The development of a next-generation human reliability analysis: Systems analysis for formal pharmaceutical human reliability (SAFPH )

Zheng, Xi; Bolton, Matthew L.; Daly, Christopher J.; Biltekoff, Elliot

doi:10.1016/j.ress.2020.106927

Cited by 19 publications

(10 citation statements)

References 35 publications

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“…The cognitive failure probabilities were calculated using the extended CREAM approach. 29 Ung estimated the collision probability of oil tankers by adapting a fuzzy BN that was built on the foundation of CREAM and Fault Tree Analysis. This adaptation took into account the weights and quantitative effects of ambient elements.…”

Section: Literature Reviewmentioning

confidence: 99%

“…They provided recommendations for enhancing the reliability of community pharmacies through HRA, which was based on the principles of the CREAM methodology. 29 Ahn and Kurt introduced a new approach based on CREAM, which offered a framework for assessing specific scenarios related to maritime human errors. 30 Yao et al suggested a Fuzzy CREAM method for analyzing human errors in a nuclear power factory.…”

Section: Literature Reviewmentioning

confidence: 99%

“…introduced a dynamic approach for assessing human reliability in manned submersibles, employing PMV‐CREAM. The cognitive failure probabilities were calculated using the extended CREAM approach 29 …”

Section: Literature Reviewmentioning

confidence: 99%

“…introduced a novel approach to HRA known as Systems Analysis for Formal Pharmaceutical Human Reliability (SAFPHR). They provided recommendations for enhancing the reliability of community pharmacies through HRA, which was based on the principles of the CREAM methodology 29 . Ahn and Kurt introduced a new approach based on CREAM, which offered a framework for assessing specific scenarios related to maritime human errors 30 .…”

Section: Literature Reviewmentioning

confidence: 99%

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A new model to optimize the human reliability based on CREAM and group decision making

Bafandegan Emroozi,

Modares,

Roozkhosh

2023

Quality & Reliability Eng

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The Cognitive Reliability and Error Analysis Method (CREAM) represents one of the second‐generation approaches to human reliability assessment, taking into account the influence of environmental conditions on human error probability (HEP). In the context of CREAM, the Common Performance Conditions (CPCs) influence error probabilities. Since not all CPCs have equal impacts, this study employs the Bayesian Best Worst Method (BWM), a novel approach in group decision‐making, to assign weights to these factors. Subsequently, two techniques based on basic CREAM are proposed. The current control mode is determined in the first technique according to the experts' opinions. Then the probability of human error is calculated based on the amount of control. It is possible to provide solutions for improving control mode, based on obtained results. Therefore, in this study, the second method has been used to make suggestions to enhance human reliability. For this purpose, in the second technique, an optimization problem is formulated to select the best applicable programs for managers to enhance human reliability. The proposed bi‐objective model tries to increase the reliability of human resources by reducing human error and costs. The proposed bi‐objective model seeks to bolster the reliability of human resources by concurrently minimizing HEP and associated costs. The efficiency of the presented methods is verified through a case study in the control room of the cement factory. The results of the first technique reveal an opportunistic control mode with a corresponding HEP of 0.0198. On the other hand, the outcomes of our proposed model underscore the greater impact of improving CPC levels in reducing the probability of human error. Ultimately, the practical programs derived from our mathematical model provide decision‐makers with valuable insights to reduce the probability of human error to a mere 0.000172 through the transition from opportunistic to strategic control.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

A new model to optimize the human reliability based on CREAM and group decision making

Bafandegan Emroozi,

Modares,

Roozkhosh

2023

Quality & Reliability Eng

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“…In several industrial sectors, there are concerns regarding human reliability 1–5 . Unfortunately, human behavior is influenced by many factors and conditions, making it considerably more unpredictable than equipment failure.…”

Section: Introductionmentioning

confidence: 99%

Importance measures for performance shaping factors of human reliability analysis

Albuquerque

Oliva

Saldanha

et al. 2022

Process Safety Progress

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The crew of a nuclear-powered submarine under development in Brasil plays an important role in its safety. For this reason, crew members must be trained in procedures against undesired events. However, information that would help to accurately decrease human error probability (HEP) on board nuclear-powered submarines is not available in the literature. Therefore, a methodology is required to obtain this information. This study introduces importance measures to evaluate the contribution of performance shaping factors (PSFs) and their levels to HEP. Importance measures Fussell-Vesely, risk reduction worth, risk achievement worth, and Birnbaum importance, commonly used in probabilistic safety assessment, are introduced to identify PSFs and PSF levels that contribute the most to HEP. The proposed approach is focused on identifying HEP's main contributors, oriented toward decision-making.The methodology uses the PSFs established in the Standardized Plant Analysis Risk-Human Reliability Analysis method and a Bayesian network to quantify HEP. Moreover, the results of an application case demonstrate the method's effectiveness in integrating expert opinions into representative values of HEP and in identifying critical PSFs and PSF levels that should be the object of the decision-making process to improve human performance.

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Evaluating human error probability in maintenance task: An integrated system dynamics and machine learning approach

Bafandegan Emroozi,

Kazemi,

Pooya

et al. 2024

Hum Ftrs & Erg Mfg Svc

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Human error is often implicated in industrial accidents and is frequently found to be a symptom of broader issues within the sociotechnical system. Therefore, research exploring human error during maintenance activities is important. This article aims to assess the probability of human error in maintenance tasks at a cement factory using the Cognitive Reliability and Error Analysis Method and System Dynamics modeling. Given that human error probability (HEP) is influenced by various common performance conditions (CPCs) and their sub‐factors, and changes dynamically in response to other variables, the SD method offers a practical approach for estimating and predicting human error behavior over time. This study identifies and quantifies the variables affecting HEP, explores their interactions and feedback in maintenance tasks, and assesses the associated costs. The machine learning technique is then used to estimate the relationship between HEP and these costs. The optimal value of the HEP function, 0.000772, is determined by identifying the minimum point of a cubic function, thereby minimizing associated costs and occupational accidents. Determining the optimal HEP is crucial for minimizing excessive costs and investing in improved ergonomics and CPCs for better performance. This addresses a significant gap in existing research where the impact of human error on maintenance tasks has not been estimated as a function. Furthermore, three scenarios are presented to help managers allocate the organization's budget more effectively.

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The development of a next-generation human reliability analysis: Systems analysis for formal pharmaceutical human reliability (SAFPH )

Cited by 19 publications

References 35 publications

A new model to optimize the human reliability based on CREAM and group decision making

A new model to optimize the human reliability based on CREAM and group decision making

Importance measures for performance shaping factors of human reliability analysis

Evaluating human error probability in maintenance task: An integrated system dynamics and machine learning approach

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