Container Vessel Accidents Analysis: A NASAHFACS Framework Study

This section explores the study encompassing the analysis of maritime accidents and the varied methodologies used since 2009.

Shipping accidents remain a primary global concern despite technological developments and safety legislation. This has emphasised the need for consistent and coherent accident investigation reports that aid isolating the most critical contributors to these events. Celik and Cebi [1] came up with an advanced version of HFACS while combining it with the Fuzzy Analytical Hierarchy Process (FAHP) to effectively assess the human error and its role in the marine accidents. This integration augments decision-making and offers a robust analytical basis for quantitatively assessing maritime events.

Celik et al. [2] suggested a risk-based model to expand shipping accident investigations (SAI). The combined the new risk framework with the Fuzzy extended Fault tree Analysis (FFTA), to tackle organizational restraints and technical failures. Their approach aids understanding and gives insight in to the various causes including operational errors and technical failures, leading to marine accidents. It stresses on the benefits of using SAI reports as a means to formulate risk mitigation strategy to address risks in marine operations. The study also looks at combining FTA with SAI with an objective to standardize data and boost maritime accident investigation and prevention process.

Schröder-Hinrichs et al. [3] used the HFACS to examine forty-one maritime accident reports related to fires and explosions in machinery spaces. Their study discovered that these investigations mainly zeroed upon the technical issues leading to the accidents but often disregarded the organizational factors. This finding brings forward and underlines a significant gap between existing investigation methods and their reports.

Salmon et al. [4] compared three modern accident analysis methods, Accimap, HFACS, and STAMP, using case studies. Accimap and STAMP offered a broader view of contributory factors, whereas HFACS stood out for its reliability and recognized taxonomy. Nonetheless, the HFACS having been designed for the aviation, encountered challenges in classifying every failure, outside the aviation sector, due to the non-availability of specific taxonomies for other fields and broader organizational levels. The study suggests improving the Accimap model's utility for future investigations and accident analyses by including flexible taxonomies at all examination levels.

Chen et al. [5] developed the Human Factors Analysis and Classification System for Maritime Accidents (HFACS-MA) to analyze human and organizational factors in marine events. This five-tiered structure integrates critical concepts from the HFACS, Reason's Swiss Cheese Model, and Hawkins' SHEL model and aligns 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.

Akyuz and Celik [6] 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 role of the human factor in marine accidents. The model provides an effective solution for users to find the pertinent causes of marine accidents. Hence, the model can identify and prioritize an accident's active or latent reasons. The applicability of the model was verified using it on a serious marine incident involving a man overboard situation during a lifeboat drill.

Wu et al. [7] introduced an enhanced version of the Cognitive Reliability and Error Analysis Method (CREAM) using an evidential reasoning approach. This refined CREAM aims to precisely evaluate linguistic variables related to the standard performance metrics in maritime accidents. Additionally, the new model addresses the inadequacies of previous CREAM models by overcoming uncertainties owing to insufficient data.

The oil and gas sector has been marred with noteworthy disasters owing to operational and organizational mistakes. As been designed for aviation sector, HFACS can have its limitations when applied to other domains, and its constraints became apparent when applied to the oil and gas sector, especially regarding regulatory oversights and issues such as sabotage. To tide over these identified gaps specific to the oil and gas industry, Theophilus et al. [8] devised a customised version, HFACS-OGI, for the oil and gas industry, enhancing the utility of HFACS in oil and gas industry, for effective investigation.

Recognizing safety as an important focus in the marine industry, Akyuz [9] presented an exclusive hybrid model to analyze shipboard accidents. The model integrates the HFACS, offering a structured framework for assessing human errors, with the Analytical Network Process (ANP) method, which examines intercomponent correlations. The outcome of this integration is a reliable instrument for ascertaining the causal factors in marine accidents.

The HFACS and the accident causation "2-4" model (24Model) are two important accident investigation models in the industry. Fu et al. [10] conducted a theoretical and application comparison of the HFACS and 24Model on the criteria, namely theoretical foundation cause classification and analysis process. It concluded following the study that the 24Model is scientific and has a certain practicability. Safety and accident investigators can use the above conclusions to select the most appropriate model for scientific research and accident causation analysis.

Kim and Na [11] suggested a human factor study methodology to ascertain and classify the fundamental variables that contribute to human error, determine the chronological order of marine accidents, and devise safety measures to mitigate the likelihood of such incidents.

Kececi and Arslan [12] devised a specialized technique, the SHip Accident Root cause Evaluation (SHARE), designed specifically for maritime incidents, as they recognized the limitations associated with the root cause analysis methodologies available for the ship operations. The methodology is structured upon the Fuzzy SWOT AHP Method, has many features like usage of standardized terminology, allowing for data quantification, identification of responsible entities for action and help devising risk-mitigation plans. The method been validated through its application to actual maritime incidents.

Total-loss marine casualties represent grave maritime disasters, causing significant economic, human, and environmental losses. Chen et al. [13] investigated the critical factors behind global total-loss marine incidents using the fuzzy matter element method. Using this model, they also performed a trend analysis on the factors related to these casualties. Based on the fuzzy matter element theory and associated principles, this research presents a comprehensive study of existing measurement methods. The study analyzes global data from 2001 to 2015 in order to test the accuracy and efficiency of their models. The study can help the policy makers in devising strategies to prevent marine accidents in future.

Batalden and Sydnes [14] evaluated marine incidents using a tailored version of the HFACS initially formulated by them in the study [15]. They analyzed investigative reports from the UK's Marine Accident Investigation Branch from July 1, 2002, to July 1, 2010. The review examined 133 contributing factors in 22 serious marine incidents, and concluded that "Very Serious" mishaps was highly related to organisational structure and provided an insight into significant problems with both onboard planning and monitoring.

Uğurlu et al. [16] reviewed a total of seventy passenger ship collision and contact incidents from the years 1991 through to 2015, analysing these events with their customized Human Factor Analysis and Classification System for Passenger Vessel accidents (HFACS-PV). The modified system based on fundamental HFACS framework has added operating conditions. The modified framework identified three main areas of concerns being abuse of authority, breach of procedures and rule violations.

Zhang et al. [17] using the ship accident investigation reports, quantitatively analyzed the relationship between ship accidents impacts and contributing factors. The study took a two-phase approach, wherein the standardization of contributing factors from the accident reports was completed, followed by a statistical analysis of this standardized data using three methods i.e., the Kruskal-Wallis test, Cramer's V analysis, and Kendall's tau coefficient, to pinpoint the primary elements influencing ship accident outcomes.

Yildiz et al. [18] assessed the potentiality of the Human Factor Analysis and Classification System for Passenger Vessel collisions (HFACS-PV) to classify marine accidents. The study spanning 51 passenger vessel groundings incidents which occurred between 1991 and 2017 and were analyzed through HFACS-PV. The research showed that HFACS-PV version for passenger vessel accidents is suitable for analysis of allision, collision, grounding and sinking incidents. The framework in addition to providing comprehensive analysis of the above incidents, can be integrated with other tools to effect qualitative and quantitative analysis.

Sánchez-Beaskoetxea et al. [19] analyzed marine accident reports by the USA National Transportation Safety Board (NTSB) from June 1975 to September 2017 to determine the impact of crew and other parties (pilots, companies, etc.) on incident causation and to identify error types. The study reviews the outcome of application of international regulations related to seafarers. Results indicate that crew errors onboard cargo and passenger fleets were exceedingly rare, highlighting the value of international conventions such as STCW. Nevertheless, the fact that pilot waters have among the highest human error rates suggests improving coordination between the bridge team. The study also highlights high crew error rates in fishing vessels and tugs, pointing to the need for specific training to lower navigation errors in these vessels.

Safe and accident-free shipping is a crucial goal for the maritime industry. Analyzing past marine accidents to identify causes and implement corrective measures is crucial. Recognizing that human error accounts for 80–85% of marine accidents, Hasanspahić et al. [20] focused on human factors and examined 135 accident reports from the UK Marine Accident Investigation Branch (MAIB) database (2010-2019) using the Human Factor Analysis and Classification System for Maritime Accidents (HFACS-MA).

The research categorizes and summarizes potentially frequent causal factors and then applies multiple linear regression to correlate the degree of accident frequency with these causes. The study suggests that focusing on two critical human factors identified by the study can help reduce accident rates and thus collectively improve safety in shipping.

Maritime risk research often needs more data for precise prediction and analysis. Li et al. [21] addressed this gap by incorporating the latest maritime accident data (2017-2021) from the Global Integrated Shipping Information System (GISIS) and Lloyd's Register Fair Play (LRF) databases into a Bayesian network (BN) model. The assessment determines 23 critical risk influential factors (RIFs) in static and dynamic risk categories, of which some are prominent. The BN-based model not only provides robust risk prediction and scenario analysis but also facilitates the formulation of accident prevention strategies in the maritime domain.

Cao et al. [22] analyzed 491 literature sources from the Web of Science database, covering the period from 2000-2022, to study marine accidents. They used CiteSpace and VOS viewer for knowledge network mapping and cluster analysis to identify research hotspots and trends. The results demonstrate strong collaboration within and among journals, researchers, institutions, and countries. The study focuses on two main aspects: integrating current and emerging technologies and discussing critical factors and outcomes of accidents. Attention is particularly given to the human aspects of remote-controlled ships and reducing accidents in the Arctic. Techniques like deep data mining and machine learning are highlighted as essential for uncovering new factors. Additionally, the study provides a theoretical foundation for enhancing marine safety, assessing trends, and visualizing cooperative networks.

2.1 Gaps identified

Container vessels are crucial to the intricate global trade network, forming the backbone of modern international commerce. Several factors have driven the increased participation of these vessels in global trade, reshaping the dynamics of international commerce and supply chains. Consequently, container trade has adapted and evolved to meet the demands of the rapidly changing global economy. The literature review reveals gaps in the analysis of container vessel accidents, particularly the need for a targeted study to address sector-specific issues, which will help identify and analyze the various causal factors and develop associated mitigating and control measures.

4.1 Word cloud for high frequency words in the reports

The word cloud (Figure 3) is generated from the coding and analysis of various accident investigation reports.

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Figure 3. Word cloud

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Figure 4. NASAFACS coded cases analysis

4.2 NASAFACS coded cases analysis

Figure 4 describes the files and the distribution of the codes assigned to them during analysis.

4.3 NASAFACS NVIVO output

After completing the coding of the causal factors as analyzed in the container vessel accident reports, hierarchy charts were generated to comprehensively understand the relationships and better understand the outcomes of the exercise. The codebook is appended in Annexure 2, providing details of the exercise. The various charts generated and their analyses are presented below:

4.3.1 NASAFACS analysis showing distribution of latent and active factors

The analysis of the distribution of latent factors and active events (Figure 5), based on the coding of investigation reports of container vessel accidents, highlights that 'L3 – Preconditions' have the highest share as the causative factor of all these accidents studied and investigated. This is followed by 'L4 – Acts,' then 'L1 - Organizational Factors,' with 'L2 – Supervision' being the least significant.

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Figure 5. Distribution of latent factors and active events

Environmental, Personnel and Individual Factors have a sizeable share within the Preconditions (Figure 6).

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Figure 6. Distribution of ‘L3 – Preconditions’

Environmental Factors: The prominence of Environmental Factors within the Preconditions category suggests that external conditions and factors contribute significantly to the accidents. These might include weather conditions, visibility issues, and sea-state. Mitigating these factors may involve improved weather forecasting, better navigational aids, and stricter safety protocols during adverse conditions. 

Personnel Factors: The fact that Personnel Factors also have a sizeable share within the Preconditions category highlights the importance of addressing communications, i.e., human-related aspects, and encompasses crew failure, task planning, risk assessment, inadequate crew coordination, lack of cross-checking, and rank intimidation. Improving personnel factors may involve training programs on resource management, task planning, risk assessment, and highlighting the importance of toolbox talk prior to the commencement of the task to ensure better crew engagement during the task.

Individual Factors: Adverse Psychological and Awareness (Cognitive) Factors make up for the Individual Factors, contributing to the role of Precondition as a significant cause of container vessel accidents. Individual Factors, though contribute on a lesser scale than Environmental and Personnel Factors.

Adverse Psychological conditions reflect the mental and emotional state of the individual, which can cause accidents. Promoting a supportive work environment and implementing mental and emotional health programs are crucial for mitigating Adverse Psychological Factors. Training programs can help individuals develop coping strategies for stress and adversity.

Awareness (Cognitive) Factors encompass and impact an individual’s mental processes and decision-making abilities. Awareness or cognitive factors are essential for maintaining situational awareness, making timely and practical assessments, and responding effectively to changing conditions. Creating an organizational culture that encourages openness and promotes the importance of cognitive skills is essential.

‘L4 – Acts’ stands next to the precondition as the causative element in the accidents (Figure 5). Acts are errors of commission/omission or violations, often observed during the mishap. Errors take a higher share in Acts than Violations. Decision-Making has the highest share within the Errors category, followed by Skill-Based and Perceptual Errors (Figure 7).

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Figure 7. Distribution of ‘L4 – Acts’

Notably, Decision-Making Errors have the highest share in the Errors category, implying that decision making plays a crucial role in accidents and can include, but is not limited to, cognitive biases, situational factors, and the ability to adapt to changing conditions.

Skill-Based Errors refer to mistakes made by individuals, despite having the necessary skills, during routine or well-learned tasks. These errors can result from slips or lapses in attention, miss-out on following procedures, and inadvertent errors, even when individuals can perform the task correctly.

Perceptual Errors include misestimating distances, misreading instruments, or simply missing essential clues by individuals. They occur when people misunderstand the information in their environment and can sometimes be critical. For example, it is more important to have a high-precision perception in navigation or while monitoring equipment; and the error will be very relevant in these scenarios.

Organizations aiming to enhance safety performance and reduce Act related incidents must address decision-making errors, skill-based errors and perceptual errors. Equally important is the commitment to continuous improvement and learning from incidents, which is pivotal in preventing accidents.

Container vessel accident analysis has shown that the Violations, too, contribute to the Acts. When policies, guidelines, norms, or standards are breached intentionally, it is a violation. Such deviations can lead to dangerous situations or behaviors that increase the risk of accidents. Violations, by definition, are acts of intention, but a person may have reasons for the deviation and may make the situation complicated. Time constraints, insufficient resources available, and organizational culture can be reasons for a person deviating from the established norms. As a result, dealing with violations necessitates a multidimensional strategy that considers both human behavior and the organizational setting in which violations occur.

‘L1 Organization’ (Figure 5), too, plays a role in container vessel accidents albeit has a lesser share as causative compared to Preconditions and Acts. Organizational Resources are the most conspicuous as a causative, followed by Organizational Operations and, finally, Organizational Culture (Figure 8).

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Figure 8. Distribution of ‘L1 – Organization’

Organizational Resources refer to the allocation and availability of resources necessary for safe operation, i.e., personnel, funds, materials/ spares, equipment, design, operational information, and infrastructure.

Organizational Operations encompass the processes, procedures, and practices that impact safety.

Organizational Culture/ Climate plays a pivotal role in shaping employee attitudes and behaviours related to safety.

‘L2 Supervision’ (Figure 5), though an essential element, has been observed to play the minimum role as a causative in container vessel accidents. ‘Inadequate Supervision’ occupies the most prominent space, followed by Planned Inappropriate Operations and Supervisory Violations (Figure 9).

Inadequate Supervision refers to situations in which the supervision provided for vessel operations is insufficient or fails to meet the necessary standards.

Planned Inappropriate Operations suggest that certain operational decisions and actions were taken, but these actions did not align with safety standards or best practices.

Supervisory Violations reflect of supervisors involving themselves in violation of rules and regulations and are a cause for serious concern.

4.3.2 NASAFACS analysis showing distribution of consequences of the accidents analyzed

Analysis of the distribution of consequences of the accidents (Figure 10) highlights Damage to Vessel as the most frequently occurring and having the highest share, followed by Container Loss or Damage and, after that, Environmental Pollution,' as these three consequential elements take a significant share. Understanding the distribution of consequences is crucial for assessing the impact of container vessel accidents. It underscores the importance of prioritising safety, environmental protection, and effective risk management in the container vessel industry.

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Figure 9. Distribution of ‘L2 – Supervision’

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Figure 10. Distribution of accident consequences

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Figure 11. NASAFACS factors and type of accident

4.3.3 Relationship between the NASAFACS factors and type of accident

Figure 11 shows the relationship between the NASAFACS factors and the type of accident which contributes significantly to understanding the underlying factors contributing to accidents in the container vessel industry. it especially highlights the role of the latent factors and active events in various accident categories.

Fire: In the case of fire incidents on container vessels, latent factors are the primary causative factors rather than active events, often due to containerized cargo's unique nature and challenges. Several factors contribute to this scenario.

- Cargo composition: The composition of cargo within the containers can vary widely, and some goods may be prone to spontaneous combustion, especially if improperly packaged/ stored or stowed onboard. This latent risk may only be apparent once a fire breaks out.

- Stuffing and stowage: Container stuffing and stowage are typically done ashore, and vessel staff may have limited control over the process or knowledge about each container's specific contents. This lack of control and information can make it challenging to assess and mitigate the fire risk associated with the cargo.

- Misdeclaration: Misdeclarations or insufficient information about the contents and weight of the containers can further complicate matters. Without accurate information, it is difficult for vessel staff to assess compatibility and stowage requirements.

Addressing fire incidents in container vessels requires improved cargo inspection, verification, and declaration processes.

Collision: The analysis of the collision accident investigation reports, as reflected in the above chart, suggests a mix of active events (operator errors) and latent factors contributing to accidents. A combination of latent factors and active events causes a majority of collisions. In contrast, a small part is caused solely by active events, underscoring the complexity of navigation and collision avoidance at sea.

The other accident categories, namely Heavy Weather Damage, Grounding, Allision, Foundering, and Explosion have both active events and latent factors as causative elements.

The analysis above highlights the intricate interplay between latent factors and active events in accidents in the container vessel industry. Recognizing and addressing these factors is essential for enhancing safety and preventing accidents.

4.3.4 Relationship between type of accident and fatality

The relationship between Type of Accident and Fatality in container vessels is significant in understanding the safety implications of the various accent categories (Figure 12). Fire and Explosion accidents that result in fatalities highlight the severe risks and potential consequences of these incidents, and underscore the immediate life-threatening dangers posed by onboard fires and explosion accidents.

4.3.5 Relationship between type of accident and vessel size

Figure 13 explores if any relationship exists between the Type of Accident and Vessel Size. It is interesting to note that the Suezmax container vessel had the highest involvement across the accident categories, followed by the ULCS container vessels, highlighting the trend that larger container vessels are more prone to accidents. Feedermax vessel’s participation in Collision, Grounding, and Allision is likely due to these vessels trading near the coast, making frequent port calls, and having higher exposure to navigation hazards.

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Figure 12. Type of accident and fatality

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Figure 13. Type of accident and vessel size

4.4 Quantitative analysis of NASAFACS NVIVO output

The results from the coding and analysis conducted by NASAFACS were adapted for use in SPSS software to perform statistical analysis and detect any notable patterns. A cross-tabulation study was conducted to analyze the relationship between the variables, followed by a chi-square test to determine the presence of any association between them. The chi-square test, a statistical method used to assess the association or independence between two categorical variables, helps evaluate whether the observed data significantly deviates from the expected data. A comparative analysis of the two datasets can determine a meaningful link between the variables. In all tests of statistical significance, a p-value of less than 0.05 indicates a statistically significant relationship between the two factors. The various cross-tabulations performed are discussed as follows:

4.4.1 Incident type and fatality

Please refer to the case processing summary in Table 4, which details the Incident Type and Fatality cross-tabulation.

Table 4. Incident type and fatality cross-tabulation

The above analysis, with a p-value of 0.119 being greater than 0.05, suggests no strong statistical evidence exists to conclude a significant relationship between the Incident Type and Fatality. When the p-value is greater than the chosen significance level (commonly set at 0.05 or 5%), it indicates that there is insufficient evidence to reject the null hypothesis, which in this case would be that there is not enough statistical evidence to claim that the type of accident is significantly related to fatalities in container vessel accidents.

Table 6. Incident type and vessel size cross-tabulation

Table 8. Incident type and operational state cross-tabulation

4.4.6 Incident type and pollution

Please refer to the case processing summary in Table 9, which details of Incident Type and Pollution cross-tabulation.

The above analysis between the Incident Type and Pollution yielded a p-value of 0.037, less than the common significance level of 0.05, indicating a strong statistical significance between the two variables within the studied dataset.

Exposure of container vessels to heavy weather and subsequent damage frequently leads to loss of container overboard. These containers may contain various goods, including hazardous materials, chemicals, and pollutants. Loss of containers at sea can result in pollution risks owing to cargo spills. Container spills at sea can cause considerable damage to the marine environment. Cargo may contain elements harmful to marine life and ecosystems. The pollution caused by lost containers can impact coastal waters, marine ecosystems, and the general health of the marine environment. There are national and international rules, e.g., IMO guidelines on container securing to address these risks, but with lashings giving way in heavy weather and frequent container loss incidents, there is a need to revisit these requirements and make them suitable for the increasing size of the container vessels, to reduce such occurrences. Preventing container loss requires improved securing procedures and compliance with container stowage regulations. Enhancing vessel construction and stability for better response in severe weather conditions will help decrease these occurrences. Container tracking can help trace the containers and aid in recovery.

Table 10. Summary of the association between attributes and accidents

4.4.7 Summary of associations between different attributes of the container vessel accidents

Table 10 summarizes the association between the various attributes and variables studied during the analysis.

4.4.8 Suggestions based on above findings to improve maritime safety

Enhanced training programs: Training programs that enhance decision-making skills, situational awareness, and response to adverse conditions should be considered. Underscoring the importance of pre-task planning and risk assessment can assist in preventing accidents.

Improved safety and operational procedures: Developing and implementing robust safety protocols for various operational phases, especially during critical phases like port calls, heavy weather scenarios can reduce accident risks. Regular audits and reviews of these procedures can ensure their effectiveness.

Mental health and support programs: Promoting mental health programs and ensuring a supportive work environment onboard vessels can help mitigate adverse psychological factors contributing to accidents. Open communication and teamwork can aid in improving overall safety.

Policy and regulatory enhancements: Regulatory bodies should consider these findings to enhance maritime safety regulations. Policies focusing on improved securing arrangements, stricter measures to ensure cargo stuffing and stowage compliance, enhanced training standards, and stricter implementation of safe practices can significantly reduce accident incidences.

Technological advancements: Investing in advanced navigational aids, weather forecasting systems, and automated safety checks can help alleviate environmental and human error factors. Technology can play a crucial role in augmenting situational awareness and decision-making capabilities.

Organizational culture: Fostering a safety-centric organizational culture is essential. Leadership commitment to safety and continuous improvement initiatives can instill a heightened safety culture among crew members.

Although the above study provides insights into container vessel accidents, it is essential to acknowledge its limitations. Every research effort has constraints that can affect the interpretation and generalization of the findings. The findings of this study may be specific to the dataset and may not fully represent the entire container vessel industry. Some aspects of incident classification, coding, and analysis may involve subjective judgments. Analysts may categorise incidents differently by introducing subjectivity into the results.

The study used NASAFACS to perform the HFACS analysis of container vessel accidents. NASAFACS has been designed and developed by NASA to analyze space accidents. Although it broadly serves the purpose of accident analysis, its adaption to maritime accident analysis will serve this purpose better in the future.

This study provides qualitative insights based on the analysis of container vessel accident investigations. Other qualitative perspectives, such as interviews or surveys of industry experts and crew members, would complement the analysis by offering deeper context and understanding.