FMEAs are like diets. There is the ‘5-bite diet’ (no need to explain this one too much) that is so simple to understand, easy to initially say ‘yes’ to, but demonstrably impossible to maintain (bordering on dangerous). Then there is the ‘werewolf diet,’ which appeals to our inner hunter-gatherer that we think we still are, but beyond that, it makes no nutritional sense. There are diets that work well for one person and not well for another. And finally, there is usually (barring medical conditions) a perfectly feasible, sensible, sustainable diet that can work for you if it is tailored to your needs, physiology, and goals.

The reality is that we cannot be truly healthy without a ‘perfect’ diet that works for us. There could be more than one ‘perfect’ diet. There are plenty of ‘mediocre’ diets. And lots of ‘terrible’ diets. 

If you are with me so far … welcome to the world of FMEAs!

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If you Google ‘fault tree analysis’ (which I know you probably haven’t), you’ll get a horrible definition that goes something like …

… a fault tree is a deductive failure analysis tool that uses Boolean logic to combine a series of lower-level events to understand the probability of a top-level system failure …

This is technically correct. Unfortunately, definitions like this don’t really help anyone. They make fault tree analysis or ‘FTA’ sound like some abstract math exercise, when in practice, fault trees are one of the most practical and human-friendly tools we have to help solve a huge range of engineering and manufacturing problems. At their core, fault trees are a way of collecting our thoughts, visualizing brainstorming sessions, and structuring messy ideas in a way that helps us make sense of what starts out as being hugely complex problems.

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High customer satisfaction with low reliability (is reliability still important?)

Most customers aren’t happy with products that break down a lot. 

Product unreliability has been behind a lot of losses, cancellations and bankruptcies. A great example is the Yugo – a small, inexpensive car from the former Yugoslavia that gained notoriety in the 1980s and 1990s. It was about half as expensive as similar cars when it hit the US market. And despite a lot of initial ‘excited’ purchasers, it simply stopped selling when people realized how comically unreliable and unsafe it was.

Now there is Rivian. When Rivian burst onto the electric vehicle (EV) scene, it quickly gained a devoted following. The Rivian R1T truck and R1S SUV were praised for their funky designs and innovative features. Owners and enthusiasts love the company’s approach to off-road lifestyles, reinforced by early partnerships with Amazon and promises of building a nationwide ‘outdoor-centric’ charging network.

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(and Why It Matters)

In the world of reliability engineering, few tools are as powerful – or as misunderstood – as Weibull analysis. It is a statistical tool, which can turn people ‘off’ straight away, as we can immediately leap to a list of equations and tables of numbers that we need to memorize and embrace. But Weibull analysis is different. You actually don’t need equations.

Check out the chart below, which is called a ‘Weibull plot.’ The different shapes and colours representing the failures of different components can tell us so many things without having to evaluate a single equation.

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… and why we still make rubbish products that break a lot

Modern militaries don’t win many wars these days. The most dominant, well-funded, highly-trained armies have consistently lost (or at least not won) the Korean War, Vietnam War, Afghanistan War, arguably the Iraq War, and plenty of others. And many dominant, well-funded, highly-trained companies are consistently spitting out unreliable or unimaginative products that smaller and less dogmatic companies have no problem bettering.

To understand why, let’s look at a man called Robert McNamara.

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