additional exploration. The parties involved in the processes described are different, which demonstrates the importance of having an appropriate working angle. This is shown by the results Fig. 6 Word cloud of literature on digital twins in seaports 123 852 J. Neugebauer et al. of the interviews. A digital twin combines real processes, planning information and technological tools, which means that employees from operations, information technology (IT) and other departments have to work closely together. Especially in the interviews with European ports, unions and work councils were mentioned as important groups which need to be integrated and informed about any project with a certain size. This is because increased transparency can often lead to more surveillance opportunities for the employer, which must be avoided according to the respective applicable law. In addition, employees working in planning often fear that a digital twin with greater optimizing possibilities could lead to their jobs being eliminated. The same applies to the employees affected by the optimization approaches, since ideally resources can be better used, therefore, reducing the need for crane operators, e.g.. Although this may be the case in future, these worries have to be addressed to facilitate the most effective use of the digital twin by all parties and for the optimization results to actually be trusted by the respective employees. The interviews showed that involving many parties and employee groups at an early stage and then focusing on the implementation of processes and their gradual implementation in the digital twin model can be a good starting point for any port or terminal that wants to implement a strict digital twin in the future. This should currently be true for almost all ports and terminals around the world. The technologies that are then used must be aligned with the goal of the digital twin and can be implemented one after the other. This also applies to the model of the digital twin and simulation or emulation, which can be developed to enable increasingly complex operations. In this way, a strict implementation of the digital twin can be done incrementally, lowering barriers to entry. Currently, many ports and terminals practice this, albeit perhaps unknowingly, as ports and terminals are already digitizing individual parts or implementing digital shadows that can become a strict digital twin in the future. With the increase in the number of implementations, research is becoming increasingly important to support the modeling and optimization approaches and, e.g., to develop new and faster simulation methods. In addition, research for applied technologies is getting more important, as the requirements for 5G networks, e.g., also increase with the complexity of the systems or processes to be interconnected. The small number of papers regarding research topics of digital twins in the special application areas of ports and container terminals is a research gap that needs to be closed. 6 Conclusion This paper presents a comprehensive study of the research and practical applications of digital twins in the context of seaports and container terminals. Subsequently, the main findings in the areas of implementation, modeling, optimization approaches, ML, simulation and applied technologies were reviewed based on a literature review and compared to practical use cases. It can be subsumed that a strict digital twin, as defined in Sect. 2, must include four parts: the integration of components or processes with their data in (near) real time, modeling of the twinned system with no specific minimum complexity required, decision support or optimization 123 Digital Twins in Ports and Container Terminals 853 functionality and automated application of the obtained decision back to the physical object or process being twinned. Creating an understanding of how a strict digital twin is defined, as well as how it is implemented in research and practice, are the main contributions of this work to the general body of knowledge focused on digital twins in seaports and terminal operations. In addition, the following learnings were identified. The literature analyzed shows an inconsistent state of research in the field of digital twins in terminals and seaports. Some papers, ports or terminals are still working on or describing basic digitization, while others are already implementing strict digital twins or researching complex methods to support the development of digital twins. This currently still leads to the incorrect use of the term digital twin, defined as “strict”digital twin in Sect. 2. The incorrect usage of the term digital twin shows the need, besides other identified open research and development areas, for further advancements in the field. The reasons for the frequent misuse of the term lie mainly in the novelty of the research area. Other reasons include the development of a strict digital twin, which is often too extensive and research subsequently focuses on partial areas. However, these should not be confused with a complete twin. In addition, for certain research areas related to digital twins, such as the implementation of sensor technology, only the consideration of data acquisition is relevant, so although this subject area can be part of a strict digital twin, it does not have to be, e.g., should no communication back to the physical object take place. With respect to the explored literature, it was noted that the largest area of research related to digital twins in the context of ports is the automated yard, specifically in regard to AGVs and ASCs. Additionally, one of the challenges identified in the literature is ensuring that the optimization approaches are aligned with the implementation and the use of ML techniques to enhance the accuracy of the digital twin’s simulation model. Furthermore, different mathematical modeling techniques for enabling performance and environmental evaluations, risk assessments and for supporting maintenance decisions, were identified as ways to improve efficiency, reduce risks and increase decision support in ports. After the literature review, the practical application of digital twins in seaports was studied through the analysis of use cases based on the conducted interviews with decision makers in the domain. The main goal of implementing digital twins at ports is to increase operational efficiency and reduce GHG emissions by minimizing travel and enforcing pollution regulations. Moreover, it was observed that while standardization is an objective for future systems, it is currently not a focal point in implementation. The functions and methodologies studied in the literature review were further discussed in the practical use cases, allowing for a deeper understanding of the challenges and opportunities of digital twin implementations in the context of seaports and container terminals. Given the increasing complexity and size of port systems and terminal infrastructures, the digital twin can be a useful tool to increase transparency and improve performance while reducing complexity. New interfaces are, on top of that, more user-friendly and facilitate the operations of future workforces that are already growing up with interfaces similar to modern video games as implemented in today’s most advanced terminals. However, this whole improvement and twinning cycle has 123 854 J. Neugebauer et al. a high degree of complexity. Therefore, it is proposed to split the different phases into different domains. For example, as already done for optimization problems in the container terminal, where the research is divided into optimization approaches supporting yard, berth, vessel planning, and more. However, a combination of these phases is also necessary for implementation and overarching research as well. Based on the practical projects and literature analyzed, several areas for further research and exploration have been identified. One area for further research is the quantification of the benefits of implementing strict digital twins in seaports. This could include a more detailed analysis of the cost savings, improved efficiency, and increased safety that can be achieved through digital twinning in ports. Another area for further investigation is the development of a general implementation framework for strict digital twins in seaports. This could include more detailed guidance on how to implement digital twins in ports, including determining a best-practice approach. The literature also highlighted the potential of sharing digital twins and their models among different stakeholders in the port ecosystem. Further research in this area could include the examination of the best practices for sharing strict digital twins and models, and the impact it has on collaboration and coordination among different parties. Additionally, further research could be conducted on the examination of the implementation process, including the investigation of the relationship between complexity and the level of detail in digital twin implementations. This could include a more in-depth analysis of the challenges and best practices for implementing digital twins in ports. Another area for further research is the advancement of optimization and simulation methods with large and always changing data sets. This could include the development of new methods and tools that can handle (near) real-time monitoring and prediction of issues that may arise in ports. Overall, the application of the potential benefits of federated digital partnerships that enable faster and more comprehensive modeling could be further explored. Finally, lessons learned from the literature review and the analysis of the use cases were discussed, highlighting the problems and opportunities of the implementation for all levels of digital twins. The need for further research in the field, specifically in terms of standardization and alignment of the models used for optimization approaches with the physical systems, was emphasized. With the increasing complexity and size of port systems and terminal infrastructures, the digital twin can be a valuable tool for increasing transparency, improving performance and reducing complexity. However, further advancements in the field are necessary to fully realize the potential of strict digital twins in ports and container terminals. Overview of the analyzed literature See Table 2. 123 Digital Twins in Ports and Container Terminals 855 Table 2 Overview and classification of the analyzed literature Reference Research scope Implementation and application methods Modeling and heuristics methods Simulation methods 5G/ LTE/ WiFi GPS/ GNSS IoT/ sensors Cameras Agarwala and Guduru (2021) Overview of 5G being used in commercial shipping, being analyzed from design to actual operations –   Agatićand Kolanović (2020) Seaport service quality and how it improves the digitalization  Agostinelli et al. (2022a) Optimizing maintenance and energy efficiency for renewables with the support of a digital twin for investigating and lowering GHG emissions – Agostinelli et al. (2022b) Renewable energy production systems in ports can improve sustainability and efficiency in urban development –– Ali-Tolppa and Kajo (2020) ML and optimization approaches to prevent service degradation of dynamic 5G coverage   Aro et al. (2020) Looking at the Baltic sea maritime industry and showing digital twins in the context of stowage and shipyards  Balaji and Chaudhry (2018) Better localization in ports using trilateration method combined with wireless sensor network (WSN) and simulation of the proposed technique  Bao et al. (2022) Asset management for decision making in industrial applications combined with an integrated digital twin frame  Battilani et al. (2022) Business process re-engineering with simulation-based validation  Boullauazan et al. (2022) Maturity model for smart ports with recommendations for a digitalization strategy – Brunetti et al. (2020) Looking at smart yards and giving a framework for simulation  123 856 J. Neugebauer et al. Table 2 continued Reference Research scope Implementation and application methods Modeling and heuristics methods Simulation methods 5G/ LTE/ WiFi GPS/ GNSS IoT/ sensors Cameras Busse et al. (2021) A framework for a digital twin for a complete supply chain  Cai et al. (2022) Digital twin application from the aspects of port operation business model and the cooperation of members mechanism  Carvalho et al. (2020) Propose a data and connectivity standard for easier communication and implementation of digital twins  Cavalli et al. (2021) Port KPIs improving by use of 5G-cellular  Chuprina et al. (2022) Learnings from the digitalization strategies of global seaports as support for governments and other authorities  Cumo (2021) The digital twin at the Ventotene Island Port (Italy) for facility management, predictive maintenance of infrastructure and for energy management – Dalaklis et al. (2021) Analysis of digital tools and technologies for future ports and giving an overview of ports around the world and the technologies already applied  Damiani et al. (2019) Energy purchasing optimization methods based on a port simulation and energy price forecasting  de la Peña Zarzuelo et al. (2020b) Technologies and tools of Industry 4.0 in the port literature being analyzed  Doleski et al. (2022) Energy system modeling for digital decarbonization in ports and logistic infrastructure  123 Digital Twins in Ports and Container Terminals 857 Table 2 continued Reference Research scope Implementation and application methods Modeling and heuristics methods Simulation methods 5G/ LTE/ WiFi GPS/ GNSS IoT/ sensors Cameras Douaioui et al. (2018) Looking at the four industrial revolutions and showing the need for further research, training and development of technologies in Industry 4.0  Du et al. (2022) 5G network simulation and evaluation based on a digital copy of a port –  Fahim et al. (2021) Track and trance as a basis for future ports and overall supplychain cooperation Gao et al. (2022a) Data governence specifically for IoT data  Gao et al. (2022c) Performance advantage of digital twin-based Q-learning solutions over Dijkstra’s algorithm for AGV path planning  Gao et al. (2022b) Simulation-based optimization for an automated storage yard in a container terminal supported by (near) real-time data of a digital twin – Gasparotti (2022) Demonstrate the benefits of introducing digital technologies in the Romanian maritime sector –   Gerlitz and Meyer (2021) Decision-making tools are proposed to facilitate and strengthen the transition in smalland medium-sized ports toward environmental responsibility, social equity, and economic efficiency – Giusti et al. (2019) Success factors and enabling technologies of synchromodality  Golovianko et al. (2021) Machine learning for image classification to replace human decision making – 123 858 J. Neugebauer et al. Table 2 continued Reference Research scope Implementation and application methods Modeling and heuristics methods Simulation methods 5G/ LTE/ WiFi GPS/ GNSS IoT/ sensors Cameras GonzálezRamírez et al. (2023) Decision support for container terminal operations with disturbances and analysis of main disturbances to port operations –– Gunes et al. (2021) Defining and managing cyber risks in ports regarding the inclusion of digital twins – Guo (2021) Digital twin for harbor hydraulic structures and concrete – Gültekin et al. (2022) Use of edge-based AI for autonomous vehicle fault detection and condition monitoring, reducing network requirements and shortening data transmission times ––  Harnischmacher et al. (2021) Supporting the power grid with AGV batteries and optimizing the dispatching based on the current grid power need  Heikkilä et al. (2022) Characterization of worldwide ports in the areas of automation, sustainability and collaboration. Showing the benefits of digital collaboration  Heilig et al. (2017a) A game theoretic framework for digitalization in seaports  Heilig et al. (2020) Data mining application in the port domain. Concluding that this research domain is yet to be developed more broadly · Heilig and Voß (2017) Overview of technologies used in smart ports and the drivers such as the PCS or the NSW  Henríquez et al. (2022) Explaining the relationship between the adoption of Industry 4.0 technologies and the seaports’business model  Hofmann and Branding (2019) Simulation-based digital twin in python  123 Digital Twins in Ports and Container Terminals 859 Table 2 continued Reference Research scope Implementation and application methods Modeling and heuristics methods Simulation methods 5G/ LTE/ WiFi GPS/ GNSS IoT/ sensors Cameras Höpfner et al. (2021) Dynamic storage space monitoring based on (near) real-time lidar information – Ibrion et al. (2019) Comparing aviation and maritime digital twins and showing the risks of digital twins and possible failures – Ichimura et al. (2022) It is discussed that all carriers are focused on digitalization  Ilin et al. (2022) Target architecture for the digitalization of the North Sea  Inkinen et al. (2021) Future prospects and scenarios as well as technological drivers in Finnish seaports  Jakovlev et al. (2021) A framework for increasing port safety and security using a digital twin  Krüger et al. (2021) Linking a static planning tool and a simulation for terminal operations including layout planning  Jedermann and Lang (2022) Showing low effort modeling and prediction methods for potential digital twins from a data perspective – Jeevan et al. (2021) It is discussed that Industry 4.0 needs investment, the right leadership and can result in greater competitiveness  Jost et al. (2022) Leverage geospatial context provided by LiDAR technology and methods to create digital twins  Karaś(2022) Literature review of smart ports, showing the importance of a standard smart port definition  Klar et al. (2022) Assessing the maturity of digital twins for ports and showing future digital twinning approaches  Koroleva et al. (2019) Comparing the digitalization of global and Russian ports  123 860 J. Neugebauer et al. Table 2 continued Reference Research scope Implementation and application methods Modeling and heuristics methods Simulation methods 5G/ LTE/ WiFi GPS/ GNSS IoT/ sensors Cameras Tubis and Poturaj (2022) Identify risks in AGV operations based on a literature review. The results show a deficiency in the identification and assessment of risks – ·  Uusitalo et al. (2021) Simulation of 5G network infra-structure to facilitate future port automation and the underlying technologies  Vakili et al. (2021) Development of a framework for a shipyard with the main objective of energy saving  Ramos Velasco et al. (2022) Transition suggestions for a fishing port, becoming less reliant on fishing – Wang et al. (2022) Ship engine system and cargo monitoring for process management using Maya and Unity 3D – Wang et al. (2021) Showing digital twins result in benefits for port management. Supporting decision making and improving resilience and performance  Wattanakul et al. (2022) Generating data with digital twins for ML regarding port capacity prediction under uncertainty  Wu et al. (2021) A digital twin of inland waterways based on 3D video fusion for improved efficiency of daily monitoring, evidence collection and emergency response  Yang et al. (2022a) Status monitoring and simulation to support container gantry cranes’transition to and decision on hydrogen power – Yang et al. (2022b) A case study on the digital twin of Qingdao Port and illustration of the development process and function of the digital twin for typical terminals –   Yang et al. (2018) Overview of underlying tech-nologies of IoT in smart ports and their challenges, e.g., comparing prices for data connectivity  123 Digital Twins in Ports and Container Terminals 867 Table 2 continued Reference Research scope Implementation and application methods Modeling and heuristics methods Simulation methods 5G/ LTE/ WiFi GPS/ GNSS IoT/ sensors Cameras Yang et al. (2022a) Overview of smart port development and future planning guidelines  Yao et al. (2021a) Digital twins and smart port concepts should be combined. Digital twin can improve the ports substantially  Yao et al. (2021b) Digital twin supported planning of automated smart ports  Zaychenko et al. (2021) Comparing Russian and global seaports regarding their digitalization level Yang et al. (2022b) Requirements and design principles for the digital twin in the port from a technical perspective  · Zhao et al. (2022) A digital twin for energy-efficient planning of multiple cranes and selection of the number of cranes is described – Zhou et al. (2022a) Review of current technology trends and relevant research topics related to the container terminals and examining the implications on how container terminals can achieve their strategic goals  Zhou et al. (2022b) A system for monitoring the operational status of port cranes based on digital twins is presented –  Zhou et al. (2018) Simulation-based optimization for improving maritime systems  Zhou et al. (2021) Digital twins and simulation for resilience improvement  123 868 J. Neugebauer et al. Table 2 continued Citation Research scope Lidar/laser Actuators Apps/ handheld-/ mobile devices 3D visualization VR/AR BIM Simulation software Agarwala and Guduru (2021) Overview of 5G being used in commercial shipping, being analyzed from design to actual operations  Agatićand Kolanović (2020) Seaport service quality and how it improves the digitalization  Agostinelli et al. (2022a) Optimizing maintenance and energy efficiency for renewables with the support of a digital twin for investigating and lowering GHG emissions  Agostinelli et al. (2022b) Renewable energy production systems in ports can improve sustainability and efficiency in urban development  Ali-Tolppa and Kajo (2020) ML and optimization approaches to prevent service degradation of dynamic 5G coverage  Aro et al. (2020) Looking at the Baltic sea maritime industry and showing digital twins in the context of stowage and shipyards   Balaji and Chaudhry (2018) Better localization in ports using trilateration method combined with WSN and simulation of the proposed technique Bao et al. (2022) Asset management for decision making in industrial applications combined with an integrated digital twin frame  Battilani et al. (2022) Business process re-engineering with simulation-based validation  Boullauazan et al. (2022) Maturity model for smart ports with recommendations for a digitalization strategy Brunetti et al. (2020) Looking at smart yards and giving a framework for simulation  123 Digital Twins in Ports and Container Terminals 869 Table 2 continued Citation Research scope Lidar/laser Actuators Apps/ handheld-/ mobile devices 3D visualization VR/AR BIM Simulation software Busse et al. (2021) A framework for a digital twin for a complete supply chain  Cai et al. (2022) Digital twin application from the aspects of port operation business model and the cooperation of members mechanism Carvalho et al. (2020) Propose a data and connectivity standard for easier communication and implementation of digital twins  Cavalli et al. (2021) Port KPIs improving by use of 5G-cellular  Chuprina et al. (2022) Learnings from the digitalization strategies of global seaports as support for governments and other authorities Cumo (2021) The digital twin at the Ventotene Island Port (Italy) for facility management, predictive maintenance of infrastructure and for energy management  Dalaklis et al. (2021) Analysis of digital tools and technologies for future ports and giving an overview of ports around the world and the technologies already applied  Damiani et al. (2019) Energy purchasing optimization methods based on a port simulation and energy price forecasting  de la Peña Zarzuelo et al. (2020b) Technologies and tools of Industry 4.0 in the port literature being analyzed  Doleski et al. (2022) Energy system modeling for digital decarbonization in ports and logistic infrastructure 123 870 J. Neugebauer et al. Table 2 continued Citation Research scope Lidar/laser Actuators Apps/ handheld-/ mobile devices 3D visualization VR/AR BIM Simulation software Douaioui et al. (2018) Looking at the four industrial revolutions and showing the need for further research, training and development of technologies in Industry 4.0   Du et al. (2022) 5G network simulation and evaluation based on a digital copy of a port  Fahim et al. (2021) Track and trance as a basis for future ports and overall supply-chain cooperation Gao et al. (2022a) Data governence specifically for IoT data Gao et al. (2022c) Performance advantage of digital twin-based Q-learning solutions over Dijkstra’s algorithm for AGV path planning  Gao et al. (2022b) Simulation-based optimization for an automated storage yard in a container terminal supported by (near) real time data of a digital twin  Gasparotti (2022) Demonstrate the benefits of introducing digital technologies in the Romanian maritime sector  Gerlitz and Meyer (2021) Decision-making tools are proposed to facilitate and strengthen the transition in smalland medium-sized ports toward environmental responsibility, social equity, and economic efficiency Giusti et al. (2019) Success factors and enabling technologies of synchromodality  Golovianko et al. (2021) Machine learning for image classification to replace human decision making González-Ramírez et al. (2023) Decision support for container terminal operations with disturbances and analysis of main disturbances to port operations  123 Digital Twins in Ports and Container Terminals 871 Table 2 continued Citation Research scope Lidar/laser Actuators Apps/ handheld-/ mobile devices 3D visualization VR/AR BIM Simulation software Gunes et al. (2021) Defining and managing cyber risks in ports regarding the inclusion of digital twins Guo (2021) Digital twin for harbor hydraulic structures and concrete  Gültekin et al. (2022) Use of edge-based AI for autonomous vehicle fault detection and condition monitoring, reducing network requirements and shortening data transmission times Harnischmacher et al. (2021) Supporting the power grid with AGV batteries and optimizing the dispatching based on the current grid power need  Heikkilä et al. (2022) Characterization of worldwide ports in the areas of automation, sustainability and collaboration. Showing the benefits of digital collaboration Heilig et al. (2017a) A game theoretic framework for digitalization in seaports  Heilig et al. (2020) Data mining application in the port domain. Concluding that this research domain is yet to be developed more broadly Heilig and Voß (2017) Overview of technologies used in smart ports and the drivers such as the PCS or the NSW  Henríquez et al. (2022) Explaining the relationship between the adoption of Industry 4.0 technologies and the seaports’business model  Hofmann and Branding (2019) Simulation-based digital twin in python  Höpfner et al. (2021) Dynamic storage space monitoring based on (near) realtime lidar information  123 872 J. Neugebauer et al. Table 2 continued Citation Research scope Lidar/laser Actuators Apps/ handheld-/ mobile devices 3D visualization VR/AR BIM Simulation software Ibrion et al. (2019) Comparing aviation and maritime digital twins and showing the risks of digital twins and possible failures Ichimura et al. (2022) It is discussed that all carriers are focused on digitalization  Ilin et al. (2022) Target architecture for the digitalization of the North Sea Inkinen et al. (2021) Future prospects and scenarios as well as technological drivers in Finnish seaports  Jakovlev et al. (2021) A framework for increasing port safety and security using a digital twin Krüger et al. (2021) Linking a static planning tool and a simulation for terminal operations including layout planning  Jedermann and Lang (2022) Showing low effort modeling and prediction methods for potential digital twins from a data perspective Jeevan et al. (2021) It is discussed that Industry 4.0 needs investment, the right leadership and can result in greater competitiveness  Jost et al. (2022) Leverage geospatial context provided by LiDAR technology and methods to create digital twins  Karaś(2022) Literature review of smart ports, showing the importance of a standard smart port definition Klar et al. (2022) Assessing the maturity of digital twins for ports and showing future digital twinning approaches  Koroleva et al. (2019) Comparing the digitalization of global and Russian ports  Kutzler et al. (2021) Using a digital twin of all information systems to improve IT-security 123 Digital Twins in Ports and Container Terminals 873 Table 2 continued Citation Research scope Lidar/laser Actuators Apps/ handheld-/ mobile devices 3D visualization VR/AR BIM Simulation software Lennon et al. (2012) Showing the advancements of the risk assessment tool and decision support system at the Port of Los Angeles/Long Beach (USA) Li et al. (2022) Modeling and reduction of energy consumption of container terminals considering, e.g., route optimization and integration of an economic benefit analysis Li and He (2020) Time predictions for berthing in container terminals using DNN Li and He (2021) A combination of automated logistics and deep learning for berth liner predictions and container terminal improvements Li et al. (2021a) Calculating and improving seaport operation performance with artificial neural networks Li and Song (2020) Discussing automated logistics in general and showing the importance based on general principles of computation for the future port including digital twins Li et al. (2021b) Analysis of the combination of ML, optimization and simulation methods in general in combination with digital twins  Li et al. (2020) Multiple simulation modules combined with a digital twin for supporting the Singapore port being planned   Li et al. (2021c) A digital twin supported terminal operation with higher efficiencies and application in AGV scheduling   Li et al. (2021d) Digital twinning supporting the AGV scheduling strategy  123 874 J. Neugebauer et al. Table 2 continued Citation Research scope Lidar/laser Actuators Apps/ handheld-/ mobile devices 3D visualization VR/AR BIM Simulation software Lind et al. (2018) Digitalization as an urgent need for optimizing communication between port actors. Amazon/Alibaba and others increasingly demanding digital services in the maritime transport domain Makkawan and Muangpan (2021) Indicators of port performance and digitalization level. Helping develop smart port performance  Mallah et al. (2020) Combining supply chain optimizing methods and industrial control systems for decision making and disturbance reaction at an export bulk port Maydanova et al. (2022) Balanced scorecard for implementing IT-architecture for digital twins  Medyakova et al. (2020) Studying digitalization efforts increasing because of a global pandemic (Covid-19)  Meyer et al. (2021) Sustainability and efficiency in small and medium sized ports in future development Mi and Liu (2022a) Maintenance using monitoring in smart ports  Mi and Liu (2022b) Overview of simulation for planning container ports. Differentiating between emulation and simulation  Min (2022) Technical port development guidelines, including integration of TOS, SCADA, ERP, sensors and many more  Morra et al. (2019) Use of a digital twin for planning alternative rail-based transportation in combination with the port area  Ni et al. (2021) AGV route and path planning based on local neural networks improving on scenarios with missing data 123 Digital Twins in Ports and Container Terminals 875 Table 2 continued Citation Research scope Lidar/laser Actuators Apps/ handheld-/ mobile devices 3D visualization VR/AR BIM Simulation software Nwakanma et al. (2022) Analysis of the prospects of introducing a digital twin in the Nigerian shipping industry  Osório et al. (2019) Showing an open integration architecture for IoT with multiple suppliers Othman (2021) Showing the correlation between Industry 4.0 application and seaport quality  Özkanli and DeniZhan (2020) Developing a roadmap for the Turkish seaports, which lack in digitalization Pacheco Bolaño and TroncosoPalacio (2021) Simulation of roll-on and -off unloading at a port for bottleneck analysis  Pagano et al. (2022) Showing the benefits of standardization for digitalization in seaports. Concluding that only few seaports are state of the art in regard to digitalization  Pang et al. (2021) Digital twin especially for production and product development in the maritime domain  Paulauskas et al. (2021) Showing a correlation between port size and digitalization level  PavlićSkender et al. (2020) Technologies used in the some of the biggest seaports worldwide   de la Peña Zarzuelo et al. (2020a) Describing simulation over the course of the digitalization in ports  Pita Costa et al. (2021) Learnings from developing smart ports and showing a rising need for sustainable digitalization in ports  123 876 J. Neugebauer et al. Table 2 continued Citation Research scope Data lake/ big data ML Predictive APIs Mirroring function Augmented visualization function Monitoring/ alerting function Control function Busse et al. (2021) A framework for a digital twin for a complete supply chain    Cai et al. (2022) Digital twin application from the aspects of port operation business model and the cooperation of members mechanism  Carvalho et al. (2020) Propose a data and connectivity standard for easier communication and implementation of digital twins Cavalli et al. (2021) Port KPIs improving by use of 5G-cellular  Chuprina et al. (2022) Learnings from the digitalization strategies of global seaports as support for governments and other authorities  Cumo (2021) The digital twin at the Ventotene Island Port (Italy) for facility management, predictive maintenance of infrastructure and for energy management    Dalaklis et al. (2021) Analysis of digital tools and technologies for future ports and giving an overview of ports around the world and the technologies already applied    Damiani et al. (2019) Energy purchasing optimization methods based on a port simulation and energy price forecasting  de la Peña Zarzuelo et al. (2020b) Technologies and tools of Industry 4.0 in the port literature being analyzed  Doleski et al. (2022) Energy system modeling for digital decarbonization in ports and logistic infrastructure 123 Digital Twins in Ports and Container Terminals 883 Table 2 continued Citation Research scope Data lake/ big data ML Predictive APIs Mirroring function Augmented visualization function Monitoring/ alerting function Control function Douaioui et al. (2018) Looking at the four industrial revolutions and showing the need for further research, training and development of technologies in Industry 4.0  Du et al. (2022) 5G network simulation and evaluation based on a digital copy of a port  Fahim et al. (2021) Track and trance as a basis for future ports and overall supply-chain cooperation  Gao et al. (2022a) Data governence specifically for IoT data  Gao et al. (2022c) Performance advantage of digital twin-based Q-learning solutions over Dijkstra’s algorithm for AGV path planning  Gao et al. (2022b) Simulation-based optimization for an automated storage yard in a container terminal supported by (near) realtime data of a digital twin  Gasparotti (2022) Demonstrate the benefits of introducing digital technologies in the Romanian maritime sector  Gerlitz and Meyer (2021) Decision-making tools are proposed to facilitate and strengthen the transition in smalland medium-sized ports toward environmental responsibility, social equity, and economic efficiency  Giusti et al. (2019) Success factors and enabling technologies of synchromodality Golovianko et al. (2021) Machine learning for image classification to replace human decision making  González-Ramírez et al. (2023) Decision support for container terminal operations with disturbances and analysis of main disturbances to port operations  123 884 J. Neugebauer et al. Table 2 continued Citation Research scope Data lake/ big data ML Predictive APIs Mirroring function Augmented visualization function Monitoring/ alerting function Control function Gunes et al. (2021) Defining and managing cyber risks in ports regarding the inclusion of digital twins  Guo (2021) Digital twin for harbor hydraulic structures and concrete  Gültekin et al. (2022) Use of edge-based AI for autonomous vehicle fault detection and condition monitoring, reducing network requirements and shortening data transmission times  Harnischmacher et al. (2021) Supporting the power grid with AGV batteries and optimizing the dispatching based on the current grid power need  Heikkilä et al. (2022) Characterization of worldwide ports in the areas of automation, sustainability and collaboration. Showing the benefits of digital collaboration    Heilig et al. (2017a) A game theoretic framework for digitalization in seaports  Heilig et al. (2020) Data mining application in the port domain. Concluding that this research domain is yet to be developed more broadly  Heilig and Voß (2017) Overview of technologies used in smart ports and the drivers such as the PCS or the NSW   Henríquez et al. (2022) Explaining the relationship between the adoption of Industry 4.0 technologies and the seaports’business model   Hofmann and Branding (2019) Simulation-based digital twin in python  Höpfner et al. (2021) Dynamic storage space monitoring based on (near) realtime lidar information  123 Digital Twins in Ports and Container Terminals 885 Table 2 continued Citation Research scope Data lake/ big data ML Predictive APIs Mirroring function Augmented visualization function Monitoring/ alerting function Control function Ibrion et al. (2019) Comparing aviation and maritime digital twins and showing the risks of digital twins and possible failures   Ichimura et al. (2022) It is discussed that all carriers are focused on digitalization  Ilin et al. (2022) Target architecture for the digitalization of the North Sea  Inkinen et al. (2021) Future prospects and scenarios as well as technological drivers in Finnish seaports  Jakovlev et al. (2021) A framework for increasing port safety and security using a digital twin    Krüger et al. (2021) Linking a static planning tool and a simulation for terminal operations including layout planning Jedermann and Lang (2022) Showing low effort modeling and prediction methods for potential digital twins from a data perspective  Jeevan et al. (2021) It is discussed that Industry 4.0 needs investment, the right leadership and can result in greater competitiveness  Jost et al. (2022) Leverage geospatial context provided by LiDAR technology and methods to create digital twins  Karaś(2022) Literature review of smart ports, showing the importance of a standard smart port definition  Klar et al. (2022) Assessing the maturity of digital twins for ports and showing future digital twinning approaches  Koroleva et al. (2019) Comparing the digitalization of global and Russian ports   Kutzler et al. (2021) Using a digital twin of all information systems to improve IT-security  123 886 J. Neugebauer et al. Table 2 continued Citation Research scope Data lake/ big data ML Predictive APIs Mirroring function Augmented visualization function Monitoring/ alerting function Control function Lennon et al. (2012) Showing the advancements of the risk assessment tool and decision support system at the Port of Los Angeles/Long Beach/Long Beach (USA)  Li et al. (2022) Modeling and reduction of energy consumption of container terminals considering, e.g., route optimization and integration of an economic benefit analysis  Li and He (2020) Time predictions for berthing in container terminals using (DNN)  Li and He (2021) A combination of automated logistics and deep learning for berth liner predictions and container terminal improvements  Li et al. (2021a) Calculating and improving seaport operation performance with artificial neural networks  Li and Song (2020) Discussing automated logistics in general and showing the importance based on general principles of computation for the future port including digital twins Li et al. (2021b) Analysis of the combination of ML, optimization and simulation methods in general in combination with digital twins  Li et al. (2020) Multiple simulation modules combined with a digital twin for supporting the Singapore port being planned  Li et al. (2021c) A digital twin supported terminal operation with higher efficiencies and application in AGV scheduling     Li et al. (2021d) Digital twinning supporting the AGV scheduling strategy  123 Digital Twins in Ports and Container Terminals 887 Table 2 continued Citation Research scope Data lake/ big data ML Predictive APIs Mirroring function Augmented visualization function Monitoring/ alerting function Control function Lind et al. (2018) Digitalization as an urgent need for optimizing communication between port actors. Amazon/Alibaba and others increasingly demanding digital services in the maritime transport domain  Makkawan and Muangpan (2021) Indicators of port performance and digitalization level. Helping develop smart port performance   Mallah et al. (2020) Combining supply chain optimizing methods and industrial control systems for decision making and disturbance reaction at an export bulk port   Maydanova et al. (2022) Balanced scorecard for implementing IT-architecture for digital twins  Medyakova et al. (2020) Studying digitalization efforts increasing because of a global pandemic (Covid-19)  Meyer et al. (2021) Sustainability and efficiency in small and medium sized ports in future development Mi and Liu (2022a) Maintenance using monitoring in smart ports   Mi and Liu (2022b) Overview of simulation for planning container ports. Differentiating between emulation and simulation Min (2022) Technical port development guidelines, including integration of TOS, SCADA, ERP, Sensors and many more    Morra et al. (2019) Use of a digital twin for planning alternative rail-based transportation in combination with the port area  Ni et al. (2021) AGV route and path planning based on local neural networks improving on scenarios with missing data   123 888 J. Neugebauer et al. Table 2 continued Citation Research scope Data lake/ big data ML Predictive APIs Mirroring function Augmented visualization function Monitoring/ alerting function Control function Nwakanma et al. (2022) Analysis of the prospects of introducing a digital twin in the Nigerian shipping industry  Osório et al. (2019) Showing an open integration architecture for IoT with multiple suppliers  Othman (2021) Showing the correlation between Industry 4.0 application and seaport quality   Özkanli and DeniZhan (2020) Developing a roadmap for the Turkish seaports, which lack in digitalization  Pacheco Bolaño and TroncosoPalacio (2021) Simulation of role-on and -off unloading at a port for bottleneck analysis Pagano et al. (2022) Showing the benefits of standardization for digitalization in seaports. Concluding that only few seaports are state of the art in regard to digitalization  Pang et al. (2021) Digital twin especially for production and product development in the maritime domain  Paulauskas et al. (2021) Showing a correlation between port size and digitalization level    PavlićSkender et al. (2020) Technologies used in the some of the biggest seaports worldwide  de la Peña Zarzuelo et al. (2020a) Describing simulation over the course of the digitalization in ports  Pita Costa et al. (2021) Learnings from developing smart ports and showing a rising need for sustainable digitalization in ports   123 Digital Twins in Ports and Container Terminals 889 Table 2 continued Citation Research scope Data lake/ big data ML Predictive APIs Mirroring function Augmented visualization function Monitoring/ alerting function Control function Portapas et al. (2021) Analysis of autonomous ships for new transport routes and digital twins for their monitoring and evaluation    Pyykkö et al. (2020) A simulation-based environment for cybersecurity training in the maritime domain  Rajput and Singh (2018) Cyber physical systems (CPS) and their implications on supply chain management. Furthermore, discussing the implementation levels of CPS’s  Ramirez et al. (2022) Network focused implementation of a basic digital twin using 5G  Rødseth and Berre (2018) Describing the need for an industrial data space in the maritime domain to support the data exchange while using digital twins   Ross et al. (2022) Analysis of a conceptual implementation of a digital twin at a British dockyard with a focus on multiobjective optimization  Rost et al. (2018) A few 5G enabled applications being tested in Hamburg (Germany)  Sadri et al. (2021) Port KPIs analyzing greenness and progressiveness of ports  Mohd Salleh et al. (2021) Defining the state of Malaysian seaport readiness regarding Industry 4.0 Garrido Salsas et al. (2022) General development perspective for the Port of Barcelona Sanchez-Gonzalez et al. (2022) Analyzing the most effective digitalization steps for maritime container handling companies    123 890 J. Neugebauer et al. Table 2 continued Citation Research scope Data lake/ big data ML Predictive APIs Mirroring function Augmented visualization function Monitoring/ alerting function Control function Sarabia-Jacome et al. (2019) Defining a seaport data space architecture and analyzing it. A standardized way of information and data sharing for interoperability is also shown  Sarabia-Jacome et al. (2020) Potential of improving the communication and communication cost by implementing a seaport data space, also partially including digital twins  Schislyaeva (2021) Examining the elements of a digital twin in logistic hubs as part of infrastructure innovation  Segovia et al. (2022) A feasibility study on emissions reductions and demand response in a seaport buildings Shcherbakov and Silkina (2021) Supply chain management integration over multiple participants. Analyzing logistics platforms and ecosystems  Shi et al. (2022) Analysis of key objects and control mechanisms as a basis for future digital twins and terminal control models Short et al. (2022) Modeling of electric gantry cranes for regeneration capture  Simoni et al. (2022) Comparing port community systems in literature and in Rotterdam and showing the potential benefits  PavlićSkender et al. (2020) Overview of modern technologies in leading global seaports, showing that future developments will lead to greater automation   Song (2021) Analysis of the supply chain in container shipping, showing how it can be improved through less fragmentation and further digitalization  123 Digital Twins in Ports and Container Terminals 891 Table 2 continued Citation Research scope Data lake/ big data ML Predictive APIs Mirroring function Augmented visualization function Monitoring/ alerting function Control function Sun (2021) 5G powered applications for the smart port realization at the Mawan and Zhoushan port (China)    Szpytko and Duarte (2019) Modeling of gantry cranes and risk simulation for creating a decision support model for integrated maintenance  Szpytko and Duarte (2021) A framework for digital twins in crane maintenance and showing a decision support model    Tam and Jones (2019) Emerging technologies in the maritime domain and their cybersecurity risks  Tardo et al. (2022) Proposal for a prototyping framework for agile development of smart services in the port, based on a set of off-the-shelf and open-source technologies  Taylor et al. (2020) Digital twins being described regarding the general maritime application and usage  Taylor et al. (2021) Differentiating simulation and modeling and digital twins. Defining the benefits of (near) real-time-data integration of digital twins  Tedeschi and Sciancalepore (2019) Advantages and disadvantages of fog and edge computing with respect to security and research challenges  Tesse et al. (2021) Digital twin projects being analyzed for creating a digital twin metamodel, which is then used as a layer in strategic management of IoT cloud architecture     Trichias et al. (2021) The “Vital-5G”project is being described, implementing “NetApps”to showcase and support the creation of 5G-based solutions to monitor, automate and improve in different parts of port logistics    123 892 J. Neugebauer et al. Table 2 continued Citation Research scope Process automation function Business intelligence function Predictive analytics function Simulation function Optimization function Understanding of digital twin Ibrion et al. (2019) Comparing aviation and maritime digital twins and showing the risks of digital twins and possible failures  Shadow Ichimura et al. (2022) It is discussed that all carriers are focused on digitalization  Shadow Ilin et al. (2022) Target architecture for the digitalization of the North Sea Twin Inkinen et al. (2021) Future prospects and scenarios as well as technological drivers in Finnish seaports n.a Jakovlev et al. (2021) A framework for increasing port safety and security using a digital twin Shadow Krüger et al. (2021) Linking a static planning tool and a simulation for terminal operations including layout planning  n.a Jedermann and Lang (2022) Showing low effort modeling and prediction methods for potential digital twins from a data perspective   Shadow Jeevan et al. (2021) It is discussed that Industry 4.0 needs investment, the right leadership and can result in greater competitiveness Shadow Jost et al. (2022) Leverage geospatial context provided by LiDAR technology and methods to create digital twins   Twin Karaś(2022) Literature review of smart ports, showing the importance of a standard smart port definition n.a Klar et al. (2022) Assessing the maturity of digital twins for ports and showing future digital twinning approaches   Twin Koroleva et al. (2019) Comparing the digitalization of global and Russian ports Digital Support System Kutzler et al. (2021) Using a digital twin of all information systems to improve IT-security Shadow 123 Digital Twins in Ports and Container Terminals 899 Table 2 continued Citation Research scope Process automation function Business intelligence function Predictive analytics function Simulation function Optimization function Understanding of digital twin Lennon et al. (2012) Showing the advancements of the risk assessment tool and decision support system at the Port of Los Angeles/Long Beach (USA) n.a Li et al. (2022) Modeling and reduction of energy consumption of container terminals considering, e.g., route optimization and integration of an economic benefit analysis n.a Li and He (2020) Time predictions for berthing in container terminals using (DNN) n.a Li and He (2021) A combination of automated logistics and deep learning for berth liner predictions and container terminal improvements   Shadow/Twin Li et al. (2021a) Calculating and improving seaport operation performance with artificial neural networks   Shadow Li and Song (2020) Discussing automated logistics in general and showing the importance based on general principles of computation for the future port including digital twins   n.a Li et al. (2021b) Analysis of the combination of ML, optimization and simulation methods in general in combination with digital twins Shadow Li et al. (2020) Multiple simulation modules combined with a digital twin for supporting the Singapore port being planned  Twin Li et al. (2021c) A digital twin supported terminal operation with higher efficiencies and application in AGV scheduling   Shadow Li et al. (2021d) Digital twinning supporting the AGV scheduling strategy   Twin 123 900 J. Neugebauer et al. Table 2 continued Citation Research scope Process automation function Business intelligence function Predictive analytics function Simulation function Optimization function Understanding of digital twin Lind et al. (2018) Digitalization as an urgent need for optimizing communication between port actors. Amazon/Alibaba and others increasingly demanding digital services in the maritime transport domain n.a Makkawan and Muangpan (2021) Indicators of port performance and digitalization level. Helping develop smart port performance n.a Mallah et al. (2020) Combining supply chain optimizing methods and industrial control systems for decision making and disturbance reaction at an export bulk port   Digital Support System Maydanova et al. (2022) Balanced scorecard for implementing IT-architecture for digital twins  Shadow Medyakova et al. (2020) Studying digitalization efforts increasing because of a global pandemic (covid-19)  Shadow/Twin Meyer et al. (2021) Sustainability and efficiency in small and medium sized ports in future development n.a Mi and Liu (2022a) Maintenance using monitoring in smart ports Digital Support System Mi and Liu (2022b) Overview of simulation for planning container ports. Differentiating between emulation and simulation   Shadow Min (2022) Technical port development guidelines, including integration of TOS, SCADA, ERP, Sensors and many more   Twin Morra et al. (2019) Use of a digital twin for planning alternative rail-based transportation in combination with the port area Digital Support System Ni et al. (2021) AGV route and path planning based on local neural networks improving on scenarios with missing data n.a 123 Digital Twins in Ports and Container Terminals 901 Table 2 continued Citation Research scope Process automation function Business intelligence function Predictive analytics function Simulation function Optimization function Understanding of digital twin Nwakanma et al. (2022) Analysis of the prospects of introducing a digital twin in the Nigerian shipping industry ·Shadow Osório et al. (2019) Showing an open integration architecture for IoT with multiple suppliers  Shadow Othman (2021) Showing the correlation between Industry 4.0 application and seaport quality n.a Özkanli and DeniZhan (2020) Developing a roadmap for the Turkish seaports, which lack in digitalization Digital Support System Pacheco Bolaño and TroncosoPalacio (2021) Simulation of role-on and -off unloading at a port for bottleneck analysis Shadow Pagano et al. (2022) Showing the benefits of standardization for digitalization in seaports. Concluding that only few seaports are state of the art in regard to digitalization n.a Pang et al. (2021) Digital twin especially for production and product development in the maritime domain Twin Paulauskas et al. (2021) Showing a correlation between port size and digitalization level n.a PavlićSkender et al. (2020) Technologies used in the some of the biggest seaports worldwide   Digital Support System/Shadow de la Peña Zarzuelo et al. (2020a) Describing simulation over the course of the digitalization in ports  Shadow Pita Costa et al. (2021) Learnings from developing smart ports and showing a rising need for sustainable digitalization in ports   Shadow 123 902 J. Neugebauer et al. Table 2 continued Citation Research scope Process automation function Business intelligence function Predictive analytics function Simulation function Optimization function Understanding of digital twin Portapas et al. (2021) Analysis of autonomous ships for new transport routes and digital twins for their monitoring and evaluation    Twin Pyykkö et al. (2020) A simulation-based environment for cybersecurity training in the maritime domain Digital Support System Rajput and Singh (2018) Cyber physical systems (CPS) and their implications on supply chain management. Furthermore, discussing the implementation levels of CPS’s   Twin Ramirez et al. (2022) Network focused implementation of a basic digital twin using 5G  Twin Rødseth and Berre (2018) Describing the need for an industrial data space in the maritime domain to support the data exchange while using digital twins  Shadow Ross et al. (2022) Analysis of a conceptual implementation of a digital twin at a British dockyard with a focus on multiobjective optimization Digital Support System Rost et al. (2018) A few 5G enabled applications being tested in Hamburg (Germany) Twin Sadri et al. (2021) Port KPIs analyzing greenness and progressiveness of ports n.a Mohd Salleh et al. (2021) Defining the state of Malaysian seaport readiness regarding Industry 4.0 n.a Garrido Salsas et al. (2022) General development perspective for the Port of Barcelona n.a Sanchez-Gonzalez et al. (2022) Analyzing the most effective digitalization steps for maritime container handling companies Twin 123 Digital Twins in Ports and Container Terminals 903 Table 2 continued Citation Research scope Process automation function Business intelligence function Predictive analytics function Simulation function Optimization function Understanding of digital twin Sarabia-Jacome et al. (2019) Defining a seaport data space architecture and analyzing it. A standardized way of information and data sharing for interoperability is also shown n.a Sarabia-Jacome et al. (2020) Potential of improving the communication and communication cost by implementing a seaport data space, also partially including digital twins n.a Schislyaeva (2021) Examining the elements of a digital twin in logistic hubs as part of infrastructure innovation   Shadow Segovia et al. (2022) A feasibility study on emissions reductions and demand response in a seaport buildings  Shadow Shcherbakov and Silkina (2021) Supply chain management integration over multiple participants. Analyzing logistics platforms and ecosystems n.a Shi et al. (2022) Analysis of key objects and control mechanisms as a basis for future digital twins and terminal control models n.a Short et al. (2022) Modeling of electric gantry cranes for regeneration capture Shadow Simoni et al. (2022) Comparing port community systems in literature and in Rotterdam and showing the potential benefits ·Shadow PavlićSkender et al. (2020) Overview of modern technologies in leading global seaports, showing that future developments will lead to greater automation Shadow Song (2021) Analysis of the supply chain in container shipping, showing how it can be improved through less fragmentation and further digitalization    Twin 123 904 J. Neugebauer et al. Table 2 continued Citation Research scope Process automation function Business intelligence function Predictive analytics function Simulation function Optimization function Understanding of digital twin Sun (2021) 5G powered applications for the smart port realization at the Mawan and Zhoushan port (China) Twin Szpytko and Duarte (2019) Modeling of gantry cranes and risk simulation for creating a decision support model for integrated maintenance Digital Support System/Shadow Szpytko and Duarte (2021) A framework for digital twins in crane maintenance and showing a decision support model   Digital Support System Tam and Jones (2019) Emerging technologies in the maritime domain and their cybersecurity risks Digital Support System Tardo et al. (2022) Proposal for a prototyping framework for agile development of smart services in the port, based on a set of off-the-shelf and open-source technologies Twin Taylor et al. (2020) Digital twins being described regarding the general maritime application and usage Shadow Taylor et al. (2021) Differentiating simulation and modeling and digital twins. Defining the benefits of (near) real-time -data integration of digital twins   Shadow Tedeschi and Sciancalepore (2019) Advantages and disadvantages of fog and edge computing with respect to security and research challenges    Shadow Tesse et al. (2021) Digital twin projects being analyzed for creating a digital twin metamodel, which is then used as a layer in strategic management of IoT cloud architecture   Shadow/Twin Trichias et al. (2021) The “Vital-5G”project is being described, implementing “NetApps”to showcase and support the creation of 5G-based solutions to monitor, automate and improve in different parts of port logistics Twin 123 Digital Twins in Ports and Container Terminals 905 Table 2 continued Citation Research scope Process automation function Business intelligence function Predictive analytics function Simulation function Optimization function Understanding of digital twin Triska et al. (2022) Maturity model for smart ports with recommendations for a digitalization strategy n.a Tubis and Poturaj (2022) Identify risks in AGV operations based on a literature review. The results show a deficiency in the identification and assessment of risks Twin Uusitalo et al. (2021) Simulation of 5G network infra-structure to facilitate future port automation and the underlying technologies  Shadow Vakili et al. (2021) Development of a framework for a shipyard with the main objective of energy saving n.a Ramos Velasco et al. (2022) Transition suggestions for a fishing port, becoming less reliant on fishing n.a Wang et al. (2022) Ship engine system and cargo monitoring for process management using Maya and Unity 3D  Shadow Wang et al. (2021) Showing digital twins result in benefits for port management. Supporting decision making and improving resilience and performance Shadow Wattanakul et al. (2022) Generating data with digital twins for ML regarding port capacity prediction under uncertainty   Digital Support System Wu et al. (2021) A digital twin of inland waterways based on 3D video fusion for improved efficiency of daily monitoring, evidence collection and emergency response  Shadow Yang et al. (2022a) Status monitoring and simulation to support container gantry cranes’transition to and decision on hydrogen power  Shadow Yang et al. (2022b) A case study on the digital twin of Qingdao Port and illustration of the development process and function of the digital twin for typical terminals   Twin 123 906 J. Neugebauer et al. Table 2 continued Citation Research scope Process automation function Business intelligence function Predictive analytics function Simulation function Optimization function Understanding of digital twin Yang et al. (2018) Overview of underlying tech-nologies of IoT in smart ports and their challenges, e.g., comparing prices for data connectivity n.a Yang et al. (2022a) Overview of smart port development and future planning guidelines Twin Yao et al. (2021a) Digital twins and smart port concepts should be combined. Digital twin can improve the ports substantially  Twin Yao et al. (2021b) Digital twin supported planning of automated smart ports   Digital Support System Zaychenko et al. (2021) Comparing Russian and global seaports regarding their digitalization level n.a Yang et al. (2022b) Requirements and design principles for the digital twin in the port from a technical perspective n.a Zhao et al. (2022) A digital twin for energy-efficient planning of multiple cranes and selection of the number of cranes is described  Twin Zhou et al. (2022a) Review of current technology trends and relevant research topics related to the container terminals and examining the implications on how container terminals can achieve their strategic goals   Shadow Zhou et al. (2022b) A system for monitoring the operational status of port cranes based on digital twins is presented   Twin Zhou et al. (2018) Simulation-based optimization for improving maritime systems  Shadow Zhou et al. (2021) Digital twins and simulation for resilience improvement Shadow 123 Digital Twins in Ports and Container Terminals 907 Funding Open Access funding enabled and organized by Projekt DEAL. This work was carried out as part of the “TwinSim”project, which is funded by the Innovative Port Technologies (IHATEC) grant program from the German Federal Ministry of Digital Affairs and Transport (BMDV)." Declarations Conflict of interest The authors declare that there are no potential conflicts of interest. All participants have declared their consent to the publication of the information from the interviews. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. 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He holds a B.Eng. degree in Transportation and Logistics and an M.Sc. degree in Logistics, Management and Engineering from Bremerhaven University of Applied Sciences, where he also worked as a project manager on researching blockchain and its application for the Bill of Lading as well as developing emissions calculations for blockchain applications and logistics transports. In his role as a researcher, he is a speaker at various scientific and business conferences, with his current research focusing on machine learning applications enabled by digital twins. He has published scientific articles in these areas. In addition, Julian is an avid pilot and has experience working with major international logistics companies. Leonard Heilig is co-founder and CTO of driveMybox, and holds a position as project leader and lecturer at the Institute of Information Systems, University of Hamburg (Germany). He received a B.Sc. degree in information systems from the University of Münster (Germany) and an M.Sc. and a doctoral degree in information systems from the University of Hamburg (Germany). In his role as CTO at driveMybox, he leads the technical development of their logistics platform, optimization, AI, and cloud infrastructure. He has published more than 50 scientific articles and books in the areas of maritime logistics, operations research, and cloud computing. He has also worked as a consultant for several international logistics projects, and served as the guest editor for several peer-reviewed journals, and provides guest lectures at various universities worldwide. Stefan Voß is professor and director of the Institute of Information Systems at the University of Hamburg. Previous positions include full professor and head of the department of Business Administration, Information Systems and Information Management at the University of Technology Braunschweig (Germany) from 1995 up to 2002. From 2020 to 2022 he served as dean of the Hamburg Business School (School of Business Administration). He holds degrees in Mathematics (diploma) and Economics from the University of Hamburg and a Ph.D. and the habilitation from the University of Technology Darmstadt. His current research interests are in quantitative / information systems approaches to supply chain management and logistics including public mass transit and telecommunications. He is author and co-author of several books and several hundred papers in various journals and compilations. In the German Handelsblatt ranking he is continuously within the top 20 professors in business administration within the German speaking countries. The most recent Wirtschaftswoche rankings list him among the top 10. Stefan Voß serves on the editorial board of some journals including being Editor of Public Transport. He is frequently organizing workshops and conferences. Furthermore, he is consulting with several companies. 123 Digital Twins in Ports and Container Terminals 917