Enhancing Container Vessel Safety: Insights from Industry Professionals

Analyzing maritime accidents and incidents is essential for comprehending their origins, identifying safety shortcomings, and implementing preventative measures to mitigate future occurrences. A variety of methodologies and approaches have been utilized or suggested for conducting such analyses. This section aims to explore these methodologies and gain insights into the process.

The maritime industry has faced incidents and accidents often tied to human error. Some gained worldwide attention, like the Exxon Valdez, while others, such as the Sea Empress, went relatively unnoticed. However, these events consistently raised awareness regarding mariner fatigue, human error, and human performance in maritime safety. International bodies like the International Maritime Organization (IMO) have initiated new regulations in line with international treaties to tackle human error. Flag State safety agencies and port authorities have enacted their own measures, and Classification Societies have issued updated rules. The work by Mccafferty and Baker [4] delves into human error in the marine sector, with a specific focus on crew fatigue as a pivotal factor. It also explores recent initiatives by maritime safety organizations aimed at addressing these issues and reducing human error incidents, concluding with the potential implications of these efforts for maritime safety.

Sánchez-Beaskoetxea et al. [5] analyzed marine incident reports from the U.S. National Transportation Safety Board (NTSB) spanning June 1975 to September 2017. Their study examined the role of ship crews and other stakeholders, such as pilots and shipping companies, in incident causation, while also assessing the impact of international regulations on seafarers. Findings revealed that crew errors on cargo and passenger vessels were rare, underscoring the effectiveness of conventions like STCW. However, high human error rates in pilot waters emphasized the need for better coordination between bridge teams. Additionally, increased crew errors in fishing vessels and tugboats highlighted the necessity for targeted training to reduce navigation mistakes.

Historically, incident investigations focused primarily on hardware problems, such as equipment faults and component failures. However, it has become increasingly evident that human factors, rather than hardware issues, are responsible for most incident precursors. Rothblum et al. [6] aimed to aid offshore and marine organizations in incorporating human elements into their investigation programs to identify the causes of incidents involving human factors and establish practical safety solutions to prevent similar incidents.

Studies on maritime accidents consistently highlight human error as a key contributing factor, despite advances in vessel technology. Singh and Raju [7] carried out a systematic literature review examining the role of risk perception, the impact of the ISM Code on safety standardization, and the use of investigation methodologies to identify both direct and systemic causes. The application of accident causation theories offers a structured understanding of the interaction between human, organizational, and external factors, supporting a move toward more integrated safety management in shipping.

To improve the investigations of shipping accidents, a risk-based model was proposed by Celik et al. [8]. Their approach integrates a new risk framework with Fuzzy Extended Fault Tree Analysis (FFTA) to address both organizational constraints and technical failures. By examining factors such as operational errors and technical malfunctions, the study provides valuable insights into the root causes of maritime accidents. It emphasizes the importance of utilizing SAI reports to develop effective risk mitigation strategies for safer marine operations. Additionally, the research explores the integration of Fault Tree Analysis (FTA) with SAI to standardize data collection and improve the overall effectiveness of maritime accident investigation and prevention.

Schröder-Hinrichs et al. [9] analyzed around 41 maritime accidents caused due to explosions and fires in machinery spaces by deploying HFACS framework. Their findings revealed that these investigations primarily focused on technical failures while often overlooking organizational factors, highlighting a gap in current investigative approaches.

In a study by Caridis [10], the Casualty Analysis Methodology for Maritime Operations (CASMET) Project was introduced. This project concentrated on coding and analyzing maritime accidents and incidents that transpire on seagoing vessels. The impetus behind this project stemmed from a lack of emphasis on organizational and human errors in European nations' investigative practices. CASMET amalgamated the best elements of existing investigative procedures, yielding positive outcomes after being applied to numerous incidents.

Grech et al. [11] investigated mariners' lack of Situation Awareness (SA) by analyzing accident reports within merchant shipping operations. They employ the Leximancer tool, recognized for its rapid textual data processing, to assess its accuracy in comparison to manual analysis conducted by two raters. The results underscore SA's pivotal role in maritime decision-making, with numerous accidents linked to SA deficiencies. Additionally, the Leximancer tool's outcomes align with manual analysis, indicating its potential for analyzing accident reports in various transportation domains.

Salmon et al. [12] used STAMP, HFACS and Accimap methods for comparison in various case studies. While Accimap and STAMP provided a broader perspective on contributing factors, HFACS was noted for its structured taxonomy and reliability. However, as HFACS was originally developed for aviation, it faced challenges in classifying failures in other industries due to the lack of sector-specific taxonomies. The study suggests enhancing Accimap by incorporating more adaptable taxonomies across different investigation levels to improve future accident analyses.

Fu et al. [13] conducted a comparative analysis of HFACS and the 24Model, evaluating their theoretical foundations, classification criteria, and analytical processes. The study found that while both models are valuable, the 24Model demonstrates strong scientific validity and practical applicability, aiding investigators in selecting suitable methodologies for accident analysis.

Wu et al. [14] introduced an improved version of the Cognitive Reliability and Error Analysis Method (CREAM) by incorporating an evidence-based reasoning approach. This enhanced model provides a more precise evaluation of language-related factors in maritime accidents while addressing data uncertainties that affected previous CREAM models.

Kim and Na [15] proposed a human factor methodology to systematically identify and categorize key contributors to human error, establish accident sequences, and develop preventive safety measures.

Kececi and Arslan [16] introduced the Ship Accident Root Cause Evaluation (SHARE) method, specifically designed for maritime incidents. Built on the Fuzzy SWOT AHP framework, SHARE standardizes terminology, quantifies data, assigns accountability, and aids in risk mitigation, with its effectiveness validated through real-world applications.

Total-loss marine accidents result in severe human, economic, and environmental consequences. Chen et al. [17] analyzed global total-loss incidents from 2001 to 2015 using the fuzzy matter element method, identifying critical factors and trends to support policymakers in accident prevention strategies.

Zhang et al. [18] conducted a quantitative analysis of ship accident reports to assess the relationship between accident severity and contributing factors. Their two-phase approach involved standardizing contributory factors from reports and statistically analyzing them using the Kruskal-Wallis test, Cramer’s V analysis, and Kendall’s tau coefficient, effectively identifying the primary determinants of ship accident outcomes.

Cao et al. [19] conducted a comprehensive review of 491 studies from the Web of Science (2000–2022) to explore trends in marine accident research. Using CiteSpace and VOSviewer for knowledge mapping and cluster analysis, the study highlighted key research areas, including the integration of emerging technologies and the impact of human factors on remote-controlled ships and Arctic operations. Advanced techniques like deep data mining and machine learning were identified as crucial for uncovering new risk factors. The study provides a strong theoretical foundation for improving maritime safety, assessing research trends, and visualizing collaboration networks among institutions and researchers.

Accurate maritime risk assessment often requires extensive data. To bridge this gap, Li et al. [20] integrated recent accident data (2017–2021) from the GISIS and LRF databases into a Bayesian network (BN) model. Their analysis identified 23 key risk-influencing factors (RIFs) across static and dynamic risk categories. The BN model enhances risk prediction and scenario analysis while supporting the development of effective accident prevention strategies.

Despite all the technological developments and enhanced legislation, shipping accidents present a major challenge and cause of global concern. This highlights the importance of producing clear and consistent accident investigation reports that help identify the key factors contributing to such incidents. To improve the assessment of human errors in maritime accidents, Fuzzy analytical hierarchy process was integrated with the Human Factors Analysis and Classification System (HFACS) in the study by Celik and Cebi [21]. This combination strengthens decision-making processes and provides a solid analytical framework for quantitatively evaluating maritime incidents.

Focusing on safety in the maritime, Wu et al. [14] and Akyuz [22] introduced a hybrid model integrating HFACS with the Analytical Network Process (ANP) to analyze shipboard accidents. This combination provides a structured framework for assessing human errors while examining intercomponent relationships, offering a reliable tool for identifying accident causes.

Chen et al. [23] developed a novel Human Factors Analysis and Classification System for Maritime Accidents (HFACS-MA) to analyse human and organizational factors in marine events. This five-tiered structure integrates critical concepts from the Reason's Swiss Cheese Model, HFACS, and the Hawkins' SHEL model and aligns them with IMO regulations. The HFACS-MA is further enriched by combining it with a Why-Because Graph, thereby allowing an additional approach to incentivize the benefits of the HFACS framework.

Ensuring safe and accident-free shipping is a key priority for the maritime industry, with human error accounting for approximately 80–85% of accidents. Hasanspahić et al. [24] investigated 135 accident reports from the UK Marine Accident Investigation Branch (2010–2019) using the HFACS-MA framework. Their study identified frequently occurring causal factors and applied multiple linear regression to analyze their correlation with accident frequency. The findings suggest that addressing two critical human factors identified in the study could significantly reduce accident rates and improve overall maritime safety.

Akyuz and Celik [25] proposed a marine accident analysis and prevention model called HFACS-CM. The model combines the HFACS with the Cognitive Map (CM) technique to analyze the contribution of the human factor in marine accidents. The model gives an effective solution for users to find the pertinent causes of marine accidents. Hence, the latent or active reasons of an accident can be identified and prioritized by this model. Using a life boat situation drill, the aptness of the model could be checked for serious marine accident involving a man.

Batalden and Sydnes [26] applied a customized HFACS framework to marine accident investigations, analyzing 22 serious incidents reported by the UK's Marine Accident Investigation Branch from 2002 to 2010. Their findings highlighted that “Very Serious” accidents were strongly linked to organizational shortcomings, particularly in onboard planning and monitoring.

The oil and gas industry has experienced severe accidents due to operational and organizational failures. Since HFACS was originally developed for aviation, its limitations became evident when applied to this sector, particularly in areas such as regulatory oversight and sabotage. To bridge these gaps, Theophilus et al. [27] developed HFACS-OGI, a tailored version of HFACS for the oil and gas industry, enhancing its effectiveness in accident investigations.

Uğurlu et al. [28] brought forward a modified HFACS for passenger vessels for better classification of the respective vessel accidents. The grounding of 51 passenger vessels which occurred between 1991 and 2017 were analysed using the modified HFACS-PV model. The integration of the framework was found to be more effective for quantitative and qualitative study of passenger vessels.

Yildiz et al. [29] examined 70 passenger ship collisions and contact incidents from 1991 to 2015 using an adapted HFACS-PV model, which incorporated additional operational conditions. Their analysis identified key concerns, including authority misuse, procedural breaches, and rule violations.

Singh and Totakura [3] examined container vessel accidents using the HFACS-derived NASAFACS framework to analyze latent factors and active errors from 2010 to 2021. Findings reveal that 'Preconditions' are the primary contributors, with vessel damage, container loss, and environmental pollution as major consequences. Collisions involve both latent and active errors, while fires are mainly latent-driven. Heavy weather damage incidents show a higher risk of pollution. The study highlights the need for better securing practices, accurate cargo declaration, and stricter stowage compliance to enhance safety and reduce environmental impact.

Despite much work that has been done to analyse marine accidents, gaps still persist, which are:

• Most models underrepresent external commercial and regulatory influences, which are particularly relevant in container shipping.

• There is limited integration of expert experiential knowledge, which can reveal systemic risks not captured in reports.

4.1 Hierarchical code–subcode model

Figure 1 shows the Hierarchical code–subcode model for the NASAFCS framework used for coding the interview responses from various respondents.

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Figure 1. Hierarchical code–subcodes model

4.2 Word cloud for high frequency words in the interview transcripts

The word cloud, generated from the coding and analysis of various interview transcripts on MAXQDA application, highlights the most frequently used words by the respondents during the interview, as shown in Figure 2.

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Figure 2. MAXQDA word cloud

4.3 Single case models for various respondents

Looking at the single case model in Figure 3, Respondent 1 gives maximum weightage to Organizational Resources, followed by Organizational Culture/ Climate (L1 – Organization). Equal weightage is given to Communication (L3 – Precondition), Supervisory Violations (L2 – Supervision) and Skills (L4 – Acts).

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Figure 3. Single case model Respondent 1

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Figure 4. Single case model Respondent 2

Looking at the above single case model in Figure 4, Respondent 2 gives maximum weightage to Organizational Resources, followed by Organizational Culture/ Climate and then Organizational Operations (L1 – Organization). Equal weightage is given to Supervisory Violations and Planned Inappropriate Operations (L2 – Supervision), Communication, Physical Environment, Adverse Physiological and Adverse Psychological (L3 – Precondition).

Looking at the single case model in Figure 5, Respondent 3 gives maximum weightage to Organizational Resources and Organizational Operations, followed by Organizational Culture/ Climate (L1 – Organization). This is followed by Communication, then Technological Environment and Physical Environment (L3 – Precondition). Supervisory Violations & Planned Inappropriate Operations also find place with lesser weightage (L2 – Supervision).

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Figure 5. Single case model Respondent 3

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Figure 6. Single case model Respondent 4

Looking at the above single case model in Figure 6, Respondent 4 gives maximum weightage to Physical Environment (L3 – Precondition), followed by Organizational Resources (L1 – Organization), followed by Supervisory Violations (L2 – Supervision) and Technological Environment (L3 – Precondition). These are followed by Communication (L3 – recondition), Planned Inappropriate Operations & Inadequate Supervision (L2 – Supervision) and Organizational Culture/ Climate (L1 – Organization). Weightage though least has also been given to Adverse Physiological and Adverse Psychological (L3 – Precondition).

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Figure 7. Single case model Respondent 5

Looking at the above single case model in Figure 7, Respondent 5 gives maximum weightage to Organizational Resources, Organizational Operations (L1 – Organization) and Physical Environment (L3 – Precondition) followed by equal weightage to Organizational Culture/ Climate (L1 – Organization), Supervisory Violations (L2 – Supervision), Adverse Psychological and Communication (L3 – Precondition).

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Figure 8. Single case model Respondent 6

Looking at the above single case model in Figure 8, Respondent 6 gives maximum weightage to Organizational Resources, Organizational Operations, Organizational Culture/ Climate (L1 – Organization), Supervisory Violations (L2 – Supervision), Physical Environment, Communication (L3 – Precondition) and Violations (L4 – Acts). The above are followed by lesser and equal weightage to Planned Inappropriate Operations (L2 – Supervision), Adverse Psychological and Technological Environment (L3 – Precondition).

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Figure 9. Single case model Respondent 7

Looking at the above single case model in Figure 9, Respondent 7 gives maximum weightage to Organizational Resources, Organizational Operations (L1 – Organization), Supervisory Violations, Inadequate Supervision (L2 – Supervision), Technological Environment and Adverse Psychological (L3 – Precondition). The above are followed by lesser and equal weightage to Organizational Culture/ Climate (L1 – Organization), Physical Environment, Communication (L3 – Precondition) and Violations (L4 – Acts).

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Figure 10. Single case model Respondent 8

Looking at the above single case model in Figure 10, Respondent 8 gives maximum weightage to Organizational Resources, Organizational Operations, Organizational Culture/ Climate (L1 – Organization), Supervisory Violations (L2 – Supervision), Technological Environment and Communication (L3 – Precondition). The above are followed by lesser and equal weightage to Planned Inappropriate Operation (L2 – Supervision) and Physical Environment (L3 – Precondition).

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Figure 11. Single case model Respondent 9

Looking at the above single case model in Figure 11, Respondent 9 gives maximum weightage to Organizational Resources, Organizational Operations, Organizational Culture/ Climate (L1 – Organization). The above are followed by lesser and equal weightage to Supervisory Violations (L2 – Supervision), Technological Environment, Physical Environment, Communication and Adverse Psychological (L3 – Precondition).

Summary of max weightage given to factors by various respondents:

Respondent 1: Organizational Resources followed by Organizational Culture/ Climate (L1).

Respondent 2: Organizational Resources followed by Organizational Culture/ Climate (L1).

Respondent 3: Organizational Resources and Organizational Operations (L1).

Respondent 4: Physical Environment (L3) followed by Organizational Resources (L1).

Respondent 5: Organizational Resources, Organizational Operations (L1) and Physical Environment (L3).

Respondent 6: Organizational Resources, Organizational Operations, Organizational Culture/ Climate (L1), Supervisory Violations (L2), Physical Environment, Communication (L3) and Violations (L4 – Acts).

Respondent 7: Organizational Resources, Organizational Operations (L1), Supervisory Violations, Inadequate Supervision (L2), Technological Environment and Adverse Psychological (L3).

Respondent 8: Organizational Resources, Organizational Operations, Organizational Culture/ Climate (L1), Supervisory Violations (L2), Technological Environment and Communication (L3).

Respondent 9: Organizational Resources, Organizational Operations, Organizational Culture/ Climate (L1).

From above, it’s evident that respondents consider Organization playing the maximum role in the causation of the incidents onboard the container, in terms of the availability of resources, culture or operations.

4.4 MAXQDA code matrix browser output–Overview of the response codes

The analysis of the distribution of latent factors and active events as depicted in Table 2 provides a detailed view of the underlying causes and contributing factors to container vessel accidents, based on the NASAFACS framework. The analysis is further detailed through the chart in Figure 12, which visually represents the distribution of these response codes. This pictorial representation helps illustrate the relative weight of different factors and allows for easy comparison.

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Figure 12. Chart showing the distribution of the response codes under NASAFACS framework

The distribution of response codes underscores the dominance of ‘L1 – Organization’ as the most significant causative factor in container vessel accidents, which is in contrast to the NASAFCS analysis of container vessel accidents, which highlights.

Following this, ‘L3 – Precondition’ factors are recognized as the next most significant contributors.

‘L2 – Supervision’ ranks third, highlighting the importance of supervisory practices, but indicating that their impact is seen as somewhat less direct compared to Organization and Precondition factors.

Finally, ‘L4 – Acts’ is seen as the least significant, emphasizing that individual actions or errors by ship staff are not considered major contributors to accidents by the respondents. This reinforces the perception that external parties and systemic issues have a more substantial impact on container vessel operations, often placing many operational aspects beyond the direct control of the ship's crew.

The distribution of NASAFACS subcodes is further detailed in Table 3, based on the feedback received from the various respondents.

Table 2. Overview of the codes assigned based on feedback from the respondents

Source: Author’s own analysis

Table 3. Distribution of NASAFACS subcodes

Source: Author’s own analysis

The analysis of the distribution of NASAFACS subcodes table (Table 3) and the codes-subcodes segment model (Figure 13) provides further insight into the hierarchy of causative factors in container vessel accidents as perceived by the respondents.

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Figure 13. Codes-subcodes segment model

L1 Organization: Organizational Resources are seen as the most significant causative factor, with Organizational Culture and Organizational Operations following behind. This suggests that respondents view effective resource availability and management as critical to preventing accidents, with the culture and operational practices of the organization and industry playing supportive roles.

L3 Preconditions: This category holds the second most weight in terms of causative factors, with Environmental Factors (both Physical and Technological) and Personnel Factors (specifically Communication) being equally significant contributors. Individual Factors, such as Adverse Physiological and Adverse Psychological conditions, are noted but are seen as less prominent.

L2 Supervision: Within this category, Supervisory Violations are viewed as the primary causative factor, followed by Planned Inappropriate Operation and Inadequate Supervision. This indicates that lapses in supervisory practices and planning are key contributors to accidents.

L4 Acts: According to the respondents, this category plays a limited role in container vessel accidents, suggesting that individual actions or mistakes are seen as less impactful compared to organizational and environmental factors.

This breakdown highlights the emphasis placed on systemic issues or ‘Latent Factors’ of ‘L1 – Organization’, ‘L3 – Preconditions’ & ‘L2 – Supervision’ and being more prevalent over individual actions or ‘Active Events’ of ‘L4 – Acts’ in the context of container vessel accidents.

4.5 MAXQDA maps

4.5.1 MAXQDA code map

The MAXQDA code map in Figure 14 is a visual tool used to display the relationships between different codes based on how frequently they are applied together in a dataset. It allows one to see patterns and connections between themes, concepts, or categories that emerge during qualitative data analysis. The code map shows the association between the codes. The more frequently two codes have been assigned together, the closer they are on the map.

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Figure 14. MAXQDA code map

4.5.2 MAXQDA code co-occurrence model (Code Intersection)

The Code Co-occurrence Model (Code Intersection) from MAXQDA in Figure 15, visually represents the relationships between key codes, showing how often specific themes (codes) appear together in the dataset. The lines connecting the nodes represent the ‘frequency of co-occurrence’ between these codes in the data. Thicker lines indicate stronger relationships, i.e., higher co-occurrence. The ‘numbers on the lines’ show the exact frequency of intersection. The codes Communication, Supervisory Violations, Organizational Culture/Climate, and Organizational Resources all have thick lines connecting them, and the frequency of 9 co-occurrences indicates a significant overlap between these issues. This suggests that in the dataset, communication failures are frequently linked to supervisory violations, which could be influenced by both organizational culture and resource allocation.

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Skill-Based has weaker links with Communication (2), Organizational Resources (2), and Organizational Culture/Climate (2), suggesting that skill-based issues are less frequently tied to these themes in the data. This could mean that while skills-based issues do contribute to problems, they are not as central as organizational factors like resources, culture, or supervision. It could also indicate that skill-based problems tend to appear in more isolated or specific contexts rather than being systemic across the organization.

4.6 Key insights from the container vessel accident analysis and respondent interview

Below are the key insights gathered during the research process.

4.6.1 Challenges with the increased size of vessel

The trend toward Ultra-Large Container Vessels (ULCVs), with capacities exceeding 20,000 TEUs, has brought notable challenges despite the economic benefits. Their substantial windage area and limited engine power create handling difficulties, particularly during adverse weather or emergency scenarios. Operationally, managing stowage and lashing with a minimal crew presents significant concerns. A prominent example is the grounding of a ULCV in the Suez Canal, which disrupted global trade, emphasizing the risks associated with these large vessels.

4.6.2 Inadequacies of port infrastructure

Port infrastructure has struggled to keep pace with the growing dimensions of container vessels. Although ports have upgraded berths and handling equipment, these changes are often reactive. Larger ships with lower engine power require more space and precise manoeuvring, particularly in constrained environments. This mismatch between ship design and port readiness increases the likelihood of incidents, especially in ports lacking advanced tugs or navigational aids.

4.6.3 Firefighting capability

Firefighting systems on modern container ships have not evolved in line with vessel size and cargo complexity. Crews of 16–18 are expected to control fires on vessels over 20,000 TEUs using methods suitable for much smaller ships. Limited accessibility to the fire origin, particularly deep within cargo stacks, further exacerbates the issue. While new technologies are being explored, the current gap in fire response capability remains a critical safety concern on ULCVs.

4.6.4 External factors in container vessel operations

Container shipping operations are significantly influenced by external factors, many of which are beyond the control of the vessel’s crew. Cargo-related incidents—such as fires or container losses—are often linked to misdeclared or improperly stowed cargo. Planning is conducted ashore due to the complexities of liner trades, which involve frequent loading and unloading at multiple ports. The crew’s role is limited to verifying vessel stability, stress parameters, and IMDG segregation compliance, relying heavily on the accuracy of shore-side cargo information. Consequently, they act more as final checkers than planners, with minimal authority to intervene in broader cargo-related decisions, despite bearing operational responsibility.

4.6.5 Misdeclaration of cargo

The container shipping industry is fraught with cargo misdeclaration challenges, both in terms of its nature and its weight, which are discussed here.

Misdeclaration of dangerous cargo (IMDG). Containerized cargo poses distinct challenges compared to bulk, oil, or general cargo, primarily due to limited transparency regarding container contents. Ship crews and operators rely heavily on the shipper’s declaration, making them vulnerable to misdeclarations. Unfortunately, such misdeclarations—especially of dangerous goods (DG)—are not uncommon and have serious safety implications. Shippers may mis-declare hazardous cargo to avoid higher freight costs, which can be three to five times greater than for non-dangerous goods, or to bypass carrier-imposed restrictions on specific DG types. When facing transport limitations, shippers may falsely label hazardous materials as harmless, concealing the actual risks. This practice endangers the vessel, crew, and environment, as misdeclared DG can trigger fires, explosions, or toxic leaks. In addition to false declarations, improper packaging of dangerous cargo further compounds the risk.

Misdeclaration of cargo weight. Another critical concern in container shipping is the misdeclaration of cargo weight. Accurate weight declaration is vital for assessing a vessel's stability, particularly its metacentric height (GM), a key determinant of safe operations at sea. Unlike bulk carriers or oil tankers, which naturally have a high GM, container vessels operate within tighter GM margins, making them more sensitive to weight inaccuracies. Misdeclared cargo, especially when heavier containers are stowed higher than intended, can distort GM calculations. While stability assessments may appear compliant on paper, the actual GM may be lower, compromising safety.

Beyond stability, accurate weight verification is essential for structural integrity, including ensuring stack weight limits on hatch covers are not exceeded. To address these risks, the Verified Gross Mass (VGM) regulation mandates that containers be weighed before loading. While this measure aims to enhance compliance, full industry adherence remains a persistent challenge.

4.6.6 Limited action possibility against faltering shippers and freight forwarders

A major challenge for shipping companies is the persistent issue of misdeclared cargo and improper packaging by freight forwarders, particularly in China, a key hub in global trade. Many Chinese freight forwarding businesses are small-scale or individually operated, making regulatory enforcement and legal action difficult. This decentralized structure hampers accountability for cargo misdeclarations.

The financial motivation to mis-declare hazardous cargo as general cargo is considerable, with DG incurring freight costs up to five times higher. This cost gap incentivizes deliberate misclassification despite safety risks. Attempts by shipping companies to enforce compliance or seek legal remedies are often hindered by complexities in the Chinese legal system.

To mitigate this risk, the industry must adopt stricter penalties and improve awareness among freight forwarders about the legal and safety consequences of misdeclaration. Strengthening enforcement and education can significantly reduce unsafe practices and enhance overall container shipping safety.

4.6.7 Role of commercial pressure

Commercial pressure presents a persistent challenge for shipmasters, especially in the container shipping sector, where schedules are set months in advance. Delays due to traffic, adverse weather, or route adjustments can disrupt tightly coordinated logistics. In such cases, masters often face strong pressure to maintain timetables, sometimes at the expense of safety.

A common consequence is high-speed navigation through congested waters, despite COLREGS requiring a safe speed based on prevailing conditions. Although weather routing services provide guidance, commercial imperatives frequently override safety considerations. Masters increasingly find their authority diminished, as decisions are influenced by shore-based commercial priorities. This imbalance can lead to risky manoeuvres in dense traffic or poor weather, raising the likelihood of incidents or cargo loss.

The same pressures are evident at ports, where rapid turnaround, reduced idle times, and high berth utilization are prioritized. Such demands may prompt pilots to approach berths at unsafe speeds, raising the risk of collisions and damage to ships or infrastructure. To foster safer operations, shipmasters must be empowered to make safety-based decisions without fear of commercial repercussions.

4.6.8 Dilution of risk assessment exercise

In practice, maritime risk assessments have become procedural formalities rather than meaningful safety evaluations. While risk management frameworks exist, commercial imperatives often override them, leading to routine approval of operations, even when significant hazards are identified.

Risk assessment outcomes are shaped by subjective factors such as the assessor’s experience, judgment, and risk tolerance. More critically, shipmasters rarely have the autonomy to halt operations based solely on their assessments. The prevailing mindset across the container industry tends to minimize operational disruption, even when residual risk is high.

As a result, risk assessments often reduce to paperwork exercises, assuming that identified hazards can be mitigated without operational delay. However, not all risks are manageable without cost. In some cases, safety may demand postponing a voyage or altering a route. Unless the industry is willing to absorb these commercial implications, genuine risk-based decision-making will remain elusive.

4.6.9 Impact of container loss on economic viability

Despite growing awareness of safety risks, container loss continues to be tolerated within the industry's economic model. Technological improvements—such as enhanced weather forecasting and vessel design to counteract phenomena like parametric rolling—have not been matched by economic incentives for operational change.

For instance, a vessel carrying 25,000 containers may remain profitable despite losing several hundred containers annually. This economic resilience discourages shipowners from investing in structural or procedural upgrades. Container loss, although undesirable, often lacks the financial consequence needed to prompt systemic change.

To shift this paradigm, economic models must place greater emphasis on safety and sustainability. Only when safety lapses result in financial or regulatory consequences will the industry move toward meaningful operational reforms.

4.6.10 Lashing issues encountered in container shipping

Lashing and securing challenges are prevalent in container vessel operations. Non-compliance with cargo securing manuals, improper weight declarations, wrong stowage, and inconsistent or degraded lashing gear contribute significantly to safety risks. Terminals sometimes deviate from the lashing plans specified by the vessel, increasing the likelihood of stack collapse, particularly in rough weather. Moreover, limited onboard manpower and maintenance opportunities cause deterioration in lashing equipment, which is often only addressed during dry dock repairs.

0 weight stress lashing beyond permissible limits (MSL), especially in multi-tier stacks. Incorrect stowage, placing heavier containers above lighter ones, can similarly cause excessive lashing loads. Furthermore, changes to GM caused by mis-stowage or incorrect container weights can either amplify rolling motions or make the ship overly tender, both of which strain lashing systems.

Block stowage, while efficient, can result in isolated “tower” stacks vulnerable in heavy weather. Errors such as mixing incompatible twist locks (left-handed vs. right-handed) pose serious hazards, as some containers may appear secured but are not. Unloading is also risky if one twist lock remains engaged.

Inadequate segregation of defective lashing gear, declining inventory levels over time, and poor lashing inventory management further aggravate safety concerns. The current regulatory requirements may be insufficient for ultra-large vessels where rolling-induced forces exceed the design limits of traditional lashing systems, necessitating a revision of standards tailored for modern container ships.

4.6.11 Impact of schedule intensity and manning levels on safety in container ship operations

Container vessels operate on some of the most demanding schedules in shipping. Rapid port turnarounds leave little room for comprehensive checks, increasing operational risk. Manning levels are often limited to the minimum required, typically 14–16 crew members, with the deck team responsible for watchkeeping, cargo supervision, lashing oversight, and regulatory compliance.

The Chief Officer, despite reliance on shore-based planners, must verify the stowage plan onboard. However, short port stays and excessive responsibilities limit the ability to inspect stowage and lashing properly. Compromised rest hours and fatigue affect crew performance, leading to elevated safety risks during complex cargo operations.

4.6.12 Quality and competency of crew in modern shipping operations

The quality of crew on container vessels is increasingly inconsistent. Rapid global fleet expansion and cost pressures have led to hiring from regions with less robust maritime training systems. While STCW certification is mandated, it does not always reflect practical readiness for modern ship operations.

Theoretical training often falls short in preparing the crew for real-world tasks. Some officers may hold valid CoCs but lack the practical competence to manage operations effectively. Recognizing this, many shipping companies invest in supplemental training programs to bridge the gap between theoretical knowledge and practical application. However, without uniform standards, competency disparities persist across the industry.

4.6.13 Execution of stowage plans and challenges during loading operations

Even well-planned stowage arrangements can falter during execution. Terminals may misplace containers or fail to follow the vessel-approved plan. In some cases, serious GM imbalances are only discovered post-loading, necessitating re-stowage or, worse, becoming apparent after departure.

Communication gaps, time constraints, and emphasis on efficiency at terminals often prevent accurate implementation. When final stowage plans are delayed or received post-sailing, vessels may be left operating with compromised stability, posing severe safety threats.

4.6.14 Crisis response and salvage capabilities in mega-sized container ships

Salvage operations for mega-container ships are increasingly complex. As ship sizes have outpaced available response equipment, salvors often face delays while designing and mobilizing suitable tools. Although casualty frequency has declined, reducing commercial incentives for equipment investment, the scale and difficulty of incidents have risen.

Crisis response protocols remain largely unchanged, but managing emergencies on ultra-large ships, whether fires, groundings, or structural failures, requires specialized gear and strategies. Without investment in scalable salvage capabilities, the maritime industry will struggle to respond effectively to such incidents.

4.6.15 Port of refuge issues

Though international conventions obligate ports to offer refuge to distressed vessels, many ports refuse entry, especially if hazardous cargo is involved. This reluctance stems not from policy gaps but from unpreparedness and risk aversion.

Refused refuge during critical emergencies may force ships to remain at sea, escalating danger. Ports need improved infrastructure, dedicated emergency handling protocols, and trained personnel to fulfil their responsibilities as safe havens.

4.6.16 Industry collaborations in the container shipping sector

Collaborative safety initiatives like Cargo Incident Notification System (CINS) have helped major shipping lines address cargo misdeclaration. By sharing data and identifying high-risk goods, CINS promotes best practices. However, its voluntary nature and lack of enforcement limit its broader impact.

The container sector contrasts with the tightly regulated tanker industry governed by OCIMF. Fragmentation in container shipping, with millions of shippers and a vast range of cargo types, makes regulatory alignment difficult. Projects like the World Shipping Council’s Top Tier, involving P&I clubs and container lines, offer promise but remain under development. More structured frameworks with broader participation and accountability are needed.

4.6.17 Environmental concerns: Emerging pollution from container ship accidents

Recent accidents, such as the MV X-Press Pearl, underscore a new class of environmental threats: microplastics. These pollutants are difficult to remove and pose long-term risks to marine ecosystems. Unlike oil spills, microplastics persist in the environment and are often underreported or underestimated.

As public and regulatory scrutiny increases, the industry must proactively develop containment and response strategies to prevent microplastic release and minimize its environmental footprint.

4.6.18 Emerging risks in shipping: Electric vehicles

The transport of electric vehicles (EVs), particularly second-hand units, introduces fire hazards due to degraded lithium-ion batteries. While new EVs often come with certification ensuring battery safety, there is no standardized process for second-hand units.

This lack of assurance creates uncertainty and risk during shipment. Even new EVs can catch fire due to thermal runaway. As EV volumes increase, establishing robust certification standards and safety protocols for battery health will be crucial to ensure safe maritime transport.

4.7 Contribution to theory

The Human Factor Analysis and Classification System (HFACS) formulated by Shappell and Wiegmann and based on the “Reason’s Swiss Cheese Model” of human error, looks at four levels of human failure, which include Unsafe Acts, Preconditions for unsafe acts, Unsafe Supervision, and Organizational Influences and classifies them into active failures and latent conditions. The Organizational Influences factored in HFACS are internal to any organization as evident in Figure 16.

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Figure 16. An accident in making (based on Reason, 1990), as adapted by Shappell and Wiegmann [31]

As observed during the study of the container shipping and associated accidents, container shipping relies heavily on the external parties like Shippers, Freight Forwarders, Ports, Terminals etc. and the actions of these external parties and external organizations have a profound impact on the safety outcomes of any container vessel. It is proposed that an additional layer of ‘External Organizational Influences’ be included in the HFACS, to account for the impact of the actions of these external parties in the container vessel accidents. The structural comparison of the existing model of the HFACS and the proposed model of HFACS for use in analysis of the container vessel accidents is as shown below in Figure 17.

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Figure 17. An accident in making (based on Reason, 1990), as adapted for container shipping HFACS-CV

This additional layer of ‘External Organizational Influences’ can also be included in the HFACS/ NASAFACS and introduced as a category to allow for categorization of factors like cargo planning discrepancies, wrong stowage, cargo misdeclaration, lashing non-compliance, etc., which are outcomes of the actions of the external parties. This maritime-specific adaptation could serve as a new theoretical model for analyzing human factors in maritime safety, especially in container shipping. Basis this additional layer, the NASAFACS structure was modified to include the impact of external organizations in the container shipping and can be used to better evaluate any accident and analyse the role of external agencies in the container vessel accidents. The ‘Space Environment’, not applying to the Maritime Accidents, has been replaced with ‘Operational Environment’ in the ‘Precondition.’ The modified structure has been named as HFACS-CV and will serve better to analyse container vessel accidents and is shown in Figure 18.

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Figure 18. Modified NASAFACS, as adapted for a container vessel (HFACS-CV)

This study advances maritime safety research by integrating the established NASAFACS framework—rooted in the HFACS—with insights derived from semi-structured interviews of experienced industry professionals. While HFACS has been extensively applied in aviation, and has been tailored for usage in general marine accident investigations (HFACS-MA), offshore installations (HFACS-OGI), passenger vessels (HFACS-PV), its application to container vessels remains limited. Moreover, previous adaptations often rely solely on historical accident data, overlooking the nuanced, evolving operational realities that practitioners face. The HFACS-CV (Container Vessel) model proposed in this research addresses this gap by combining empirical accident classification with contextual, experience-based perspectives. This dual-input methodology ensures that latent organizational and operational factors—often obscured in retrospective reports—are brought to the forefront through expert testimony. this hybrid approach facilitates a more grounded, domain-specific understanding of error pathways in the container shipping sector.