This is a generally well-known topic, but I do still get asked the question fairly regularly so, in this post, I’m going to provide a brief outline on the difference between Qualitative and Quantitative Risk Analysis.

In a nutshell, **Quantitative Risk Analysis** uses available data to produce a numerical value which is then used to predict the probability (and hence, acceptability) of a risk event outcome. **Qualitative Risk Analysis**, on the other hand, applies a subjective assessment of risk occurrence likelihood (probability) against the potential severity of the risk outcomes (impact) to determine the overall severity of a risk.

Risk management (in it’s very loosest form) can be traced back to the beginning of human origins, but it was only towards the end of the 19^{th} century, when high-rise buildings, complex railway infrastructures, large dams and canals started being built, that formalised project risk management techniques became more widespread in helping determine the outcome of a project. At that stage, however, risk management techniques were all still largely qualitative. Meaning the management of risk focussed only on identifying threats (or opportunities), subjectively establishing the likelihood of risk event occurrence and identifying the potential impacts of the risk. This 3-step process remains the foundation of qualitative risk analysis today, although we now have formalised methods and guide-lines to help us establish the severity of these risks, which would typically be done using a Probability/Impact ranking matrix.

The first known quantitative risk analysis method was developed by Henry Gantt in 1917 in the form of the Gantt Chart which, at the time, was used exclusively for schedule risk analysis. Gantt charts still form the basis of most schedule applications used today but, in terms of Quantitative Risk Analysis, the available methods have evolved and diversified greatly, providing us with a range of options specific to different risk types and their impacts. Quantitative Risk Analysis methods include, amongst others, Monte-Carlo Analysis, Layers of Protection Analysis (LOPA), Failure Mode and Effect Analysis (FMEA), Markov Analysis and Bayesian Analysis. Most of these methods will be meaningless to all but trained Risk Engineers and Managers, so let me try and describe Quantitative Risk Analysis by way of example.

In most operating facilities where there are inherent life threatening risks, such as oil & gas handling facilities, chemical processing plants, timber mills, mines etc. it has become mandatory practice to carry out a technical safety Quantitative Risk Analysis (QRA) to evaluate the risk to personnel working on the facility. The primary objectives of this type of QRA are to establish the Individual Risk per Annum (IRPA) and Potential for Loss of Life (PLL), and to then recommend measures to ensure the risks are kept As Low as Reasonably Practical (ALARP).

IRPA is calculated by multiplying the Location Specific Individual Risk (LSIR) by the proportion of time an individual spends in that location, and PLL is calculated by multiplying the IRPA by the number of personnel working within the location. LSIR is calculated as the sum of the frequency of each anticipated Major Accident Event (MAE) multiplied by the probability of fatality due to an MAE at that location. These calculations may be defined mathematically as follows:

LSIR = ∑ (F × P)

IRPA = LSIR x T

PLL = IRPA x N

The results of these calculations are then compared against a set of Risk Tolerability Criteria and, if they fall outside the acceptable range, mitigation measures need to be taken to reduce the results to fall within the acceptability criteria (or ALARP).

In our next post, we will consider another Quantitative Risk Analysis method, that being Monte-Carlo Simulation, and examine how this method is used to help predict the probability of outcome of certain events.

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