Prevent Tragedies - Accident Analysis Models and ProSREM
Figure - Accident analysis models
Analysis
models and methods
Accident analysis models and methods
provide safety professionals with a means of understanding why accidents occur.
Choosing an analysis technique is, however, not a simple process. A wide range
of methods are available; each offering various theoretical and practical
benefits and drawbacks. Furthermore, individuals engaged in accident
investigation are subjected to various factors, e.g. budgetary and time
constraints, which can influence their selection and usage of an analysis tool.
Ref: Accident Analysis
Models and Methods: Guidance for Safety
Professionals
Peter Underwood and Dr. Patrick Waterson
A key driver for the continued rise in
analysis model and method numbers is the ever-increasing complexity of
socio-technical systems (which are comprised of interacting human,
technological and environmental components) and the resulting change in
accident causation mechanisms. As researchers have sought to account for these
changes, the ensuing development of analysis techniques can be described as
having gone through three major phases, i.e. sequential, epidemiological and
systemic. This categorization relates to the different underlying assumptions
of accident causation (Hollnagel and Goteman, 2004). This distinction is not
obligatory and other classification systems based on differing accident
characteristics exist (e.g. Kjellén, 2000) (Katsakiori et al., 2009). However,
it helps explain the desire of researchers to introduce systems theory concepts
into accident analysis, as detailed in the following sections.
Sequential techniques
The sequential class of models and methods describes
accidents as the result of time-ordered sequences of discrete events. They
assume that an undesirable event, i.e. a ‘root cause’, initiates a sequence of
events that lead to an accident and that the cause-effect relation between
consecutive events is linear and deterministic. This implies that the accident
is the result of this root cause which, if identified and removed, will prevent
a recurrence of the accident. Examples include the Domino model (Heinrich,
1931), Fault Tree Analysis (Watson, 1961 cited in Ericson, 1999) and the Five
Whys method (Ohno, 1988).
These methods work well for losses caused
by physical component failures or the actions of humans in relatively simple
systems and generally offer a good description of the events leading up to an
accident (Leveson, 2004). However, the cause-effect relationship between the
management, organisational and human elements in a system is poorly defined by
these techniques and they are unable to depict how these causal factors
triggered the accident (Rathnayaka et al., 2011). From the end of the 1970’s it
became apparent that the sequential tools were unable to adequately explain a
number of major industrial accidents, e.g. Three Mile Island, Chernobyl and
Bhopal.
Consideration for the role that
organisational influences play in accidents was required and resulted in the
creation of the epidemiological class of analysis tools.
Epidemiological techniques
Epidemiological models and methods view
accidents as a combination of ‘latent’ and ‘active’ failures within a system,
analogous to the spreading of a disease (Qureshi, 2007). Latent conditions,
e.g. management practices or organisational culture, are likened to resident
pathogens and can lie dormant within a system for a long time (Reason et al.,
2006). Such organizational factors can
create conditions at a local level, i.e. where operational tasks are conducted,
which negatively impact on an individual’s performance (e.g. fatigue or high
workload). The scene is then set for ‘unsafe acts’, such as errors and
violations, to occur. Therefore, the adverse consequences of latent failures
only become evident when they combine with unsafe acts, i.e. active failures,
to breach the defences of a system. The most well-known epidemiological
technique is the Swiss Cheese model (Reason, 1990, 1997), which has formed the
conceptual basis for various analysis methods, e.g. the Human Factors Analysis
& Classification System (HFACS) (Wiegmann and Shappell, 2003) and Tripod
Beta.
The epidemiological class of techniques
better represent the influence of organisational factors on accident causation,
when compared with the sequential tools. Given that they require an individual
to look beyond the proximal causes of an accident and examine the impact of a
system’s latent conditions, a more comprehensive understanding of an accident
can be achieved. However, many are still based on the cause-effect principles
of the sequential models, as they describe a linear direction of accident
causation (Hollnagel, 2004). From the late 1990’s, a number of researchers e.g.
(Rasmussen, 1997; Leveson, 2001; Svedung
and Rasmussen, 2002) argued that these epidemiological techniques were no
longer able to account for the increasingly complex nature of socio-technical
system accidents. The application of systems theory was subsequently proposed
as a solution to this issue.
Systemic techniques
Systems theory is designed to understand
the structure and behaviour of any type of system. Rather than treating
accidents as a sequence of cause-effect events, it describes losses as the
unexpected behaviour of a system resulting from uncontrolled relationships
between its constituent parts. In other words, accidents are not created by a
combination of latent and active failures; they are the result of humans and
technology operating in ways that seem rational at a local level but
unknowingly create unsafe conditions within the system that remain uncorrected.
From this perspective, simply removing a ‘root cause’ from a system will not
prevent the accident from recurring. A holistic approach is required whereby
safety deficiencies throughout the entire system must be identified and
addressed. A range of systemic tools exist which enable the application of the
systems approach, e.g. the Systems Theoretic Analysis Model and Processes model
(STAMP) (Leveson, 2004, 2011), the Functional Resonance Analysis Method (FRAM)
(Hollnagel, 2004, 2012) and the Accimap (Rasmussen, 1997).
Whilst these systemic techniques appear to
provide a deeper understanding of accident causation, various studies suggest
they are more resource intensive and require considerable amounts of domain and
theoretical knowledge to apply (e.g. Ferjencik, 2011; Johansson and Lindgren,
2008).
Furthermore, the latest version of the
Swiss Cheese model (see Reason, 1997) acknowledges that active failures are not
always required for an accident to happen; long-standing latent conditions are
sometimes all that is required, as was the case in the Kings Cross, Piper Alpha
and the space shuttles Challenger and Columbia accidents (see Reason et al.,
2006). It also acknowledges that latent conditions can be better described as organizational
factors, rather than management failures. This represents top-level managerial
decisions as ‘normal behaviour’ influenced by the local conditions, resource
constraints and objectives of an organisation.
The distinction between the epidemiological
and systemic perspective of accidents, therefore, seems to be a subtle one.
However, a number of studies have compared systemic methods with established
Swiss Cheese based methods, such as HFACS (Salmon et al. 2012) and the Systemic
Occurrence Analysis Methodology (e.g. Arnold, 2009) and commented that the
systemic techniques do provide a deeper understanding of how the behaviour of
the entire system can contribute to an accident.
Whilst the ‘systems approach’ is arguably
the dominant concept within accident analysis research, systemic models and
methods are yet to gain widespread acceptance within the practitioner community
(Underwood and Waterson, 2013).
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