
Let’s get back to the Herald ferry accident for a moment. Its investigation was a remarkably curious – and quite unusual at the time – example of an analysis technique that sometimes goes under the name of the cause-and-effect tree.
The cause-and-effect tree is an iterative reasoning exercise intended to explore the cause-and-effect relationships behind a problem. Its primary purpose is to help the investigator resist the temptation to stop at the first plausible explanation and avoid jumping to superficial conclusions by forcing him or her to keep asking the same question (the ‘why’) over and over again, even if the answer has seemingly been found.
Cause-and-effect trees belong to a wider family of causal-analysis tools, ranging from the simple Five Whys game often attributed to Sakichi Toyoda, to highly formalized Fault Tree Analysis. The precise technique matters less here than the habit of thinking they encourage: resisting the temptation to stop, and looking one step further, one chapter earlier, one level deeper. The tree structure also helps us realise that a specific failure may have more than one cause. The captain, for example, remained unaware that the ship was leaving with its bow doors open both because there was no warning light and because the company relied on negative reporting. You begin building your tree by placing the problem at its root, and start growing it upwards by asking ‘why’ at every stop. Every ‘because’ adds another branch at your current stop. Once you have added branches for all the plausible “becauses” you can identify, you move upwards and ask ‘why?’ of each one in turn. The more answers you get for a why, the wider your tree becomes. The more ‘why’s you ask in any one subtree, the taller it grows.
Let’s consider the Herald accident. Leaving aside broader aspects of the vessel’s design, we can end up with the following tree (click to enlarge):
A cause-and-effect tree of the Herald accident investigation
It is very helpful to build this diagram collaboratively, where one subgroup of the team provides their ‘because’ while the other provides counterarguments: “Why didn’t the assistant bosun close the doors? Because he slept through! But he wasn’t the first person to do so. Doesn’t that suggest there may be something else worth investigating?” Team members taking sides can make the exercise considerably more productive as it facilitates the search for less obvious causal explanations. It also helps keep the blame game under control and reminds everyone that humans are, well, humans.
Looking at our simplified tree, we can conclude that many of the deepest actionable causes lead back to management decisions: the company’s failure to create a safe working environment through clear, workable procedures and appropriate safeguards, and its failure to establish a safe and productive work culture.
Note that the tree doesn’t stop on “human trait” or “human weakness” causes. Fatigue, forgetfulness, confirmation bias, complacency and distraction are all part of what makes us human. We cannot eliminate those qualities from people. They are not moral failings in themselves, nor useful places to stop an investigation. They are predictable characteristics of human beings – and therefore risks that a well-designed system should anticipate and mitigate.
Only when we start looking at the problem from that angle can we do what we ultimately want to do – make our systems and our world safer. Humans get tired. Humans make mistakes. Humans underestimate their role in the overall safety of the system. Replace one bosun without changing anything else, and another may make exactly the same mistake – as had already happened on another company ferry. Add redundancy, positive confirmation and independent checks, and you remove important routes by which that mistake can turn into a disaster.
There’s another curious thing about that tree. Nearly every leaf that is not a human trait involves a human decision – but those humans occupy very different positions in the system. This brings us back to the earlier point: the accident was undoubtedly a product of human error. And yet saying that tells us surprisingly little until we ask which human, making what decision, under what conditions, and how far upstream.
And one more thing.
It is vitally important to resist the urge to be dismissive of people when analysing causes. Kindness and curiosity are unexpectedly close relatives. Try your best to be kind. Try to be understanding. Try to postpone blame until you understand what happened. Accountability can come afterwards; explanation has to come first.
Most mistakes are not acts of malice. People make poor decisions, overlook things, and sometimes break rules, but there is often context that helps explain why their actions made sense to them at the time.
You may eventually conclude that the person genuinely behaved negligently. But you arrive there after investigation rather than before it. The greatest value of causal-analysis tools – whether Five Whys, fishbone diagrams or formal fault-tree analysis – is that they cultivate the habit of asking one more question after we think we already know the answer.
Sometimes the next “why?” finds a broken procedure. Sometimes it finds a bad decision. And sometimes it merely finds a tired human being. But it is usually worth asking.











