CC BY
2UDK 656.614.3
Korostin Oleksandr
individual researcher, master's degree
INNOVATIONS IN THE AUTOMATION OF ELECTRONIC MESSAGE PROCESSING FOR MARITIME SHIPPING
Abstract: The article investigates innovations in the automation of electronic message processing in the MTSh sector. It analyzes problems of information overload and errors in manual data processing. The potential of applying artificial intelligence (AI) to improve communication efficiency is examined. It highlights that AI technologies, such as natural language processing and machine learning, enhance the accuracy and speed of data processing. Ethical aspects and challenges of AI implementation in the maritime sector are also considered.
Keywords: Automation, electronic messages, maritime shipping, artificial intelligence, natural language processing, machine learning, innovations.
INTRODUCTION
The maritime shipping (MTSh) industry, a vital component of global trade, depends extensively on effective and reliable communication. Email remains a principal method for coordinating operations, managing logistics, and ensuring compliance with international regulations. The growing volume and complexity of email traffic present substantial challenges to traditional processing methods. Problems such as information overload, response delays, and human errors not only reduce operational efficiency but also affect decision-making processes. These difficulties necessitate the exploration of advanced technologies, particularly Artificial Intelligence (AI), to enhance and streamline communication within the maritime sector.
The primary aim of this study is to explore the theoretical foundations of AI-driven automation in email processing for MTSh. The potential benefits, challenges, and ethical considerations related to AI implementation are examined.
ХОЛОДНАЯ НАУКА №6/2024
MAIN PART
Maritime transport continues to navigate the post-COVID-19 landscape, grappling with the aftermath of global supply chain disruptions from 2021-2022, a downturn in the container shipping market, and shifts in trade and shipping patterns. The industry faces numerous challenges, including heightened trade policy tensions and geopolitical frictions, while adapting to changes in globalization patterns. Additionally, the maritime sector must transition towards more sustainable practices, reduce carbon emissions, and embrace digital technologies. The industry's ability to balance these interconnected factors will determine how effectively it can adapt to the evolving operational and regulatory environment while meeting global trade demands. Despite these challenges, the sector remains resilient, with moderate growth expected through 2024-2028. According to UNCTAD forecasts, maritime trade volumes grew by 2.4% in 2023 following a contraction of 0.4% in 2022. The growth rates of freight volume in ton-miles exceed the corresponding indicator in tonnes in 2022 and 2023, and in the forecasts for 2024 (fig. 1).
□ Change in % compared to the previous year
В Growth in sea freight volume in tonnes ] Growth in sea freight volume in ton-miles
10
В л A
Figure 1. Seaborne trade growth, tons and ton-miles [1] Adopting advanced technologies, promoting sustainable practices, and increasing efficiency through digitalization will help the industry sustain resilience and competitiveness in a changing global environment.
ELECTRONIC MESSAGING IN MARITIME TRANSPORT Electronic messaging plays a critical role in the maritime transport sector, facilitating communication between various stakeholders including shipping
companies, port authorities, logistics providers, and regulatory bodies. The effective use of electronic messaging technologies is essential for ensuring the smooth operation of maritime logistics, compliance with international regulations, and timely information exchange. Several key technologies are utilized in electronic messaging within maritime transport, each serving specific purposes:
• Traditional email systems are widely used for general communication, sharing documents, and coordinating logistics. They provide a straightforward and reliable method for exchanging information across various platforms and geographic locations.
• Electronic Data Interchange (EDI) is a standardized method for exchanging business documents between organizations electronically. In maritime transport, EDI facilitates the automated transfer of shipping manifests, bills of lading, customs documents, and other critical data, reducing the need for manual data entry and minimizing errors.
• Maritime Single Window (MSW) systems streamline the submission of regulatory information by allowing maritime stakeholders to submit all necessary documentation through a single electronic platform [2]. This reduces the administrative burden on shipping companies and enhances compliance with international regulations.
Despite the advancements in electronic messaging technologies, the maritime transport sector faces several challenges that hinder the efficient and effective use of these systems (table 1).
Table 1. Challenges in electronic messaging within maritime transport [3,4]
Problem Description Impact
Information overload The high volume of emails and messages can overwhelm users, making it difficult to identify and prioritize critical information. Delays in decision-making, increased risk of missing important messages, and reduced productivity.
Data inconsistency Inconsistent data formats and standards across different systems and stakeholders lead to discrepancies and mi scommunicati on. Increased errors, duplication of efforts, and difficulties in integrating information from multiple sources.
Cybersecurity risks Electronic messaging systems are vulnerable to cyber threats such as Potential data breaches, financial losses, and damage to
phishing, malware, and unauthorized access. reputation.
Regulatory compliance Ensuring compliance with varying international regulations and requirements can be complex and time-consuming. Administrative burden, risk of non-compliance penalties, and delays in operations.
Lack of integration Many electronic messaging systems operate in silos, with limited integration between different platforms and technologies. Inefficiencies in data sharing, redundant processes, and challenges in achieving end-to-end visibility.
Manual processing A significant amount of electronic messaging still involves manual processing and intervention. Increased labor costs, higher risk of human error, and slower processing times.
From the author's perspective, the maritime transport sector's dependence on electronic messaging presents several significant challenges that affect operational efficiency and decision-making. Mistakes arising from information overload, inconsistent data, and manual processing are particularly harmful, as they can result in delays, higher labor costs, and the potential for miscommunication. By integrating AI-driven solutions, the maritime transport sector can greatly enhance its responsiveness, precision, and capacity to adjust to the evolving demands of global trade, thus preserving its resilience and competitiveness in a dynamic operational environment.
THEORETICAL FOUNDATIONS OF AI-ENHANCED EMAIL
AUTOMATION IN MTSH
AI encompasses a wide range of technologies that enable machines to perform tasks that typically require human intelligence. In the context of email automation, several key AI concepts are particularly relevant, including natural language processing (NLP), machine learning (ML), and automated classification systems [5]. These technologies are foundational in developing systems capable of understanding, processing, and managing email communications efficiently.
Natural Language Processing (NLP) is a branch of AI focused on the interaction between computers and human language. NLP enables machines to comprehend, interpret, and generate human language in a valuable way. In email automation, NLP algorithms can analyze the content of emails to extract meaningful
information, classify messages, and generate appropriate responses. Techniques such as sentiment analysis, named entity recognition, and topic modeling are commonly used in NLP to enhance email processing. For instance, sentiment analysis can determine the tone of an email, which is crucial in prioritizing customer service responses, while named entity recognition helps identify key information such as dates, names, and locations.
Another component, ML involves the development of algorithms that allow computers to learn from and make decisions based on data. In email automation, ML models can be trained to recognize patterns and trends within large datasets of email communications. This capability is essential for tasks such as spam detection, predictive text input, and personalized email sorting. Supervised learning, where models are trained on labeled data, and unsupervised learning, which involves finding hidden patterns in unlabeled data, are both widely applied in email automation [6]. Reinforcement learning, another ML approach, can be utilized to optimize email response strategies based on feedback and interaction outcomes.
Automated classification systems in AI are essential for organizing and managing email traffic. These systems utilize NLP and ML techniques to sort emails into predefined categories such as spam, important, promotions, and social updates. The classification relies on the analysis of content, context, and metadata. By automating this process, the need for manual sorting is significantly reduced, which enhances efficiency and minimizes the risk of human error [7]. Advanced algorithms, including deep learning neural networks, achieve high accuracy in email categorization due to their capability to handle large datasets and identify complex patterns.
AI-driven automation in email processing can incorporate additional features such as language translation, summarization, and scheduling. Language translation algorithms enable the automatic translation of emails written in different languages, facilitating seamless communication in multinational maritime operations. Summarization techniques condense lengthy emails into brief summaries, allowing users to quickly grasp the essential information without reading the entire content.
Scheduling algorithms can automate the process of arranging meetings and appointments based on email interactions, streamlining operational logistics.
The integration of NLP, ML, and automated classification systems within AI frameworks offers significant potential to enhance email automation in MTSh. These technologies not only improve the efficiency and accuracy of email processing but also support better decision-making and communication strategies.
Integrating AI into maritime email processing involves several strategic steps and a consideration of both technical and operational challenges. The initial step in this integration process is a comprehensive assessment of the existing email processing system to identify key areas where AI can add value. This involves analyzing the volume and type of emails handled, the common issues encountered, and the specific needs of the organization. Following this assessment, the next step is selecting appropriate AI technologies, such as natural NLP and ML models, tailored to the unique requirements of maritime email communication.
The implementation strategy should include the development of a robust data infrastructure capable of handling large volumes of email data efficiently. This infrastructure must support the training and deployment of AI models, ensuring that they can process and analyze email content accurately and in real-time. Additionally, integrating AI into existing email systems requires the development of custom algorithms to automate tasks such as email categorization, sentiment analysis, and predictive analytics. Training these algorithms involves using historical email data to teach the AI systems to recognize patterns and make informed decisions. In Table 2, the technical and operational challenges of implementing AI in maritime email processing are outlined and their impacts on the industry.
Table 2. Technical and operational challenges of implementing AI in maritime Email
processing [8]
Challenges Description Impact
Data privacy and security Ensuring compliance with international data protection regulations to prevent breaches and maintain trust. Potential data breaches, financial losses, and damage to reputation.
Resistance to change Overcoming organizational inertia and employee apprehension Reduced adoption rates, potential conflicts, and slower
towards new technologies disrupting established workflows. integration of AI systems.
Maintenance and updates Continuous monitoring and updating of AI systems to ensure they adapt to evolving email patterns and business needs. Increased operational costs and the need for dedicated AI specialists and IT support.
Integration complexity Integrating AI seamlessly with existing email systems and other IT infrastructure. Technical difficulties, potential downtime during integration, and increased complexity of IT management.
Cost of implementation High costs associated with deploying and maintaining AI technologies, particularly for smaller maritime companies. Financial strain on smaller companies, slower adoption rates, and potential exclusion from the benefits of AI.
Ethical considerations Addressing ethical issues such as bias in AI algorithms, transparency in AI decisionmaking, and accountability. Potential for biased decisions, lack of transparency, and challenges in ensuring ethical compliance.
Despite these challenges, the implementation of AI in automating email processing for maritime transport holds significant potential. Future prospects for AI in this area are particularly promising, especially regarding systems designed to recognize the content of emails related to freight offers or newly available positions on cargo ships. Currently, this market operates through manual processing, with operators handling hundreds of emails daily. This approach is time-consuming and prone to human error, making it ideal for AI-driven automation.
AI technologies such as NLP and ML can be employed to automatically identify and extract key details from emails, such as cargo type, volume, destination, and shipping dates. This information can then be integrated into a centralized database and displayed on a user-friendly interface on the company's website.
The automation of email processing will streamline communication workflows, allowing operators to quickly access and respond to relevant shipping opportunities. AI-driven email processing can provide valuable insights and analytics, helping companies optimize their operations and better understand market trends. In the long term, the adoption of AI for email processing in maritime transport will lead to more dynamic and responsive logistics operations. Companies will be able to allocate resources more effectively, reduce operational costs, and improve service quality.
CONCLUSION
The integration of AI into the automation of email processing in maritime transport holds tremendous potential. Despite the technical, operational, and ethical challenges, AI technologies like NLP and ML offer promising solutions to improve efficiency, accuracy, and decision-making. These advancements can significantly reduce manual processing, enhance the ability to handle large volumes of emails, and improve communication workflows. As AI continues to evolve, its applications in maritime email processing will become more sophisticated, enabling the industry to better adapt to the demands of global trade. This transformation is crucial for maintaining competitiveness and ensuring that maritime transport remains efficient and reliable in an increasingly complex and dynamic environment.
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