A Review: Research Trend of Digitalization Centered Approach in Risk Management of Logistics Loss from FPSO to Tanker

A Review: Research Trend of Digitalization Centered Approach in Risk Management of Logistics Loss from FPSO to Tanker | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review A Review: Research Trend of Digitalization Centered Approach in Risk Management of Logistics Loss from FPSO to Tanker Habibi Palippui, Daniel Mohammad Rosyid, Silvianita, Juswan Sade This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7218259/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The increasing complexity of offshore oil and gas operations, particularly in the cargo transfer process from Floating Production Storage and Offloading (FPSO) units to tankers, demands robust and adaptive risk management strategies. This study presents a systematic literature review (SLR) of 194 peer-reviewed articles published between 2010 and 2023 in SJR-indexed journals structured according to the PRISMA 2020 guidelines. The objective was to identify research trends, methodological patterns, and the extent of digital technology adoption in managing logistics loss risks. Using content analysis across seven dimensions ranging from risk typology to data analysis methods, the findings indicate a dominance of modeling approaches (42%), with limited integration of advanced digital tools such as artificial intelligence (AI), digital twins, and blockchain. Human factors are increasingly emphasized but remain insufficiently linked with technological frameworks. This study highlights a research gap in real-time, AI-enabled, and human-centered systems for maritime logistics. It proposes future directions, including digital twin-based monitoring, integrated human-technology interfaces, and predictive analytics for enhanced operational safety and efficiency. This review contributes to the establishment of a comprehensive knowledge base for advancing digital risk management in offshore logistics contexts. Ocean Engineering Digitalization and Maritime Safety Logistics Risk Management FPSO-Tanker Transfer Systematic Literature Review Offshore Oil and Gas Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION The logistics transfer process from Floating Production Storage and Offloading (FPSO) units to tankers is critical in offshore oil and gas production. This operation is inherently vulnerable to multiple forms of risk, including operational disruptions, human errors, environmental unpredictability, and logistical coordination failure(C.-S. Kim et al., 2022 ; Meier et al., 2023 ). As offshore production becomes more frequent and global demand intensifies, the need for reliable, safe, and efficient oil transfer mechanisms has become an operational imperative. In response to these risks, digitalization has emerged as a strategic enabler for advanced risk management in maritime logistics. Technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and digital twins provide real-time monitoring, predictive analytics, and intelligent automation(Madusanka et al., 2023 ; Sur & Kim, 2020a ). These tools align with broader frameworks, such as Industry 4.0 and Maritime 5.0, which advocate autonomous, interconnected, and intelligent maritime operations(Heilig et al., 2017 ; Monferdini et al., 2025 ). However, despite their technological maturity, the integration of such tools into risk management systems for FPSO-to-tanker logistics remains limited and fragmented. Although several studies have examined logistics risk management in various industrial and maritime contexts(Rameshkumar, 2020 ; Sur & Kim, 2020a ), most have emphasized traditional modeling techniques, such as Bayesian networks, fault tree analysis, fuzzy logic, and scenario-based simulations(K. X. Li et al., 2023 ). These approaches are often disconnected from real-time data systems or digital platforms, leaving a gap between theoretical models and practical technology-enabled implementations. Furthermore, while human factors are increasingly acknowledged as a critical component of maritime safety, they are rarely considered within the context of digital-human system integration(Chauvin, 2011 ), especially in high-stakes offshore environments. To address these gaps, this study undertakes a systematic literature review (SLR) of 194 peer-reviewed articles published between 2010 and 2023 in SJR-indexed journals. The review was structured in accordance with the PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines(Page et al., 2021 ), ensuring methodological transparency and reproducibility. Systematic content analysis was applied to map patterns across seven analytical dimensions: risk types, digital intervention methods, research design, treatment, stakeholder involvement, and data analysis techniques(Mouschoutzi & Ponis, 2022 ; Pham, 2023a ). This review investigates how digitalization-centered approaches have been applied to mitigate logistics loss risks during FPSO-to-tanker operations and identifies key research gaps. The synthesis contributes to the advancement of theory and practice by proposing a future research agenda centered on AI integrated digital twins, real-time monitoring infrastructures, and human-technology interface models. These elements are essential for achieving safer, smarter, and more resilient maritime logistics operations in this era of digital transformation(Govindan, 2023 ; Sunitiyoso et al., 2022 ; C. Wang et al., 2022 ). To visually contextualize the scope of this study, Fig. 1 presents a conceptual mapping of FPSO–tanker risk domains, illustrating the interactions among logistics activities, risk categories, and opportunities for digital intervention. This visual framework serves as a foundation for understanding the complexity of a system and highlights the strategic role of technology in achieving holistic risk mitigation. To systematically explore these trends and address the identified research gaps, the present study applied a systematic literature review guided by PRISMA 2020 protocols, ensuring rigor, transparency, and reproducibility in the synthesis process. METHODE Research Design This study adopted a systematic literature review (SLR) approach guided by the PRISMA 2020 framework(Page et al., 2021 ). This method was selected for its ability to ensure rigor, transparency, and reproducibility in evidence synthesis. This review focuses on risk management strategies in offshore oil logistics, specifically the transfer process between Floating Production Storage and Offloading (FPSO) units and tankers under the lens of digitalization. This approach enables the identification of dominant themes, research gaps, and evolution of digital interventions in maritime logistics(Mouschoutzi & Ponis, 2022 ; Pham, 2023a ). The review process comprised four PRISMA-aligned stages: identification, screening, eligibility, and inclusion. As shown in Fig. 2 , 230 articles published between 2010 and 2023 were initially retrieved from SJR-indexed journals. After removing 22 duplicates, 208 articles were screened on the basis of their titles and abstracts. Following full-text assessments, 14 articles were excluded owing to thematic irrelevance or lack of methodological clarity, resulting in 194 articles included in the final synthesis. Data Sources and Selection Criteria The primary data for this study comprised peer-reviewed scientific articles obtained from journals indexed in the Scimago Journal & Country Rank (SJR) database. The literature search focuses on journals relevant to maritime logistics, port management, offshore operations, and digital risk management. Using systematic keyword combinations, 28 SJR-indexed journals specializing in maritime studies were identified and scanned. These queries targeted articles discussing key themes, such as efficiency, competitiveness, sustainability, safety, and digitalization in global maritime logistics systems. A set of inclusion and exclusion criteria were applied to ensure the relevance and quality of the selected studies. Articles were included if they (1) addressed digital risk management in the context of maritime logistics, (2) employed either empirical or model-based research designs, (3) were published between 2010 and 2023, (4) were written in English, and (5) provided access to full text. Conversely, articles were excluded if they (1) were editorials, opinion pieces, or lacked peer review; (2) focused exclusively on land-based logistics contexts; or (3) did not demonstrate sufficient methodological transparency. These criteria ensured the selection of high-quality thematically relevant studies for systematic analysis. Research Instrument and Analytical Dimensions This review employed a content analysis instrument consisting of seven analytical dimensions designed to categorize and synthesize selected articles, as shown in Table 1 . Table 1 Aspects and Categories Used for Content Analysis in this Study Dimension Description (1) Year of Publication Trend analysis across 2010–2023 (2) Type of Research Qualitative, Quantitative, Mixed, Modeling, R&D (3) Research Subject Ports, shipping, human factors, policy, sustainability (4) Maritime Logistics Topics Technology, competitiveness, oil transfer operations (5) Treatment Modeling, simulation, case-based, etc. (6) Data Collection Instruments Surveys, interviews, secondary databases (7) Data Analysis Methods DEA, statistical analysis, AI, fuzzy logic, etc. Categories in dimensions (1), (4), and (5) were inductively determined through the initial reading. Meanwhile, dimensions (2), (3), (6), and (7) were defined deductively based on the existing literature and the reviewed frameworks(Y.-S. Choi & Yeo, 2023 ; Sunitiyoso et al., 2022 ). Data Analysis Strategy Data analysis in this study was conducted using a structured two-phase process. In the first phase, a classification process was applied to each of the 194 articles selected. Articles were categorized based on key analytical dimensions, including the type of digital technology used, nature of the risk addressed, specific logistics context, and methodological features of the study. This classification was performed using content extracted from the abstracts, methods, and discussion sections of each article(X. Chen et al., 2022 ; C.-S. Kim et al., 2022 ). In the second phase, a pattern analysis was conducted using a combination of longitudinal, frequency, and thematic techniques to identify prevailing trends in research designs, the adoption of digital interventions, and the focal topics addressed across studies(Jiang et al., 2018 ; T. L. H. Nguyen et al., 2020 ). Visualization tools such as bar charts, frequency tables, and temporal trend lines were employed to facilitate interpretation, offering clear representations of the analyzed data. A dedicated longitudinal analysis was also conducted to examine changes in research focus over time, specifically covering the period from 2010 to 2023(H. E. Haralambides et al., 2010 ; S. H. Park et al., 2018 ). This two-tiered analytical strategy enables a comprehensive synthesis of research patterns and provides critical insights into the evolution of digital transformation in maritime logistics risk management. This approach ensures both depth and breadth in understanding how scholarly discourse has addressed digitalization in offshore oil logistics, with particular attention to the FPSO-to-tanker interface(Balci & Surucu-Balci, 2021 ; Poornikoo & Øvergård, 2022 ). Furthermore, each selected article was systematically coded based on its research design, qualitative, quantitative, simulation-based, or mixed methods, as well as the data collection instruments used, including surveys, interviews, secondary data sources, and modeling approaches. This step enabled a more granular analysis of the methodological trends in maritime logistics research, particularly regarding the adoption of digital risk management strategies. The coding process provided a foundation for examining how different methodological choices influenced the framing and outcomes of studies. Based on this structured classification and pattern analysis approach, the following section presents the key findings. These include trends in publication volume, dominant research designs and instruments, major topical themes, and analytical techniques adopted in digital maritime logistics studies between 2010 and 2023. FINDINGS Number of Publications Figure 3 shows a steady rise in maritime logistics publications from 2010 to 2023, with a notable surge from 2018 onward, and a peak in 2023. This reflects the growing academic interest in themes such as operational efficiency, sustainability, and competitiveness, particularly driven by digital transformation in the maritime sector. Studies highlight that digitalization has become central to improving the performance across logistics chains. Research spans on the efficiency analysis of logistics providers and port competitiveness (Dang & Yeo, 2017 ; C.-S. Kim et al., 2022 ; H. G. Park & Lee, 2015 )to evaluate port productivity under risk (T. M. Choi & Siqin, 2022 ) and the rationalization of container terminals(S. Kim et al., 2022 ). Other studies underscore the role of ports in energy transition (Notteboom & Haralambides, 2022 ) and explore the potential of autonomous vessels, although gaps remain in economic and risk-based analyses(Munim & Haralambides, 2022 ). Further developments include digital initiatives in global shipping alliances, port integration, and emission regulations(S. Chen et al., 2022 ; H. Haralambides, 2023 ; K. X. Li et al., 2023 ), suggesting a comprehensive shift in maritime logistics priorities. However, despite the rising volume of research, a gap persists in explicitly linking digitalization with structured risk mitigation, particularly regarding logistics losses, system resilience, and real-time maritime safety. Types of Research Figure 4 reveals that qualitative research dominates the field of maritime logistics digitalization, accounting for 76 studies. This reflects the complexity of human-technology interactions in maritime operations, which demand deep contextual understanding(Y.-S. Choi & Yeo, 2023 ; C. Kim & Shin, 2019 ; Pham, 2023b ). Qualitative methods offer rich insights into the sociotechnical dynamics that quantitative approaches may overlook(Mouschoutzi & Ponis, 2022 ; Senarak, 2021 ). Quantitative research, with 48 studies, is gaining prominence owing to the rise of big data and analytical tools enabling evidence-based decisions in logistics optimization. Modeling and simulation (33 studies) also play a pivotal role by enabling the virtual testing of logistics systems, although their adoption is often limited by computational demands (Wang et al., 2020 ; Zhou et al., 2023 ; Kim et al., 2023 ). Other approaches, such as R&D (25), multicriteria (8), and case studies (9), are less common, but remain valuable for innovation and localized understanding(J. Liu & Wang, 2019 ; Y. Il Park et al., 2019 ; Vaferi et al., 2018 ). Figure 5 further breaks down qualitative methodologies, showing that field studies dominate (45 studies), highlighting the industry's need for real-world insights into digital implementation (Le et al., 2020 ; T. L. H. Nguyen et al., 2020 ; Sunitiyoso et al., 2022 ). Literature reviews and bibliometric analysis (11 studies), along with analytical research (10), help build conceptual frameworks and track emerging patterns in maritime digitalization (Y.-S. Choi & Yeo, 2023 ; Y. Li et al., 2022 ; Mouschoutzi & Ponis, 2022 ). Although there are fewer optimization studies (6), they contribute substantially to the development of algorithmic models to enhance fleet management, route planning, and decision-making(B. Dong, Christiansen, Fagerholt, & Chandra, 2020 ; C. Kim & Shin, 2019 ; Zhou et al., 2023 ). Empirical (3) and editorial (1) studies, although limited in number, offer additional perspectives and directions for future research. Overall, the variety of research types reflects the multifaceted nature of maritime digitalization. However, future studies should consider hybrid designs, such as integrating field studies with optimization models to capture both context-specific insights and scalable digital solutions ((Y.-S. Choi & Yeo, 2023 ; Y. Li et al., 2022 ; T. L. H. Nguyen et al., 2020 ). Research Subjects Figure 6 shows that Smart Ports (36 studies) are the most researched topic, emphasizing their role in enhancing efficiency, sustainability, and port operations through digital integration(K. X. Li et al., 2023 ; Notteboom & Haralambides, 2022 ; Pham, 2023b ). Human Factors and Ship Automation follow closely (29 studies each), reflecting an interest in ergonomic design, crew training, and autonomous systems for safer and more efficient operations(Y.-N. Kim et al., 2023 ; Munim & Haralambides, 2022 ; Rameshkumar, 2020 ). Digital Transformation (28 studies) focuses on restructuring logistics through AI, big data, and IoT(Y.-S. Choi & Yeo, 2023 ; S. Park, Lee, et al., 2023 ), while Blockchain (25 studies) offers solutions for transparency and secure maritime transactions(S. Nguyen et al., 2022 ; Zhou et al., 2023 ). Research on Vehicle Technology (23 studies) and System Design (24 studies) targets navigation, energy efficiency, and integrated logistics platforms(Dobrovnik et al., 2018 ; Helo et al., 2021 ). Despite growing interest, a key gap persists in the limited integration between technical system design and human-centered approaches, particularly in high-risk contexts, such as FPSO-to-tanker transfer operations. This underscores the need for interdisciplinary frameworks that combine digital innovation, safety, and operational resilience(Y.-N. Kim et al., 2023 ; Munim & Haralambides, 2022 ). Maritime Digitalization Topics Selected in Carrying Out a Cargo Loss Study Table 2 summarizes the three main themes in maritime digitalization studies linked to cargo loss: port operations (11 articles), maritime safety and human factors (seven articles), and logistics and supply chains (three articles). The dominant themes are port and terminal operations, highlighting the use of IoT and terminal management systems to boost operational efficiency and reduce cargo loss risks(S. Kim et al., 2022 ; K. X. Li et al., 2023 ; Pham, 2023a ; Sunitiyoso et al., 2022 ). These studies emphasize digital solutions for managing port congestion and the loading/unloading processes. Table 2 Maritime Digitalization Topics Selected in Carrying Out Cargo Loss Studies Topics Number of Articles Port and Terminal Operations 11 Maritime Safety and Human Factors 7 Maritime Logistics and Supply Chain 3 Maritime safety and human factors were addressed in seven studies, stressing crew training, communication, and decision making supported by AIS and VMS technologies to prevent incidents(G. Kim et al., 2023 ; Rameshkumar, 2020 ; Sur & Kim, 2020b ). In contrast, maritime logistics and supply chain integration are underrepresented with only three studies. Despite its crucial role in cargo tracking and improving end-to-end visibility, this topic has not been sufficiently explored(C.-S. Kim et al., 2022 ; Rameshkumar, 2020 ; Sur & Kim, 2020a ). This review reveals that limited research has been conducted on the integration of digital technologies into holistic maritime supply chain management to mitigate cargo losses. Therefore, future research should explore system-wide digital frameworks that connect ports, vessels, and logistics networks more effectively. Treatment This section examines the treatments or independent variables employed in maritime logistics studies, particularly those exploring digitalization to reduce logistics-related risk. These treatments reflect the methodological strategies adopted by the researchers to test this hypothesis(G. Kim et al., 2023 ; K. X. Li et al., 2023 ; Pham, 2023a ; Sunitiyoso et al., 2022 ). The main categories identified included Modeling, Surveys, and Data Analysis, which are summarized in Table 3 . Table 3 Research Treatments dalam in Maritime Logistic Research Treatments/Independent Variables Number of Articles Data Analysis 3 Modeling 42 Survey 10 Among these, modeling is the most prominent method, with 42 articles utilizing simulation techniques and predictive algorithms to assess and manage risks, particularly in the context of FPSO-to-tanker oil transfers(Sur and Kim, 2020a ). The survey approach, featured in ten studies, captures practical insights from field professionals about risk perceptions and mitigation practices(Rameshkumar, 2020 ; G. Kim et al., 2023 ). Although only discussed in three articles, data analysis plays a key role in digital risk management by enabling data-driven decisions through interpretation of digitally acquired operational information. The dominance of modeling highlights the field’s emphasis on proactive, technology-supported strategies to optimize logistics safety and efficiency, reinforcing the relevance of digitalization in this sector(Kim et al., 2022 ; Sunitiyoso et al., 2022 ; Li et al., 2023 ; Pham, 2023a ). Data Collection Instruments To examine digitalization-based approaches to managing logistics losses from FPSOs to tankers, researchers utilize diverse data collection instruments, as summarized in Table 4 . These include surveys, secondary databases, simulations, and mixed methods (Kim, Roh and Seo, 2022 ; Sunitiyoso et al., 2022 ; Li et al., 2023 ; Pham, 2023a ). Digital tools have become increasingly central to capturing real-time data and supporting predictive risk analysis(Rameshkumar, 2020 ; Park, Kim and Kwon, 2023 ), offering greater accuracy than manual methods(Sur and Kim, 2020a ). Table 4 Instruments developed to collect previous research data No. Instrument Group Total Examples 1 Questionnaires and Surveys 30 Structured questionnaires, Expert surveys, AHP questionnaires 2 Secondary Data and Databases 37 World Bank data, AIS data, Panel data 3 Interviews 8 Face-to-face interviews, Telephone interviews 4 Observation and Document Analysis 3 Direct observation, Finance report analysis 5 Mixed Methods 4 Combination of qualitative and quantitative data 6 Simulation and Modeling 2 Simulation data, Mathematical models 7 Not Specifically Mentioned 21 Methods not described in detail Overall Total 105 Among the 105 instruments analyzed, secondary data dominated (35.24%, n = 37), reflecting a preference for validated datasets from global institutions and academic sources(Kim, Roh and Seo, 2022 ; Sunitiyoso et al., 2022 ). Questionnaires and surveys followed (28.57%, n = 30), indicating active primary data collection(Li et al., 2023 ; Pham, 2023a ). However, 21 studies (20%) did not clearly report their instruments, indicating a methodological transparency gap. Less frequent methods included interviews (7.62%, n = 8), mixed methods (3.81%), observation (2.86%), and simulation (1.90%), yet each adds a valuable perspective, especially for complex or context-specific topics(Rameshkumar, 2020 ; Sur and Kim, 2020a ; Y.-N. Kim et al., 2023 ). This pattern underscores both reliance on established data and the need for diversified and well-documented research tools in maritime logistics. Data Analysis Methods In digitalization-driven risk management research within maritime logistics, particularly involving FPSO operations, choosing appropriate data analysis methods is vital to ensure research validity(Kim et al., 2022 ; Sunitiyoso et al., 2022 ; Pham, 2023a ). With the growing complexity of operational data, advanced analytical tools are becoming increasingly essential for accurate interpretation and decision-making(Li and Yuen, 2022 ). Table 5 The various data analysis methods used in research related to logistics loss risk management No. Analysis Method Total Example Methods Literature 1 Statistical Analysis 37 Regression, Correlation, SEM (Lee and Han, 2018 ; Hirata, 2019 ; Jung, Kim and Shin, 2019 ; Park et al., 2019 , 2023 ; Rahman et al., 2019 ; Asian, Wang and Dickson, 2020 ; Dong, Christiansen, Fagerholt and Bektaş, 2020 ; Dong, Christiansen, Fagerholt and Chandra, 2020 ; Nguyen and Kim, 2020 ; Giamouzi and Nomikos, 2021 ; Kim, Roh and Seo, 2022 ; Lai et al., 2022 ; Li et al., 2022 ; Lai, Feng and Zhu, 2023 ; Michail and Melas, 2023 ) 2 Qualitative Analysis 15 Content Analysis, Thematic Analysis (Senarak, 2020 ; Mouschoutzi and Ponis, 2022 ; Sunitiyoso et al., 2022 ; Choi and Yeo, 2023 ; G. Kim et al., 2023 ; Nanyam and Kumar Jha, 2023 ; Nong, 2023 ; Pham, 2023a ) 3 Mathematical Modeling and Simulation 11 Mathematical Models, Multi-Agent Simulation (Dong, Christiansen, Fagerholt and Bektaş, 2020 ; Wang et al., 2020 ; Li and Yuen, 2022 ; Teweldebrhan, Maghelal and Galadari, 2022 ; G. Kim et al., 2023 ; Park, Kim and Kwon, 2023 ) 4 Data Envelopment Analysis (DEA) 8 DEA-Window, SBM-DEA (Dang and Yeo, 2017 ; Park, Pham and Yeo, 2018 ; Adler et al., 2022 ; Kim et al., 2022 ; Nong, 2023 ) 5 Multi-Criteria Decision-Making Methods 8 AHP, TOPSIS (Nguyen, 2018 ; Jung, Kim and Shin, 2019 ; Park et al., 2019 ; Yazır, Şahin and Yip, 2021 ; Rojanaleekul, Pungchompoo and Sirivongpaisal, 2022 ) 6 Fuzzy Methods 7 Fuzzy AHP, Fuzzy TOPSIS (Nguyen, 2018 ; Sur and Kim, 2020b ; Yazır, Şahin and Yip, 2021 ; Teweldebrhan, Maghelal and Galadari, 2022 ) 7 Multivariate Analysis 5 PCA, Cluster Analysis (Kim et al., 2022 ; Rojanaleekul, Pungchompoo and Sirivongpaisal, 2022 ; Lai, Feng and Zhu, 2023 ) 8 Network Analysis 5 SNA, Analytic Network Analysis (Chi, Phong and Hanh, 2023 ; Choi and Yeo, 2023 ) 9 Optimization Methods 4 Genetic Algorithm, Metaheuristics (Galvao, Wang and Mileski, 2016 ; Nguyen, 2016 ; Dong et al., 2023 ) 10 Bibliometric Analysis 4 VOSviewer, WordCloud (Mouschoutzi and Ponis, 2022 ; Li et al., 2023 ; Pham, 2023a ) 11 Econometric Models 3 VAR, VEC (Giamouzi and Nomikos, 2021 ; Michail and Melas, 2023 ; Park, Kim and Kwon, 2023 ) 12 Efficiency and Productivity Analysis 3 Malmquist Productivity Index (Adler et al., 2022 ; Nanyam and Kumar Jha, 2023 ; Nong, 2023 ) 13 Machine Learning and AI 2 Random Forest, Deep Learning LSTM (Li et al., 2022 ; G. Kim et al., 2023 ; Palippui, 2024 ) 14 Specific Logistics and Maritime Methods 2 Additive Market Share Analysis (Teweldebrhan, Maghelal and Galadari, 2022 ) 15 Others 6 ISM, Markov Chain Model (Lee, 2018 ; Kim, Sur and Cho, 2023 ; Nanyam and Kumar Jha, 2023 ) 16 Not Specifically Mentioned 22 - (Chauvin, 2011 ; Chiu et al., 2011 ; Yeo, 2012 , 2018 ; Dombrowski et al., 2016 ; Nguyen, 2016 ; Rafke and Lestari, 2017 ; Cho and Lee, 2020 ; Wang, Yang and Zhang, 2020 ; Wijaya et al., 2020 ; Akbar et al., 2021 ; Amin et al., 2021 ; Firlej and Taeihagh, 2021 ; Helo, Paukku and Sairanen, 2021 ; Page et al., 2021 ; Thi Nong, 2022 ; Haralambides, 2023 ) The analysis of 142 methods revealed a strong quantitative dominance, led by statistical approaches such as SEM and regression(Kim et al., 2022 ; Park et al., 2023 ). Qualitative techniques are often employed for content and thematic analysis(Sunitiyoso et al., 2022 ; Pham, 2023a ), while modeling and simulation approaches are increasingly used to replicate complex maritime systems(Wang, Yang and Zhang, 2020 ; G. Kim et al., 2023 ). Other notable methods include DEA and MCDM for performance and decision modeling(Dang and Yeo, 2017 ; Jung, Kim and Shin, 2019 ), fuzzy logic for uncertainty analysis(Sur and Kim, 2020a ), and bibliometric tools for mapping research trends(Mouschoutzi and Ponis, 2022 ; Li et al., 2023 ). Despite increasing relevance, machine learning and AI applications remain underutilized(Li et al., 2022 ; Palippui, 2024 ), and 22 studies (15.5%) lacked clear methodological reporting, highlighting the need for greater transparency. This methodological diversity underscores evolving practices in maritime risk research and points to opportunities for the further integration of AI, hybrid models, and domain-specific analytics to advance digital maritime safety. Discussion Methodological Landscape and Dominance of Statistical Approaches This review highlights the pronounced reliance on traditional statistical analyses in maritime logistics and risk management research. With 37 occurrences, this method has emerged as the most frequently employed across various studies(Kim et al., 2022 ; Sunitiyoso et al., 2022 ; Pham, 2023a ). The dominance of regression, structural equation modeling (SEM), and correlation-based techniques reflects the emphasis on quantifiable, replicable outcomes, aligning with Helo et al. (2019), who advocate for empirical rigor in analyzing logistics systems(Helo, Paukku and Sairanen, 2021 ). Such methodologies are especially favored in risk management because of their robustness in validating relationships among variables (Rameshkumar, 2020 ; Sur and Kim, 2020a ; Kim et al., 2022 ; Li et al., 2023 ). Emergence of Qualitative Insights and Contextual Depth While quantitative techniques prevail, the notable presence of qualitative analysis (15 studies, or 10.56%) underscores the growing recognition of non-numeric contextual exploration in maritime digitalization(Mouschoutzi and Ponis, 2022 ; Sunitiyoso et al., 2022 ). Studies have increasingly relied on content and thematic analysis to capture human-technology interactions, institutional behavior, and socio-technical dynamics (Rameshkumar, 2020 ; Kim et al., 2022 ). This finding supports Notteboom et al. (2021), who highlight the importance of understanding the underlying motivations and tacit knowledge in maritime operations (Notteboom and Haralambides, 2022 ). Mathematical Modeling and Simulation: A Growing Paradigm Mathematical modeling and simulation (11 occurrences) represent an evolving approach for analyzing complex maritime systems. These models allow for predictive scenario-based analyses that support decision-making in logistics optimization. Research by Wang et al. ( 2020 ) on dynamic pricing and Dong et al. ( 2020 ) on route planning demonstrates how multi-agent simulation and optimization techniques contribute to resilient system design(Dong, Christiansen, Fagerholt and Bektaş, 2020 ; Wang, Yang and Zhang, 2020 ). This aligns with Tran et al. ( 2017 ), who emphasized model-based strategies in maritime supply chain prediction(Tran, Haasis and Buer, 2017 ). Methodological Diversity and Hybrid Techniques An interesting aspect is the methodological pluralism evident in approaches such as Data Envelopment Analysis (DEA), Multi-Criteria Decision-Making (MCDM), and fuzzy methods. DEA techniques have been widely applied to assess port and vessel performance(Dang and Yeo, 2017 ; Park, Pham and Yeo, 2018 ), while fuzzy logic models enhance decision making in uncertain risk environments(Sur and Kim, 2020a ). This variety suggests an increasing appreciation for multidimensional decision support systems, confirming the complexity and heterogeneity of maritime digitalization challenges(Yan et al., 2017 ). Lag in Adoption of Machine Learning and AI Despite the recognized potential of Artificial Intelligence (AI) and Machine Learning (ML) in digital transformation, their application remains minimal only 2 studies employed such methods(Liu et al., 2022 ; Kim, Sur and Cho, 2023 ). This discrepancy contrasts starkly with projections by(Heilig, Lalla-Ruiz and Voß, 2017 ), who emphasize AI's transformative capabilities of AI. Possible barriers include limited access to quality training data, domain-specific AI models, and industry resistance to black box systems. However, promising examples, such as the LSTM-based energy prediction at Busan Port, (G. Kim et al., 2023 ; Y.-N. Kim et al., 2023 ) show early momentum in this area. Transparency in Methodological Reporting One methodological concern was the lack of specificity in several studies; 22 articles did not clearly report their analysis methods. This reflects gaps in methodological transparency, which undermines reproducibility and hampers comparative review. Davarzani et al. ( 2016 ) emphasized the need for standardized reporting frameworks, and this review supports this recommendation. In contrast, studies like (2023) serve as models for systematic reporting through detailed bibliometric techniques(Davarzani et al., 2016 ; Pham, 2023a ). Synthesis and Future Direction Collectively, the findings emphasize that while statistical and modeling methods are firmly entrenched in maritime research, emerging qualitative and hybrid techniques play a crucial role in exploring the social, technical, and systemic complexity of digital transformation. Importantly, AI and advanced analytics remain underutilized, indicating a clear research opportunity. Future research should actively pursue integrative analytical models that combine simulation, machine learning, and empirical insights to better address cargo risk, port inefficiencies, and human-system interaction in the digital era. CONCLUSION This systematic literature review investigates the application of digitalization-based approaches in managing logistics loss risks during FPSO-to-tanker oil transfer operations. Using the PRISMA 2020 protocol, 194 peer-reviewed articles from 2010 to 2023 were analyzed across seven analytical dimensions, offering a comprehensive synthesis of trends in risk typology, digital intervention, research design, data instruments, and analytical methods. The findings revealed a steady increase in academic attention since 2018, with a clear focus on modeling-based strategies and statistical analysis. Research subjects predominantly focus on port operations, safety, and human factors, while logistics and supply chain integration remain underrepresented. Although traditional quantitative techniques remain dominant, the emergence of qualitative and hybrid methods has indicated an expanding methodological landscape. Notably, the adoption of AI and advanced digital technologies, such as digital twins, blockchain, and machine learning, is still nascent. Despite the momentum of digital transformation in maritime logistics, there is limited integration between human-centric safety systems and real-time digital platforms, particularly in high-risk operations, such as FPSO-to-tanker transfers. Moreover, transparency in methodological reporting remains inconsistent, hindering replicability and cumulative knowledge-building. This study contributes to the literature by mapping the current state and identifying research gaps, particularly the lack of holistic digital frameworks that integrate ports, vessels, and logistics networks. Future research should prioritize (1) system-wide AI-enabled solutions, (2) digital twin-based infrastructure for predictive monitoring, and (3) interdisciplinary approaches that bridge human factors with intelligent systems. 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Deepwater Gulf of Mexico Oil Spill Scenarios Development and Their Associated Risk Assessment Deepwater Gulf Of Mexico Oil Spill Scenarios Development And Their Associated Risk Assessment A Dissertation . https://www.researchgate.net/publication/308100250 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7218259","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":491110537,"identity":"ad2389a9-0fc6-442e-b5ec-fd3f8703d4c7","order_by":0,"name":"Habibi Palippui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACAwYehsMMBjYMDBJQETaGBKK0pJGohZkBqAuuhYGQFnP2swcPFxScT9wu3fz4A0ONHQMfOwEtlj15CYdnGNxO3DnnmJkEw7FkBjaeBwQcdiDH4DAPUMuGGwlmQI8cYGCTIOSX829AWs4BtaR//sDwjxgtN8C2HABqyTGQYGwjSss7kF+SjYFayiQS+5J5CPvlfO7hzwV/7GSBDtv84cM3Ozn5dgK2oAKgYh5S1I+CUTAKRsEowAEA3epGCD837nkAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-7396-1847","institution":"Hasanuddin University","correspondingAuthor":true,"prefix":"","firstName":"Habibi","middleName":"","lastName":"Palippui","suffix":""},{"id":491110876,"identity":"e5ab1e79-2ec7-4a7c-9247-6c6b436f55e8","order_by":1,"name":"Daniel Mohammad Rosyid","email":"","orcid":"https://orcid.org/0009-0002-4551-3320","institution":"Intitut Teknolgi Sepuluh Nopember","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"Mohammad","lastName":"Rosyid","suffix":""},{"id":491110926,"identity":"e4a7ea39-62a4-42c5-8a84-97353184d3a7","order_by":2,"name":"Silvianita","email":"","orcid":"https://orcid.org/0000-0001-5487-5521","institution":"Intitut Teknolgi Sepuluh Nopember","correspondingAuthor":false,"prefix":"","firstName":"","middleName":"","lastName":"Silvianita","suffix":""},{"id":491111078,"identity":"d7cd19a0-add8-487a-b71d-70dd75f33c2e","order_by":3,"name":"Juswan Sade","email":"","orcid":"https://orcid.org/0009-0006-3124-2637","institution":"Hasanuddin University","correspondingAuthor":false,"prefix":"","firstName":"Juswan","middleName":"","lastName":"Sade","suffix":""}],"badges":[],"createdAt":"2025-07-26 03:57:20","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7218259/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7218259/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87813666,"identity":"8d3c4f26-e81c-49d0-937d-2bb1c1214708","added_by":"auto","created_at":"2025-07-29 09:41:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":434250,"visible":true,"origin":"","legend":"\u003cp\u003eLoad transfer process FPSO-Tanker (Zulqarnain \u0026nbsp;\u0026amp; Muhammad Zulqarnain, 1999).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7218259/v1/5deec835d589e63bf94f45db.png"},{"id":87812156,"identity":"8b795418-87a2-467f-9c4b-2d0d6f53eb89","added_by":"auto","created_at":"2025-07-29 09:33:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58497,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA 2020-based flow of article selection process\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7218259/v1/baf1f663ee250fc78c912457.png"},{"id":87812158,"identity":"b3d241d6-63fd-4faa-b587-f32ecd6c9fb2","added_by":"auto","created_at":"2025-07-29 09:33:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":57500,"visible":true,"origin":"","legend":"\u003cp\u003eThe trend of increasing number of studies\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7218259/v1/e70e3b3db9c5d116538e960e.png"},{"id":87813664,"identity":"854ec23a-b51d-4405-961b-b53999893252","added_by":"auto","created_at":"2025-07-29 09:41:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":52202,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Types of Research in research that focuses on maritime logistics related to digitalization\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7218259/v1/bf783f6a61e145000e36d185.png"},{"id":87812162,"identity":"64096561-a3ff-441a-9f3f-0e5a42435727","added_by":"auto","created_at":"2025-07-29 09:33:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":50687,"visible":true,"origin":"","legend":"\u003cp\u003eVarious qualitative research methodologies used in the field of maritime logistics\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7218259/v1/4a7dc0007e15a554156d671f.png"},{"id":87813665,"identity":"e7154f25-c1c3-4708-b72d-c98825aa0a87","added_by":"auto","created_at":"2025-07-29 09:41:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":81072,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of research subjects related to maritime logistics and digitalization.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7218259/v1/453fa8b22c43080971bcb686.png"},{"id":87815189,"identity":"4dd5bb8e-720f-4c9c-9ce8-05dc0ab46da7","added_by":"auto","created_at":"2025-07-29 09:57:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1761847,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7218259/v1/ca77aa2b-e56c-4583-9cff-3c8cad8ba131.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eA Review: Research Trend of Digitalization Centered Approach in Risk Management of Logistics Loss from FPSO to Tanker\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe logistics transfer process from Floating Production Storage and Offloading (FPSO) units to tankers is critical in offshore oil and gas production. This operation is inherently vulnerable to multiple forms of risk, including operational disruptions, human errors, environmental unpredictability, and logistical coordination failure(C.-S. Kim et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Meier et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As offshore production becomes more frequent and global demand intensifies, the need for reliable, safe, and efficient oil transfer mechanisms has become an operational imperative.\u003c/p\u003e\u003cp\u003eIn response to these risks, digitalization has emerged as a strategic enabler for advanced risk management in maritime logistics. Technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and digital twins provide real-time monitoring, predictive analytics, and intelligent automation(Madusanka et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sur \u0026amp; Kim, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). These tools align with broader frameworks, such as Industry 4.0 and Maritime 5.0, which advocate autonomous, interconnected, and intelligent maritime operations(Heilig et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Monferdini et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, despite their technological maturity, the integration of such tools into risk management systems for FPSO-to-tanker logistics remains limited and fragmented.\u003c/p\u003e\u003cp\u003eAlthough several studies have examined logistics risk management in various industrial and maritime contexts(Rameshkumar, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sur \u0026amp; Kim, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e), most have emphasized traditional modeling techniques, such as Bayesian networks, fault tree analysis, fuzzy logic, and scenario-based simulations(K. X. Li et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These approaches are often disconnected from real-time data systems or digital platforms, leaving a gap between theoretical models and practical technology-enabled implementations. Furthermore, while human factors are increasingly acknowledged as a critical component of maritime safety, they are rarely considered within the context of digital-human system integration(Chauvin, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), especially in high-stakes offshore environments.\u003c/p\u003e\u003cp\u003eTo address these gaps, this study undertakes a systematic literature review (SLR) of 194 peer-reviewed articles published between 2010 and 2023 in SJR-indexed journals. The review was structured in accordance with the PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines(Page et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), ensuring methodological transparency and reproducibility. Systematic content analysis was applied to map patterns across seven analytical dimensions: risk types, digital intervention methods, research design, treatment, stakeholder involvement, and data analysis techniques(Mouschoutzi \u0026amp; Ponis, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pham, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis review investigates how digitalization-centered approaches have been applied to mitigate logistics loss risks during FPSO-to-tanker operations and identifies key research gaps. The synthesis contributes to the advancement of theory and practice by proposing a future research agenda centered on AI integrated digital twins, real-time monitoring infrastructures, and human-technology interface models. These elements are essential for achieving safer, smarter, and more resilient maritime logistics operations in this era of digital transformation(Govindan, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sunitiyoso et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; C. Wang et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo visually contextualize the scope of this study, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a conceptual mapping of FPSO–tanker risk domains, illustrating the interactions among logistics activities, risk categories, and opportunities for digital intervention. This visual framework serves as a foundation for understanding the complexity of a system and highlights the strategic role of technology in achieving holistic risk mitigation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo systematically explore these trends and address the identified research gaps, the present study applied a systematic literature review guided by PRISMA 2020 protocols, ensuring rigor, transparency, and reproducibility in the synthesis process.\u003c/p\u003e"},{"header":"METHODE","content":"\u003cp\u003e\u003cb\u003eResearch Design\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study adopted a systematic literature review (SLR) approach guided by the PRISMA 2020 framework(Page et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This method was selected for its ability to ensure rigor, transparency, and reproducibility in evidence synthesis. This review focuses on risk management strategies in offshore oil logistics, specifically the transfer process between Floating Production Storage and Offloading (FPSO) units and tankers under the lens of digitalization. This approach enables the identification of dominant themes, research gaps, and evolution of digital interventions in maritime logistics(Mouschoutzi \u0026amp; Ponis, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pham, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe review process comprised four PRISMA-aligned stages: identification, screening, eligibility, and inclusion. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, 230 articles published between 2010 and 2023 were initially retrieved from SJR-indexed journals. After removing 22 duplicates, 208 articles were screened on the basis of their titles and abstracts. Following full-text assessments, 14 articles were excluded owing to thematic irrelevance or lack of methodological clarity, resulting in 194 articles included in the final synthesis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Sources and Selection Criteria\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe primary data for this study comprised peer-reviewed scientific articles obtained from journals indexed in the Scimago Journal \u0026amp; Country Rank (SJR) database. The literature search focuses on journals relevant to maritime logistics, port management, offshore operations, and digital risk management. Using systematic keyword combinations, 28 SJR-indexed journals specializing in maritime studies were identified and scanned. These queries targeted articles discussing key themes, such as efficiency, competitiveness, sustainability, safety, and digitalization in global maritime logistics systems.\u003c/p\u003e\u003cp\u003eA set of inclusion and exclusion criteria were applied to ensure the relevance and quality of the selected studies. Articles were included if they (1) addressed digital risk management in the context of maritime logistics, (2) employed either empirical or model-based research designs, (3) were published between 2010 and 2023, (4) were written in English, and (5) provided access to full text. Conversely, articles were excluded if they (1) were editorials, opinion pieces, or lacked peer review; (2) focused exclusively on land-based logistics contexts; or (3) did not demonstrate sufficient methodological transparency. These criteria ensured the selection of high-quality thematically relevant studies for systematic analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResearch Instrument and Analytical Dimensions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis review employed a content analysis instrument consisting of seven analytical dimensions designed to categorize and synthesize selected articles, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAspects and Categories Used for Content Analysis in this Study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDimension\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(1) Year of Publication\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTrend analysis across 2010–2023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(2) Type of Research\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative, Quantitative, Mixed, Modeling, R\u0026amp;D\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(3) Research Subject\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePorts, shipping, human factors, policy, sustainability\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(4) Maritime Logistics Topics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTechnology, competitiveness, oil transfer operations\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(5) Treatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModeling, simulation, case-based, etc.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(6) Data Collection Instruments\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSurveys, interviews, secondary databases\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(7) Data Analysis Methods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDEA, statistical analysis, AI, fuzzy logic, etc.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eCategories in dimensions (1), (4), and (5) were inductively determined through the initial reading. Meanwhile, dimensions (2), (3), (6), and (7) were defined deductively based on the existing literature and the reviewed frameworks(Y.-S. Choi \u0026amp; Yeo, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sunitiyoso et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Analysis Strategy\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData analysis in this study was conducted using a structured two-phase process. In the first phase, a classification process was applied to each of the 194 articles selected. Articles were categorized based on key analytical dimensions, including the type of digital technology used, nature of the risk addressed, specific logistics context, and methodological features of the study. This classification was performed using content extracted from the abstracts, methods, and discussion sections of each article(X. Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; C.-S. Kim et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the second phase, a pattern analysis was conducted using a combination of longitudinal, frequency, and thematic techniques to identify prevailing trends in research designs, the adoption of digital interventions, and the focal topics addressed across studies(Jiang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; T. L. H. Nguyen et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Visualization tools such as bar charts, frequency tables, and temporal trend lines were employed to facilitate interpretation, offering clear representations of the analyzed data. A dedicated longitudinal analysis was also conducted to examine changes in research focus over time, specifically covering the period from 2010 to 2023(H. E. Haralambides et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; S. H. Park et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis two-tiered analytical strategy enables a comprehensive synthesis of research patterns and provides critical insights into the evolution of digital transformation in maritime logistics risk management. This approach ensures both depth and breadth in understanding how scholarly discourse has addressed digitalization in offshore oil logistics, with particular attention to the FPSO-to-tanker interface(Balci \u0026amp; Surucu-Balci, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Poornikoo \u0026amp; Øvergård, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, each selected article was systematically coded based on its research design, qualitative, quantitative, simulation-based, or mixed methods, as well as the data collection instruments used, including surveys, interviews, secondary data sources, and modeling approaches. This step enabled a more granular analysis of the methodological trends in maritime logistics research, particularly regarding the adoption of digital risk management strategies. The coding process provided a foundation for examining how different methodological choices influenced the framing and outcomes of studies.\u003c/p\u003e\u003cp\u003eBased on this structured classification and pattern analysis approach, the following section presents the key findings. These include trends in publication volume, dominant research designs and instruments, major topical themes, and analytical techniques adopted in digital maritime logistics studies between 2010 and 2023.\u003c/p\u003e"},{"header":"FINDINGS","content":"\u003cp\u003e\u003cb\u003eNumber of Publications\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows a steady rise in maritime logistics publications from 2010 to 2023, with a notable surge from 2018 onward, and a peak in 2023. This reflects the growing academic interest in themes such as operational efficiency, sustainability, and competitiveness, particularly driven by digital transformation in the maritime sector.\u003c/p\u003e\u003cp\u003eStudies highlight that digitalization has become central to improving the performance across logistics chains. Research spans on the efficiency analysis of logistics providers and port competitiveness (Dang \u0026amp; Yeo, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; C.-S. Kim et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; H. G. Park \u0026amp; Lee, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)to evaluate port productivity under risk (T. M. Choi \u0026amp; Siqin, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and the rationalization of container terminals(S. Kim et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Other studies underscore the role of ports in energy transition (Notteboom \u0026amp; Haralambides, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and explore the potential of autonomous vessels, although gaps remain in economic and risk-based analyses(Munim \u0026amp; Haralambides, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurther developments include digital initiatives in global shipping alliances, port integration, and emission regulations(S. Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; H. Haralambides, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; K. X. Li et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), suggesting a comprehensive shift in maritime logistics priorities. However, despite the rising volume of research, a gap persists in explicitly linking digitalization with structured risk mitigation, particularly regarding logistics losses, system resilience, and real-time maritime safety.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTypes of Research\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveals that qualitative research dominates the field of maritime logistics digitalization, accounting for 76 studies. This reflects the complexity of human-technology interactions in maritime operations, which demand deep contextual understanding(Y.-S. Choi \u0026amp; Yeo, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; C. Kim \u0026amp; Shin, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pham, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). Qualitative methods offer rich insights into the sociotechnical dynamics that quantitative approaches may overlook(Mouschoutzi \u0026amp; Ponis, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Senarak, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eQuantitative research, with 48 studies, is gaining prominence owing to the rise of big data and analytical tools enabling evidence-based decisions in logistics optimization. Modeling and simulation (33 studies) also play a pivotal role by enabling the virtual testing of logistics systems, although their adoption is often limited by computational demands (Wang et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOther approaches, such as R\u0026amp;D (25), multicriteria (8), and case studies (9), are less common, but remain valuable for innovation and localized understanding(J. Liu \u0026amp; Wang, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Y. Il Park et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Vaferi et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e further breaks down qualitative methodologies, showing that field studies dominate (45 studies), highlighting the industry's need for real-world insights into digital implementation (Le et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; T. L. H. Nguyen et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sunitiyoso et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Literature reviews and bibliometric analysis (11 studies), along with analytical research (10), help build conceptual frameworks and track emerging patterns in maritime digitalization (Y.-S. Choi \u0026amp; Yeo, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Y. Li et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mouschoutzi \u0026amp; Ponis, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough there are fewer optimization studies (6), they contribute substantially to the development of algorithmic models to enhance fleet management, route planning, and decision-making(B. Dong, Christiansen, Fagerholt, \u0026amp; Chandra, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; C. Kim \u0026amp; Shin, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Empirical (3) and editorial (1) studies, although limited in number, offer additional perspectives and directions for future research.\u003c/p\u003e\u003cp\u003eOverall, the variety of research types reflects the multifaceted nature of maritime digitalization. However, future studies should consider hybrid designs, such as integrating field studies with optimization models to capture both context-specific insights and scalable digital solutions ((Y.-S. Choi \u0026amp; Yeo, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Y. Li et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; T. L. H. Nguyen et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eResearch Subjects\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that Smart Ports (36 studies) are the most researched topic, emphasizing their role in enhancing efficiency, sustainability, and port operations through digital integration(K. X. Li et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Notteboom \u0026amp; Haralambides, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pham, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). Human Factors and Ship Automation follow closely (29 studies each), reflecting an interest in ergonomic design, crew training, and autonomous systems for safer and more efficient operations(Y.-N. Kim et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Munim \u0026amp; Haralambides, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rameshkumar, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Digital Transformation (28 studies) focuses on restructuring logistics through AI, big data, and IoT(Y.-S. Choi \u0026amp; Yeo, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; S. Park, Lee, et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), while Blockchain (25 studies) offers solutions for transparency and secure maritime transactions(S. Nguyen et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Research on Vehicle Technology (23 studies) and System Design (24 studies) targets navigation, energy efficiency, and integrated logistics platforms(Dobrovnik et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Helo et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite growing interest, a key gap persists in the limited integration between technical system design and human-centered approaches, particularly in high-risk contexts, such as FPSO-to-tanker transfer operations. This underscores the need for interdisciplinary frameworks that combine digital innovation, safety, and operational resilience(Y.-N. Kim et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Munim \u0026amp; Haralambides, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMaritime Digitalization Topics Selected in Carrying Out a Cargo Loss Study\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the three main themes in maritime digitalization studies linked to cargo loss: port operations (11 articles), maritime safety and human factors (seven articles), and logistics and supply chains (three articles).\u003c/p\u003e\u003cp\u003eThe dominant themes are port and terminal operations, highlighting the use of IoT and terminal management systems to boost operational efficiency and reduce cargo loss risks(S. Kim et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; K. X. Li et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pham, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Sunitiyoso et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These studies emphasize digital solutions for managing port congestion and the loading/unloading processes.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMaritime Digitalization Topics Selected in Carrying Out Cargo Loss Studies\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of Articles\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePort and Terminal Operations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaritime Safety and Human Factors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaritime Logistics and Supply Chain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eMaritime safety and human factors were addressed in seven studies, stressing crew training, communication, and decision making supported by AIS and VMS technologies to prevent incidents(G. Kim et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rameshkumar, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sur \u0026amp; Kim, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). In contrast, maritime logistics and supply chain integration are underrepresented with only three studies. Despite its crucial role in cargo tracking and improving end-to-end visibility, this topic has not been sufficiently explored(C.-S. Kim et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rameshkumar, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sur \u0026amp; Kim, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). This review reveals that limited research has been conducted on the integration of digital technologies into holistic maritime supply chain management to mitigate cargo losses. Therefore, future research should explore system-wide digital frameworks that connect ports, vessels, and logistics networks more effectively.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTreatment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis section examines the treatments or independent variables employed in maritime logistics studies, particularly those exploring digitalization to reduce logistics-related risk. These treatments reflect the methodological strategies adopted by the researchers to test this hypothesis(G. Kim et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; K. X. Li et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pham, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Sunitiyoso et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The main categories identified included Modeling, Surveys, and Data Analysis, which are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResearch Treatments dalam in Maritime Logistic Research\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreatments/Independent Variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of Articles\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eData Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModeling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurvey\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eAmong these, modeling is the most prominent method, with 42 articles utilizing simulation techniques and predictive algorithms to assess and manage risks, particularly in the context of FPSO-to-tanker oil transfers(Sur and Kim, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). The survey approach, featured in ten studies, captures practical insights from field professionals about risk perceptions and mitigation practices(Rameshkumar, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; G. Kim et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although only discussed in three articles, data analysis plays a key role in digital risk management by enabling data-driven decisions through interpretation of digitally acquired operational information.\u003c/p\u003e\u003cp\u003eThe dominance of modeling highlights the field’s emphasis on proactive, technology-supported strategies to optimize logistics safety and efficiency, reinforcing the relevance of digitalization in this sector(Kim et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sunitiyoso et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pham, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Collection Instruments\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo examine digitalization-based approaches to managing logistics losses from FPSOs to tankers, researchers utilize diverse data collection instruments, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. These include surveys, secondary databases, simulations, and mixed methods (Kim, Roh and Seo, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sunitiyoso et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pham, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Digital tools have become increasingly central to capturing real-time data and supporting predictive risk analysis(Rameshkumar, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Park, Kim and Kwon, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), offering greater accuracy than manual methods(Sur and Kim, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInstruments developed to collect previous research data\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInstrument Group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExamples\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuestionnaires and Surveys\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStructured questionnaires, Expert surveys, AHP questionnaires\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary Data and Databases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWorld Bank data, AIS data, Panel data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInterviews\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFace-to-face interviews, Telephone interviews\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObservation and Document Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDirect observation, Finance report analysis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMixed Methods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCombination of qualitative and quantitative data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSimulation and Modeling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSimulation data, Mathematical models\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot Specifically Mentioned\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMethods not described in detail\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eOverall Total\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eAmong the 105 instruments analyzed, secondary data dominated (35.24%, n = 37), reflecting a preference for validated datasets from global institutions and academic sources(Kim, Roh and Seo, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sunitiyoso et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Questionnaires and surveys followed (28.57%, n = 30), indicating active primary data collection(Li et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pham, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). However, 21 studies (20%) did not clearly report their instruments, indicating a methodological transparency gap.\u003c/p\u003e\u003cp\u003eLess frequent methods included interviews (7.62%, n = 8), mixed methods (3.81%), observation (2.86%), and simulation (1.90%), yet each adds a valuable perspective, especially for complex or context-specific topics(Rameshkumar, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sur and Kim, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Y.-N. Kim et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This pattern underscores both reliance on established data and the need for diversified and well-documented research tools in maritime logistics.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Analysis Methods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn digitalization-driven risk management research within maritime logistics, particularly involving FPSO operations, choosing appropriate data analysis methods is vital to ensure research validity(Kim et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sunitiyoso et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pham, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). With the growing complexity of operational data, advanced analytical tools are becoming increasingly essential for accurate interpretation and decision-making(Li and Yuen, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe various data analysis methods used in research related to logistics loss risk management\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnalysis\u0026nbsp;Method\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExample\u003c/p\u003e\u003cp\u003eMethods\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLiterature\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStatistical Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRegression, Correlation, SEM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(Lee and Han, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hirata, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jung, Kim and Shin, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Park et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rahman et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Asian, Wang and Dickson, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dong, Christiansen, Fagerholt and Bektaş, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dong, Christiansen, Fagerholt and Chandra, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nguyen and Kim, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Giamouzi and Nomikos, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kim, Roh and Seo, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lai et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lai, Feng and Zhu, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Michail and Melas, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContent Analysis, Thematic Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(Senarak, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mouschoutzi and Ponis, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sunitiyoso et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Choi and Yeo, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; G. Kim et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nanyam and Kumar Jha, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nong, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pham, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMathematical Modeling and Simulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMathematical Models, Multi-Agent Simulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(Dong, Christiansen, Fagerholt and Bektaş, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li and Yuen, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Teweldebrhan, Maghelal and Galadari, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; G. Kim et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Park, Kim and Kwon, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eData Envelopment Analysis (DEA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDEA-Window, SBM-DEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(Dang and Yeo, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Park, Pham and Yeo, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Adler et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nong, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMulti-Criteria Decision-Making Methods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAHP,\u0026nbsp;TOPSIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(Nguyen, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jung, Kim and Shin, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Park et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yazır, Şahin and Yip, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rojanaleekul, Pungchompoo and Sirivongpaisal, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFuzzy Methods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFuzzy AHP, Fuzzy TOPSIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(Nguyen, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sur and Kim, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Yazır, Şahin and Yip, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Teweldebrhan, Maghelal and Galadari, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultivariate Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePCA, Cluster Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(Kim et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rojanaleekul, Pungchompoo and Sirivongpaisal, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lai, Feng and Zhu, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNetwork Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSNA, Analytic Network Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(Chi, Phong and Hanh, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Choi and Yeo, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOptimization Methods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGenetic Algorithm, Metaheuristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(Galvao, Wang and Mileski, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nguyen, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Dong et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBibliometric Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVOSviewer, WordCloud\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(Mouschoutzi and Ponis, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pham, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEconometric Models\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVAR,\u0026nbsp;VEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(Giamouzi and Nomikos, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Michail and Melas, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Park, Kim and Kwon, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEfficiency and Productivity Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMalmquist Productivity Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(Adler et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nanyam and Kumar Jha, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nong, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMachine Learning and AI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRandom Forest, Deep Learning LSTM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(Li et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; G. Kim et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Palippui, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecific Logistics and Maritime Methods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAdditive Market Share Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(Teweldebrhan, Maghelal and Galadari, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eISM, Markov Chain Model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(Lee, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kim, Sur and Cho, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nanyam and Kumar Jha, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot Specifically Mentioned\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(Chauvin, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chiu et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Yeo, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dombrowski et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nguyen, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rafke and Lestari, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cho and Lee, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang, Yang and Zhang, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wijaya et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Akbar et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Amin et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Firlej and Taeihagh, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Helo, Paukku and Sairanen, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Page et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Thi Nong, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Haralambides, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe analysis of 142 methods revealed a strong quantitative dominance, led by statistical approaches such as SEM and regression(Kim et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Park et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Qualitative techniques are often employed for content and thematic analysis(Sunitiyoso et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pham, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e), while modeling and simulation approaches are increasingly used to replicate complex maritime systems(Wang, Yang and Zhang, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; G. Kim et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Other notable methods include DEA and MCDM for performance and decision modeling(Dang and Yeo, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jung, Kim and Shin, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), fuzzy logic for uncertainty analysis(Sur and Kim, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e), and bibliometric tools for mapping research trends(Mouschoutzi and Ponis, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite increasing relevance, machine learning and AI applications remain underutilized(Li et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Palippui, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and 22 studies (15.5%) lacked clear methodological reporting, highlighting the need for greater transparency.\u003c/p\u003e\u003cp\u003eThis methodological diversity underscores evolving practices in maritime risk research and points to opportunities for the further integration of AI, hybrid models, and domain-specific analytics to advance digital maritime safety.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cb\u003eMethodological Landscape and Dominance of Statistical Approaches\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis review highlights the pronounced reliance on traditional statistical analyses in maritime logistics and risk management research. With 37 occurrences, this method has emerged as the most frequently employed across various studies(Kim et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sunitiyoso et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pham, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). The dominance of regression, structural equation modeling (SEM), and correlation-based techniques reflects the emphasis on quantifiable, replicable outcomes, aligning with Helo et al. (2019), who advocate for empirical rigor in analyzing logistics systems(Helo, Paukku and Sairanen, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such methodologies are especially favored in risk management because of their robustness in validating relationships among variables (Rameshkumar, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sur and Kim, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eEmergence of Qualitative Insights and Contextual Depth\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhile quantitative techniques prevail, the notable presence of qualitative analysis (15 studies, or 10.56%) underscores the growing recognition of non-numeric contextual exploration in maritime digitalization(Mouschoutzi and Ponis, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sunitiyoso et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Studies have increasingly relied on content and thematic analysis to capture human-technology interactions, institutional behavior, and socio-technical dynamics (Rameshkumar, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This finding supports Notteboom et al. (2021), who highlight the importance of understanding the underlying motivations and tacit knowledge in maritime operations (Notteboom and Haralambides, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMathematical Modeling and Simulation: A Growing Paradigm\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMathematical modeling and simulation (11 occurrences) represent an evolving approach for analyzing complex maritime systems. These models allow for predictive scenario-based analyses that support decision-making in logistics optimization. Research by Wang et al. (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) on dynamic pricing and Dong et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) on route planning demonstrates how multi-agent simulation and optimization techniques contribute to resilient system design(Dong, Christiansen, Fagerholt and Bektaş, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang, Yang and Zhang, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This aligns with Tran et al. (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), who emphasized model-based strategies in maritime supply chain prediction(Tran, Haasis and Buer, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethodological Diversity and Hybrid Techniques\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAn interesting aspect is the methodological pluralism evident in approaches such as Data Envelopment Analysis (DEA), Multi-Criteria Decision-Making (MCDM), and fuzzy methods. DEA techniques have been widely applied to assess port and vessel performance(Dang and Yeo, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Park, Pham and Yeo, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), while fuzzy logic models enhance decision making in uncertain risk environments(Sur and Kim, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). This variety suggests an increasing appreciation for multidimensional decision support systems, confirming the complexity and heterogeneity of maritime digitalization challenges(Yan et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eLag in Adoption of Machine Learning and AI\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDespite the recognized potential of Artificial Intelligence (AI) and Machine Learning (ML) in digital transformation, their application remains minimal only 2 studies employed such methods(Liu et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kim, Sur and Cho, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This discrepancy contrasts starkly with projections by(Heilig, Lalla-Ruiz and Vo\u0026szlig;, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), who emphasize AI's transformative capabilities of AI. Possible barriers include limited access to quality training data, domain-specific AI models, and industry resistance to black box systems. However, promising examples, such as the LSTM-based energy prediction at Busan Port, (G. Kim et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Y.-N. Kim et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) show early momentum in this area.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTransparency in Methodological Reporting\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOne methodological concern was the lack of specificity in several studies; 22 articles did not clearly report their analysis methods. This reflects gaps in methodological transparency, which undermines reproducibility and hampers comparative review. Davarzani et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) emphasized the need for standardized reporting frameworks, and this review supports this recommendation. In contrast, studies like (2023) serve as models for systematic reporting through detailed bibliometric techniques(Davarzani et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pham, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSynthesis and Future Direction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCollectively, the findings emphasize that while statistical and modeling methods are firmly entrenched in maritime research, emerging qualitative and hybrid techniques play a crucial role in exploring the social, technical, and systemic complexity of digital transformation. Importantly, AI and advanced analytics remain underutilized, indicating a clear research opportunity.\u003c/p\u003e\u003cp\u003eFuture research should actively pursue integrative analytical models that combine simulation, machine learning, and empirical insights to better address cargo risk, port inefficiencies, and human-system interaction in the digital era.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis systematic literature review investigates the application of digitalization-based approaches in managing logistics loss risks during FPSO-to-tanker oil transfer operations. Using the PRISMA 2020 protocol, 194 peer-reviewed articles from 2010 to 2023 were analyzed across seven analytical dimensions, offering a comprehensive synthesis of trends in risk typology, digital intervention, research design, data instruments, and analytical methods.\u003c/p\u003e\u003cp\u003eThe findings revealed a steady increase in academic attention since 2018, with a clear focus on modeling-based strategies and statistical analysis. Research subjects predominantly focus on port operations, safety, and human factors, while logistics and supply chain integration remain underrepresented. Although traditional quantitative techniques remain dominant, the emergence of qualitative and hybrid methods has indicated an expanding methodological landscape. Notably, the adoption of AI and advanced digital technologies, such as digital twins, blockchain, and machine learning, is still nascent.\u003c/p\u003e\u003cp\u003eDespite the momentum of digital transformation in maritime logistics, there is limited integration between human-centric safety systems and real-time digital platforms, particularly in high-risk operations, such as FPSO-to-tanker transfers. Moreover, transparency in methodological reporting remains inconsistent, hindering replicability and cumulative knowledge-building.\u003c/p\u003e\u003cp\u003eThis study contributes to the literature by mapping the current state and identifying research gaps, particularly the lack of holistic digital frameworks that integrate ports, vessels, and logistics networks. Future research should prioritize (1) system-wide AI-enabled solutions, (2) digital twin-based infrastructure for predictive monitoring, and (3) interdisciplinary approaches that bridge human factors with intelligent systems.\u003c/p\u003e\u003cp\u003eBy addressing these opportunities, future studies can support the development of safer, smarter, and more sustainable offshore logistics systems that advance both academic rigor and industrial resilience in the era of maritime digitalization.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdler, N., Hirte, G., Kumar, S., \u0026amp; Niemeier, H.-M. (2022). The impact of specialization, ownership, competition and regulation on efficiency: a case study of Indian seaports. \u003cem\u003eMaritime Economics \u0026amp; Logistics\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(3), 507\u0026ndash;536. https://doi.org/10.1057/s41278-021-00200-y\u003c/li\u003e\n\u003cli\u003eAkbar, A., Aasen, A. K. A., Msakni, M. 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(1999). \u003cem\u003eDeepwater Gulf of Mexico Oil Spill Scenarios Development and Their Associated Risk Assessment Deepwater Gulf Of Mexico Oil Spill Scenarios Development And Their Associated Risk Assessment A Dissertation\u003c/em\u003e. https://www.researchgate.net/publication/308100250\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Hasanuddin University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Digitalization and Maritime Safety, Logistics Risk Management, FPSO-Tanker Transfer, Systematic Literature Review, Offshore Oil and Gas","lastPublishedDoi":"10.21203/rs.3.rs-7218259/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7218259/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe increasing complexity of offshore oil and gas operations, particularly in the cargo transfer process from Floating Production Storage and Offloading (FPSO) units to tankers, demands robust and adaptive risk management strategies. This study presents a systematic literature review (SLR) of 194 peer-reviewed articles published between 2010 and 2023 in SJR-indexed journals structured according to the PRISMA 2020 guidelines. The objective was to identify research trends, methodological patterns, and the extent of digital technology adoption in managing logistics loss risks. Using content analysis across seven dimensions ranging from risk typology to data analysis methods, the findings indicate a dominance of modeling approaches (42%), with limited integration of advanced digital tools such as artificial intelligence (AI), digital twins, and blockchain. Human factors are increasingly emphasized but remain insufficiently linked with technological frameworks. This study highlights a research gap in real-time, AI-enabled, and human-centered systems for maritime logistics. It proposes future directions, including digital twin-based monitoring, integrated human-technology interfaces, and predictive analytics for enhanced operational safety and efficiency. 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