Rooting out Causes in Failure Analysis
the road? The corner was blind. He was driving too fast. He was drunk and his reflexes were slow.
Causal mapping is an attempt to formalise the interaction between deterministic causes; Bayesian networks do the same statistically, introducing a probabilistic, quantitative element. The fault tree explicates the causal relationships using Boolean logical operators. The ‘five whys’ method is a simpler approach that focuses attention down single causal chains: the cause of the cause of the cause, etc. (times five).
Causal Relations, Root Causes and Relevance These methods are useful to establish causal relations. They help to identify ‘root’ causes, i.e. causes that lie at the root of several chains leading to the final failure. Our hapless driver’s depression is a root cause: it causes his drinking, the neglect that led to the bald tyre and the recklessness that led to speeding without a seatbelt. These methods help us to manage and quantify complicated interdependencies, especially when those interdependencies are statistical rather than deterministic.
None of these methods is very clear about what a cause is, though our intuitive notion of a cause tends to serve us very well. What is more problematic is that causal chains are ‘dense’. The blind corner and the oncoming car were not immediate causes of the crash; they caused a swerve. That and the bald tyre and icy road caused a skid. That and the driver’s drunkenness caused him to brake, etc. We can always drill down into any given cause and break it into smaller causes. When to stop is left as a question of judgement.
Figure 1: A Fishbone Diagram for David Lewis’ Example of a Car Accident Equipment
Bald tyres
Driving too fast
Process
Drunk driver
Crash Icy road Materials Environment Driver depressed Management
Nor is it always clear from these methods how far back in a causal chain it is useful to go (though the ‘five whys’ method has a pretty big clue in its title). The driver’s abusive childhood is undoubtedly a cause and one that might be of interest to a forensic psychologist investigating the case, though of little interest to the road safety expert doing the same.
Figure 3 shows a development of the causal map for our unfortunate motorist. Taking the causal map seriously and conscientiously following the ‘why’ methodology quickly leads to a bewildering multiplicity of causes and information overload.
In practice, investigators use their experience and judgement to decide how far back to regress along a causal chain and how much to drill into it, but this can make the results too subjective and dependent on the prejudices and preoccupations of the investigator.
People
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