Systemic accident analysis: Examining the gap between research and practice

Underwood, Peter; Waterson, Patrick

doi:10.1016/j.aap.2013.02.041

Cited by 186 publications

(116 citation statements)

References 30 publications

Supporting

Mentioning

113

Contrasting

Order By: Relevance

“…However, if the critiques of the SCM are justified then the continued use of this (arguably outdated) model means accident investigations may not achieve the necessary understanding of major accidents to prevent recurrence. Given that the SCM is in widespread use throughout various industries and SAA methods are yet to be widely adopted by practitioners (see Underwood and Waterson, 2013), the outcome of this debate has clear ramifications with regards to improving safety. Therefore, it is important to understand whether or not the SCM can provide a systems thinking approach and remain a viable option for accident analysis.…”

Section: Performing Saa With the Scm?mentioning

confidence: 99%

See 1 more Smart Citation

Systems thinking, the Swiss Cheese Model and accident analysis: A comparative systemic analysis of the Grayrigg train derailment using the ATSB, AcciMap and STAMP models

Underwood

Waterson

2014

Accident Analysis & Prevention

Self Cite

252

138

View full text Add to dashboard Cite

Section: Performing Saa With the Scm?mentioning

confidence: 99%

“…Salmon et al, 2012;Stanton et al, 2012). It views accidents as the result of unexpected, uncontrolled relationships between a system's constituent parts with the requirement that systems are analysed as whole entities, rather than considering their parts in isolation (Underwood and Waterson, 2013).…”

Section: Introductionmentioning

confidence: 99%

Systems thinking, the Swiss Cheese Model and accident analysis: A comparative systemic analysis of the Grayrigg train derailment using the ATSB, AcciMap and STAMP models

Underwood

Waterson

2014

Accident Analysis & Prevention

Self Cite

252

138

View full text Add to dashboard Cite

“…crowding). In what follows, we summarise the findings as they relate to a selection of the factors (further details are available in [8]). IET Intell.…”

Section: Factors Contributing To Older Passengers Stfs At Train Stationsmentioning

confidence: 99%

Probing deeper into the risks of slips, trips and falls for an ageing rail passenger population: applying a systems approach

Waterson

Kendrick

Ryan

et al. 2016

IET Intelligent Transport Systems

View full text Add to dashboard Cite

. (2016) Probing deeper into the risks of slips, trips and falls for an ageing rail passenger population: applying a systems approach. IET Intelligent Transport Systems, 10 (1). pp. 25-31. ISSN 1751-9578Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/32480/1/ITS-SI-2015-0047.pdf Copyright and reuse:The Nottingham ePrints service makes this work by researchers of the University of Nottingham available open access under the following conditions. This article is made available under the University of Nottingham End User licence and may be reused according to the conditions of the licence. For more details see: http://eprints.nottingham.ac.uk/end_user_agreement.pdf A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Abstract: In this study, the authors report the findings from a study of the contributory factors leading to slips, trips and falls (STFs) amongst elderly passengers at train stations and how these are likely to change in the future over the medium to long term (the period 2035-2050). Their data draws on: stakeholder interviews with rail personnel and elderly passengers; a set of station observations carried out across the UK; and, a survey of the views of station managers. The findings point to a set of 22 contributory factors covering aspects of organisational, station environment and passenger (individual) influence on STFs. Amongst the factors which most concern station managers at the present and over the next few decades are: rushing behaviour on train platforms; the consumption of alcohol by passengers; aspects of station design (e.g. flooring); and, training for station staff as regard the risks of STFs. The authors summarise their findings in the form of a systems model which highlights priorities with regard to STFs in terms of all of the stakeholders taking part in the study. A final section discusses a set of issues which might form the basis for a future agenda for research and practice in this area.

show abstract

“…In accordance, providing techniques and system designs that help people and organizations cope with complexity might thus be one method to enhance system resilience. However, without a clear understanding of what manifestations of resilience look like (Back et al 2008), it will be difficult to identify such manifestations in practice and quantify the theoretical models developed, creating a research-practice gap (Underwood and Waterson 2013). This is especially true when focusing on quantifying resilience for infrastructural systems, in which the current quantification methods used (e.g., graph theory: Berche et al 2009; fuzzy interference: Heaslip et al 2010) emanate from other well-established and well-elaborated methodological frameworks but as such are not fully capable of capturing the underlying interrelations of system modules (Tamvakis and Xenidis 2013).…”

Section: Introductionmentioning

confidence: 99%

“…The resilience (systems) approach is considered to be the next step (e.g., Qureshi 2008) and has become, arguably, the dominant paradigm in the study of complex sociotechnical systems (Underwood and Waterson 2013). Resilience engineering can be defined as a proactive approach concerned with enhancing organizations' intrinsic abilities to reorganize and manage their functioning and adaptive capacity prior to, during or following events, so that the system can sustain the required level of operations under both expected and unexpected conditions Hollnagel 2014).…”

Section: Introductionmentioning

confidence: 99%

Toward quantifying metrics for rail-system resilience: identification and analysis of performance weak resilience signals

2015

View full text Add to dashboard Cite

This paper aims to enhance tangibility of the resilience engineering concept by facilitating understanding and operationalization of weak resilience signals (WRSs) in the rail sector. Within complex socio-technical systems, accidents can be seen as unwanted outcomes emerging from uncontrolled sources of entropy (functional resonance). Various theoretical models exist to determine the variability of system interactions, the resilience state and the organization's intrinsic abilities to reorganize and manage their functioning and adaptive capacity to cope with unexpected and unforeseen disruptions. However, operationalizing and measuring concrete and reliable manifestations of resilience and assessing their impact at a system level have proved to be a challenge. A multimethod, ethnographic observation and resilience questionnaire, were used to determine resilience baseline conditions at an operational rail traffic control post. This paper describes the development, implementation and initial validation of WRSs identified and modeled around a 'performance system boundary.' In addition, a WRS analysis function is introduced to interpret underlying factors of the performance WRSs and serves as a method to reveal potential sources of future resonance that could comprise system resilience. Results indicate that performance WRSs can successfully be implemented to accentuate relative deviations from resilience baseline conditions. A WRS analysis function can help to interpret these divergences and could be used to reveal (creeping) change processes and unnoticed initiating events that facilitate emergence that degrades rail-system resilience. Establishing relevant change signals in advance can contribute to anticipation and awareness, enhance organizational learning and stimulate resilient courses of action and adaptive behavior that ensures rail operation reliability.

show abstract

Systemic accident analysis: Examining the gap between research and practice

Cited by 186 publications

References 30 publications

Systems thinking, the Swiss Cheese Model and accident analysis: A comparative systemic analysis of the Grayrigg train derailment using the ATSB, AcciMap and STAMP models

Systems thinking, the Swiss Cheese Model and accident analysis: A comparative systemic analysis of the Grayrigg train derailment using the ATSB, AcciMap and STAMP models

Probing deeper into the risks of slips, trips and falls for an ageing rail passenger population: applying a systems approach

Toward quantifying metrics for rail-system resilience: identification and analysis of performance weak resilience signals

Contact Info

Product

Resources

About