Answered July 19 2019
Some of the most common root cause analysis mistakes involve poor definitions or focusing too much on the wrong thing. Others simply ignore root causes entirely, rendering the process pointless.
One of the most important parts of root cause analysis is defining the problem well. When defining the problem at hand, just saying something is wrong isn’t enough. Rather, you want to dig into the specifics of when it occurs, how prevalent it is, and any domino effects it causes.
Often, businesses don’t get specific enough about the actual problem, and that leads their RCA down the wrong path.
Another frequent issue is a tendency to go on odd tangents instead of focusing on the root processes.
For example, if you need to rework a corrective maintenance task performed last week, you might end up looking too much at your people and not enough at procedures. This might take you down a tangent where you find the work failed because Jerry wasn’t paying attention, which happened because he was tired from lack of sleep because the baby wouldn’t stop crying. It might be a root cause, but there’s nothing actionable there.
A better route would be to see how your maintenance processes might have failed to account for human error. The job failed because there were no quality control processes in place in case someone did something wrong. Unlike Jerry’s sleep schedule, that’s something you can change.
Conversely, instead of branching out on weird tangents, another common mistake is taking your RCA too narrow. If you have a complex systemic issue, you’ll need to account for multiple contributing causes, not just one. Yet companies often focus on just one issue to the exclusion of all else.
To solve this problem, use a tool that will help you look at multiple factors, such as fault tree analysis or a fishbone diagram.
Once you get some potential root causes, you need to make plans based on your findings. Teams will sometimes revert to the more superficial “causes” in their analysis, ignoring the root causes entirely. In doing so, they end up treating symptoms rather than problems.
Ignoring data is actually pretty common. Oil rigs might ignore 99% of their sensor data. Management teams might ignore the true causes of their problems.
Basically, when you get to the end or your RCA, find solutions to the root problems you find first.
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