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Article

Assessment of Differences Between the Ports of Rotterdam, Valparaíso and San Antonio Towards Smart Ports, Emphasising Digital Technologies

by
Luis Valenzuela-Silva
1,
Miguel Muñoz
1,
Carolina Lagos
2,
J. P. Sepúlveda-Rojas
3,* and
Raúl Carrasco
4,*
1
Departamento de Economía, Recursos Naturales y Comercio Internacional, Facultad de Administración y Economía, Universidad Tecnológica Metropolitana, Santiago 7500998, Chile
2
Facultad de Ingeniería y Negocios, Universidad de Las Américas, Providencia, Santiago 7500975, Chile
3
Departamento de Ingeniería Industrial, Universidad de Santiago Chile, Santiago 9170124, Chile
4
Departamento de Contabilidad y Gestión Financiera, Facultad de Administración y Economía, Universidad Tecnológica Metropolitana, Santiago 7500998, Chile
*
Authors to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2025, 13(12), 2220; https://doi.org/10.3390/jmse13122220
Submission received: 4 October 2025 / Revised: 18 November 2025 / Accepted: 19 November 2025 / Published: 21 November 2025
(This article belongs to the Special Issue Smart Seaport and Maritime Transport Management, Second Edition)

Abstract

The objective is to evaluate the differences between the Chilean ports of Valparaíso and San Antonio and the port of Rotterdam in their journey towards smart ports, focusing on Blockchain (BC) technologies, Artificial Intelligence (AI), big data, Internet of Things (IoT), 5G networks and Digital Twins (DT), according to Port 4.0 and 5.0 models. The methodology is a qualitative assessment based on scores from the analysis of Port 4.0 technology information, including labour relations, environmental care and community integration for Port 5.0. The results confirm Rotterdam as representative of a ‘Smart Port’ for Ports 4.0 and 5.0, showing gaps with Chilean ports, which are rated as ‘incipient implementation’ in Port 4.0 and ‘in transition’ in Port 5.0. These differences are due to factors such as investment, financing, infrastructure, governance, regulation, digital human capital, organisational culture and innovation, and the characteristics of the port ecosystem.

1. Introduction

Seaports are fundamental to open economies in a globalised world [1,2,3], as they link production and consumption, both domestic and foreign [4], and are decisive in the growth and development strategies of many countries [5,6]. It is estimated that maritime transport accounts for nearly 80% of the volume of world trade and 70% of its value [7]. Ports handle both mass-market goods and other high-value goods [8]. Ports are necessary for keeping global supply chains active, and their management reflects the operational efficiency of their port activity [9].
Ports have been advancing in digitisation and automation in order to improve the efficiency of their logistics [10,11]. In recent decades, different generations of ports have been recognised, in line with logistical challenges and the emergence of new technologies. Flynn et al. [12] suggests adding a new category of ports to the four previously recognised, by incorporating the concept of fifth-generation ports (Port 5.0). The incorporation of Port Community System (PCS) technology in ports, as well as its subsequent development, has driven improvements in port management, reducing costs and streamlining operations through more fluid communication and greater digitisation of tasks [13]. However, in order to achieve a smart port concept, whether under a Port 4.0 or Port 5.0 model, the presence and integration of all available technologies that harmonise and facilitate its activity is required [14,15]. This is a major challenge, which makes it advisable to first look at the experiences of the world’s leading smart ports, such as those in Rotterdam, Hamburg and Shanghai [16].
The fundamentals of the Port 4.0 model are based on the digitisation of processes and the integration of advanced technologies, such as Blockchain (BC), Artificial Intelligence (AI), big data, Internet of Things (IoT), 5G networks and Digital Twins (DT). The aim is to optimise the efficiency and safety of port operations, which are being subjected to increasing pressures resulting from the growth in global commercial traffic and the larger size of cargo ships [17,18]. Here, big data and its real-time analysis are of great importance in achieving these goals [19]. For its part, port automation reduces idle time and minimises costs. For example, in cargo handling, it reduces waiting times and facilitates real-time transport management.
The transition from the Port 4.0 to Port 5.0 model represents a vision that considers the community in which it is embedded. In Port 5.0, working conditions, environmental considerations and community relations become relevant [20]. This also involves collaboration with stakeholders to maintain efficient [21], safe operations with minimal environmental impact [22]. Digital technologies are used in this model with a view to achieving safer and more collaborative working environments, with sustainable practices and incorporating citizen participation [23]. Community participation contributes to social cohesion and is necessary for strengthening a sometimes conflictive relationship [24]. The Port 5.0 model also requires that technological advances be put at the service of environmental sustainability and its ecological objectives [25], such as minimising the carbon footprint and improving environmental management [26], developing environmentally friendly operations [27].
The transition from conventional ports to the Port 4.0 model poses an enormous challenge, which is compounded by the additional requirements imposed by Port 5.0. First, these advanced technologies must be available in the context of adequate infrastructure support, so that they can then be efficiently integrated into port activities. At the same time, it is necessary to raise awareness and train the workforce in the digital transformation of their jobs, understanding that some of them may resist change and/or see their jobs at risk. Finally, the impact that this modernisation may have on local communities must be addressed, through dialogue with their spokespersons and authorities, who often and for various reasons tend to hinder projects promoted by ports [28]. This phenomenon of resistance to technological change has been widely studied in the literature [29].
Within the framework of studies that consider assessments of port digitization, it is worth highlighting some of the efforts that have been made. For example, using the AHP technique from Buiza-Camacho-Camacho et al. [30], they focused on generating indicators and associated weightings for the assessment of Mediterranean ports. Nguyen et al. [31], meanwhile, considers the technological and environmental challenges facing ports and discusses the approaches that have been taken to technological applications in smart port management and energy systems, with a focus on Vietnam. Among their findings, they highlight the importance of different perspectives on the prioritisation of technologies implemented in ports. On the other hand, in a more recent study, Iberahim et al. [32] focus on Malaysia’s main port to understand the impacts of digital technology, aimed at port employees, on port management. They found varying degrees of preparedness among port employees, identifying factors that inhibit the adoption of port technologies. However, previous studies do not consider the transnational application of indicators that allow for the comparison of port performance. This study also seeks to contribute to filling that gap.
The aim of this article is to qualitatively assess the gaps or differences between the Chilean ports of Valparaíso and San Antonio and the port of Rotterdam in terms of their progress or trajectory towards smart ports, with an emphasis on digital technologies such as BC, AI, big data, IoT, 5G networks and DT, as well as their derivatives machine learning and automation, using Port 4.0 and Port 5.0 as models. The above can be summarised in three questions to be answered, in accordance with the qualitative evaluation methodology outlined below: (1) Is the port of Rotterdam, considered one of the most automated globally, a good example of these models? (2) How far are the Chilean ports of Valparaíso and San Antonio from the evaluation carried out on the port of Rotterdam? and (3) What factors explain these differences? It is hoped that this work will provide critical insights and meaningful comparisons that will guide the way towards a more efficient, sustainable and inclusive through learning best practices. However, it should be borne in mind that a number of factors affect the capacity to adopt technological capabilities, such as [33]: (a) Adopting port’s characteristics: Know-how, Organization Support, Organizational structure, Financial capacity, A port’s network embeddedness, Risk taking. (b) Characteristics of the innovation: Costs, Relative advantage/perceived usefulness, Complexity/perceived ease of use, Compatibility, Trialability, Observability. (c) Stakeholder pressures: Customer, Competitive port, Regulatory body, Societal.
The work is organised as follows: Section 2 explains the methodological characteristics of the research. Next, Section 3 provides an exhaustive review of each of the six technologies identified in the previous paragraph, in terms of their benefits or usefulness and the challenges they face. Section 4 presents the results of the evaluation for the ports of Rotterdam, Valparaíso and San Antonio. It describes how these technologies interact in a smart port and indicates the evaluation associated with each port. Finally, Section 5 offers some conclusions.

2. Materials and Methods

The qualitative assessment methodology assigns scores on a scale of 1 to 5 to each of these six technologies for each port that make up a Port 4.0, according to the existence, implementation, and impact of each of them on their respective port activities. At the extremes, a score of 1 represents that there is no record or public evidence of the existence and implementation of the technology analysed, and a score of 5 represents that the technology has a total and strategic presence, meaning that it is fully integrated into the management and operations of the port. The scoring scale determined to evaluate each of the six technologies (Port 4.0) is shown in Table 1:
The final score for each port in terms of technological relevance corresponds to the simple average of the scores obtained for each of its technologies. In the case of technologies that replace any of those reviewed and considered here, before assigning a rating, their relevance and ability to deliver the expected achievements or benefits were considered, given the characteristics of the port.
The six technologies are given equal weighting, as there is no theoretical consensus on the importance of each one and, as Nardo et al. [34] points out, weightings are naturally value judgements. Furthermore, some advantages of this form of evaluation have to do with its simplicity when explaining it and its usefulness, especially when there is no agreement among stakeholders and when the elements evaluated are considered equally important [35]. The technologies address complementary aspects of port performance. As these are three different ports, with different needs, characteristics and strategies, there are no solid reasons to claim that one technology is more important than the others. Therefore, the use of the simple arithmetic mean is the most appropriate approach.
On the other hand, the Port 5.0 assessment is configured by adding three dimensions for a holistic evaluation: (1) environmental care, (2) working conditions and labour treatment, and (3) community integration [20,22,36]. Using a scale from 1 to 5, at the extremes, a score of 1 represents poor, highly problematic or absent practices, and a score of 5 represents effective practices that serve as benchmarks at the national and international levels (see Table 2).
Taking into account the same reasons as in the evaluation of the six technologies, the elements that make up Port 5.0 are given equal weight in the evaluation. Due to the importance that the model gives to these requirements, the final result for Port 5.0 is calculated as the simple average of both assessments, i.e., that of Port 4.0 and that of these latter dimensions, giving them the same relative weight. The evaluation obtained will correspond to the categories of “Early Implementation”, “In Transit” and “Smart Port”, in accordance with Table 3 and Figure 1.
The relevance of these three dimensions in the Port 5.0 model led to them being given the same weighting as technological factors. Therefore, the final result in this case is based on the simple average of both evaluations.
Now, for both models, if there is a clear prospect that a ‘project or initiative being implemented’ will enable the port to achieve the next higher score within a reasonable time frame, in the case of scores 2, 3 and 4, the score will be expressed as 2.5, 3.5 and 4.5, respectively. With the aim of allowing greater sensitivity to capture more accurate variations of subjective reality in the assessment.
The ports evaluated were chosen for their relevance: the port of Rotterdam because it is one of the western ports with the highest container traffic and a global benchmark for performance [37]. Meanwhile, the ports of Valparaíso and San Antonio in Chile are the most important ports in that country in terms of container traffic [38].
As the work is based on official sources, press reports and specialised websites, the first step was to organise and summarise the information on each port, translating it into useful knowledge, and then validate and interpret it to enable a structured and contextualised comparison between the ports. This information was compiled between 10 November 2024 and 10 June 2025 (see Appendix A). Information sources published in English and Spanish were consulted. Marketing materials and opinions reproduced from written sources were excluded.
Table 4 shows the profiles of the port performance assessors and the validator of the assessment tool used.
The sole purpose of this evaluation methodology is to highlight the differences between the ports examined. Therefore, it does not seek to guide decision-making as in the case of techniques such as MCDM, AHP, ANP, DEX, MACBETH, MAUT, DEMATEL and DRSA.

3. Technologies for Optimising Port Activity

3.1. Blockchain in Port Optimisation

BC technology is defined as a distributed and immutable digital ledger, composed of transaction records in blocks, cryptographically chained, which allows modifications to be detectable and difficult to alter without the consensus of the network. This structure guarantees traceability, data integrity, and the difficulty of modification in decentralized environments [39,40,41].
In the port sector, this technology can make transactions more transparent, providing systems with real-time functionality and streamlining maritime communications by addressing challenges related to data security and transaction visibility. It can be applied in areas such as Radio-Frequency Identification (RFID), digital navigation, and mobile devices, among others [42,43,44].
BC is a digital technology that acts as an immutable and decentralised ledger, thereby eliminating the need for intermediaries and guaranteeing the integrity of the information exchanged [45]. It uses robust encryption mechanisms, such as secure hash and consensus algorithms, which protect confidential information [46], reducing the risk of fraudulent manipulation. This is highly relevant for the security of port logistics operations [47], where sensitive information is exchanged [48]. Real-time information exchange streamlines the coordination of port activities, resulting in more efficient resource allocation [49]. Furthermore, as a reliable technology, it allows for the elimination of physical documentation that can be manipulated, such as bills of lading [50], which in turn translates into cost savings for port operators and logistics companies, as well as contributing to a reduction in carbon footprint [51,52].
Its implementation and integration with other technologies and/or platforms enables better port management. For example, with IoT and RFID tracking technology [53], accurate traceability of containers throughout the supply chain can be achieved [54]. The use of near-field communication chips in containers allows traceability information to be securely stored and retrieved [55]. And platforms such as Hyperledger Fabric can guarantee security and robust access control, appropriate for the sensitive nature of port operations [56]. All these examples have a positive impact on the quality of service offered by ports.
Another application of BC is smart contracts, which are digitised and automated, eliminating the need for paper and significantly increasing the speed of these types of operations [57], streamlining administrative procedures, reducing operational risks [58] and consequently improving workflow management [56]. This application makes it possible to overcome classic logistics traceability challenges, such as unreliable data and opaque information [59].
Like any relatively new, complex technology requiring high initial investment, the adoption of BC in ports faces several challenges ahead. The first is a thorough understanding of this technology and how it interacts with others [25]. It is likely to face resistance to change from its participants and operators, who are traditionally conservative when it comes to technological advances [60,61]. A new challenge facing this technology is that of cybersecurity [62,63]. Other challenges relate to legal uncertainty in the face of current regulatory frameworks and their changing nature [64,65], its limited scalability in the face of a large volume of logistics operations [66,67], the limited interoperability between different BC [68], and its lack of integration with legacy systems [43]. Based on the background information presented, the evaluation of the ports of San Antonio, Valparaíso, and Rotterdam as Ports 4.0 requires that technology be fully integrated into port operations. For example, the port must have a fully operational PCS Portbase operating with blockchain.

3.2. Artificial Intelligence in Port Optimisation

AI is a discipline based on the study of computer science, capable of performing tasks associated with human intelligence, such as learning, reasoning, perception, decision-making, and understanding natural language. These systems are based on algorithms and models that allow them to process information, acquire data patterns, and adapt to new situations [69,70].
AI is a relevant technology to optimize port activity and achieve logistic efficiency, reduce costs, and minimize errors in the supply chain [71]. The proof of this can be seen in the successful implementations in the ports of Rotterdam, Hamburg, and Singapore [72]. AI drives automation and enables predictive analysis and machine learning.
The automation of cranes, for example, reveals a port’s shift towards smart and sustainable practices [73], as does the automation of docks. In turn, predictive analysis is relevant for strategic decision-making, such as space optimisation and capital and operating cost estimates [72]. Predictive maintenance, supported by AI and data from sensors installed on port equipment, makes it possible to predict potential failures in advance, extending the useful life of equipment and contributing to greater safety and operational efficiency [74,75]. With the right algorithms and machine learning models, it is possible to accurately predict ship delivery and waiting times [76], as well as arrival times, which is important for terminals to minimise disruptions [77].
Other associated uses of this technology include artificial vision that monitors dock cranes, ensuring greater workplace safety [78], and video surveillance systems that detect and respond to threats in real time [79,80], before they cause significant damage [81]. Thus, AI stands as a relevant tool for defending against evolving cyber threats [82].
In terms of environmental sustainability practices, AI plays a role in optimising energy use and reducing greenhouse gas emissions, as well as reducing sulphur content [83] and substantially improving fuel efficiency, as demonstrated by the case of Maersk Line and the port of Rotterdam [84].
The implementation of AI in ports also faces challenges. First, it requires investment in advanced technological infrastructure, hardware and software [84], and specialised professionals [85]. Integration with legacy systems is complex, as it would require the creation of a hybrid infrastructure that takes advantage of both systems [86]. Other challenges relate to data and privacy. Large amounts of data from diverse sources and formats in real time require advanced data integration and engineering processes [87]. Facial recognition poses privacy risks, so it is necessary to create government frameworks that prioritise human rights and ethics [88,89]. The idea behind this regulation would be to ensure data confidentiality without hindering research and applications [90].
In short, it is possible to highlight the application of various AI tools in port areas, such as the following: (1) Berth planning and allocation. Optimisation and reinforcement learning algorithms (models that learn to distribute berths based on times, tides, size and priority of ships) [91,92,93]. (2) Yard management and container movement. Deep learning together with simulation and prediction models (to forecast flows, congestion and optimal vehicle routes) [94,95,96]. (3) Predictive maintenance of port equipment. Supervised machine learning (regression models or neural networks that analyse data from IoT sensors to anticipate failures) [97,98]. (4) Security, surveillance and access/cargo control. Computer vision and deep learning in Convolutional Neural Network (CNN) (for number plate and face recognition or automatic anomaly detection) [99,100]. The elements considered allow us to determine that, for a port to obtain the highest score on the Port 4.0 assessment scale, it is required that an AI-based system be fully integrated into port activities such as productive maintenance and traffic management, to mention a few areas.

3.3. Big Data in Port Optimisation

Big data is an environment that allows the processing of large volumes of data, in different formats, at high speed [101].
Big data analysis is used in ports to optimise the logistics chain, which reduces costs and enables just-in-time operations [102]. Its integration into this chain also aims to improve service quality, which is important for effective logistics planning [103].
Big data enables causality and correlation analyses, which help determine weather routing and tracking [104], as well as ship performance and navigation data [105]. Similarly, real-time analysis allows for the optimisation of route and fleet management, reducing delays and ensuring service levels [106]. All of these are relevant aspects for efficient port management. Here, machine learning is fundamental in predicting ship arrival times, as these models can determine arrival times fairly accurately based on historical trajectories [77].
Big data analytics also provide detailed, real-time information on the condition of goods, including temperature and humidity, to verify that transported products maintain their quality [107]. Big data and deep learning promote proactive management of port terminals, adapting processes in real time [108]. Big data, together with AI and DT, optimise the supply chain by creating digital replicas of assets and processes, strengthening the resilience of the supply chain [109].
In terms of port security, big data contributes to improving emergency alert and management systems, promoting a safer logistics environment [110]. Advanced data visualisation techniques can be used to identify trends, patterns and anomalies, reducing the risks of port management [111]. In conjunction with DT and AI techniques such as CNN and Long Short-Term Memory (LSTM), which provide accurate predictions, it is feasible to improve security management in port operations [112].
At the same time, big data also contributes to greater fuel efficiency, reducing polluting emissions by analysing ship routes and environmental conditions [113], contributing to the ecological transition of modern ports [102] and to constant progress in the safety and energy efficiency of maritime operations [105]. Big data also enables the monitoring of environmental parameters, such as emissions and energy consumption, which are crucial for ensuring compliance with environmental regulations [114]. Big data information encourages sustainable practices, such as alternative energy vehicles, with the aim of minimising the environmental impact of logistics operations [115].
Big data environments present challenges. The complexity of big data systems can complicate the identification and mitigation of security threats, whether unauthorised access, data leaks, or other potential internal dangers [116]. Furthermore, the integration of data from various sources, such as shipyards and ship operations, adds complexity to analyses [117]. This is due to the diverse and geographically dispersed nature of maritime data sources [113]. Qualified personnel are also required in ports to use these tools effectively, although resistance to change can hinder data-driven management [118]. An additional challenge is data protection, which requires the implementation of robust access control mechanisms alongside clear data governance frameworks [119,120,121]. The integration of Big Data in the execution of advanced and real-time analysis of the port system implies, according to the evaluation system developed in this work, that the port receives the highest score in terms of Port 4.0.

3.4. IoT in Port Optimisation

IoT technology relies on sensors and devices that are integrated into various assets, allowing them to collect and transmit data autonomously [122,123], connectivity that is crucial for supporting data-driven decision-making [124]. This reduces asset downtime and ensures smoother operation [125].
Data interpretation and response automation can utilise machine learning models [126]. The role of IoT in the automation of port operations makes it possible to perform tasks such as predictive maintenance and process control, which are important for the proper functioning of port facilities [127], thus ensuring the reliability of automated systems [128]. AI and big data enable remote management, which is key to maintaining an efficient and safe port environment [129]. The automation of repetitive, rule-based tasks using robotic processes can be of great value in dynamic environments such as ports [130].
With IoT, it is possible to monitor cargo containers in real time, which aids logistics planning and improves security measures, reducing the likelihood of damage to goods [131] and improving the visibility and control of port operations [132]. With real-time data, IoT can significantly reduce the risk of theft and loss by alerting stakeholders to anomalies or unauthorised access [131]. It can also be used to control ship traffic, optimising the flow of vessels, reducing congestion and minimising waiting times [133]. Its implementation in the cold chain guarantees its maintenance, ensuring product quality during transit [134].
IoT can collect real-time data on environmental parameters relating to air quality, water, noise levels and energy consumption, which are parameters for identifying patterns and trends and defining environmental management strategies [135,136]. They also contribute to sustainability by optimising fuel consumption, managing emissions and ensuring compliance with environmental regulations [137,138,139].
Despite its many applications, the implementation of IoT in port logistics faces challenges. To begin with, it must be considered that IoT solutions in ports require large investments in hardware and infrastructure, as they demand a large number of sensors and robust networks to support data transmission and processing [140,141].
IoT devices are exposed to security threats such as data leaks, malware attacks, and unauthorised access, vulnerabilities that can stem from inadequate encryption, insufficient authentication, and data collection [142,143]. Data governance is relevant in smart ports, bearing in mind that it is necessary to improve the data lifecycle and its management to maximise the potential of IoT in terms of operational efficiency and sustainability [144].
Within the framework of the evaluation being conducted by this study, for a port to obtain the highest score in its Port 4.0 rating, it requires that the IoT be integrated into the port’s management and operations. For example, through the deployment of sensors throughout the port area, supported by a specialized platform.

3.5. 5G Network in Port Optimisation

5G refers to the set of fifth-generation mobile technologies and networks, which have a latency of less than 1 ms and a speed of up to 10 Gbps, improving the connectivity of different devices [145].
Ultra-reliable, low-latency 5G technology facilitates rapid communication between IoT devices. This enables high-speed data processing and is useful for monitoring, controlling and making real-time decisions about port activity, resulting in increased productivity, reduced downtime and greater efficiency [146,147], as well as improvements in the safety and accuracy of automated and remote equipment operations [148].
Data collection and analysis facilitates predictive maintenance [149], which is valuable in port ecosystems where real-time decisions are necessary for logistics optimisation [150] and for achieving greater operational efficiency [151,152]. AI combined with 5G can automate complex tasks [153] and thus optimise resource allocation [154]. AI and automation can improve the quality of service offered, which is relevant for maintaining the continuity of port operations [155]. Reliable 5G connectivity is appropriate for remotely operated cranes and drones, as any delay could have serious consequences [156]. It also facilitates maritime traffic management by providing real-time information on vessel routes and port conditions, allowing for better coordination of activities, reducing congestion and delivery times [157], information that promotes better maritime traffic management [158]. This technology also enables full connectivity between different modes of transport, whether road, rail or sea [159].
5G enables the use of portable devices to monitor workers, which increases their safety and well-being [160]. With AI and 5G, cargo can be monitored in real time, reducing theft and smuggling, which is very important for ensuring the integrity of port operations [161]. A very useful implementation is the segmentation of the 5G network, which helps to manage the cybersecurity risks associated with this technology [162]. Zero Trust architectures are even more reliable, as they require authentication for every access request, regardless of its origin [163].
Like all the technologies reviewed above, 5G networks face several challenges. The base infrastructure has high implementation costs, which are necessary to realise its full potential [164], requiring devices and infrastructure to be upgraded to make them compatible with 5G. Network segmentation can reduce costs by optimising resource utilisation [165,166]. As the increased connectivity of this technology exposes it to new cybersecurity threats, network segmentation is necessary to mitigate these risks [167,168]. Regulatory compliance, in terms of limited high-frequency spectrum availability and emission standards, is another aspect to consider and manage [169]. It also requires ongoing technical training of personnel to ensure the effective operation of 5G-compatible systems [170].
5G technology offers reduced latency and high capacity for simultaneous connections, enabling real-time port operations, equipment automation and constant communication between IoT systems, something that was not possible with previous networks such as 4G or Wi-Fi. The highest score in the Port 4.0 assessment can be achieved to the extent that it has been integrated into its operations and management, for example, through the deployment of 5G networks in its smart port strategies.

3.6. Digital Twins in Port Optimisation

Several simulation tools have long been used in port management, but it is the convergence of the digital and physical worlds, via the IoT, that has enabled the creation of a modern, globally connected port ecosystem [171]. Digital twin technology was designed to reproduce the state of physical operating systems, creating a dynamic virtual representation [172]. The integration of technologies such as IoT, AI and 3D modelling are useful for capturing and simulating the real dynamics of the port environment [173]. The incorporation of big data is required for the proper functioning of DT, as it is necessary for real-time updates and analysis [174].
By creating virtual prototypes, DT promote innovation even before their implementation in the real world [175]. This capability helps to optimise designs by minimising the risks associated with construction and operational changes [176], and to discern how equipment would behave under different conditions and levels of stress or wear in order to more accurately predict potential failures [177].
In ports, DT can integrate physical infrastructure models and their operational flows and movements, including ships, trucks, and containers. This integration is facilitated by IoT and real-time data, creating an interactive and synchronised virtual replica of the port [178], which improves port security and operational control [179] and will ultimately optimise continuous decision-making, making a difference compared to traditional simulation methods [180].
These dynamic models are constantly updated with real-time data to reflect the current state of the port [181]. AI and machine learning further enhance their potential for analysis and simulation with advanced data [182,183]. DT enable real-time monitoring and management of port infrastructure, leading to increased logistical efficiency through predictive maintenance [184], improved resource allocation and risk mitigation [185], optimising maintenance programmes and reducing unplanned downtime, thereby extending the useful life of equipment [186,187].
The accuracy of DT requires real-time data collection in order to properly monitor port activities and environmental conditions [171]. They can also contribute information on air quality and environmental sustainability, enabling the development of strategies to improve environmental outcomes [188].
The costs of developing and implementing DT are very high, as they require appropriate IT infrastructure and components, along with highly qualified professionals capable of digitising physical equipment and processes, as well as modelling and integrating systems. Generating a digital representation that covers all assets and processes is a daunting task, given the complexity and scale of port operations [178,189]. Another significant challenge in implementing this technology lies in the integration of different data sources and interoperability between various software programmes, in order to avoid isolated information storage systems and ensure uninterrupted operation [190]. It is also important to ensure the privacy and security of information for the successful implementation of DT [191,192]. Other challenges include encouraging innovation and the use of digital technologies [193,194] and investing in training programmes to develop the required skills [195].
To be evaluated as Port 4.0 with the maximum score, ports must fully integrate these technologies into their management and operations, for example, through models that allow the simulation of port operations.

4. Port 4.0 and Port 5.0: Differences Between the Ports of Rotterdam, Valparaíso and San Antonio

4.1. Interaction and Integration of Technologies for a Smart Port

Another relevant element in smart ports is the integration and interaction of the technologies reviewed above. Both models (4.0 and 5.0) highlight the importance of achieving improvements in efficiency, sustainability, and competitiveness through the digital transformation of port operations.
BC is a technology available for protecting critical data and transactions in IoT networks, facilitating access control and communication for IoT devices, which is required for sustaining the operational security of smart ports [196], and ensuring the traceability of transactions within the port system [197]. In addition, it contributes to better container management, ensuring data transparency throughout the supply chain [198]. The combination of BC with big data and IoT can facilitate port operations by providing useful information and improving operational efficiency [199]. The ports of Valparaíso and San Antonio had a BC tool since 2019, but in 2022 it ceased to be operational, resulting in a lower rating for both ports.
For its part, the incorporation of AI into logistics encourages automation and intelligent processing, increasing efficiency and reducing costs [73]. AI and machine learning algorithms enable early intrusion prevention, which is crucial for maintaining cybersecurity in smart ports [200], while also ensuring the reliability of communication systems. It also improves the analysis of the information received, enabling predictive maintenance, efficient port operations and greater navigation safety [201]. In conjunction with big data, AI makes it possible to predict equipment failures, making port operations more efficient and safer [202]. In addition, AI can predict environmental impacts and propose corrective measures to minimise the environmental footprint [84,203].
In this integration of digital technologies, data and its quality are necessary for accurate analysis, so standardising it is a challenge that must be addressed [153,201]. In other words, when there are multiple sources of information, sophisticated algorithms and methodologies are needed to ensure that it is accurate and processable [204]. Big data plays a fundamental role here when it comes to analysing enormous amounts of data generated by IoT devices and then incorporating these analyses into decision-making processes [25]. AI analyses these large volumes of data in real time, making it possible to detect anomalies and respond to threats, thereby increasing the security of IoT ecosystems [205]. Similarly, they can detect unusual activities in order to take measures to improve the physical security of smart ports [206]. All of this aims to improve the overall competitiveness of ports by providing useful information and reducing downtime [72,207].
In turn, IoT devices are very important for collecting enormous amounts of information from different port operations, such as containers and ship traffic, providing real-time location, status and security, which are crucial aspects for efficient port management [133]. 5G-compatible IoT allows large volumes of data to be processed at high speed, delivering relevant information for efficient decision-making in smart port operations [153,160,208]. This is because 5G increases transmission speed and reliability, enabling real-time communication for decision-making in the port environment [209,210]. Its high bandwidth and low latency allow cybersecurity risks and interoperability issues to be addressed in real time [160].
This technology is required for the remote control of cranes and other port equipment, as well as for autonomous systems and predictive maintenance, which require ultra-reliable, low-latency communication [146,211]. This allows operations to be carried out efficiently and safely from a distance, reducing labour requirements and minimising human error [212,213]. The synergy between AI and 5G enables ports to react in real time to operational changes, making smart ports more agile and better able to respond to market demands [153,211].
When combined with 5G, DT provide a virtual representation of port operations, accurately assisting navigation and docking processes, thereby reducing environmental impact by optimising fuel consumption [210]. The predictive capacity of DT, in conjunction with real-time data and advanced analytics, reduces downtime and extends the useful life of port infrastructure [212,214].
The integration and interaction of these technologies creates synergies: IoT and 5G act as the nerve centre, collecting and transmitting information in real time from the physical environment of the port. Big data and AI function as the brain, processing and analysing this information to extract operational intelligence, make predictions and optimise decisions. BC provides integrity, trust and security for this information and exchanges or transactions. And DT provide the interface to comprehensively visualise, simulate and control this complex ecosystem. These interactions are the most complete manifestation of a virtual port operating in real time, that is, a smart port.
There are challenges and limitations associated with its implementation that must be kept in mind. These include: the transition to a smart port requires significant investment; port authorities must be open to technological innovation and new management practices; data and transaction privacy and security; complex integration processes and interoperability challenges; latency issues; and high energy consumption.
Gradually overcoming the challenges associated with each technology, especially those related to its effective integration, is key to realising the vision of a smart port and optimising port activity in all its dimensions. It is not only a technological transformation, but also a strategic shift towards the future of smarter and more sustainable port management. Despite these challenges, the development and growing adoption of these technologies will continue to transform the maritime industry, driving efficiency, sustainability and innovation in port ecosystems globally.

4.2. Port Assessment

With regard to the assessment of the ports of Rotterdam, Valparaíso and San Antonio concerning the use of technologies and the implementation of measures specific to ports 4.0 and ports 5.0, the results are summarised in Table 5.
With the information analysed and the methodology adopted, the results reveal a clear superiority of the port of Rotterdam over the ports of Valparaíso and San Antonio, both in the implementation of Port 4.0 technologies and in the additional dimensions of Port 5.0. This difference is reflected in the final average scores for Port 4.0: Rotterdam (4.5), Valparaíso (2.7) and San Antonio (2.8); and for Port 5.0: Rotterdam (4.7), Valparaíso (3.1) and San Antonio (3.1). This confirms Rotterdam as a smart Port 4.0 and 5.0, while Valparaíso and San Antonio reflect an incipient, limited and fragmented adoption of the technologies that make up Port 4.0, both being rated as ‘in transition’ due to their scores of around 3.0 when it comes to Port 5.0. There is no contradiction in these results for Chilean ports, given that the first model is partial or technological and the second is holistic or comprehensive, which better reflects the actors involved and the desirable characteristics to be considered a smart port.
Rotterdam exhibits a comprehensive approach, combining advanced technological implementation with robust environmental, labour and community management, making it a global role model. The Chilean ports analysed show a considerable technological gap compared to Rotterdam, with incipient efforts, initiatives under development or limited implementation, such as in the area of IoT in both cases, and in 5G or a good substitute for it in the case of Valparaíso, still far from accessing DT technology. Rotterdam’s superiority is attributed to a comprehensive strategy, supported by its workers and the Dutch authorities, both locally and nationally. Chilean ports not only face a significant technological gap, but also structural and political challenges, particularly in their relationship with the community.
Figure 2a shows the performance gap between the port of Rotterdam and the ports of San Antonio and Valparaíso. The greatest gap is observed in the deployment of DT technology, while the smallest gap or separation with respect to the Chilean ports occurs in: 5G technology (Port of San Antonio) and BC (Port of Valparaíso). In contrast, Figure 2b shows an overlap in the performance of the ports of San Antonio and Valparaíso.
The gaps identified for Chilean ports cannot be attributed to a single cause, but rather to a combination of factors, including differences in investment levels and access to financing, the state of enabling infrastructure, governance and regulatory frameworks, the availability of human capital with digital skills, organisational culture towards innovation, and the characteristics of each port ecosystem. One obstacle in this area, which is considered decisive, is excessive environmental permitting, which hinders or delays port expansion and improvement projects. This not only rejects or delays physical works and their tenders, increasing project costs, but also postpones the implementation of the infrastructure that supports the new technologies required by modern ports. In Valparaíso, there is the case of the Cerros de Valparaíso Terminal or Expansion of Terminal 2.
The project began in 2013, with a projected investment of USD 600 million, but the multiple Consolidated Report on Requests for Clarifications, Corrections or Additions (CRRCCA) generated by the Environmental Assessment Service determined that it was not until December 2024, more than a decade later, that it was included among the five largest ports under environmental assessment, with no clear implementation date to date. Construction is estimated to begin in 2028–2029 and operation in the 2030s. Two other projects are facing complex situations: the Cruise and Breakbulk Terminal Project, complementary to Terminal 2, and the Single Concession Model. In San Antonio, the Outer Port Project has been under evaluation since 2020 with significant delays due to environmental permits CRRCCA, collection of studies and postponed tenders. The activities planned for 2026–2028 have been postponed for more than four years. This is in stark contrast to Rotterdam, where regulations require that deadlines be met and where the most complex port projects, corresponding to integrated permits, must not exceed 26 weeks or 6 months. This limited timeframe allows for reliable planning and avoids arbitrary decisions.
The interaction and integration of the technologies analysed is essential for their implementation. In other words, the implementation of each technology depends, in turn, on its ability to integrate and interact with other technologies. Thus, the rating achieved by each technology also reflects its level of integration and interaction with others. Thus, in cases where the technology is only just being implemented as a pilot project (for which it receives a rating of 2 points), its interaction with other technologies is less than in cases where the technology has a full and strategic presence in port management and operations (in which case it receives 5 points). On the other hand, the integration of working conditions, environmental protection measures and community integration initiatives complement the technological assessment, insofar as they progress from absent or problematic practices (associated with 1 point in the assessment) to practices that establish ports as national and international benchmarks (for which the port obtains 5 points).

Limitations

A limitation of this study is the asymmetrical possibility of access to port authorities, determined by the resources available. The methodology relied mainly on secondary sources. Despite the methodology used, it is recognised that the subjective nature of the assessment could limit the value of the results [215].

5. Conclusions

The Chilean ports of Valparaíso and San Antonio perform best in the implementation of PCS and BC technologies. However, they score low in 5G network and DT application. In particular, they show the greatest gap with respect to the port of Rotterdam in the latter technology, which translates into a significant area for improvement.
Future projections for the path to smart ports favour San Antonio. Its new PCS with Single Port Window being implemented and its Outer Port Project, which is close to being approved, may positively change the results and ratings obtained here. The projection for Valparaíso is more conservative, given that it faces geographical, heritage and urban restrictions that limit its capacity for expansion. If the scenario is challenging for San Antonio, it is even more so for Valparaíso.
Chile is a small country with an open economy in a globalised world, where foreign trade and, consequently, its ports play a key role in the national growth and development strategy, which makes it urgent to close the gaps that have been identified. In this regard, it is important for local or national authorities with some responsibility for the development and maturation of Chilean port activity to consider the following: (1) formulate a roadmap for the digital transformation of ports with deadlines that cannot be postponed; (2) re-examine environmental regulations, severely limiting evaluation deadlines with compliance requirements; (3) promote and support investment in port infrastructure and technology; (4) prioritise technological adoption with a strategic approach; (5) promote port governance models that drive innovation and effective collaboration among all actors in the ecosystem; and (6) establish a system for continuously monitoring port progress in all possible areas, using key performance indicators and conducting periodic benchmarking, with public access to non-strategic information. And, in the specific cases of Valparaíso and San Antonio, continue to advance in the three additional dimensions required by Port 5.0, particularly in consolidating their relationship with the community.
Due to the changing nature of technology and port management processes in areas such as those mentioned above, the scores provided may change, especially in the face of new challenges. In this regard, one of the most important developments is the incorporation of sixth-generation maritime communication networks [216], but also the use of green hydrogen for decarbonisation [217], to name a few.
In future studies, it is essential to include key quantitative indicators of port performance, such as TEU traffic, average vessel waiting time, and PCS adoption rates, to provide context on technological readiness.
On the other hand, future studies may provide the levels of importance of the technologies used in ports, with their respective weightings. In this regard, it is worth highlighting the importance that the incorporation of new technologies has acquired in ports, generating improvements in performance, efficiency, time reduction, and quality of work, among others.

Author Contributions

Conceptualization, L.V.-S.; methodology, L.V.-S. and J.P.S.-R.; software, L.V.-S., M.M., C.L., J.P.S.-R. and R.C.; validation, L.V.-S., M.M., C.L., J.P.S.-R. and R.C.; formal analysis, L.V.-S., M.M., C.L., J.P.S.-R. and R.C.; investigation, L.V.-S., M.M., C.L., J.P.S.-R. and R.C.; data curation, L.V.-S. and R.C.; writing—review and editing, L.V.-S., M.M., C.L., J.P.S.-R. and R.C.; visualization, R.C.; supervision, L.V.-S., M.M. and R.C.; project administration, L.V.-S., M.M. and R.C.; funding acquisition, J.P.S.-R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AIArtificial Intelligence
BCBlockchain
CNNConvolutional Neural Network
CRRCCAConsolidated Report on Requests for Clarifications, Corrections or Additions
DTDigital Twins
IoTInternet of Things
LSTMLong Short-Term Memory
PCSPort Community System
RFIDRadio-Frequency Identification

Appendix A

URL for Table 5. Information was collected between 10 November 2024 and 10 June 2025, and the URL with the last access date is provided.
URL: 1 https://www.portbase.com/en/about-us/, (accessed on 5 April 2025); 2 https://customers.xebia.com/improve-continuous-innovation-through-cloud-initiative, (accessed on 19 May 2025); 3 https://www.offshore-energy.biz/dutch-portbase-connects-with-tradelens-blockchain-platform/, (accessed on 24 April 2025); 4 https://www.supplychaindive.com/news/Rotterdam-blockchain-test-digital-booking/540342/, (accessed on 31 May 2025); 5 https://www.ship-technology.com/news/blockchain-port-of-rotterdam/, (accessed on 24 May 2025); 6 https://www.ledgerinsights.com/port-of-rotterdam-msc-cma-cgm-to-pilot-blockchain-tokens-for-container-release/, (accessed on 24 May 2025); 7 https://www.porttechnology.org/news/port-of-rotterdam-introduces-quay-connect-blockchain-technology/, (accessed on 3 June 2025); 8 https://www.porttechnology.org/news/rotterdam_to_deliver_blockchain_boost/, (accessed on 12 May 2025); 9 https://www.portbase.com/en/news/launch-of-portbase-marketplace/, (accessed on 31 May 2025); 10 https://www.portbase.com/en/news/hutchison-ports-ect-rotterdam-will-start-sharing-data-elements-in-cargo-controller-by-december-2023/, (accessed on 7 May 2025); 11 https://www.portofrotterdam.com/en/news-and-press-releases/secure-data-sharing-in-the-port-of-rotterdam, (accessed on 28 May 2025); 12 https://www.portbase.com/en/news/portbase-launches-secure-chain-now-its-up-to-the-market/, (accessed on 17 May 2025); 13 https://portxl.org/alumni-feature/t-mining-secure-container-release/, (accessed on 29 May 2025); 14 https://www.puertovalparaiso.cl/puerto-valparaiso-expone-sobre-avances-tecnologicos-en-la-cadena-logistica, (accessed on 20 May 2025); 15 https://www.malagahoy.es/empresas-al-dia/Indra-participa-digital-chileno-Valparaiso_0_1642936229.html, (accessed on 21 May 2025); 16 https://www.puertovalparaiso.cl/noticias/puerto-valparaiso-obtiene-premio-asiva-por-pcs-silogport-3, (accessed on 26 April 2025); 17 https://www.noviscorp.com/us/sap/port-of-valparaiso-upgrades-its-sap-systems-successfully/, (accessed on 30 May 2025); 18 * https://www.empresaoceano.cl/puertos/valparaa-so/tps/tps-es-el-primer-terminal-portuario-en-chile-que-se-une-a-la-plataforma, (accessed on 6 May 2025); 19 * https://www.pugh.cl/pugh/sala-de-prensa/2020/proyecto-busca-convertir-a-valparaiso-en-la-primera-region-con, (accessed on 7 June 2025); 20 https://es.linkedin.com/pulse/puerto-san-antonio-avanza-firmemente-en-la-implementaci%C3%B3n-qh0le?utm_source=chatgpt.com, (accessed on 22 April 2025); 21 https://www.puertosanantonio.com/puerto-san-antonio-destaco-avances-en-la-implementacion-del-port, (accessed on 11 April 2025); 22 https://www.soychile.cl/San-Antonio/Puerto-y-Logistica/2025/03/04/897438/puerto-san-antonio-digitalizacion-tecnologia.html, (accessed on 1 April 2025); 23 https://www.atalayar.com/en/articulo/economy-and-business/indra-digitises-processes-and-operations-in-the-logistics-chain-of-the-port-of-san-antonio-in-chile/20250129133000210400.html, (accessed on 3 April 2025); 24 https://porthink.com/pcs-puerto-de-san-antonio-revolucionando-la-gestion-portuaria, (accessed on 6 May 2025); 25 https://www.aapaseaports.com/index.php/2023/06/16/outer-harbor-the-largest-port-expansion-project-in-chiles-history/, (accessed on 25 April 2025); 26 https://www.infraestructurapublica.cl/puerto-san-antonio-avanza-en-la-tramitacion-ambiental-del-proyecto-puerto-exterior-y-se-prepara-para-adjudicar-obras-en-2026/, (accessed on 14 April 2025); 27 * https://es.cointelegraph.com/news/chile-the-san-antonio-international-terminal-registered-40-000-movements-with-blockchain, (accessed on 5 May 2025); 28 https://alog.cl/puerto-san-antonio-presenta-el-nuevo-port-community-system/, (accessed on 7 June 2025);29 https://inspenet.com/articulo/tecnologia-en-puertos-maritimos-del-futuro/, (accessed on 29 April 2025); 30 https://www.bestpractice.ai/ai-case-study-best-practice/the_port_of_rotterdam_authority_reduces_vessel_waiting_times_by_20%25_with_machine_learning, (accessed on 28 May 2025); 31 https://www.portofrotterdam.com/en/news-and-press-releases/port-rotterdam-authority-tests-autonomous-navigation-floating-lab, (accessed on 28 May 2025); 32 https://portalportuario.cl/gerente-de-logistica-de-puerto-valparaiso-apunta-a-la-implementacion-de-inteligencia-artificial-para-mejorar-procesos/, (accessed on 9 June 2025); 33 https://portalportuario.cl/puerto-valparaiso-reduce-congestion-en-ruta-la-polvora-gracias-a-uso-de-inteligencia-artificial/, (accessed on 7 May 2025); 34 https://www.agendalogistica.cl/puerto-de-valparaiso-valparaiso-zeal/puerto-de-valparaiso-camiones-que-ingresan-al-zeal-redujeron-su-tiempo-de-espera-en-15-minutos/1730118, (accessed on 29 May 2025); 35 https://www.empresaoceano.cl/con-inteligencia-artificial-sti-refuerza-seguridad-y-optimiza-operaciones, (accessed on 7 May 2025); 36 https://www.soychile.cl/San-Antonio/Sociedad/2024/12/13/887814/sti-utilizara-ia-camaras.html, (accessed on 11 April 2025); 37 https://portalportuario.cl/san-antonio-terminal-internacional-refuerza-seguridad-y-optimiza-operaciones-con-inteligencia-artificial/, (accessed on 6 June 2025); 38 https://dcsa.org/newsroom/track-trace-ready-for-use-in-port-of-rotterdam, (accessed on 15 May 2025); 39 https://www.agendalogistica.cl/digitalizacion-logistica-port-communty-system/diego-alarcon-en-el-puerto-de-valparaiso-estamos-llegando-al-100-de-digitalizacion/1817171, (accessed on 19 April 2025); 40 https://www.puertovalparaiso.cl/silogport-prepara-upgrade-tecnologico-y-logistico-para-que-valparaiso-se, (accessed on 12 May 2025); 41 https://www.indracompany.com/es/noticia/indra-digitaliza-procesos-operaciones-cadena-logistica-puerto-san-antonio-chile, (accessed on 7 June 2025); 42 https://www.portofrotterdam.com/en/news-and-press-releases/port-rotterdam-puts-internet-things-platform-operation, (accessed on 3 June 2025); 43 https://airspan.com/project/port-of-rotterdam/, (accessed on 7 June 2025); 44 https://blogs.cisco.com/industrial-iot/cisco-edge-intelligence-globally-available-as-port-of-rotterdam-completes-field-trial, (accessed on 1 May 2025); 45 https://maritimefairtrade.org, (accessed on 19 April 2025); 46 https://www.seatrade-maritime.com/ports-logistics/port-of-rotterdam-teams-up-with-ibm-to-digitise-operations-using-iot, (accessed on 3 June 2025); 47 https://aquisanantonio.cl/puerto-san-antonio-inicia-implementacion-del-port-community-system/, (accessed on 7 May 2025); 48 https://www.huawei.com/en/news/2018/11/huawei-KPN-Shell-5G-Applications-Rotterdam, (accessed on 14 April 2025); 49 https://www.ericsson.com/en/cases/2019/rotterdam-world-gateway–empower-digital-port-with-private-lte, (accessed on 31 May 2025); 50 https://www.rcrwireless.com/20190306/internet-of-things/rotterdam-extends-private-lte-setup, (accessed on 9 June 2025); 51 https://www.fierce-network.com/private-wireless/private-lte-keeps-rotterdam-terminal-afloat, (accessed on 9 May 2025); 52 https://s3.amazonaws.com/gobcl-prod/public_files/Campa%C3%B1as/Cuenta-P%C3%BAblica-2023/CP-sectoriales/Ministerio-de-Transportes-y-Telecomunicaciones.pdf, (accessed on 7 May 2025); 53 https://www.nperf.com/en/map/CL/3868626.Valparaiso/163636.Claro-Movil/signal?ll=-33.03932&lg=-71.62725&zoom=12, (accessed on 18 April 2025); 54 https://portalportuario.cl/nokia-es-seleccionada-para-desplegar-red-privada-lte-industrial-en-san-antonio-terminal-internacional/?utm_source=chatgpt.com, (accessed on 4 April 2025); 55 https://safety4sea.com/san-antonio-terminal-chile-to-improve-connectivity/, (accessed on 3 May 2025); 56 https://www.ericsson.com/es/blog/latin-america/2021/el-5g-es-el-ingrediente-clave-para-los-puertos-sostenibles-y-conectados-del-futuro, (accessed on 30 May 2025); 57 https://aquisanantonio.cl/san-antonio-terminal-internacional-es-el-primer-puerto-en-contar-con-red-lte-4g-privada/, (accessed on 13 May 2025); 58 https://www.opportimes.com/en/port-of-rotterdam-the-digital-twin/, (accessed on 6 June 2025); 59 https://www.worldports.org/digital-twins-revolutionizing-seaports/, (accessed on 2 May 2025); 60 https://www.asisonline.org/security-management-magazine/articles/2024/04/industry-news/, (accessed on 15 May 2025); 61 https://www.fundacion.valenciaport.com/en/project/logistics-simulation-chile-multidimensional-survey-of-logistics-chain-procedures-for-the-port-of-san-antonio-and-proposed-optimization-solutions/, (accessed on 10 June 2025); 62 https://www.ecoports.com/news/port-of-rotterdam-gets-pers-certified-for-the-fifth-time, (accessed on 12 May 2025); 63 https://uk-ports.org/espo-congratulates-port-of-rotterdam-for-renewing-its-ecoports-environmental-management-standard-pers/, (accessed on 23 April 2025); 64 https://www.cadenadesuministro.es/monograficos/espsmartports24/nuevos-avances-digitalizacion-bunkering_1506400_102.html, (accessed on 11 April 2025); 65 https://inspenet.com/noticias/el-puerto-de-rotterdam-disminuyo-emisiones/, (accessed on 20 May 2025); 66 https://www.mdpi.com/2071-1050/13/7/3959, (accessed on 5 June 2025); 67 https://www.portofrotterdam.com/en/about-port-authority/port-authority-society/corporate-social-responsibility, (accessed on 30 April 2025); 68 https://publications.portofrotterdam.com/port-of-rotterdam-magazine-editie-2-2024/building-the-future, (accessed on 27 May 2025); 69 https://www.netherlandscircularhotspot.nl/case/port-of-rotterdam-cooperation-on-a-local-level/, (accessed on 18 April 2025); 70 https://www.puertovalparaiso.cl/puerto-valparaiso-recibe-certificaciones-iso-aplicables-a-servicios-de, (accessed on 8 April 2025); 71 https://www.puertovalparaiso.cl/puerto-valparaiso-ratifica-su-compromiso-sostenible-con-el-sello-huella, (accessed on 14 May 2025); 72 https://www.abs-qe.com/Knowledge/Projects/Chilean-Maritime-Terminal-Optimizes-Energy-Efficiency-and-Environmental-Responsibility-with-ISO-Certification/, (accessed on 28 April 2025); 73 https://www.puertovalparaiso.cl/epv/site/docs/20240404/20240404165521/1604679005_medioambienteyprotecciondelentorno.pdf, (accessed on 1 April 2025); 74 https://municipalidaddevalparaiso.cl/category/medio-ambiente/, (accessed on 1 May 2025); 75 https://www.indracompany.com/es/noticia/indra-lidera-transformacion-digital-puerto-valparaiso-eficiente-sostenible-operacion, (accessed on 17 May 2025); 76 https://www.unep.org/explore-topics/transport/what-we-do/global-clean-ports/port-valparaiso-chile, (accessed on 26 April 2025); 77 https://www.maritimoportuario.cl/mp/epv-destaca-el-acuerdo-por-valparaiso-en-su-reporte-integrado-2023/, (accessed on 30 April 2025); 78 https://portalportuario.cl/puerto-valparaiso-solicita-ampliacion-de-plazo-para-evaluacion-ambiental-del-proyecto-terminal-2/, (accessed on 3 June 2025); 79 https://logistica360chile.cl/san-antonio-terminal-internacional-renueva-certificaciones-iso-9001-e-iso-14001/, (accessed on 30 May 2025); 80 https://www.empresaoceano.cl/sti-es-el-primer-puerto-en-obtener-certificacion-de-excelencia-huella, (accessed on 23 April 2025); 81 https://portalportuario.cl/fepasa-consigue-sello-de-cuantificacion-por-medicion-de-huella-de-carbono/, (accessed on 26 April 2025); 82 https://www.puertosanantonio.com/medio-ambiente-y-salud, (accessed on 26 April 2025); 83 https://www.puertosanantonio.com/politica-de-sostenibilidadepsa, (accessed on 5 June 2025); 84 https://www.colsa.cl/quienes_somos/, (accessed on 15 April 2025); 85 https://www.puertosanantonio.com/estrategia-ambiental, (accessed on 24 April 2025); 86 https://www.rsm.nl/news/detail/15570-grant-for-research-on-port-automation-and-inequality/, (accessed on 1 May 2025); 87 https://theloadstar.com/maersk-skips-call-at-rotterdam-as-labour-issues-bring-delay/, (accessed on 10 June 2025); 88 https://www.sevenseasworldwide.com/blog/posts/2025/february/strike-action-at-the-port-of-rotterdam-causing-delays/, (accessed on 28 April 2025); 89 https://blogs.tradlinx.com/lsp-alerts-rotterdam-antwerp-munich-facing-delays/, (accessed on 3 May 2025); 90 https://ritra.nl/en/strikes-cause-delays-for-shippers/, (accessed on 22 May 2025);91 https://www.empresaoceano.cl/puerto-valparaiso-recibe-acreditacion-sac-por-parte-de-ist, (accessed on 31 May 2025); 92 https://www.intelligentcio.com/latam/2022/03/04/port-of-valparaiso-increases-its-security-to-the-highest-international-standards/, (accessed on 10 April 2025); 93 https://resilientmaritimelogistics.unctad.org/guidebook/case-study-7-port-valparaiso-chile, (accessed on 13 May 2025); 94 https://www.24horas.cl/actualidad/nacional/comienza-paro-portuario-barricadas-en-valparaiso-san-antonio-tocopilla, (accessed on 25 April 2025); 95 https://www.portalfruticola.com/noticias/2024/04/04/actualizacion-paro-portuario-en-chile/, (accessed on 30 April 2025); 96 https://www.adnradio.cl/2024/04/04/trabajadores-portuarios-de-chile-inician-paro-nacional-hoy-jueves-4-de-abril-exigiendo-respuestas-del-gobierno/, (accessed on 20 May 2025); 97 https://www.3blmedia.com/news/dp-world-launches-revolutionary-moormastertm-technology-chile, (accessed on 19 April 2025); 98 https://www.agendalogistica.cl/conflicto-laboral-ley-de-puertos-paro-laboral/trabajadores-de-la-bahia-de-san-antonio-inician-movilizacion-exigiendo-ley-de-puertos/1756166, (accessed on 7 April 2025); 99 https://www.sanantonio.cl/municipalidad/noticias/item/13447-san-antonio-conmemora-a-lideres-sindicales-asesinados-en-1973-en-homenaje-a-la-lucha-por-derechos-laborales.html, (accessed on 25 April 2025); 100 https://www.biobiochile.cl/noticias/economia/negocios-y-empresas/2018/06/22/la-conflictiva-relacion-laboral-que-anticipo-al-cierre-de-maersk-en-san-antonio.shtml, (accessed on 3 April 2025); 101 https://www.portofrotterdam.com/en/about-port-authority/port-authority-society/corporate-citizenship, (accessed on 1 May 2025); 102 https://knowledgehub.clc.gov.sg/publications-library/rotterdam-port-and-the-city-heart-of-innovation-clc, (accessed on 27 May 2025); 103 https://www.rotterdamportwelfare.com/en/volunteer-initiatives/submit-a-project/, (accessed on 12 May 2025); 104 https://1library.net/article/guaranteeing-the-support-of-the-local-population.zw1wmrvq?utm_source=chatgpt.com#google_vignette, (accessed on 30 May 2025); 105 https://www.puertovalparaiso.cl/puertovalparasolanzareporteintegrado2024, (accessed on 26 May 2025); 106 https://www.trade.gov/market-intelligence/chile-marine-technology-seaports-expansion-projects-and-concessions, (accessed on 29 April 2025); 107 https://cooperativa.cl/noticias/pais/region-de-valparaiso/gobierno-reactivo-obras-en-el-parque-baron-de-valparaiso/2025-01-08/210716.html, (accessed on 11 April 2025); 108 https://www.empresaoceano.cl/vecinos-y-comercio-valoran-apertura-de-baron-pero-discrepan-sobre-sus-usos, (accessed on 5 May 2025); 109 https://www.upla.cl/noticias/2018/08/21/proyecto-parque-baron-sumo-el-apoyo-del-consejo-de-rectores-de-valparaiso/, (accessed on 14 April 2025); 110 https://www.observador.cl/mas-de-11-mil-portenos-votaron-para-elegir-proyecto-urbanistico-en-el-muelle-baron/, (accessed on 29 April 2025); 111 https://www.elmostrador.cl/mercados/2018/01/04/se-acabo-el-mall-baron-en-valparaiso/?, (accessed on 7 May 2025); 112 https://www.biobiochile.cl/noticias/nacional/region-de-valparaiso/2019/02/01/transportistas-y-portuarios-rechazan-construccion-de-paseo-peatonal-en-muelle-baron-de-valparaiso.shtml, (accessed on 26 May 2025); 113 https://www.aivp.org/en/newsroom/new-port-city-road-map-for-valparaiso-chile/, (accessed on 29 May 2025); 114 https://sustainableworldports.org/project/valparaiso-port-company-valparaiso_puerto-plus-project/, (accessed on 23 April 2025); 115 https://www.asiva.cl/2024/06/03/puerto-valparaiso-obtiene-premio-asiva-por-silogport-3-innovadora-tecnologia-que-optimiza-los-procesos-logisticos/, (accessed on 24 April 2025); 116 https://www.puertovalparaiso.cl/puerto-valparaiso-destaca-avances-y-celebra-a-organizaciones?, (accessed on 4 June 2025); 117 https://www.puertovalparaiso.cl/fondos-concursables-puerto-valparaiso-aumentan-recursos-y-numero-de, (accessed on 10 April 2025); 118 https://www.tps.cl/mision-cumplida-iniciativas-sociales-son-ejecutadas-gracias-a-los-fondos, (accessed on 29 April 2025); 119 https://www.mundomaritimo.cl/noticias/consejo-ciudad-puerto-san-antonio-realiza-su-primera-sesion-2025-con-el-desafio-de-optimizar-el-trabajo-de-sus-comisiones, (accessed on 5 June 2025); 120 https://www.revistalogistec.com/inicio/noticias-industria/6186-puerto-san-antonio-y-el-municipio-firman-una-agenda-de-trabajo-para-el-ano-2025-que-incluye-proyectos-de-desarrollo-urbano, (accessed on 6 May 2025); 121 https://www.aivp.org/en/newsroom/san-antonio-port-incentivising-port-city-integration-through-citizen-participation/, (accessed on 16 May 2025); 122 https://eldesconcierto.cl/2023/04/03/puerto-exterior-de-san-antonio-se-aplaza-2-anos-para-responder-observaciones-ambientales, (accessed on 4 June 2025); 123 https://eldesconcierto.cl/2020/05/06/organizaciones-sociales-y-ambientales-advierten-sobre-los-impactos-del-proyecto-puerto-exterior-de-san-antonio, (accessed on 11 April 2025); 124 https://es.wikipedia.org/wiki/Conflicto_socioambiental_por_el_megapuerto_exterior_de_San_Antonio, (accessed on 5 April 2025); 125 https://g5noticias.cl/2022/09/22/core-de-valparaiso-suspende-pronunciamiento-sobre-proyecto-puerto-exterior-de-san-antonio/, (accessed on 17 May 2025); 126 https://www.elespectador.cl/dirigentes-portuarios-de-san-antonio-manifestaron-preocupacion-por-el-atraso-y-concrecion-del-puerto-exterior/, (accessed on 23 April 2025); 127 https://portalportuario.cl/ambientalistas-piden-retirar-del-sea-el-proyecto-de-puerto-exterior-de-san-antonio/, (accessed on 23 May 2025); 128 https://portalportuario.cl/dirigente-de-san-antonio-advierte-a-sus-pares-de-valparaiso-que-el-puerto-exterior-es-una-politica-de-estado/, (accessed on 23 April 2025); 129 https://infoinvi.uchilefau.cl/san-antonio-los-lamentos-de-una-ciudad-fallida/, (accessed on 25 April 2025); 130 https://gensuite.cl/medio-ambiente/san-antonio-historia-proyectos-fallidos/, (accessed on 10 April 2025); 131 https://www.puertosanantonio.com/nosotros/comunidades, (accessed on 23 April 2025); 132 https://portal.sanantonio.cl/municipalidad/noticias/item/13268-los-habitantes-de-san-antonio-se-unen-para-honrar-el-patrimonio-cultural-y-local-de-su-comuna.html, (accessed on 31 May 2025); 133 https://www.puertosanantonio.com/prensa/copia-de-la-delegada-presidencial-caroline-sireau-visito-el-nuevo, (accessed on 14 May 2025); 134 https://elproa.cl/2025/05/puerto-san-antonio-inauguro-segunda-etapa-del-paseo-borde-costero-norte/, (accessed on 26 May 2025); 135 https://www.aivp.org/es/newsroom/puerto-san-antonio-fomentando-la-integracion-ciudad-puerto-a-traves-de-la-participacion-ciudadana/, (accessed on 10 May 2025).
Note: * operational 2019–2022.

References

  1. Belmoukari, B.; Audy, J.F.; Forget, P. Smart port: A systematic literature review. Eur. Transp. Res. Rev. 2023, 15, 4. [Google Scholar] [CrossRef]
  2. Yang, Y.C.; Ge, Y.E. Adaptation strategies for port infrastructure and facilities under climate change at the Kaohsiung port. Transp. Policy 2020, 97, 232–244. [Google Scholar] [CrossRef]
  3. Kearney, A.; Harrington, D.; Kelliher, F. Executive capability for innovation: The Irish seaports sector. Eur. J. Train. Dev. 2018, 42, 342–361. [Google Scholar] [CrossRef]
  4. Sekar, D.M. Efficiency of Port Operation Its Influence on Supply Chain Network Design. Manag. Account. J. 2022, 57, 39. [Google Scholar] [CrossRef]
  5. Yang, B.; Mao, J. Knowledge Service Model of Port Supply Chain Enterprise Based on Ontology. J. Phys. Conf. Ser. 2020, 1575, 12003. [Google Scholar] [CrossRef]
  6. Wong, K.H.T.; Shou, E.C.; Zhang, H.; Ng, A.K.Y. Strategy formulation of new generation ports: A case study of Hong Kong International Terminals Ltd. (HIT). Res. Transp. Bus. Manag. 2017, 22, 239–254. [Google Scholar] [CrossRef]
  7. United Nations. Reveiw of Maritime Transport 2018; United Nations: New York, NY, USA, 2019. [Google Scholar]
  8. Yagci, M.; Noordali, M. Maritime Trade: Riding the Waves of Commerce and Weathering the Storms of Disruption; Islamic Development Bank Institute: Jeddah, Saudi Arabia, 2024. [Google Scholar] [CrossRef]
  9. Obasi, C.; Oyakegha, S.; Monday, O.A. Port Logistics and Supply Chain Management: An Empirical Review. Afr. J. Econ. Sustain. Dev. 2024, 7, 82–91. [Google Scholar] [CrossRef]
  10. Chen, S.; Wang, Z.; Xiao, G. Quantitative analysis of the impact of port economic development on maritime logistics and supply chain efficiency. Appl. Math. Nonlinear Sci. 2024, 9, 1–15. [Google Scholar] [CrossRef]
  11. Yarova, N.; Vorkunova, O. Global economic concept of creating a logistics center based on a maritime commercial port. Dev. Manag. Entrep. Methods Transp. 2022, 81, 27–42. [Google Scholar] [CrossRef]
  12. Flynn, M.; Lee, T.; Notteboom, T. The next step on the port generations ladder: Customer-centric and community ports. In Current Issues in Shipping, Ports and Logistics; Notteboom, T., Ed.; UPA—University Press Antwerp: Antwerp, Belgium, 2011; pp. 497–510. [Google Scholar]
  13. Sadia, R.; Tuli, F.A.; Lal, K. Digitization History and its Impact on the Economy, Employment, and Society. Glob. Discl. Econ. Bus. 2023, 12, 15–24. [Google Scholar] [CrossRef]
  14. EnerTIC. XIII Guía de Referencia Smart Energy; Technical Report; Plataforma enerTIC: Madrid, Spain, 2024. [Google Scholar]
  15. Molavi, A.; Lim, G.J.; Race, B. A framework for building a smart port and smart port index. Int. J. Sustain. Transp. 2020, 14, 686–700. [Google Scholar] [CrossRef]
  16. Jahn, C.; Nellen, N. Smart Port Concept: Strategic Development, Best Practices, Perspectives of Development. In Arctic Maritime Logistics: The Potentials and Challenges of the Northern Sea Route; Ilin, I., Devezas, T., Jahn, C., Eds.; Contributions to Management Science; Springer International Publishing: Cham, Switzerland, 2022; pp. 81–93. [Google Scholar] [CrossRef]
  17. Behdani, B. Port 4.0: A conceptual model for smart port digitalization. Transp. Res. Procedia 2023, 74, 346–353. [Google Scholar] [CrossRef]
  18. Kusumawati, E.D.; Karjono, K.; Karmanis, K. Review of Port Management Integrated Digitization System: A Pathway to Efficient and Sustainable Port Operations. Marit. Park J. Marit. Technol. Soc. 2023, 2, 52–57. [Google Scholar] [CrossRef]
  19. Paliwal, T.; Sikdar, A.; Kachhi, Z. Integration of Advanced Technologies for Industry 4.0. In AI-Driven IoT Systems for Industry 4.0; Jose, D., Nanjundan, P., Paul, S., Mohanty, S.N., Eds.; CRC Press: Boca Raton, FL, USA, 2024; pp. 114–142. [Google Scholar] [CrossRef]
  20. Molina, R. La revolución digital del mar: Los puertos del futuro. Rev. Obras Públicas 2018, 165, 66–71. [Google Scholar]
  21. Durán, C.; Fernández-Campusano, C.; Carrasco, R.; Carrillo, E. DMLBC: Dependable machine learning for seaports using blockchain technology. J. King Saud Univ.-Comput. Inf. Sci. 2024, 36, 101918. [Google Scholar] [CrossRef]
  22. Kaliszewski, A. Porty pia̧tej oraz szóstej generacji (5GP, 6GP)—ewolucja ekonomicznej i społecznej roli portów. Stud. Mater. Inst. Transp. Handlu Morskiego 2017, 14, 93–123. [Google Scholar] [CrossRef]
  23. Vetrivel, S.C.; Mohanasundaram, T. Industry 5.0 From Automation to Autonomy: Engineering the Shift. In Innovations in Engineering and Food Science; Mehta, S., Islam, F., Imran, A., Eds.; IGI Global: Hershey, PA, USA, 2024; Chapter 4; pp. 88–118. [Google Scholar] [CrossRef]
  24. Valionienė, E.; Župerkienė, E. Port-City Cultural Interaction’s Influence on the Sustainable Coastal Development. Cah. Sci. Transp.-Sci. Pap. Transp. 2024, 82, 133–151. [Google Scholar] [CrossRef]
  25. Hirata, E.; Watanabe, D.; Lambrou, M. Shipping Digitalization and Automation for the Smart Port. In Supply Chain—Recent Advances and New Perspectives in the Industry 4.0 Era; Bányai, T., Bányai, Á., Kaczmar, I., Eds.; IntechOpen: Rijeka, Croatia, 2022; Chapter 7; p. 102015. [Google Scholar] [CrossRef]
  26. Giraldo, J.D.; Castaño, T.; González, J.; López, V.; Velásquez, P.; Tamayo, J. Utilidad de las tecnologías de las industria 4.0 en los smart ports. Ing. Compet. 2024, 26, e-30212814. [Google Scholar] [CrossRef]
  27. Karagkouni, K.; Boile, M. Classification of Green Practices Implemented in Ports: The Application of Green Technologies, Tools, and Strategies. J. Mar. Sci. Eng. 2024, 12, 571. [Google Scholar] [CrossRef]
  28. Abaza, W.; Shalaby, A.F.; Yehia, M. Constructing a Theoretical Framework of the Urban Transformation Processes of the Port City Interface towards Resilient Egyptian Port Cities. Civ. Eng. Archit. 2022, 10, 71–92. [Google Scholar] [CrossRef]
  29. Markus, M.L. Technochange Management: Using IT to Drive Organizational Change. J. Inf. Technol. 2004, 19, 4–20. [Google Scholar] [CrossRef]
  30. Buiza-Camacho-Camacho, G.; del Mar Cerbán-Jiménez, M.; González-Gaya, C. Assessment of the factors influencing on a smart port with an analytic hierarchy process. Rev. DYNA 2016, 91, 498–501. [Google Scholar]
  31. Nguyen, H.P.; Pham, N.D.K.; Bui, V.D. Technical-Environmental Assessment of Energy Management Systems in Smart Ports. Int. J. Renew. Energy Dev. 2022, 11, 889–901. [Google Scholar] [CrossRef]
  32. Iberahim, H.; Albashri, N.Z.; Warnoh, I.E.; Rushdan, A.; Matsuura, Y. Port digitalisation: Technology readiness assessment and segmentation profile of malaysian port operators. J. Sustain. Sci. Manag. 2024, 19, 104–122. [Google Scholar] [CrossRef]
  33. Sooprayen, K.; Van de Kaa, G.; Pruyn, J.F.J. Factors for innovation adoption by ports: A systematic literature review. J. Ocean Eng. Mar. Energy 2024, 10, 953–962. [Google Scholar] [CrossRef] [PubMed]
  34. Nardo, M.; Saisana, M.; Saltelli, A.; Tarantola, S.; Hoffman, A.; Giovannini, E. Handbook on Constructing Composite Indicators: Methodology and User Guide; Number 2005/03; OECD Publishing: Paris, France, 2005. [Google Scholar] [CrossRef]
  35. Greco, S.; Ishizaka, A.; Tasiou, M.; Torrisi, G. On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness. Soc. Indic. Res. 2019, 141, 61–94. [Google Scholar] [CrossRef]
  36. Gonçalves, A.; de Aguiar Dutra, A.R.; de Andrade Guerra, J.B.S.O.; Dutra, A. Ports and Climate Change: A Systematic Review Aligned With the Sustainable Development Goals. Sustain. Dev. 2025, in press. [Google Scholar] [CrossRef]
  37. International Bank for Reconstruction and Development. The Container Port Performance Index 2023: A Comparable Assessment of Performance Based on Vessel Time in Port; World Bank Publications: Washington, DC, USA, 2024; p. 82. [Google Scholar]
  38. Directemar. Boletín Estadistico Marítimo: Datos 2023; Technical Report; Armada de Chile: Valparaíso, Chile, 2024. [Google Scholar]
  39. Igonor, O.S.; Amin, M.B.; Garg, S. The Application of Blockchain Technology in the Field of Digital Forensics: A Literature Review. Blockchains 2025, 3, 5. [Google Scholar] [CrossRef]
  40. Morales-Alarcón, C.H.; Bodero-Poveda, E.; Villa-Yánez, H.M.; Buñay-Guisñan, P.A. Blockchain and Its Application in the Peer Review of Scientific Works: A Systematic Review. Publications 2024, 12, 40. [Google Scholar] [CrossRef]
  41. Almarri, S.; Aljughaiman, A. Blockchain Technology for IoT Security and Trust: A Comprehensive SLR. Sustainability 2024, 16, 10177. [Google Scholar] [CrossRef]
  42. Tsiulin, S.; Reinau, K.H.; Hilmola, O.P.; Goryaev, N.; Karam, A. Blockchain-based applications in shipping and port management: A literature review towards defining key conceptual frameworks. Rev. Int. Bus. Strateg. 2020, 30, 201–224. [Google Scholar] [CrossRef]
  43. Guan, P.; Wood, L.C.; Wang, J.X.; Duong, L.N.K. Blockchain adoption in the port industry: A systematic literature review. Cogent Bus. Manag. 2024, 11, 2431650. [Google Scholar] [CrossRef]
  44. Alahmadi, D.H.; Baothman, F.A.; Alrajhi, M.M.; Alshahrani, F.S.; Albalawi, H.Z. Comparative analysis of blockchain technology to support digital transformation in ports and shipping. J. Intell. Syst. 2021, 31, 55–69. [Google Scholar] [CrossRef]
  45. Kasaei, A.; Albadvi, A. Cargo chain: Cargo Management in Port Logistics with Blockchain Technology. Res. Sq. 2023; preprint. [Google Scholar] [CrossRef]
  46. Andrushchak, I. Aspects of blockchain technology as a component of information security. In Technical, Agricultural and Physical Sciences as the Main Sciences of Human Development; International Science Group, Ed.; Primedia eLaunch LLC: Dallas, TX, USA, 2024; pp. 292–300. [Google Scholar] [CrossRef]
  47. Ahmad, R.W.; Hasan, H.; Jayaraman, R.; Salah, K.; Omar, M. Blockchain applications and architectures for port operations and logistics management. Esearch Transp. Bus. Manag. 2021, 41, 100620. [Google Scholar] [CrossRef]
  48. Farah, M.B.; Ahmed, Y.; Mahmoud, H.; Shah, S.A.; Al-kadri, M.O.; Taramonli, S.; Bellekens, X.; Abozariba, R.; Idrissi, M.; Aneiba, A. A survey on blockchain technology in the maritime industry: Challenges and future perspectives. Futur. Gener. Comput. Syst. 2024, 157, 618–637. [Google Scholar] [CrossRef]
  49. Dwinovan, N.; Dillah, A.; Najmuddin, F.; Verawati, K. Eksplorasi Potensi Penggunaan Blockchain Dalam Optimalisasi Manajemen Pelabuhan di Indonesia: Tinjauan Literatur. J. Multidisiplin Dehasen 2024, 3, 277. [Google Scholar] [CrossRef]
  50. Liaqat, M.; Almazroi, A.A.; Shuja, J.; Mustafa, E. Securing oil port logistics: A blockchain framework for efficient and trustworthy trade documents. PLoS ONE 2024, 19, e0309526. [Google Scholar] [CrossRef]
  51. Derpich, I.; Duran, C.; Carrasco, R.; Moreno, F.; Fernandez-Campusano, C.; Espinosa-Leal, L. Pursuing Optimization Using Multimodal Transportation System: A Strategic Approach to Minimizing Costs and CO2 Emissions. J. Mar. Sci. Eng. 2024, 12, 976. [Google Scholar] [CrossRef]
  52. Durán, C.; Derpich, I.; Carrasco, R. Optimization of Port Layout to Determine Greenhouse Gas Emission Gaps. Sustainability 2022, 14, 13517. [Google Scholar] [CrossRef]
  53. Fuertes, G.; Alfaro, M.; Soto, I.; Carrasco, R.; Iturralde, D.; Lagos, C. Optimization model for location of RFID antennas in a supply chain. In Proceedings of the 2018 7th International Conference on Computers Communications and Control (ICCCC), Oradea, Romania, 8–12 May 2018; pp. 203–209. [Google Scholar] [CrossRef]
  54. Nasih, S.; Arezki, S.; Gadi, T. Blockchain Technology for tracking and tracing containers: Model and conception. Data Metadata 2024, 3, 373. [Google Scholar] [CrossRef]
  55. Wang, S.; Zhen, L.; Xiao, L.; Attard, M. Data-Driven Intelligent Port Management Based on Blockchain. Asia-Pac. J. Oper. Res. 2021, 38, 2040017. [Google Scholar] [CrossRef]
  56. Alkhaldi, B.; Al-Omary, A. Supply-Blockchain Functional Prototype for Optimizing Port Operations Using Hyperledger Fabric. Blockchains 2024, 2, 217–233. [Google Scholar] [CrossRef]
  57. Sangeerth, P.S.; Lakshmy, K.V. Blockchain based Smart Contracts in Automation of Shipping Ports. In Proceedings of the 2021 6th International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 20–22 January 2021; pp. 1248–1253. [Google Scholar] [CrossRef]
  58. Shamala, L.M.; Balasaraswathi, V.R.; Gayathri, R. Revolutionizing Industry and Business Processes with Smart Contracts in Blockchain. In Decentralizing the Online Experience With Web3 Technologies, 11th ed.; Darwish, D., Ed.; IGI Global: Hershey, PA, USA, 2024; pp. 225–245. [Google Scholar] [CrossRef]
  59. An, H.; Yu, L.; Li, Y.; Chen, C.; Liang, X.; Jiao, Y.; Zhao, G. Research on Logistics Traceability Application Based on Blockchain Technology. In Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering (EITCE ’23), New York, NY, USA, 20–22 October 2024; pp. 1691–1696. [Google Scholar] [CrossRef]
  60. Khan, M.R.; Masood, F.; Gul, I.; Khan, M.R. Blockchain Technology in Supply Chain Management: Opportunities and Challenges. In Convergence of Industry 4.0 and Supply Chain Sustainability; IGI Global: Hershey, PA, USA, 2024; pp. 275–295. [Google Scholar] [CrossRef]
  61. Karjono, K.; Kusumawati, E.D.; Pambudi, M.A.L.; Karmanis, K. Maritime Supply Chain Optimisation: A Case Study of Blockchain Integration in Port Logistics Management. Marit. Park J. Marit. Technol. Soc. 2024, 3, 135–141. [Google Scholar] [CrossRef]
  62. Abdallah, R.; Besancenot, J.; Bertelle, C.; Duvallet, C.; Gilletta, F. Assessing Blockchain Challenges in the Maritime Sector. In Blockchain and Applications, 4th International Congress; Prieto, J., Benítez Martínez, F.L., Ferretti, S., Arroyo Guardeño, D., Tomás Nevado-Batalla, P., Eds.; Lecture Notes in Networks and Systems; Springer: Cham, Switzerland, 2023; Volume 595, pp. 13–22. [Google Scholar] [CrossRef]
  63. Kapnissis, G.; Leligou, E.E.; Vaggelas, G. Blockchain Challenges in Maritime Industry: An Empirical Investigation of the Willingness and the Main Drivers of Adoption by the Hellenic Shipping Industry. Open J. Appl. Sci. 2020, 10, 779–790. [Google Scholar] [CrossRef]
  64. Szabo, J.; Bernard, C.; Philip, L. Legal Implications and Challenges of Blockchain Technology and Smart Contracts. Comput. Life 2024, 12, 6–10. [Google Scholar] [CrossRef]
  65. Pejović, Č.; Lee, U. Blockchain Bills of Lading: A New Generation of Electronic Transport Documents. Pored. Pomor. Pravo 2022, 61, 31–62. [Google Scholar] [CrossRef]
  66. Moraes, K.K.; Ganga, G.M.D.; Godinho Filho, M.; Santa-Eulalia, L.A.; Tortorella, G.L. Overcoming technological barriers for blockchain adoption in supply chains: A diffusion of innovation (DOI)-informed framework proposal. Supply Chain Manag. Int. J. 2024, 30, 19–49. [Google Scholar] [CrossRef]
  67. Sinniati, S.; Darma, G.S. The promise of blockchain: Analysing potentials and barriers in supply chain management. BISMA (Bisnis Dan Manaj.) 2023, 16, 87–114. [Google Scholar] [CrossRef]
  68. Jin, H.; Dai, X.; Xiao, J. Towards a Novel Architecture for Enabling Interoperability amongst Multiple Blockchains. In Proceedings of the 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), Vienna, Austria, 2–6 July 2018; pp. 1203–1211. [Google Scholar] [CrossRef]
  69. Xu, Y.; Liu, X.; Cao, X.; Huang, C.; Liu, E.; Qian, S.; Liu, X.; Wu, Y.; Dong, F.; Qiu, C.W.; et al. Artificial intelligence: A powerful paradigm for scientific research. Innovation 2021, 2, 100179. [Google Scholar] [CrossRef] [PubMed]
  70. Malik, S.; Muhammad, K.; Waheed, Y. Artificial intelligence and industrial applications-A revolution in modern industries. Ain Shams Eng. J. 2024, 15, 102886. [Google Scholar] [CrossRef]
  71. Zevallos, J.C.L.N.; Converso, G.; Hernández, A.D.; Doria-Andrade, J. Machine Learning and Automation Systems to Improve Port and Maritime Logistics Efficiency. J. Ecohumanism 2025, 4, 625–631. [Google Scholar] [CrossRef]
  72. Alonso Medina, P.; Sanz Sáiz, R. Soluciones basadas en inteligencia artificial para el desarrollo de negocios en entornos portuarios. Rev. Ord. Sect. Marítimo 2024, 2, 35–51. [Google Scholar] [CrossRef]
  73. Liu, X. The Collaborative Application of Internet of Things and Artificial Intelligence in Smart Logistics. Front. Comput. Intell. Syst. 2023, 6, 35–38. [Google Scholar] [CrossRef]
  74. Chaibi, M.; Daghrir, J. Artificial Intelligence for Predictive Maintenance of Port Equipment: A Revolution in Progress. In Design and Modeling of Mechanical Systems—VI; Chouchane, M., Abdennadher, M., Aifaoui, N., Chaari, F., Bouaziz, S., Affi, Z., Haddar, M., Romdhane, L., Benamara, A., Eds.; Lecture Notes in Mechanical Engineering; Springer: Cham, Switzerland, 2024; pp. 332–340. [Google Scholar]
  75. Nadaf, S. AI for Predictive Maintenance in Industries. Int. J. Res. Appl. Sci. Eng. Technol. 2024, 12, 2013–2017. [Google Scholar] [CrossRef]
  76. Dinh, G.H.; Pham, H.T.; Nguyen, L.C.; Dang, H.Q.; Pham, N.D.K. Leveraging Artificial Intelligence to Enhance Port Operation Efficiency. Pol. Marit. Res. 2024, 31, 140–155. [Google Scholar] [CrossRef]
  77. Mekkaoui, S.E.; Benabbou, L.; Berrado, A. Machine Learning Models for Efficient Port Terminal Operations: Case of Vessels’ Arrival Times Prediction. IFAC-PapersOnLine 2022, 55, 3172–3177. [Google Scholar] [CrossRef]
  78. Lourakis, M.; Pateraki, M. Computer vision for increasing safety in container handling operations. In Human Factors and Systems Interaction, Proceedings of the AHFE 2022, New York, NY, USA, 24–28 July 2022; AHFE International: Honolulu, HI, USA, 2022; Volume 52. [Google Scholar] [CrossRef]
  79. Sivakumar, C.; Vali, T.K.; Reddy, P.S.B.; Meghana, M.L.; Sukumar, Y. AI-Powered Video Surveillance for Enhanced Intrusion Detection. In Proceedings of the 2024 International Conference on IoT Based Control Networks and Intelligent Systems, Bengaluru, India, 17–18 December 2024; pp. 1630–1634. [Google Scholar] [CrossRef]
  80. Sivapriya, J.; Ramani, D.R.; Srivastava, R.P.; Kumar, K.; Nair, R.V. AI-Powered Anomaly and Threat Detection for Surveillance Footage Analysis. In Proceedings of the 2024 8th International Conference on Inventive Systems and Control, Coimbatore, India, 29–30 July 2024; pp. 240–247. [Google Scholar] [CrossRef]
  81. Vasanthageethan, S.G.D.A. Examining of the Impact of Artificial Intelligence on Threat Detection and Response Systems. Res. Arch. Rising Sch. 2025; preprint. [Google Scholar] [CrossRef]
  82. Khot, A.; Potadar, O.; Mitragotri, P. Artificial Intelligence in Cybersecurity. Int. J. Res. Appl. Sci. Eng. Technol. 2024, 12, 2025–2029. [Google Scholar] [CrossRef]
  83. Ceyhun, G.Ç. Recent Developments of Artificial Intelligence in Business Logistics: A Maritime Industry Case. In Digital Business Strategies in Blockchain Ecosystems: Transformational Design and Future of Global Business; Hacioglu, U., Ed.; Contributions to Management Science (MANAGEMENT SC.); Springer International Publishing: Cham, Switzerland, 2019; pp. 343–353. [Google Scholar] [CrossRef]
  84. Durlik, I.; Miller, T.; Kostecka, E.; Łobodzińska, A.; Kostecki, T. Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities. Appl. Sci. 2024, 14, 5994. [Google Scholar] [CrossRef]
  85. Safuan; Syafira, A. Artificial Intelligence in Indonesian Ports: Opportunities and Challenges. Trans. Marit. Sci. 2024, 13, 1–17. [Google Scholar] [CrossRef]
  86. Kapoor, A. Big Data Infrastructure: Integrating Legacy Systems with AI-Driven Platforms. In Proceedings of the 10th International Conference Computer Science & Information Technology, Sydney, Australia, 19–20 October 2024; pp. 145–152. [Google Scholar] [CrossRef]
  87. Reddy Kovvuri, V.K. The Role of AI in Data Engineering and Integration in Cloud Computing. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. 2024, 10, 616–623. [Google Scholar] [CrossRef]
  88. Wang, X.; Wu, Y.C.; Zhou, M.; Fu, H. Beyond surveillance: Privacy, ethics, and regulations in face recognition technology. Front. Big Data 2024, 7, 1337465. [Google Scholar] [CrossRef] [PubMed]
  89. Capasso, C.; Zingoni, A.; Calabro, G.; Sterpa, A. Legal and Technical Answers to Privacy Issues raised by AI-based Facial Recognition Algorithms. In Proceedings of the 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, Milano, Italy, 25–27 October 2023; pp. 575–580. [Google Scholar] [CrossRef]
  90. Alsaigh, R.; Mehmood, R.; Katib, I.; Liang, X.; Alshanqiti, A.; Corchado, J.M.; See, S. Harmonizing AI governance regulations and neuroinformatics: Perspectives on privacy and data sharing. Front. Neuroinform. 2024, 18, 1472653. [Google Scholar] [CrossRef] [PubMed]
  91. Lee, D.; Ko, Y.M. Learning-Driven Berth Allocation Optimization With Port Authority Behavior. IEEE Access 2025, 13, 173832–173843. [Google Scholar] [CrossRef]
  92. Wang, P.; Li, J.; Cao, X. Discrete Dynamic Berth Allocation Optimization in Container Terminal Based on Deep Q-Network. Mathematics 2024, 12, 3742. [Google Scholar] [CrossRef]
  93. Makhado, N.; Paepae, T.; Sejeso, M.; Harley, C. Berth Allocation and Quay Crane Scheduling in Port Operations: A Systematic Review. J. Mar. Sci. Eng. 2025, 13, 1339. [Google Scholar] [CrossRef]
  94. Luan, Y.; Jia, Q.S.; Xing, Y.; Li, Z.; Wang, T. An Efficient Real-Time Railway Container Yard Management Method Based on Partial Decoupling. IEEE Trans. Autom. Sci. Eng. 2025, 22, 14183–14200. [Google Scholar] [CrossRef]
  95. Zheng, S.; Sha, J.; Kong, Y.; Wang, Y. Research on artificial intelligence-driven container relocation problem for green ports. Front. Mar. Sci. 2025, 12, 1614356. [Google Scholar] [CrossRef]
  96. Kolangiammal, S.; Prabha, S.; Sivalakshmi, P.; P, N.; Kalaichelvi, S.; Sujatha, S. Transforming Yard Management for Optimizing Efficiency through IoT and AI Integration. In Proceedings of the 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), Kirtipur, Nepal, 3–5 October 2024; pp. 218–223. [Google Scholar] [CrossRef]
  97. Aslam, S.; Navarro, A.; Aristotelous, A.; Garro Crevillen, E.; Martınez-Romero, A.; Martínez-Ceballos, Á.; Cassera, A.; Orphanides, K.; Herodotou, H.; Michaelides, M.P. Machine Learning-Based Predictive Maintenance at Smart Ports Using IoT Sensor Data. Sensors 2025, 25, 3923. [Google Scholar] [CrossRef]
  98. Kalafatelis, A.S.; Nomikos, N.; Giannopoulos, A.; Alexandridis, G.; Karditsa, A.; Trakadas, P. Towards Predictive Maintenance in the Maritime Industry: A Component-Based Overview. J. Mar. Sci. Eng. 2025, 13, 425. [Google Scholar] [CrossRef]
  99. Samonte, M.J.C.; Laurenio, E.N.B.; Lazaro, J.R.M. Enhancing Port and Maritime Cybersecurity Through AI—Enabled Threat Detection and Response. In Proceedings of the 2024 8th International Conference on Smart Grid and Smart Cities, Shanghai, China, 25–27 October 2024; pp. 412–420. [Google Scholar] [CrossRef]
  100. Pohontu, A.; Ermolai, V. Artificial Intelligence in Maritime Domain Awareness Applications: Trends and Prospects. In Digital Transformation; Ivascu, L., Cioca, L.I., Doina, B., Filip, F.G., Eds.; Intelligent Systems Reference Library; Springer Nature Switzerland: Cham, Switzerland, 2024; Volume 257, pp. 193–204. [Google Scholar] [CrossRef]
  101. Chen, J.; Zhang, Q.; Liang, M.; Peng, C.; Chen, C. Big-data-driven vessel destination prediction for smart port management. Eng. Appl. Artif. Intell. 2025, 154, 110829. [Google Scholar] [CrossRef]
  102. Cacho, J.L.; Tokarski, A.; Thomas, E.; Chkoniya, V. Port Dada Integration: Opportunities for Optimization and Value Creation. In Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry; Chkoniya, V., Ed.; Advances in Business Information Systems and Analytics (ABISA) Book Series; IGI Global: Hershey, PA, USA, 2021; Chapter 1; pp. 1–22. [Google Scholar] [CrossRef]
  103. Moldagulova, A.; Satybaldiyeva, R.; Kuandykov, A. Application of Big Data in Logistics. In Proceedings of the 6th International Conference on Engineering & MIS 2020 (ICEMIS’20), New York, NY, USA, 14–16 September 2020; pp. 1–6. [Google Scholar] [CrossRef]
  104. Jović, M.; Tijan, E.; Marx, R.; Gebhard, B. Big Data Management in Maritime Transport. J. Marit. Transp. Sci. 2019, 57, 123–141. [Google Scholar] [CrossRef]
  105. Mirović, M.; Miličević, M.; Obradović, I. Veliki skupovi podataka u pomorskoj industriji. Naše More 2018, 65, 56–62. [Google Scholar] [CrossRef]
  106. Palippui, H. Integration of Technology and Regulations for Safe and Efficient Marine Logistics. Collab. Eng. Dly. Book Ser. 2024, 2, 1–7. [Google Scholar] [CrossRef]
  107. Ishii, S. Global logistics visibility. In ITS for Freight Logistics; Kawashima, H., Ed.; Institution of Engineering and Technology: Hertfordshire, UK, 2022. [Google Scholar] [CrossRef]
  108. Metzger, A.; Franke, J.; Jansen, T. Data-driven deep learning for proactive terminal process management. In Proceedings of the BPM (Industry Forum), Vienna, Austria, 1–6 September 2019; pp. 190–201. [Google Scholar]
  109. Espinosa-Jaramillo, M.T.; Chenet Zuta, M.E.; Koneti, C.; Jayasundar, S.; Olivares Zegarra, S.d.R.; Carvajal-Ordoñez, V.F.M. Digital twins in supply chain operations bridging the physical and digital worlds using ai. J. Electr. Syst. 2024, 20, 1764–1774. [Google Scholar] [CrossRef]
  110. Sang, X.; Huang, J. Thinking on the Application of Big-Data in Port Security Integration. In Proceedings of the International Conference on Management, Computer and Education Informatization; Advances in Computer Science Research; Atlantis Press: Dordrecht, The Netherlands, 2015; pp. 37–40. [Google Scholar] [CrossRef]
  111. Ayoola, I. Enhancing Business Decision-Making with Advanced Data Visualization: A Sectoral Comparative Analysis. Int. J. Res. Innov. Soc. Sci. 2024, VIII, 1–8. [Google Scholar] [CrossRef]
  112. Wang, K.; Xu, H.; Wang, H.; Qiu, R.; Hu, Q.; Liu, X. Digital twin-driven safety management and decision support approach for port operations and logistics. Front. Mar. Sci. 2024, 11, 1455522. [Google Scholar] [CrossRef]
  113. Herodotou, H.; Aslam, S.; Holm, H.; Theodossiou, S. Big Maritime Data Management. In Maritime Informatics; Lind, M., Michaelides, M., Ward, R., T. Watson, R., Eds.; Progress in IS; Springer International Publishing: Cham, Switzerland, 2021; pp. 313–334. [Google Scholar] [CrossRef]
  114. Borgi, T.; Zoghlami, N.; Abed, M. Big data for transport and logistics: A review. In Proceedings of the 2017 International Conference on Advanced Systems and Electric Technologies, Hammamet, Tunisia, 14–17 January 2017; pp. 44–49. [Google Scholar] [CrossRef]
  115. Li, Z. Big Data Management: Empowering Sustainable Logistics with Data-Driven Operation Optimization. Adv. Econ. Manag. Polit. Sci. 2023, 54, 64–68. [Google Scholar] [CrossRef]
  116. Ramani, K. Impact of Big Data on Security: Big Data Security Issues and Defense Schemes. In Cloud Security: Concepts, Methodologies, Tools, and Applications; IGI Global: Hershey, PA, USA, 2019; pp. 2014–2038. [Google Scholar] [CrossRef]
  117. Ramalingeswara Rao, B.; Amritha, C. Challenges and Opportunities of Big Data Analytics for Maritime and Shipping Industry. Int. J. Eng. Technol. Manag. Sci. 2024, 8, 83–90. [Google Scholar] [CrossRef]
  118. Madhavaram, C.; Sunkara, J.R.; Bauskar, S.R.; Galla, E.P.; Gollangi, H.K. Data-driven management: The impact of visualization tools on business performance. SSRN Electron. J. 2025. [Google Scholar] [CrossRef]
  119. Yang, L.; Li, J.; Elisa, N.; Prickett, T.; Chao, F. Towards Big data Governance in Cybersecurity. Data-Enabled Discov. Appl. 2019, 3, 10. [Google Scholar] [CrossRef]
  120. Charłampowicz, J. Assessment of the Interplay of Various Factors in Port Governance: Development of the Theoretical Framework. Sci. J. Gdynia Marit. Univ. 2025, 133, 7–17. [Google Scholar] [CrossRef] [PubMed]
  121. Issa-Zadeh, S.B.; Garay-Rondero, C.L. Maritime Pilotage and Sustainable Seaport: A Systematic Review. J. Mar. Sci. Eng. 2025, 13, 945. [Google Scholar] [CrossRef]
  122. Sabino Soares, C.C.; da Silva, E.; da Rocha Fernandes, A.; Delcio Parreira, W. Sensores Inteligentes em Sistemas Assistivos para Surdos: Uma Revisão Sistemática da Literatura. In Proceedings of the Anais do XIV Computer on the Beach—COTB’23, Florianópolis, Brasil, 30 March–1 April 2023; Volume 14, pp. 518–520. [Google Scholar] [CrossRef]
  123. Khan, J.Y.; Yuce, M.R. Internet of Things (IoT): Systems and Applications; Jenny Stanford Publishing: New York, NY, USA, 2019; p. 366. [Google Scholar] [CrossRef]
  124. Chhabra, Y.; Jadhav Bhatt, A. IoT Networks. In Network Optimization in Intelligent Internet of Things Applications; Khurana Batra, P., Mehra, P.S., Tanwar, S., Eds.; Chapman and Hall/CRC: New York, NY, USA, 2024; Chapter 1; pp. 3–18. [Google Scholar] [CrossRef]
  125. Kumar, G.; Godihal, J.H. IoT in Logistics and Transportation. In Connected Horizons Exploring IoT Applications in Infrastructure|Agriculture|Environment and Design; Godihal, J.H., Ed.; Iterative International Publishers, Selfypage Developers Pvt Ltd.: Novi, MI, USA, 2024; Volume 3, Chapter 6; pp. 54–62. [Google Scholar] [CrossRef]
  126. Vila Gómez, M. IoT Semantic-Based Monitoring of Infrastructures Using a Microservices Architecture; Tesi Doctoral, upc, Departament de Ciències de la Computació, Universitat Politècnica de Catalunya: Barcelona, Spain, 2024. [Google Scholar] [CrossRef]
  127. Kumar, M.R.; Devi, B.R.; Rangaswamy, K.; Sangeetha, M.; Kumar, K.V.R. IoT-Edge Computing for Efficient and Effective Information Process on Industrial Automation. In Proceedings of the 2023 International Conference on Networking and Communications, Chennai, India, 5–6 April 2023; pp. 1–6. [Google Scholar] [CrossRef]
  128. Rao, B.S.; Gupta, M.M.; Sheikameer Batcha, S.; Katherin Mathew, S.; Ram, M.S.; Imanbayeva, Z. Deep Learning and Internet of Things (IoT) based Industrial Automation and Human Error Reduction. In Proceedings of the 2022 4th International Conference on Inventive Research in Computing Applications, Coimbatore, India, 21–23 September 2022; pp. 917–923. [Google Scholar] [CrossRef]
  129. Hussein, W.N.; Kamarudin, L.M.; Hussain, H.N.; Zakaria, A.; Badlishah Ahmed, R.; Zahri, N.A.H. The Prospect of Internet of Things and Big Data Analytics in Transportation System. J. Phys. Conf. Ser. 2018, 1018, 12013. [Google Scholar] [CrossRef]
  130. Premavathi, T.; Shekhar, A.; Raj, A.; Mohan, K.; Palaniappan, D.; Shukla, M. The Utilization of Internet of Things (IoT) in the Field of Robotics Process Automation. In Applications of New Technology in Operations and Supply Chain Management; Taghipour, A., Ed.; IGI Global: Hershey, PA, USA, 2024; pp. 337–359. [Google Scholar] [CrossRef]
  131. Syaputra, A.; Sutabri, T. Perancangan Sistem Monitoring Barang Logistik Berbasis IoT. Switch J. Sains Dan Teknol. Inf. 2024, 2, 102–111. [Google Scholar] [CrossRef]
  132. Pethe, S.; Sahu, A.; Kodarlikar, S.; Vamshidhar, M. IoT Research in Supply Chain Management and Logistics: Real-Time Asset Tracking and Inventory Management. In Proceedings of the 2024 International Conference on Innovations and Challenges in Emerging Technologies, Nagpur, India, 7–8 June 2024; pp. 1–5. [Google Scholar] [CrossRef]
  133. Priya, S.; Sairam, A.; Azath, H.; Manivannan, S.K.; Mohankumar, N.; Vedasundara Vinayagam, P. Smart Ports Solutions for Cargo Container Tracking and Vessel Traffic Counting Systems using IoT and Cloud Computing. In Proceedings of the 2024 10th International Conference on Communication and Signal Processing, Melmaruvathur, India, 12–14 April 2024; pp. 1106–1111. [Google Scholar] [CrossRef]
  134. Cil, A.Y.; Abdurahman, D.; Cil, I. Internet of Things enabled real time cold chain monitoring in a container port. J. Shipp. Trade 2022, 7, 9. [Google Scholar] [CrossRef]
  135. Radha, C.; Madheswaran, M.; Lokesh, M.; Althaf, M.M. Environmental Monitoring in Internet of Things (IOT). Int. J. Res. Appl. Sci. Eng. Technol. 2024, 12, 1658–1663. [Google Scholar] [CrossRef]
  136. Ahmad, S.J. Environmental Monitoring Using IoT. J. Electr. Comput. Exp. 2023, 1, 36–39. [Google Scholar] [CrossRef]
  137. Nathisiya, B.M.; Radhakrishnan, A. Leveraging IoT Technology for Transformative Impact in the Maritime Sector. Salud Cienc. Tecnol.-Ser. Conf. 2024, 3, 1253. [Google Scholar] [CrossRef]
  138. Bulak, M.E. A Frontier Approach to Eco-Efficiency Assessment in the World’s Busiest Sea Ports. Sustainability 2024, 16, 1142. [Google Scholar] [CrossRef]
  139. Lestre, G.; Robaina, M.; Matias, J.; Oliveira, M. From Port to Policy: Studying Societal Impacts of Seaport Decarbonization. In Proceedings of the 2024 20th International Conference on the European Energy Market, Istanbul, Turkiye, 10–12 June 2024; pp. 1–6. [Google Scholar] [CrossRef]
  140. Merino, J.; Sasidharan, M.; Herrera, M.; Zhou, H.; Crespo del Castillo, A.; Parlikad, A.K.; Brooks, R.; Poulter, K. Lessons learned from an IoT deployment for condition monitoring at the Port of Felixstowe. IFAC-PapersOnLine 2022, 55, 217–222. [Google Scholar] [CrossRef]
  141. Yang, Y.; Zhong, M.; Yao, H.; Yu, F.; Fu, X.; Postolache, O. Internet of things for smart ports: Technologies and challenges. IEEE Instrum. Meas. Mag. 2018, 21, 34–43. [Google Scholar] [CrossRef]
  142. Kori, A.; Channaveeramma, E.; Miskin Quadri, S.; Venkatachalam, K. IOT Privacy & Security. In Futuristic Trends in IOT; Iterative International Publishers, Selfypage Developers Pvt Ltd.: Karnataka, India, 2024; Volume 3, Chapter 3; pp. 50–64. [Google Scholar] [CrossRef]
  143. Anil, A.; Babu, A.R.; Antony, J.; Vilson, K.E.; Koshy, S. Security And Privacy Concern In IoT Devices. Int. J. Eng. Technol. Manag. Sci. 2023, 7, 491–502. [Google Scholar] [CrossRef]
  144. Gao, J.; Sun, Y.; Rameezdeen, R.; Chow, C. Understanding data governance requirements in IoT adoption for smart ports – a gap analysis. Marit. Policy Manag. 2024, 51, 617–630. [Google Scholar] [CrossRef]
  145. Taboada, I.; Shee, H. Understanding 5G technology for future supply chain management. Int. J. Logist. Res. Appl. 2020, 24, 392–406. [Google Scholar] [CrossRef]
  146. Pandikumar, S.; Shaheena, K.V.; Dinesh, T. Upgrading Industrial Automation with 5G and IoT. In Innovations and Trends in Modern Computer Science Technology—Overview, Challenges and Applications; Pandikumar, S., Thakur, M.K., Eds.; QTanalytics India: Delhi, India, 2024; pp. 57–77. [Google Scholar] [CrossRef]
  147. Purohit, A.; Kaushik, R.; Sharma, M.K. 5G and its Impact on IoT: A Review. J. Nonlinear Anal. Optim. 2023, 14, 31–42. [Google Scholar] [CrossRef]
  148. Qi, S.; Sun, W.; Zong, Y. Research on Ship Remote Monitoring and Intelligent Decision-making System Supported by 5G Communication. In Proceedings of the 2024 IEEE 6th International Conference on Civil Aviation Safety and Information Technology, Hangzhou, China, 23–25 October 2024; pp. 1399–1405. [Google Scholar] [CrossRef]
  149. Sharma, N.; Ahlawat, P. 5G for IoT: Between Reality and Friction. In Fundamental and Supportive Technologies for 5G Mobile Networks; El-Kader, S.M.A., Hussein, H., Eds.; IGI Global: Hershey, PA, USA, 2020; Chapter 1; pp. 1–23. [Google Scholar] [CrossRef]
  150. Shravika, J.; Shreya, P.; Shreya, R.; Shreyas, K. The Impact of 5G on IoT Ecosystems. Int. J. Netw. Syst. 2024, 13, 40–44. [Google Scholar] [CrossRef]
  151. HR, N.; Bargavi, S.K.M. 5G IoT Networks. Int. J. Adv. Res. Sci. Commun. Technol. 2024, 4, 679–683. [Google Scholar] [CrossRef]
  152. Goswami, S.; Mondal, S. The role of 5G in enhancing IOT connectivity: A systematic review on applications challenges and future prospects. Big Data Comput. Visions 2024, 4, 314–325. [Google Scholar] [CrossRef]
  153. Galati, M. Unleashing the Power of IoT with 5G and AI: A Paradigm Shift in Connectivity Services. SSRN Electron. J. 2023. [Google Scholar] [CrossRef]
  154. Haider, N.; Baig, M.Z.; Imran, M. Artificial Intelligence and Machine Learning in 5G Network Security: Opportunities, advantages, and future research trends. arXiv 2020, arXiv:2007.04490. [Google Scholar] [CrossRef]
  155. Manda, J.K. Infrastructure Management for 5G Networks: Optimizing Infrastructure Management Practices to Support the Deployment and Maintenance of 5G Networks, Aligning with Your Expertise in Managing Complex Telecom Infrastructure Projects. SSRN Electron. J. 2024. [Google Scholar] [CrossRef]
  156. Pragadeswaran, S.; Subha, N.; Arun Kumar, V.; Vijay Anand, D.; Veera Boopathy, E.; Bharathi, V. The Effects of 5g Technology on Wireless Sensor Networks: Innovations and Challenges. SSRN Electron. J. 2025. [Google Scholar] [CrossRef]
  157. Uusitalo, M.A.; Viswanathan, H.; Kokkoniemi-Tarkkanen, H.; Grudnitsky, A.; Moisio, M.; Harkonen, T.; Yli-Paunu, P.; Horsmanheimo, S.; Samardzija, D. Ultra-Reliable and Low-Latency 5G Systems for Port Automation. IEEE Commun. Mag. 2021, 59, 114–120. [Google Scholar] [CrossRef]
  158. Harish, T.; Suriya, V.; Velan, R. 5G/Next Generation Networks. In Proceedings of the International Conference on Recent Trends in Computing & Communication Technologies, Tamilnadu, India, 20 November 2024; pp. 590–600. [Google Scholar] [CrossRef]
  159. Andriani, O.F.; Nashiruddin, M.I.; Adriansyah, N.M. 5G Private Network Assessment for Port Industrial Area: Study case in Port of Tanjung Priok. In Proceedings of the 2023 IEEE International Conference on Communication, Networks and Satellite, Malang, Indonesia, 23–25 November 2023; pp. 584–590. [Google Scholar] [CrossRef]
  160. Pradeep, S.; Venkatesh, K.; Bhagavatula, S.; Roy, R.; Bhargavi, P.; Gupta, A. The Impact of 5G on Real-Time IoT Data Processing: Exploring Challenges and Innovative Solutions. In Proceedings of the 2024 International Conference on Electrical Electronics and Computing Technologies, Greater Noida, India, 29–31 August 2024; pp. 1–6. [Google Scholar] [CrossRef]
  161. Farroha, B.S.; Farroha, D.L.; Cook, J.D.; Dutta, A. Exploring the security and operational aspects of the 5th generation wireless communication system. In Proceedings of the SPIE 11015, Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2019, Baltimore, MD, USA, 14–18 April 2019; Volume 11015, p. 1101508. [Google Scholar] [CrossRef]
  162. Costa-Pérez, X.; Garcia-Saavedra, A.; Giust, F.; Sciancalepore, V.; Li, X.; Yousaf, Z.; Liebsch, M. Network Slicing for 5G Networks. In 5G Networks: Fundamental Requirements, Enabling Technologies, and Operations Management; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2018; Chapter 9; pp. 327–370. [Google Scholar] [CrossRef]
  163. Das, H.S.; Samanta, S.; Metia, R.; Samanta, D.; Bag, B. Cyber Security Techniques for 5G Networks. In Advanced Cyber Security Techniques for Data, Blockchain, IoT, and Network Protection; IGI Global: Hershey, PA, USA, 2024; pp. 123–146. [Google Scholar] [CrossRef]
  164. Sahu, V.; Sahu, N.; Sahu, R. Challenges and Opportunities of 5G Network: A Review of Research and Development. Am. J. Electr. Comput. Eng. 2024, 8, 11–20. [Google Scholar] [CrossRef]
  165. Messaoudi, F.; Bertin, P.; Ksentini, A. Towards the quest for 5G Network Slicing. In Proceedings of the 2020 IEEE 17th Annual Consumer Communications & Networking Conference, Las Vegas, NV, USA, 10–13 January 2020; pp. 1–7. [Google Scholar] [CrossRef]
  166. Taleb, T.; Mada, B.; Corici, M.I.; Nakao, A.; Flinck, H. PERMIT: Network Slicing for Personalized 5G Mobile Telecommunications. IEEE Commun. Mag. 2017, 55, 88–93. [Google Scholar] [CrossRef]
  167. Subramanian, B.; Al Naamani, K.S.H.; Sagayee, G.M.A. Innovative Architectures and Management Strategies in 5G Communication Networks. Int. J. Comput. Math. Comput. Sci. 2024, 1, 1–7. [Google Scholar] [CrossRef]
  168. Singh, P.K.; Brahma, M.; Nath, P.; Ghosh, U. A Study on Secure Network Slicing in 5G. In Proceedings of the 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops, Bangalore, India, 1–4 May 2023; pp. 52–61. [Google Scholar] [CrossRef]
  169. Taheribakhsh, M.; Jafari, A.; Peiro, M.M.; Kazemifard, N. 5G Implementation: Major Issues and Challenges. In Proceedings of the 2020 25th International Computer Conference, Computer Society of Iran, Tehran, Iran, 1–2 January 2020; pp. 1–5. [Google Scholar] [CrossRef]
  170. Potter, A.; Wang, Y.; Naim, M. Scaling-up 5G adoption in smart ports: Barriers and enablers. Marit. Policy Manag. Manag. 2024, 52, 517–534. [Google Scholar] [CrossRef]
  171. De la Peña Zarzuelo, I.; Freire Seoane, M.J.; López Bermúdez, B.; Pais Montes, C. The Role of Simulation in the Ports and Maritime Industry: Practical Experiences and Outlook for the New Generation of Ports 4.0. In Proceedings of the 2019 World of Shipping Portugal, an International Research Conference on Maritime Affairs Summary Report, Carcavelos, Portugal, 21–22 November 2019; pp. 35–36. [Google Scholar]
  172. David, I.; Syriani, E. Automated Inference of Simulators in Digital Twins. In Handbook of Digital Twins; Lyu, Z., Ed.; CRC Press: Boca Raton, FL, USA, 2024; Chapter 8; pp. 122–148. [Google Scholar] [CrossRef]
  173. Mihai, S.; Yaqoob, M.; Hung, D.V.; Davis, W.; Towakel, P.; Raza, M.; Karamanoglu, M.; Barn, B.; Shetve, D.; Prasad, R.V.; et al. Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects. IEEE Commun. Surv. Tutor. 2022, 24, 2255–2291. [Google Scholar] [CrossRef]
  174. Abdulhayan, S.; Abd, S.A. Big Data and Digital Twins. In Handbook of Industrial and Business Applications with Digital Twins; Krishnan, S., Anand, A.J., Sendhilkumar, S., Eds.; CRC Press: Boca Raton, FL, USA, 2024; Chapter 7; pp. 154–183. [Google Scholar] [CrossRef]
  175. Lachvajderova, L.; Trebuna, M.; Kadarova, J. Unlocking Industry Potential: The Evolution and Impact of Digital Twins. Acta Mech. Slovaca 2024, 28, 46–51. [Google Scholar] [CrossRef]
  176. Eom, J.; Kim, J.; Lee, S.; Yoon, J.; Kim, S. Digital twin development for berthing planning of ships. J. Inst. Control Robot. Syst 2022, 28, 724–732. [Google Scholar] [CrossRef]
  177. Eddy, C.W.; Castanier, M.P.; Wagner, J.R. Predictive Maintenance of a Ground Vehicle Using Digital Twin Technology. SAE Int. J. Adv. Curr. Pract. Mobil. 2024, 7, 865–876. [Google Scholar] [CrossRef]
  178. Yao, H.; Wang, D.; Su, M.; Qi, Y. Application of Digital Twins in Port System. J. Phys. Conf. Ser. 2021, 1846, 12008. [Google Scholar] [CrossRef]
  179. Oliveira, L.; Castro, M.; Ramos, R.; Santos, J.; Silva, J.; Dias, L. Digital Twin for Monitoring Containerized Hazmat Cargo in Port Areas. In Proceedings of the 2022 17th Iberian Conference on Information Systems and Technologies, Madrid, Spain, 22–25 June 2022; pp. 1–4. [Google Scholar] [CrossRef]
  180. Zhou, E. Data-Driven Simulation Optimization in the Age of Digital Twins: Challenges and Developments. In Proceedings of the 2024 Winter Simulation Conference, Orlando, FL, USA, 15–18 December 2024; pp. 31–45. [Google Scholar] [CrossRef]
  181. Ok, S.Y. A Large-Scale 3D Visualization System for Port Container Terminal Simulation. J. Korea Inst. Inf. Commun. Eng. 2015, 19, 119–126. [Google Scholar] [CrossRef]
  182. Pasupuleti, M.K. Revolutionizing Industries with Digital Twin Technology. In Digital Twin Technology; National Education Services: Chicago, IL, USA, 2024; Chapter 7; pp. 61–79. [Google Scholar] [CrossRef]
  183. Chandaluri, R.; Nelakuditi, U. Monograph on Components, Design, and Applications of Digital Twin. Int. J. Next-Gener. Comput. 2022, 13. [Google Scholar] [CrossRef]
  184. Zeneli, M.; Marinova, G. Navigating the Future: Digital Twin in Maritime Industry. In Proceedings of the 2024 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications, Graz, Austria, 9–11 July 2024; pp. 1–6. [Google Scholar] [CrossRef]
  185. Fernandes, A.; Gutierres, D.; Fugihara, M.; De Norman, B. Port Management Digital Twin and Control Tower Integration: An Approach to Support Real-Time Decision Making. In Proceedings of the 2024 Winter Simulation Conference, Orlando, FL, USA, 15–18 December 2024; pp. 2821–2831. [Google Scholar] [CrossRef]
  186. Mohapatra, A. Generative AI for Predictive Maintenance: Predicting Equipment Failures and Optimizing Maintenance Schedules Using AI. Int. J. Sci. Res. Manag. 2024, 12, 1648–1672. [Google Scholar] [CrossRef]
  187. Kane, A.P.; Kore, A.S.; Khandale, A.N.; Nigade, S.S.; Joshi, P.P. Predictive Maintenance using Machine Learning. arXiv 2022, arXiv:2205.09402. [Google Scholar] [CrossRef]
  188. Double Check. Traffic Technol. Int. 2023, 2023, 40–41. [CrossRef]
  189. Bao, X.; Jia, F.; Zhong, J.; Zhang, L.; Liu, C.; Chen, L.; Zheng, Y. Digital twin system for automated container terminal and a case study. In Proceedings of the 2024 IEEE 20th International Conference on Automation Science and Engineering, Bari, Italy, 28 August–1 September 2024; pp. 142–147. [Google Scholar] [CrossRef]
  190. Gebreab, S.; Musamih, A.; Salah, K.; Jayaraman, R.; Boscovic, D. Accelerating Digital Twin Development With Generative AI: A Framework for 3D Modeling and Data Integration. IEEE Access 2024, 12, 185918–185936. [Google Scholar] [CrossRef]
  191. Abayadeera, M.R.; Ganegoda, G. Digital Twin Technology: A Comprehensive Review. Int. J. Innov. Sci. Res. Technol. 2024, 10, 1485–1504. [Google Scholar] [CrossRef]
  192. Ly, R.; Shojaei, A.; Naderi, H. DT-DAO: Digital Twin and Blockchain-Based DAO Integration Framework for Smart Building Facility Management. In Proceedings of the Construction Research Congress 2024, Iowa, IA, USA, 20–23 March 2024; pp. 796–805. [Google Scholar] [CrossRef]
  193. Esiri, A.E.; Sofoluwe, O.O.; Ukato, A. Digital twin technology in oil and gas infrastructure: Policy requirements and implementation strategies. Eng. Sci. Technol. J. 2024, 5, 2039–2049. [Google Scholar] [CrossRef]
  194. Marino, A.; Pariso, P.; Picariello, M. Digital Twin in SMEs: Implementing Advanced Digital Technologies for Engineering Advancements. Macromol. Symp. 2024, 413, 2300176. [Google Scholar] [CrossRef]
  195. Wuni, I.Y.; Abankwa, D.A.; Koc, K.; Adukpo, S.E.; Antwi-Afari, M.F. Critical barriers to the adoption of integrated digital delivery in the construction industry. J. Build. Eng. 2024, 83, 108474. [Google Scholar] [CrossRef]
  196. Tyagi, A.K. Blockchain and Artificial Intelligence for Cyber Security in the Era of Internet of Things and Industrial Internet of Things Applications. In AI and Blockchain Applications in Industrial Robotics; Biradar, R.C., Geetha, D., Tabassum, N., Hegde, N., Lazarescu, M., Eds.; IGI Global: Hershey, PA, USA, 2023; Chapter 7; pp. 171–199. [Google Scholar] [CrossRef]
  197. Trivedi, N.K.; Tiwari, R.G.; Jain, A.K.; Sharma, V.; Gautam, V. Impact Analysis of Integrating AI, IoT, Big Data, and Blockchain Technologies: A Comprehensive Study. In Proceedings of the 2023 3rd Asian Conference on Innovation in Technology, Ravet IN, India, 25–27 August 2023; pp. 1–6. [Google Scholar] [CrossRef]
  198. Nasih, S.; Arezki, S.; Gadi, T. Tracking and Tracing Containers Model Enabled Blockchain Basing on IOT Layers. In Innovations in Smart Cities Applications Volume 7; Ben Ahmed, M., Boudhir, A.A., El Meouche, R., Karas, I.R., Eds.; Lecture Notes in Networks and Systems; Springer Nature Switzerland: Cham, Switzerland, 2024; Volume 906, pp. 136–147. [Google Scholar] [CrossRef]
  199. Du, Y.; Li, C.; Wang, T.; Xu, Y. Special issue on “Smart port and shipping operations” in Maritime Policy & Management. Marit. Policy Manag. 2023, 50, 413–414. [Google Scholar] [CrossRef]
  200. Pathak, J.P.; Singh, K.; Roy, S. Role of Artificial Intelligence and Blockchain on Cyber Security: A PRISMA-Compliant Systematic Literature Review. In Data Visualization Tools for Business Applications; Muniasamy, M.A., Naim, A., Kumar, A., Eds.; IGI Global: Hershey, PA, USA, 2024; Chapter 13; pp. 287–320. [Google Scholar] [CrossRef]
  201. Durlik, I.; Miller, T.; Cembrowska-Lech, D.; Krzemińska, A.; Złoczowska, E.; Nowak, A. Navigating the Sea of Data: A Comprehensive Review on Data Analysis in Maritime IoT Applications. Appl. Sci. 2023, 13, 9742. [Google Scholar] [CrossRef]
  202. Parveen, K. Securing Breakneck Pace of 5G Networks Air Interfaces Through Proactive AI Monitoring. Int. J. Electron. Crime Investig. 2024, 8, 20–26. [Google Scholar] [CrossRef]
  203. Ameh, B. Digital tools and AI: Using technology to monitor carbon emissions and waste at each stage of the supply chain, enabling real-time adjustments for sustainability improvements. Int. J. Sci. Res. Arch. 2024, 13, 2741–2757. [Google Scholar] [CrossRef]
  204. Chung, S.H. Applications of smart technologies in logistics and transport: A review. Transp. Res. Part E Logist. Transp. Rev. 2021, 153, 102455. [Google Scholar] [CrossRef]
  205. Tauseef, M.; Kounte, M.R.; Nalband, A.H.; Ahmed, M.R. Exploring the Joint Potential of Blockchain and AI for Securing Internet of Things. Int. J. Adv. Comput. Sci. Appl. 2023, 14, 885–895. [Google Scholar] [CrossRef]
  206. Muhati, E.; Rawat, D.B.; Sadler, B.M. A New Cyber-Alliance of Artificial Intelligence, Internet of Things, Blockchain, and Edge Computing. IEEE Internet Things Mag. 2022, 5, 104–107. [Google Scholar] [CrossRef]
  207. Vani, G.; Naveenkumar, R.; Singha, R.; Sharkar, R.; Kumar, N. Advancing Predictive Data Analytics in IoT and AI Leveraging Real time Data for Proactive Operations and System Resilience. Nanotechnol. Perceptions 2024, 20, 568–582. [Google Scholar] [CrossRef]
  208. Mukherjee, S.; Gupta, S.; Rawlley, O.; Jain, S. Leveraging big data analytics in 5G-enabled IoT and industrial IoT for the development of sustainable smart cities. Trans. Emerg. Telecommun. Technol. 2022, 33, e4618. [Google Scholar] [CrossRef]
  209. Tyler, N. The Smart Port Network. New Electron. 2020, 53, 10–12. [Google Scholar] [CrossRef]
  210. Yigit, Y.; Nguyen, L.D.; Ozdem, M.; Kinaci, O.K.; Hoang, T.; Canberk, B.; Duong, T.Q. TwinPort: 5G drone-assisted data collection with digital twin for smart seaports. Sci. Rep. 2023, 13, 12310. [Google Scholar] [CrossRef]
  211. Gunturu, V.; Ranga, J.; Murthy, C.R.; Swapna, B.; Balaram, A.; Raja, C. Artificial Intelligence Integrated with 5G for Future Wireless Networks. In Proceedings of the 2023 International Conference on Inventive Computation Technologies, Lalitpur, Nepal, 26–28 April 2023; pp. 1292–1296. [Google Scholar] [CrossRef]
  212. Nian, L.; Zhengwei, Z.; Yuandong, S.; Dongyang, Y. Wisdom Tower Crane Network Control System Based on 5G Technology. In Proceedings of the 2023 International Conference on Computers, Information Processing and Advanced Education, Ottawa, ON, Canada, 26–28 August 2023; pp. 284–287. [Google Scholar] [CrossRef]
  213. Kokkoniemi-Tarkkanen, H.; Horsmanheimo, S.; Grudnitsky, A.; Moisio, M.; Li, Z.; Uusitalo, M.A.; Samardzija, D.; Härkönen, T.; Yli-Paunu, P. Enabling Safe Wireless Harbor Automation via 5G URLLC. In Proceedings of the 2019 IEEE 2nd 5G World Forum, Dresden, Germany, 30 September–2 October 2019; pp. 403–408. [Google Scholar] [CrossRef]
  214. Golovan, A.; Mateichyk, V.; Gritsuk, I.; Lavrov, A.; Smieszek, M.; Honcharuk, I.; Volska, O. Enhancing Information Exchange in Ship Maintenance through Digital Twins and IoT: A Comprehensive Framework. Computers 2024, 13, 261. [Google Scholar] [CrossRef]
  215. Guyo, G.D. The Limitations of Research Findings behind the Veil of Subjectivities: Subjective Values and Extra-Subjective Challenges. Gadaa J. 2024, 7, 108–125. [Google Scholar]
  216. Xylouris, G.; Nomikos, N.; Kalafatelis, A.; Giannopoulos, A.; Spantideas, S.; Trakadas, P. Sailing into the future: Technologies, challenges, and opportunities for maritime communication networks in the 6G era. Front. Commun. Netw. 2024, 5, 1439529. [Google Scholar] [CrossRef]
  217. Pivetta, D.; Dall’Armi, C.; Sandrin, P.; Bogar, M.; Taccani, R. The role of hydrogen as enabler of industrial port area decarbonization. Renew. Sustain. Energy Rev. 2024, 189, 113912. [Google Scholar] [CrossRef]
Figure 1. Ranges of the general assessment scale.
Figure 1. Ranges of the general assessment scale.
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Figure 2. Results of the qualitative assessment of the Ports of Rotterdam, Valparaíso, and San Antonio (Table 5).
Figure 2. Results of the qualitative assessment of the Ports of Rotterdam, Valparaíso, and San Antonio (Table 5).
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Table 1. Port 4.0 assessment scale.
Table 1. Port 4.0 assessment scale.
ValueDescription
1There is no public record or evidence of the existence and/or implementation of the technology analysed (non-existent or not implemented)
2A pilot project or similar initiative is being implemented (incipient presence)
3There is a project in operation, but with limited or expanding scope (observable presence)
4The technology is used significantly in several port operations (established presence)
5The technology has a comprehensive and strategic presence, meaning it is fully integrated into port management and operations (comprehensive and strategic presence and implementation)
Table 2. Port 5.0 assessment scale.
Table 2. Port 5.0 assessment scale.
ValueDescription
1Practices largely absent or problematic
2Minimal compliance with standards in response to pressure
3Systematic efforts observed, although with challenges or areas for improvement
4Sustained good performance and proactive practices
5Benchmark practices at national and international level
Table 3. Ranges of the general assessment scale.
Table 3. Ranges of the general assessment scale.
RankColourDefinition
2 , 3 Early implementation
3 , 4 In transit
4 , 5 Smart Port
Table 4. Qualification of Assessors and Validators.
Table 4. Qualification of Assessors and Validators.
TipeDetail
Assessor 1Economist and expert in port development.
Assessor 2PhD, Expert in International Trade.
Assessor 3PhD, Researcher in data management.
ValidatorPhD, Researcher in logistics and port management.
Note: In order to ensure the reliability of the evaluations, after each evaluator assigned the corresponding scores to each port, in cases where there were differences, they agreed on an appropriate score. Therefore, it is a consensus assessment.
Table 5. Qualitative Assessment Ports of Rotterdam, Valparaíso and San Antonio.
Table 5. Qualitative Assessment Ports of Rotterdam, Valparaíso and San Antonio.
TechnologyPort of RotterdamPort of ValparaísoPort of San Antonio
PCS4.5 1,24 14–163.5 20,28
BC4.5 3–134 17–193.5 21–27
AI5 29–313 32–343.5 35–37
Big data5 29,383 39,402.5 41
IoT5 29,42–4622 47
5G a4 43,48–512 52,533.5 54–57
DT5 58,591 601 61
Port 4.0 (average)4.52.72.8
AssessmentSmart Port 4.0Early ImplementationEarly Implementation
Environmental care5 62–694 34,70–784 25,26,79–85
Working conditions and labour treatment4.5 67,86–903.5 35,91–963.5 37,84,94–100
Community integration4.5 67,101–1043 77,105–1183 83,84,106–135
Partial Port 5.0 (average)4.73.53.5
Port 5.0 (average)4.73.13.1
AssessmentSmart port 5.0In transitionIn transition
Note: Superscripts see Appendix A. a Private industrial networks.
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MDPI and ACS Style

Valenzuela-Silva, L.; Muñoz, M.; Lagos, C.; Sepúlveda-Rojas, J.P.; Carrasco, R. Assessment of Differences Between the Ports of Rotterdam, Valparaíso and San Antonio Towards Smart Ports, Emphasising Digital Technologies. J. Mar. Sci. Eng. 2025, 13, 2220. https://doi.org/10.3390/jmse13122220

AMA Style

Valenzuela-Silva L, Muñoz M, Lagos C, Sepúlveda-Rojas JP, Carrasco R. Assessment of Differences Between the Ports of Rotterdam, Valparaíso and San Antonio Towards Smart Ports, Emphasising Digital Technologies. Journal of Marine Science and Engineering. 2025; 13(12):2220. https://doi.org/10.3390/jmse13122220

Chicago/Turabian Style

Valenzuela-Silva, Luis, Miguel Muñoz, Carolina Lagos, J. P. Sepúlveda-Rojas, and Raúl Carrasco. 2025. "Assessment of Differences Between the Ports of Rotterdam, Valparaíso and San Antonio Towards Smart Ports, Emphasising Digital Technologies" Journal of Marine Science and Engineering 13, no. 12: 2220. https://doi.org/10.3390/jmse13122220

APA Style

Valenzuela-Silva, L., Muñoz, M., Lagos, C., Sepúlveda-Rojas, J. P., & Carrasco, R. (2025). Assessment of Differences Between the Ports of Rotterdam, Valparaíso and San Antonio Towards Smart Ports, Emphasising Digital Technologies. Journal of Marine Science and Engineering, 13(12), 2220. https://doi.org/10.3390/jmse13122220

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