Wood and Sweginnis (1995) commented that in selecting methods of analysis, there is no 'one size fits all'. This view was supported by Munson's evaluation
(Munson, 1999), where he found that for his particular purpose (investigating accidents which occurred when fighting forest fires) the best solution appeared to be to use two methods in combination, MES and Fault Tree Analysis. Some methods are appropriate only for major investigations, because they require considerable
resources, such as MORT (Johnson, 1980). Others have limitations in depth of investigation, which may be immaterial in simple cases. For example MES would have difficulty in identifying latent failures, but this limitation might be unimportant
of MES's ability to arrange concrete information, preparatory to further investigation by some other method, as Munson found. The DOE (1999) approach could be regarded as an attempt to combine features of Benner's (1994) and Reason's (1991) methods. This is similar to the experience of Zotov (1996) and Johnson (1999), both of whom found that a combination of methods was advisable for the investigation of systemic errors. It could be considered to be, in effect, an application of the multi- faceted approach recommended by Johnson (1980), and by Ammerman (1998).
Summary
To summarise this section of the review, the present conception of an accident is that a process leads to an undesirable occurrence. The challenge is to understand the systemic weaknesses which resulted in the loss, so that they may be averted in future. Various graphical schemes have been devised as an aid to understanding the complex interactions which may have taken place. It is likely that more than one analytical approach may be needed for full understanding, especially of complex accidents.
Business Analysis Methods
An accident can be regarded as a process with an undesirable outcome. Processes are commonplace in business, and since not all processes may work perfectly, there are methods to control or improve them. Such methods will be examined next, to see whether they have anything to offer in the way of analysing accidents or making safety recommendations.
Control of processes in industry can be considered at three levels: 1. Prevention of deficiencies; for example, through the use of standardised
procedures. This is the area of quality assurance, as exemplified by ISO 9000 and its derivatives (International Organisation for Standards, 1994).
2. Detection that some deficiency has occurred, by product sampling. This is the traditional method of quality control.
3. Problem solving, where a deficiency has occurred, so that remedies can be devised. Pareto analysis and Ishikawa Diagrams are techniques used in this area.
The quality control and quality assurance approaches are used both by the aviation industry, and regulatory authorities, in trying to ensure that routine operations are being performed safely. The ISO 9000 approach is that every operation should be reduced to procedures and documented (International Organisation for Standards, 1994). In this context, an unsafe operation must mean that a procedure has not been followed, or an additional procedure is necessary. However, merely determining that some procedure was not followed says nothing about why it was not followed, let alone how to ensure that it will be followed in future. Further, in aircraft maintenance it has been found that standard procedures have not been followed about one third of the time (van Avermaete & Hakkeling-Mesland, 2001); other domains might show similar results. It thus seems unlikely that a recommendation to add yet more procedures would of itself bring about an improvement in safety.
Pareto analysis originated in economics, but has been found to be applicable in other domains (Dixon, 2000). Empirical analysis has found that, in many different domains, about 80% of problems arise from about 20% of the possible causal factors. Accordingly, best returns from the available effort can be achieved by focussing on the important few factors, rather than on the trivial many. However, in order to produce the cumulative distribution function which will identify the important causal factors, there have to be multiple data points, i.e. many accidents. Also, since it is accepted that accidents each have multiple causes (ICAO, 1994), it would be
necessary to weight the causal factors in some way, because only the most important could be used in a Pareto analysis. This approach, of weighting the causal factors, could be similar to that adopted by O'Hare et al. (1994); they used the factor which initiated the accident sequence to generate their frequency diagrams. Pareto analysis might have merit in reviewing an accident database, but has no relevance in the analysis of an individual accident.
Ishikawa (1985) developed the Ishikawa Diagram in the 1960s as a way of displaying the way in which factors in a number of areas (e.g. policy, skills and so on) could contribute to an undesired effect (Vanderbilt University, 2005). It is sometimes
removed from the main stem are considered to be ‘root causes,’ the same usage of the term as in Johnson (1980). The fishbone diagram is something like a fault tree
diagram, on its side. The fishbone diagram has the same limitation as a fault tree, in that it cannot readily show interactions between branches.
While the fishbone diagram purports to show causation, and might therefore be put in the same class as Why-Because Analysis, its primary use today is in ‘brainstorming’: it allows the ideas generated, in the search for causes of some undesired effect, to be linked to the outcome and grouped into classes. Since it does not have the rigorous analysis of causation found in WBA, and lacks the ability to show networks of factors, it has little to offer for accident analysis.
Figure 13. Fishbone Diagram.
Source: Cause and effect diagram (2005). [Internet]. Skymark Corporation. Retrieved 6th June, 2005, from the World Wide Web:
Total Quality Management was an industrial approach to achieving high quality products, primarily through involvement of the workforce in achieving the best results in their own areas. Continuous incremental improvement in every area was sought (Schenker, 1998), analogous to the ‘creeping tide’ approach to safety recommendations. However, on the production side at least, this approach appeared to have reached a point of diminishing returns. Despite efforts to improve in every area, no overall improvement was being achieved (Goldratt, 1984). Improved output in several areas, for example by introducing automation, could result in little or no increase in output from the factory. This problem motivated Goldratt to develop his Theory of Constraints (Goldratt, 1990b). The Theory of Constraints suggests that, instead of seeking piecemeal improvements, it is much more effective to find the one
(or a few) factor(s) constraining the overall performance, and focus on fixing those. Goldratt developed a set of ‘thinking tools’ in the form of logical flow charts, to enable the constraints to be identified, and to discover how best to fix them without introducing other problems. This approach has now found wide application in business (Mabin & Balderstone, 1998; Mabin & Davies, 2003), in a wide variety of domains. The ability to identify underlying ‘core problems’ (Dettmer, 1997) and devise remedies for them could make this a promising approach to making safety recommendations. It will be considered in detail in the next section.