Can a risk assessment reliably predict if you will have an accident today?
April 14, 2011 Leave a comment
Statistics is one of those areas which can look very impressive in mathematical circles. In a nutshell, given sufficient samples and assuming all the factors are considered, past trends can be a reasonable indicator of future events. However, as weather forecasters know all to well, the field of statistics can only result in a probability. It may be predicted that the chances of rain today are 80%. But there is always a chance of 20% that it will not rain. Reliance on probabilities to precisely forecast individual specific events is therefore flawed because a probability is by definition based on an uncertain set of factors and the probability is only valid given sufficient samples.
In the natural world risk is a reality. Throughout history, humans have endeavoured to mitigate against risks to their safety, from avoiding sabre toothed lions to carefully depressurising a vessel before drilling into it for maintenance. Those risks that cannot be adequately mitigated need to be avoided or simply accepted (i.e move far away from sabre toothed lions or don’t ever drill into a pressure vessel).
In hazardous industries , risk assessments are a fundamental part of the management of safety. Risk assessments endeavour to identify those serious risks that demand attention. A common approach used in industry to quantify risk is to consider the probability of an incident on a scale of 1 to 5, and at the same time the consequences should the incident occur, also on a scale of 1 to 5. The product of the two numbers is the overall risk figure. The risk can be plotted on a graph or so called “heat map” where the top right quadrant shows risks with a high probability and a serious consequence, and the bottom left quadrant shows low probability and low consequence.
In general, because with finite resources companies cannot concentrate on all risks they tend to look at the “top 10” or some other ranking. These top 10 risks are typically found in the “hot” zone of the heat map (top right quadrant).
This approach is simple, practical and quite useful. It is however flawed in three main respects:
(1) The probability of a risk occurring is based on judgement, is a statistical metric and is therefore imprecise in predicting specific future events.
(2) The risks with very low probabilities and very high consequences are sometimes not in the “Top 10”. (For example a nuclear accident, high consequences, low probability).
(3) The risk can change over time for any number of reasons such as plant modifications, operational changes or new factors. The time between the risk assessment and the actual work in hazardous environments can be the difference between an accident taking place or apparent “safe work”.
Leading indicators of safety are sometimes used to statistically predict the underlying probability of an incident. Whether or not this is a reliable tool is a whole debate in its own right, but companies often use these because they are practical and useful. For example the number of accidents per manhour worked, or the number of near misses etc are both leading indicators that can predict an increase in the underlying probability of an accident. Furthermore, a near miss usually results in some actions taken to avoid the incident in future, thereby over time reducing risk. When these indicators increase, further action needs to be taken (so the theory says) to address those factors that are resulting in unsafe conditions. Again this approach can be flawed if it not realised that leading indicators are also statistically derived and therefore imprecise. Also, management are often totally unaware of what action is actually required to contain rising indicators, especially if the causes are behavioural or cultural in nature.
Software systems that address safety holistically need to consider several factors. They need to recognise the value of leading indicators and have a good incident and near miss management capability and handle behavioural based safety observations and measurements. They need to recognise the importance of assessing safety related risks at multiple levels – in the engineering and design process (e.g. HAZOP outputs), as well as in the actual operations (e.g. permit to work). They need to recognise the dynamic nature of operational environments and have good change management processes to measure the impact of modifications on operational risk. Finally, they need to have the capability to relate patterns and links in the data to warn people of risks that are the combined result of multiple simultaneous factors. For example, maintenance work on equipment + recent modification to equipment + previous incidents related to equipment + standing work procedure in use = overall risk. This overall risk is something for example that is not evident to people who inspect the work sites, but is the result of advanced system analytics that can correlate data intelligently to derive new insights. Few EHS systems achieve this level of vital insight which is likely to be successfully developed only by those vendors who focus on operational safety systems.
Clearly the whole subject of risk in a safety context is vast and cannot be covered in a short article of this nature. My only advice is to be extremely sceptical of inappropriate statistics and oversimplified risk management processes. Be extremely thorough in approach and have multiple strategies to manage safety. Finally seek systems that have a holistic view on safety and at the same time are practical and easy to use. Once the system is in place, look to continuously improving the quality of risk information by adding modules such as incident management, permit to work, engineering change management and advanced analytics that generate new safety related insights.
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