Success Likelihood Index Method Research Articles

A conceptual risk modelling for cargo tank fire/explosion in chemical tanker by using Evidential Reasoning -SLIM and Bayesian belief network approach

The transportation of hazardous cargoes, particularly in chemical tankers, represents significant risks such as fire and explosion, which can have catastrophic consequences for both human life, commodity and maritime environment. This article presents a conceptual risk modelling for cargo tank fire/explosion incidents in chemical tanker ships under the Evidential Reasoning (ER) - Success likelihood index method (SLIM) approach with the Bayesian belief network (BBN) to provide a practical tool for assessing and managing the associated risks. Whilst the ER-SLIM approach offers a systematic human error probability (HEP) prediction and handling uncertainty in risk, allowing for the incorporation of expert judgments and data-driven information, the BBN provides a probabilistic graphical model to represent the causal relationships among various risk factors. The result shows that the risk of cargo tank fire/explosion during the cargo tank cleaning operation is calculated as 2.83E-02. The findings of the research including consequences analysis provide valuable insights for decision-makers, safety managers, superintendents, ship masters and officers in the maritime industry to prioritize risk management strategies and enhance the safety of chemical tanker operations.

Estimating the Human Error Probability during Lifeboat Drills

Lifeboats are life-saving equipment used when it is necessary to abandon a ship or, in some ships, for man-overboard situations (to collect persons from water). Every seafarer onboard a ship has a task related to lifeboat operation in an emergency. In order to master and practise the assigned tasks, be ready to react at any moment, and efficiently use life-saving equipment and appliances, seafarers on ships perform drills at prescribed intervals. Effective drill performance is of paramount importance, as it improves safety and enables crew members to practise lifeboat operations. However, although their primary role is life-saving, lifeboat drills have resulted in numerous accidents, causing injuries and fatalities, besides equipment damage. Therefore, it is necessary to prevent such unwanted events and discover their root causes. As the human factor is considered a significant cause of marine accidents, this paper aims to quantify human error probability (HEP) during lifeboat drills. In addition, because lifeboat drill accident data are scarce, this study adopted the Success Likelihood Index Method (SLIM) for human reliability analysis (HRA). Based on expert judgments, the tasks with the highest probability of human error and factors significantly influencing human performance during lifeboat drills are identified. According to the study results, the recovery of the lifeboat is the most hazardous phase with the highest HEP. In addition, the BN-SLIM is adopted to estimate the probability of human error during the recovery of the lifeboat. The task with the largest HEP is confirming the release lever is properly rested and hooks locked (HEP = 4.5%). Furthermore, the design and condition of equipment and Crew Competence are identified as the most important Performance-Shaping Factors (PSFs) that affect crew members’ performance. The BN-SLIM model was verified utilising a sensitivity analysis and validated by analysing real-life lifeboat drill accidents that occurred during lifeboat recovery. The results confirmed that the model could be used to analyse lifeboat accidents and for proactive preventive measures because most influencing factors are recognised, and acting on them can significantly reduce the HEP of the overall task, improve lifeboat safety, and save lives at sea.

Investigating the influence of human errors in master-pilot information exchange on maritime accident risk during pilotage

Pilotage-related errors have led to too many accidents in recent years, resulting in significant economic losses and disruptions in global logistics. Despite its importance, Master-Pilot Exchange (MPX) can be overlooked due to several factors. This study focuses on MPX, a standard protocol for information exchange between shipmasters and maritime pilots to ensure safe ship handling. Deficiencies in MPX procedures have been highlighted in various accident reports, emphasizing the need for improvement. This research aims to identify an optimal MPX process, evaluate potential shortcomings, assess their likelihood and severity, and analyse the human factors contributing to these deficiencies. The study utilizes a fuzzy Bayesian Network (BN) to model the MPX process and identify factors that may lead to its failure. It also employs the Success Likelihood Index Method (SLIM) approach to calculate the probability of human errors in the MPX process and identify the performance-shaping factors triggering them. The finding of the Bayesian method shows that the machinery problem and the navigational equipment problem are the two most significant root causes of MPX process failure. According to the SLIM findings, providing details of "the ship's contingency plan", and "navigational information" are the two most significant tasks for this process. Among the identified performance-shaping factors, the degree of teamwork and harmony, experience and knowledge appear to be the three most significant contributors with the highest human error probability (HEP) in mentioned tasks. This study represents a significant contribution to research on master-pilot interactions, addressing a critical aspect of maritime safety.

Investigating the Human Reliability in the Healthcare Sector Using the Fuzzy Analytic Network Process and the Success Likelihood Index Method.

We aimed to identify the factors contributing to human error in hospital emergency departments using scientific methods. We used the Fuzzy Analytical Network Process (FANP) and Success Likelihood Index Method (SLIM) to investigate human reliability in 54 hospital emergency departments in 15 provinces of Iran from 2021 to 2022. The study classified 17 general factors affecting human errors in hospital emergency departments. Organizational (0.349), occupational (0.330), and personal factors (0.320) had the most significant impact on human error. Based on a matrix of paired comparisons for nine emergency tasks using the probability of success index method, "checking test results and diagnosis" had the highest probability of error when referring patients to intensive care or discharge. Although the study prioritized patients, there was still a cumulative probability of human error before disease diagnosis at 0.01332, highlighting the need for further training to minimize these risks. The FANP and SLIM were effective in identifying the factors contributing to human error in hospital emergency departments. Doctors and nurses working in these departments require more knowledge, experience, and responsibility to avoid errors. By identifying factors influencing the occurrence of human error and finding solutions to reduce risks, hospitals can improve the quality of their care and prevent errors.

Assessment of human error contribution to container loss risk under fault tree analysis and interval type-2 fuzzy logic-based SLIM approach

Human is a key element of the safety of life on board ships and a significant contributing factor to most of the accidents and incidents in the maritime industry. At this point, risk analysis plays a critical role in ensuring operational safety and maritime transportation sustainability. This paper aims to systematically evaluate how human errors (HEs) contribute to operational risks. Based on this, Fault Tree Analysis (FTA) is combined under an Interval Type-2 Fuzzy Logic environment with Success Likelihood Index Method (SLIM). Whilst the FTA evaluates the criticality of the operational activities, the Interval Type-2 Fuzzy Sets (IT2FS) deals with vagueness and subjectivity in using experts’ judgements, and the SLIM estimates the probabilities for the human error-related basic events. Since container losses can lead to severe damage and catastrophic events in a container terminal, loading operation was investigated as a case study. Safety culture, experience, and fatigue were observed as highly effective factors in crew performance. The obtained results also indicate that this hybrid approach can effectively be applied to determine the operational vulnerabilities in high-risk industries. The paper intends to improve safety control levels and lower losses in the future of maritime container transport besides emphasising the potential consequences of failures and crucial human errors in the operational process.

An extended fuzzy bow-tie approach with SLIM to assess risk for anchor dredging in cargo ships

Anchoring is one of the frequent processes undertaken by the crew on-board ship. Improper anchoring has significant consequences, such as anchor dredging, which may pose serious risks to the ship crew, marine environment and commodities. This paper aims to present a robust approach combining bow-tie approach with SLIM (Success Likelihood Index Method) under fuzzy sets theory. Since the fuzzy bow-tie represents a comprehensive quantitative risk assessment (QRA) method, the SLIM predicts the probability of a human error occurring during a specific task's completion. In this context, anchor dredging risk can be calculated for a specific case. This paper's novelty lies in consideration of human error's contribution to risk for ship anchor dredging and combining quantitative and qualitative analysis in maritime risk assessment. The finding of the paper shows that the occurrence probability of anchor dredging risk for cargo ships is 7.31E-02. Besides its robust theoretical background, cargo ship owners, superintendents, safety inspectors, and health, safety, environment and quality (HSEQ) managers can utilise the paper's findings to minimise potential risk and enhance operational safety during anchoring operations.

Prediction of human error probabilitiy for officers during watchkeeping process under SLIM approach

ABSTRACT Safety is the first priority for the maritime industry as it relates to the environment, human life, and the cargo carried. The human factor is vital as it critically impacts increasing safety on board. The human factor also has an essential place in watchkeeping operations on the bridge. At the same time, estimating human error has always been difficult, as data are difficult to obtain in the maritime industry. The Success Likelihood Index Method (SLIM) is often used in human error (HE) evaluation in the maritime industry, where it is difficult to obtain human error data. Therefore, this study aims to evaluate the human error (HE) probabilities in watchkeeping operations on the bridge by using the SLIM. In this study, the findings obtained from the SLIM method can also be used as a decision-making tool by all ship crews and ship management companies to minimise the possibility of human error occurring during the watchkeeping and increase the safety level on the board.

Assessment of human error contribution to maritime pilot transfer operation under HFACS-PV and SLIM approach

Human factor plays an important role in sustainable maritime transportation. Human errors are the leading cause of death or injuries during maritime pilot transfer operations despite international regulations. This paper proposes a hybrid human factors analysis model, comprised of qualitative and quantitative approaches by combining Success Likelihood Index Method (SLIM) and Human Factor Analysis and Classification System (HFACS) methods, to obtain the seafarers’ errors in the pilot boarding and disembarking. Human errors in the maritime pilot transfer process are determined by examining accident reports, literature reviews and expert judgments. Accidents and near misses are often poorly reported in the maritime industry. Therefore, determining human error probability (HEP) is of paramount importance in maritime transportation. This paper conducts an empirical human error prediction for a maritime pilot transfer operation to enhance operational safety and minimize human errors, providing a methodological extension through the integration of the HFACS technique into the SLIM. Utilizing HFACS and SLIM constitutes the unique contribution of this paper by predicting the possibility of human error since it presents the first application of the maritime industry. Thus, possible loss of life and injury will be prevented, the sustainability of maritime safety will be contributed, and pilots will be provided to work in a safe environment.

The hybrid method combined STPA and SLIM to assess the reliability of the human interaction system to the emergency shutdown system of LNG ship-to-ship bunkering

By introducing autonomous or software-controlled systems, human operators are increasingly required to perform cognitive-intensive tasks in addition to existing labour-intensive tasks. As a result, it will be more difficult to identify human roles in future complex systems with traditional approaches such as hierarchical task analysis used in the conventional HRA. This paper proposes a novel systematic approach for a human reliability assessment to better understand human activities in complex systems. The proposed framework is a hybrid method combining the System Theoretic Process Analysis (STPA) and the Success Likelihood Index Method (SLIM) to assess the system reliability. The STPA is adopted to analyse the interaction relationship between different system components. The primary purpose of STPA is to find and analyse human activities that affect the risk in human-machine interaction systems. Then the identified human activities are evaluated and quantified by the SLIM as a probability of human error. The system reliability block diagram represents the derived human error probabilities to assess the entire system for a probabilistic risk assessment. Furthermore, the study proposed system alternations by comparing three different system configurations. Results demonstrate the importance of human performance in a complex system where humans, machines, and software interact.

New success likelihood index model for large group human reliability analysis considering noncooperative behaviors and social network

Human reliability analysis (HRA) is a systematic method to qualitatively and quantitatively assess and analyze potential human errors for enhancing the safety and reliability of complex socio-technical systems. As a representative HRA approach, the success likelihood index method (SLIM) has been widely applied to quantify the effects of performance shaping factors on human performance and derive the human error probabilities of operational tasks. The influence quantification of performance shaping factors mainly relies on experts’ subjective judgments, where conflict opinions inevitably exist because of their different knowledge and experiences. Besides, the current SLIM studies often involved a small group of experts and assumed them to be independent, which is insufficient to tackle increasingly complicated HRA problems. In this article, a large group SLIM model is developed to calculate the human error probabilities of operational tasks considering experts’ noncooperative behaviors and social relations. Specifically, a large group of experts are included to provide task state assessments under complex uncertain linguistic environment, and clustered into subgroups based on a social network analysis. Then a consensus reaching process is performed to tackle the noncooperative behaviors of experts to obtain a consensus human error probability estimation. Finally, a case study of cargo loading process is provided to illustrate the practicality and effectiveness of the proposed large group SLIM model.

A large-scale group Success Likelihood Index Method to estimate human error probabilities in the railway driving process

• Success Likelihood Index Method (SLIM) is widely used to estimate human error probability. • A large-scale group SLIM (LG-SLIM) model is proposed to provide fuzzy ratings and negotiate to reduce biases for enormous experts. • A new consensus model establishes an effective balance between the subjectivities and compromises of experts. • The combination of the Bayesian network and the Monte Carlo simulation verifies the results of the proposed LG-SLIM model. Success Likelihood Index Method (SLIM) is widely used to estimate human error probability. However, the traditional SLIM relies on a small number of experts, which may cause biases and undermine its effectiveness. To overcome this drawback, a large number of experts participate to provide fuzzy ratings and negotiate to reduce biases, which forms a large-scale group SLIM (LG-SLIM) problem. The LG-SLIM model is used to negotiate among experts to reach a unanimous agreement of the group. We improve the consensus model by constructing two mathematical programming models that provide the recommendations of both optimum preference adjustments and weight adjustment for the non-cooperative subgroup, which establishes an effective balance between the subjectivities and compromises of experts. Some typical human errors concerning target tasks are estimated in the railway driving process. To verify the proposed LG-SLIM model, the results obtained from it and individuals are compared with the simulation values from the Bayesian network respectively. The comparison shows that the results are consistent with the simulation results and conform to the experience and knowledge in railway driving. The LG-SLIM model is helpful to human error assessment when critical operations need enormous experts to obtain reliable and accurate results.

Human Operating Risk Assessment for Outdoor Terminal Box of Electric Power

The outdoor terminal box is the basic node of the power Internet of Things as an intermediate link between outdoor electrical equipment and indoor equipment, like measurement and control, protection, communication and other equipment. Terminal boxes and terminals need to be regularly checked on site, loop tests, transformations, and equipment replacement. Operator errors have become one of the main factors in outdoor terminal box accidents. In terms of human risk assessment of outdoor terminal boxes, few papers are involved. In order to correctly evaluate the influence of human factors on the failure of outdoor terminal boxes, the paper first analyzes the human behavior factors of outdoor terminal box operators, the common performance conditions of the CREAM (Cognitive Reliability and Error Analysis Method) model is used to analyze the human behavior mechanism and behavior reliability factors during the operation of the terminal box. Then, the SLIM (Success Likelihood Index Method) model is used to calculate the probability of human error. The proportional failure model is used to calculate the failure rate of the outdoor terminal box itself. Finally, taking a circuit breaker terminal box as an example for simulation, the probability of human error is 1.56%, the equipment failure rate is 0.84%, the risk value of the system is 10.7%, and the risk level of the system is 3. From the probabilistic perspective, it shows that human factors have a greater influence on the causes of accidents.

A slim based approach for human error probability of steel scrap cargo operations as a critical process in the maritime sector

The transportation of steel scrap cargoes in global trade has been increasing over the years. According to the researches, the demand of steel scrap materials will be more than double in the late 21st century whereby forecasting of steel scrap production and so steel scrap cargo operations will also evenly rise in ports. Increasing steel scrap cargo operations will bring along several undesirable accidents and injuries. Investigations show that the most paramount reason for these incidents or accidents related with steel scrap cargo operations in maritime sector is human error. In this sense, it is aimed to identify human error probabilities (HEPs) for steel scrap cargo operation that is performed frequently in maritime sector, especially in bulk carrier vessels. In this study, Success Likelihood Index Method (SLIM), which is one of the methods for Human Reliability Analysis (HRA), is used to determine HEPs in steel scrap cargo operations due to the limited data availability on this topic. Accordingly, the most common error modes that are determined via detailed literature review are ranked in accordance with HEP values. It is put forward which and how much error mode is affected from Performance Shaping Factor (PSF) such as education, supervision, environmental condition, equipment and tool condition, and experience mostly. According to the analysis results, “the falling piece of steel scrap on the deck during steel scrap loading or unloading operation” has the most probability occurrence. Consequently, it is understood that training and experience factors are critically important for preventing errors in steel scrap cargo operation in overall view. On the other hand, environmental condition, supervision, and equipment and tool condition factors include the prominent level of significance to bring down the probability of accruing of some specific errors. Accordingly, the proposed approach not only make a theory-based contribution to the maritime literature, but also to active contribution to the sector involving P&I Clubs, shipping companies, and classification societies toward focusing point for minimizing the accidents about steel scrap cargo operations.

ИСПОЛЬЗОВАНИЕ ИНТЕРВАЛЬНЫХ МОДЕЛЕЙ НЕОПРЕДЕЛЕННОСТЕЙ ДЛЯ ОЦЕНКИ НАДЕЖНОСТИ ЧЕЛОВЕКА-ОПЕРАТОРА С ПОМОЩЬЮ МЕТОДА SLIM

Relevance The model «Success Likelihood Index Method» (SLIM) and its corresponding method belong to the first generation of models and methods designed to analyze the reliability of a human operator in complex technological systems. For almost forty years of their existence, they have been used to assess the contribution of the human factor to the reliability of various systems and objects. The results of this application confirmed their effectiveness. However, the approach underlying SLIM has a number of disadvantages. The main one is the need for experts to set fixed («point») values of indices characterizing the factors that affect the probabilities of errors of a human operator. In addition, it is required to have at least two fixed values of such a probability. At the same time, obtaining these a priori probability values based on experimental data in real situations is difficult. Aim of research In this work, the task is to improve the traditional version of the SLIM method by eliminating the noted shortcomings. The main goal is to provide the expert with the opportunity to give not the point values of the indices under consideration, but the intervals of these values. Thus, the transition to interval models of uncertainties is carried out, which makes the conclusions more adequate to real conditions. Research methods To achieve this goal, descriptions of variables and functions used in the framework of interval analysis are used. In practice, this means the possibility of finding solutions that are true not only for fixed values of the initial data, but also for limited sets of these values. Results A modified version of the model and the corresponding SLIM method have been developed, which allow one to operate with information about the bounds of the intervals of values involved in calculating the probabilities of successful task completion. The final probability is also determined up to the bounds of the range of its values. A numerical example is presented that demonstrates the main stages of calculations using the new model.

Evaluation of human factor engineering influence in nuclear safety using probabilistic safety assessment techniques

Abstract This paper provides the empirical basis to support predictions of the Human Factor Engineering (HFE) influences in Human Reliability Analysis (HRA). A few methods were analyzed to identify HFE concepts in approaches of Performance Shaping Factors (PSFs): Technique for Human Error Rate Prediction (THERP), Human Cognitive Reliability (HCR) and Cognitive Reliability and Error Analysis Method (CREAM), Success Likelihood Index Method (SLIM) Plant Analysis Risk – Human Reliability Analysis (SPAR-H), A Technique for Human Error Rate Prediction (ATHEANA) and Man-Machine-Organization System Analysis (MMOSA). Also, in order to identify other necessary PSFs in HFE, an additional investigation process of human performance (HPIP) in event occurrences was used. Thus, the human error probability could be reduced and its evaluating can give out the information for error detection and recovery. The HFE analysis model developed using BHEP values (maximum and pessimistic) is based on the simplifying assumption that all specific circumstances of HFE characteristics are equal in importance and have the same value of influence on human performance. This model is incorporated into the PSA through the HRA methodology. Finally, a clarification of the relationships between task analysis and the HFE is performed, ie between potential human errors and design requirements.

SLIM based methodology for human error probability calculation of bunker spills in maritime operations

System reliability assessment is among the main acts in the operation and maintenance of every industrial and service sector, which also applies to maritime transportation system. Safety is an important parameter for system reliability assessment and a top priority for maritime transportation sector due to its direct effects on the environment, human life, and goods transported. Because human factor plays a very critical role on improving safety onboard, reliability assessment becomes vital for researchers and decision makers. The human factor also has an important role in bunkering operations. Bunkering is a routine operation on a ship which has been the cause of several accidental oil spills in the past. To prevent environmental damages caused by oil spill, bunkering operation requires careful planning, utmost care and high performance of the ship's crew. In this regard, the prediction of human error probabilities offers an advantage to prevent environmental pollution at sea and also provides a high level of safety in maritime industry. Therefore, the aim of this study is to calculate human error probabilities in bunkering operation by using Success Likelihood Index Method (SLIM). This study can also be used as a decision-making tool by ship's crew for improving safety at sea and preventing ship-based oil spill through providing a strategic and effective approach in bunkering operations.

The Role of Human Factors in Maritime Environmental Risk Assessment: A Case Study of Oil Spill Response

Human error is one of the significant factors attributed to marine accidents. This paper aims to assess the potential contribution of human errors in improving maritime environmental risk management. Success Likelihood Index Method (SLIM), has been adopted to systematically estimate human error potentials for designated tasks in pollution prevention, along with fuzzy sets to deal with subjectivity in the process of using experts’ judgments. An oil spill response operation was investigated due to its considerable risks for the marine environment. Environmental factors, familiarity, and fatigue were observed as having a high impact on human performance. Besides its theoretical insight, the paper provides practical insights into the prevention of marine pollution. This study is presented as a reference providing a contribution to estimating the impact of human errors on maritime accidents. The paper is also intended to contribute to other risky industries where human errors can lead to fatal consequences.

Prediction of human–machine interface (HMI) operational errors for maritime autonomous surface ships (MASS)

The human factor is a hot topic for the maritime industry since more than 80 percent of maritime accidents are due to human error. Minimizing human error contributions in maritime transportation is vital to enhance safety levels. At this point, the maritime autonomous surface ships (MASS) concept has become one of the most significant aspects to minimize human errors. The objective of this research is to predict the human–machine interface (HMI)-based operational errors in autonomous ships to improve safety control levels. At this point, the interaction between shore-based operator and controlling system (cockpits) can be monitored and potential HMI operational errors can be predicted. This research utilizes a Success Likelihood Index Method (SLIM) under an interval type-2 fuzzy sets (IT2FSs) approach. While the SLIM provides a prediction of the human–machine interface (HMI) operational errors, the IT2FSs tackles uncertainty and vagueness in the decision-making process. The findings of this paper are expected to highlight the importance of human–machine interface (HMI) operational errors in autonomous ships not only for designers but also for operational aspects.

An interval type-2 fuzzy SLIM approach to predict human error in maritime transportation

The human factor is regarded as one of the most significant topics for sustainable maritime transportation since increasing shipping activities can pose potential hazards to human life, the environment, and commodity. The paper aims to assess the potential contribution of human errors in the maritime industry. Hence, Success Likelihood Index Method (SLIM) was employed by incorporating interval type-2 fuzzy sets (IT2FSs). While SLIM systematically estimates human error probability (HEP) for the designated task, IT2FS deals with subjectivity in the process of using experts’ judgements. A loading operation onboard containership was investigated due to its considerable risks for the marine environment. Safety culture, experience, fatigue, and time limitation were observed as highly effective on crew performance. The obtained results indicate that the IT2FS-SLIM approach can effectively be applied for determining the vulnerabilities and critical human errors in the operational process. The paper is also intended to enhance safety control levels and minimize potential errors in maritime transportation.

PENILAIAN PROBABILITAS HUMAN ERROR DI INDUSTRI TAMBANG BATUBARA

Based on data from the Health and Safety Executive, it can be concluded that as many as 90% of accidents are caused by human error. Coal mining is an industry that has a high risk of work accidents. If the mining process is not following procedures, then lives will be at stake. Human error probability assessment needs to be done because most accidents that occur in coal mining are caused by human error. HRA (Human Reliability Analysis) is part of the risk assessment process which aims to calculate the HEP value. HRA has been used in many studies to assess the risks involved in large, complex, and dangerous systems. The Success Likelihood Index Method (SLIM) is a method of analysis for human reliability. Prevention and control of human error need to be done at every stage of work. SLIM can analyze HEP at each stage of the work. SLIM aims to obtain a HEP value. To get the HEP value, we must first find the SLI (Success Likelihood Index) value. Finding the SLI value comes from a weighting questionnaire and PSF (Performance Shaping Factor) assessment that has been filled in by an expert judgment. After the HEP value is obtained, it can be seen which jobs fall into the safe risk level and which the risk cannot be accepted. Furthermore, risk reduction is carried out by making a task analysis of jobs that have a high hazard risk. Based on the research results, it can be seen the factors that cause a human error, namely unsafe conditions, unsafe actions, personal factors, and job factors. While the task that has the highest HEP value is on task 4, which is equal to 0,006932. The task with the smallest HEP value is task 1, amounting to 0,006478.