Accident prevention is an important part of the process for any industry. By now, a great number of theories and models have been developed to explain the causes and prevent accidents from happening in the future. There is still a debate, however, on how effectively can those theories be used in practice. While some believe that it is possible to establish the root cause of an accident, and, therefore, prevent it from happening again, others argue that the nature of an accident is too complex and that there are too many variables to consider.
There are different analysis techniques that can be used to create accident causation models and theories. Technological advances and ubiquitous implementation of technology also lead to an ever-increasing amount of theories, as the nature of accident causation mechanisms becomes more and more complex (Larouzee and Guarnieri, 2015). Sequential techniques present an accident as a result of a chain of events that was triggered by a root cause. The implication of the sequential technique is that establishing the root cause of the accident will help prevent it from reoccurring. One of the most known examples of this technique is the Domino Theory, developed by Herbert Heinrich in 1931, which serves as a basis for many of the following theories (Griffin, Young & Stanton, 2015, p.31). However, some critics of the sequential technique consider it to be too simplistic and find that it does not account for organizational influences.
Epidemiological techniques are based on the idea that accidents are a result of a combination of both active and latent factors. The latent factors can exist in a system undetected for a long time, as they create conditions for an accident to occur, but do not trigger it directly. The best example of the epidemiological technique is the Swiss Cheese Model, created by James Reason in the early 1990s. According to Li and Thimbleby (2014), the Swiss Cheese Model “visualises incidents as the result of the accumulation of multiple failures in defences (represented as the holes in slices of cheese)” (p. 116). This kind of technique accounts for more factors and provides a better understanding of the nature of an accident. Despite that, some people argue that because of the increasing technological advances and more complex socio-technical systems this technique has become obsolete.
A more holistic approach is represented by the systemic techniques. It diverges from the cause-effect view of an accident and rather “describes losses as the unexpected behaviour of a system resulting from uncontrolled relationships between its constituent parts”, as noted by Underwood and Waterson (2013, p. 4). While the systemic techniques seem to allow for a deeper understanding of the nature of an accident, other techniques can be used depending on the type of system in which an accident occurs. However, the possibilities of all those techniques are still limited. For example, according to Shappell and Wiegmann (2012), “humans have played a progressively more important causal role in both civilian and military aviation accidents as aircraft equipment has become more reliable” (p. 11). It shows that the human factor might just be too unpredictable, and, unlike technology, have less room for improvement.
The Domino Theory, however, is still relevant in many cases. A lot of disasters or potential disasters can be viewed as a chain of events, directly triggered by the first event. Things like wars and revolutions can often be analyzed using the theory.
Understanding the cause of an accident is an important step in preventing it and is a big part of safety management. Different theories and models exist to help achieve that; they can be used independently or as a combination. None of them, however, can always guarantee a successful result.
References
Griffin, T. G., Young, M. S., & Stanton, N. A. (2015). Human factors models for aviation accident analysis and prevention. United Kingdom, London: Ashgate Publishing.
Larouzee, J., & Guarnieri, F. (2015). From theory to practice: itinerary of Reasons’ Swiss Cheese Model. CRC Press, Safety and Reliability of Complex Engineered Systems, 1(2), 817-824.
Li, Y., & Thimbleby, H. (2014). Hot cheese: A processed swiss cheese model. The Journal of the Royal College of Physicians of Edinburgh, 44(2), 116-121.
Shappell, S. A., & Wiegmann, D. A. (2012). A human error approach to aviation accident analysis: The human factors analysis and classification system. United Kingdom, London: Ashgate Publishing.
Underwood, P., & Waterson, P. (2013). Accident analysis models and methods: guidance for safety professionals. United Kingdom, Loughborough: Loughborough University.