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In our day to day practice we frequently see risk assessments methodologies stretched to «fit the clients’ case». In other words, users select improper tools to deliver results. This is a brief summary for the Failure Modes and Effects Analysis (FMEA) risk matrix one of the most common risk assessments methodologies. In the coming weeks we will review several of these methodologies and will end the review with a comparison.
FMEA was one of the first systematic techniques for preventative failure analysis. It allowed to study problems that might arise from malfunctions of military systems (1950). It involves reviewing as many components, assemblies, and subsystems as possible to identify potential failure modes, and their causes and effects (consequences). FMEA identifies failure modes, not hazards (malfunctions), like HAZOP does. That’s a subtle, but significant philosophical shift. Failure modes and effects analysis constitute today the backbone of Risk Assessment common practices.
FMEA starts with an event, a failure, due to an hazard. However, but it does not require a detailed identification of all the possible hazards (like HAZOP) . Subsequently it evaluates failures‘ effects, often following simplified methodologies as described below. FMEA does not explicitly require a detailed understanding/modelling of the systems’ functional relationships.
In FMEA a failure probability can only be estimated or reduced by understanding its mechanism. Therefore if the system is not well understood or an inexperienced reviewer starts the exercise, it is very likely that some failure mode will be left-out. Inter-dependencies are generally neglected, unless a specific effort is made to include cascading events (domino effects). FMEA generally give a false sense of precision and simplicity of risk matters to their users.
Common practice FMEA generally do not include detailed consequences’ analyses. The risk is not properly calculated (oftentimes underestimated). It is common for example, when applying FMEA, to see teams selecting the worst among financial, human, or environmental category of consequences and forgetting their possible combinations.
Probability Impact Graphs (PIGs risk matrix) often display the results. PIGs 4×4 or 5×5 risk matrix cells coloring gives a sense for risk criticality. Many interpretative problems afflict PIGs. Events with low p, high C are very different than those with high p, low C. PIGS will however prioritize then similarly (See Figure above).
FMEAs require time. For the curious readers here are two extreme examples: