Incorporating organizational factors into Probabilistic Risk Assessment (PRA) of complex socio-technical systems: A hybrid technique formalization

Mohaghegh, Zahra; Kazemi, Reza; Mosleh, Ali

doi:10.1016/j.ress.2008.11.006

Cited by 176 publications

(66 citation statements)

References 33 publications

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“…Of the risks discussed in this report, technical risk may be the one NASA traditionally assesses the most (for instance, see Weber et al, 2012 for a review of use of Bayesian networks; other examples include Frank, 1995;Paté-Cornell, 2001;Mohaghegh, Kazemi, and Mosleh, 2009;Boyer and Hamlin, 2011). As a result, NASA has guidelines for technical risk.…”

Section: Technical Risksmentioning

confidence: 99%

Developing a Risk Assessment Methodology for the National Aeronautics and Space Administration

Gerstein¹,

Kallimani²,

Mayer³

et al. 2016

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This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited. Permission is given to duplicate this document for personal use only, as long as it is unaltered and complete. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial use. For information on reprint and linking permissions, please visit www.rand.org/pubs/permissions.The RAND Corporation is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous. RAND is nonprofit, nonpartisan, and committed to the public interest.RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.Support RAND Make a tax-deductible charitable contribution at www.rand.org/giving/contribute www.rand.org For more information on this publication, visit www.rand.org/t/RR1537 Library of Congress Cataloging-in-Publication Data is available for this publication. : 978-0-8330-9563-3 Published by the RAND Corporation, Santa Monica, Calif. ISBN © Copyright 2016 RAND CorporationR® is a registered trademark. Cover image: NASA. CubeSats deployed from the International Space Station, May 17, 2016 Cover design by Eileen Delson La Russo iii Preface The StudyThis study was conducted in response to a request from the National Aeronautics and Space Administration (NASA) to assist in gaining an understanding of how various disparate factors contribute to overall risk associated with NASA-level decisions. The specific mandate was to "provide NASA's Office of Strategy and Plans with one or more risk frameworks that integrate risk factors and risk management approaches tailored to NASA's management, operations, and acquisition structures."NASA confronts a variety of organizational-level risks within its programs. Comparing, contrasting, and mitigating these risks require developing a common lens through which to view the risks. Such an evaluation can provide opportunity to gain an overall understanding of the risks associated with NASA-level decisions. This study developed such a methodology. Specifically, it resulted in the development of a single risk-informed decision support and includes the identification of various techniques for conducting risk assessments and the definition of the seven risk factors identified as being important for NASA-level decision consideration.The report also includes identification of the risk factors, components, indicators, and mitigation strategies for two case studies: (1) cancellation of the Space Shuttle program and commercialization of transport to the International Space Station and (2) the Cislunar Habitat.This report is written for a non-expert audience so that any practitioner or decisionmaker, with any level of training, could make use of the methodology. It therefore does not assum...

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Section: Technical Risksmentioning

confidence: 99%

Developing a Risk Assessment Methodology for the National Aeronautics and Space Administration

Gerstein¹,

Kallimani²,

Mayer³

et al. 2016

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“…27 -35, DOI 10.2478/v10281-012-0007-8 software becoming available that takes away the computational burden, it has the last few years found very useful applications in medical science, social sciences, economy and fi nance, and software reliability determination. Recently it has been applied in aviation safety (Mohaghegh et al, 2009;Groth et al, 2010;Ale et al, 2009) and is starting to fi nd its way in Quantitative Risk Analysis of processes in on-and offshore (Khakzad et al, 2011;Pasman and Rogers, 2012a a,b;Vinnem et al, 2012).…”

Section: Fig 7 Aggregation Levels Of Indicators Accordingmentioning

confidence: 99%

Process Safety Performance Indicators

Pasman¹

2012

Transactions of the VSB - Technical University of Ostrava, Safety Engineering Series

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Abstract:For over 50 years to measure safety performance the Lost Time Incident Rate, LTIR was used. Fortunately, over the years the learning attitude towards accidents changed from a retrospective to a pro-active one. In the 90-s the safety management system was introduced. No management though, without the Deming cycle of Plan, Do, Check, Act, and checking, means the need of indicators. Existing LTIR-values were used not realizing these refl ect personal rather than process safety. In 2005 after the BP Texas City refi nery vapor cloud explosion, awareness of the difference broke through and Process Safety Leading and Lagging Metrics were formulated. In January 2012 an international conference was held in Brussels organized by EPSC and CEFIC. Results will be summarized. The paper will explain briefl y, where we are now, and what still is ahead.

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“…For some years now, employing industrial forecasting models in accident forecasting relying on multiple factors has been justified by the fact that causal factors of accidents are attributed to human, equipment and managerial deficiencies (Cooke and Rohleder, 2006;Mohaghegh et al 2009;Rathnayaka et al 2011). Although proponents of models further justify the use of multiple causal factors, they also acknowledge that their degrees of interactions are also complex (Qureshi 2008;Stringfellow 2010).…”

Section: Introductionmentioning

confidence: 99%

A novel grey–fuzzy–Markov and pattern recognition model for industrial accident forecasting

2017

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Industrial forecasting is a top-echelon research domain, which has over the past several years experienced highly provocative research discussions. The scope of this research domain continues to expand due to the continuous knowledge ignition motivated by scholars in the area. So, more intelligent and intellectual contributions on current research issues in the accident domain will potentially spark more lively academic, value-added discussions that will be of practical significance to members of the safety community. In this communication, a new grey-fuzzy-Markov time series model, developed from nondifferential grey interval analytical framework has been presented for the first time. This instrument forecasts future accident occurrences under timeinvariance assumption. The actual contribution made in the article is to recognise accident occurrence patterns and decompose them into grey state principal pattern components. The architectural framework of the developed grey-fuzzyMarkov pattern recognition (GFMAPR) model has four stages: fuzzification, smoothening, defuzzification and whitenisation. The results of application of the developed novel model signify that forecasting could be effectively carried out under uncertain conditions and hence, positions the model as a distinctly superior tool for accident forecasting investigations. The novelty of the work lies in the capability of the model in making highly accurate predictions and forecasts based on the availability of small or incomplete accident data.

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Incorporating organizational factors into Probabilistic Risk Assessment (PRA) of complex socio-technical systems: A hybrid technique formalization

Cited by 176 publications

References 33 publications

Developing a Risk Assessment Methodology for the National Aeronautics and Space Administration

Developing a Risk Assessment Methodology for the National Aeronautics and Space Administration

Process Safety Performance Indicators

A novel grey–fuzzy–Markov and pattern recognition model for industrial accident forecasting

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