Human and organizational error data challenges in complex, large-scale systems

Grabowski, Martha; You, Zhuyu; Zhou, Zhi; Song, Huawei; Steward, M. W.; Steward, Brittany

doi:10.1016/j.ssci.2009.01.008

Cited by 47 publications

(17 citation statements)

References 11 publications

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“…GIS has been used to analyze and visualize field data from historical accidents (Van der Perk, Burrough, & Voigt, ), and generate spatial risk analysis based on postulated hazards (Rigina & Baklanov, , ). Several radiological hazard codes are designed to generate outputs usable in GIS geodatabase and “shapefile” formats (Grabowski et al., ; Rentai, ; Rigina & Baklanov, , ). For example, the NRC's Radiological Assessment Systems for Consequence Analysis (RASCAL) tool can produce shapefiles for GIS (NRC, ).…”

Section: Methodological Development For External‐explicit Integrationmentioning

confidence: 99%

GIS‐Based Integration of Social Vulnerability and Level 3 Probabilistic Risk Assessment to Advance Emergency Preparedness, Planning, and Response for Severe Nuclear Power Plant Accidents

et al. 2018

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In the nuclear power industry, Level 3 probabilistic risk assessment (PRA) is used to estimate damage to public health and the environment if a severe accident leads to large radiological release. Current Level 3 PRA does not have an explicit inclusion of social factors and, therefore, it is not possible to perform importance ranking of social factors for risk‐informing emergency preparedness, planning, and response (EPPR). This article offers a methodology for adapting the concept of social vulnerability, commonly used in natural hazard research, in the context of a severe nuclear power plant accident. The methodology has four steps: (1) calculating a hazard‐independent social vulnerability index for the local population; (2) developing a location‐specific representation of the maximum radiological hazard estimated from current Level 3 PRA, in a geographic information system (GIS) environment; (3) developing a GIS‐based socio‐technical risk map by combining the social vulnerability index and the location‐specific radiological hazard; and (4) conducting a risk importance measure analysis to rank the criticality of social factors based on their contribution to the socio‐technical risk. The methodology is applied using results from the 2012 Surry Power Station state‐of‐the‐art reactor consequence analysis. A radiological hazard model is generated from MELCOR accident consequence code system, translated into a GIS environment, and combined with the Center for Disease Control social vulnerability index (SVI). This research creates an opportunity to explicitly consider and rank the criticality of location‐specific SVI themes based on their influence on risk, providing input for EPPR.

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Section: Methodological Development For External‐explicit Integrationmentioning

confidence: 99%

GIS‐Based Integration of Social Vulnerability and Level 3 Probabilistic Risk Assessment to Advance Emergency Preparedness, Planning, and Response for Severe Nuclear Power Plant Accidents

et al. 2018

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“…Research has identified increases of SCR from organisational errors (Roberts, 2001;Grabowski et al, 2009), operational errors (Helmreich and Foushee, 1993), and challenges in training and development, particularly shortages of SC managers with necessary skills and competencies (Ellinger and Ellinger, 2013). In addition, focusing on a linear rather than collaborative structure (GAO, 2003) and failing to address and manage emerging organisational changes (Lord et al, 2015) contributes to higher SCR.…”

Section: Organisational Design and Behaviourmentioning

confidence: 99%

An extended framework for supply chain risk management: incorporating the complexities of emerging industries and large-scale systems

Burns

2017

IJMTM

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Over the last two decades, firms, governmental agencies, and non-governmental organisations have sought improved understanding of supply chain management and supply chain risk. Much supply chain risk research has been in large and mature industries such as automotive, aerospace, and energy production. Yet, additional supply chain risk areas are of concern to executives, including emerging industries, emerging supply chains, governmental agencies, quasi-governmental agencies, non-governmental agencies, and large-scale systems engineering projects that cope with complex technical challenges and multi-year development cycles. Through involvement with commercial and governmental research partners, we developed an extended and more generalised framework of supply chain risk that categorises dimensions of SCR and highlights the complexity that must be addressed by organisations. Key findings include that academics must address the complexity that firms face rather than assume simpler risk models, and that firms need additional academic thought leadership for developing improved approaches to manage supply chain risk.

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“…Entretanto, a literatura oferece poucos métodos para minimizar a subjetividade envolvida na classificação de um erro. Além disso, as classificações existentes assumem a existência de tipos de erros mutuamente exclusivos, o que pode ser uma simplificação demasiada e fonte de incerteza nas tabulações de dados (GRABOWSKI et al, 2009 Basicamente, o método consiste de um algoritmo com uma série de perguntas, com respostas do tipo sim ou não, que permite classificar os tipos de erros de operadores de linha de frente com base na classificação SRK (skill, rule and knowldege based errors) proposta por Reason (1990Reason ( , 1997. Nessa classificação, os erros são diferenciados de acordo com os níveis de desempenho cognitivo em que eles ocorrem, constituindo uma classificação mais abstrata do que aquelas baseadas em características observáveis de comportamento (por exemplo, omissões e repetições), bem como classificações que destacam fatores locais contextuais, tais como estresse, interrupções e distrações (REASON, 2008).…”

Section: Introductionunclassified

Método para classificação de tipos de erros humanos: estudo de caso em acidentes em canteiros de obras

Saurin

Grando²,

Costella

2012

Prod.

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ResumoEste trabalho tem como objetivo principal desenvolver melhorias em um método de classificação de tipos de erros humanos de operadores de linha de frente. Tais melhorias foram desenvolvidas com base no teste do método em canteiros de obras, um ambiente no qual ele ainda não havia sido aplicado. Assim, foram investigados 19 acidentes de trabalho ocorridos em uma construtora de pequeno porte, sendo classificados os tipos de erros dos trabalhadores lesionados e de colegas de equipe que se encontravam no cenário do acidente. Os resultados indicaram que não houve nenhum erro em 70,5% das 34 vezes em que o método foi aplicado, evidenciando que as causas dos acidentes estavam fortemente associadas a fatores organizacionais. O estudo apresenta ainda recomendações para a interpretação das perguntas que constituem o método, bem como modificações em algumas dessas perguntas em comparação às versões anteriores. Palavras-chaveInvestigação de acidentes. Erro humano. Segurança no trabalho. Construção civil.

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Human and organizational error data challenges in complex, large-scale systems

Cited by 47 publications

References 11 publications

GIS‐Based Integration of Social Vulnerability and Level 3 Probabilistic Risk Assessment to Advance Emergency Preparedness, Planning, and Response for Severe Nuclear Power Plant Accidents

GIS‐Based Integration of Social Vulnerability and Level 3 Probabilistic Risk Assessment to Advance Emergency Preparedness, Planning, and Response for Severe Nuclear Power Plant Accidents

An extended framework for supply chain risk management: incorporating the complexities of emerging industries and large-scale systems

Método para classificação de tipos de erros humanos: estudo de caso em acidentes em canteiros de obras

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