{"title":"分析有助于制造业大流行风险评估的人力和组织因素的模型:FBN-HFACS模型","authors":"Amirhossein Khoshakhlagh, Saber Moradi Hanifi, Fereydoon Laal, Esmaeil Zarei, Fatemeh Dalakeh, Hamid Safarpour, Rohollah Fallah Madvari","doi":"10.1080/1463922x.2023.2223254","DOIUrl":null,"url":null,"abstract":"This study presents a holistic model based on Fuzzy Bayesian Network-Human Factor Analysis and Classification System (FBN-HFACS) to analyze contributing factors in the pandemic, Covid 19, risk management under uncertainty. The model contains three main phases include employing a) HFACS to systematically identify influencing factors based on validation using content validity indicators, b) Fuzzy Set Theory to obtain the prior probability distribution of contributing factors in pandemic risk and address the epistemic uncertainty and subjectivity, and finally, c) Bayesian network to develop causality model of the risk, probabilistic inferences and handle parameter and model uncertainties. The Ratio of Variation (RoV), as BN-driven importance measures, is utilized to conduct sensitivity analysis and explore the most critical factors that yield effective safety countermeasures. The model is tested to investigate four large manufacturing industries in South Khorasan (Iran). It provided a deep understanding of influencing human and organizational factors and captured dependencies among those factors, while quantitative finding paves a way to efficiently make risk-based decisions to deal with the pandemic risks under uncertainty.","PeriodicalId":22852,"journal":{"name":"Theoretical Issues in Ergonomics Science","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A model to analyze human and organizational factors contributing to pandemic risk assessment in manufacturing industries: FBN-HFACS modelling\",\"authors\":\"Amirhossein Khoshakhlagh, Saber Moradi Hanifi, Fereydoon Laal, Esmaeil Zarei, Fatemeh Dalakeh, Hamid Safarpour, Rohollah Fallah Madvari\",\"doi\":\"10.1080/1463922x.2023.2223254\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study presents a holistic model based on Fuzzy Bayesian Network-Human Factor Analysis and Classification System (FBN-HFACS) to analyze contributing factors in the pandemic, Covid 19, risk management under uncertainty. The model contains three main phases include employing a) HFACS to systematically identify influencing factors based on validation using content validity indicators, b) Fuzzy Set Theory to obtain the prior probability distribution of contributing factors in pandemic risk and address the epistemic uncertainty and subjectivity, and finally, c) Bayesian network to develop causality model of the risk, probabilistic inferences and handle parameter and model uncertainties. The Ratio of Variation (RoV), as BN-driven importance measures, is utilized to conduct sensitivity analysis and explore the most critical factors that yield effective safety countermeasures. The model is tested to investigate four large manufacturing industries in South Khorasan (Iran). It provided a deep understanding of influencing human and organizational factors and captured dependencies among those factors, while quantitative finding paves a way to efficiently make risk-based decisions to deal with the pandemic risks under uncertainty.\",\"PeriodicalId\":22852,\"journal\":{\"name\":\"Theoretical Issues in Ergonomics Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2023-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Theoretical Issues in Ergonomics Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/1463922x.2023.2223254\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ERGONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Theoretical Issues in Ergonomics Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/1463922x.2023.2223254","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ERGONOMICS","Score":null,"Total":0}
引用次数: 0
摘要
提出基于模糊贝叶斯网络-人因分析与分类系统(FBN-HFACS)的整体模型,分析不确定条件下新冠肺炎风险管理的影响因素。该模型包括三个主要阶段:a) HFACS基于内容效度指标的验证,系统识别影响因素;b)模糊集理论,获得大流行风险影响因素的先验概率分布,解决认知不确定性和主观性;c)贝叶斯网络,建立风险因果关系模型,进行概率推理,处理参数和模型的不确定性。变化率(Ratio of Variation, RoV)作为bn驱动的重要性度量,用于进行敏感性分析,探索产生有效安全对策的最关键因素。通过对南呼罗珊(伊朗)四个大型制造业的调查,对该模型进行了检验。它提供了对影响人类和组织因素的深刻理解,并捕获了这些因素之间的依赖关系,而定量发现为有效地做出基于风险的决策,以应对不确定情况下的大流行风险铺平了道路。
A model to analyze human and organizational factors contributing to pandemic risk assessment in manufacturing industries: FBN-HFACS modelling
This study presents a holistic model based on Fuzzy Bayesian Network-Human Factor Analysis and Classification System (FBN-HFACS) to analyze contributing factors in the pandemic, Covid 19, risk management under uncertainty. The model contains three main phases include employing a) HFACS to systematically identify influencing factors based on validation using content validity indicators, b) Fuzzy Set Theory to obtain the prior probability distribution of contributing factors in pandemic risk and address the epistemic uncertainty and subjectivity, and finally, c) Bayesian network to develop causality model of the risk, probabilistic inferences and handle parameter and model uncertainties. The Ratio of Variation (RoV), as BN-driven importance measures, is utilized to conduct sensitivity analysis and explore the most critical factors that yield effective safety countermeasures. The model is tested to investigate four large manufacturing industries in South Khorasan (Iran). It provided a deep understanding of influencing human and organizational factors and captured dependencies among those factors, while quantitative finding paves a way to efficiently make risk-based decisions to deal with the pandemic risks under uncertainty.