QuikSigma: Failure Modes and Effects Analysis Part 3

We continue our lessons from part 1 and part 2 with Failure Modes and Effects Analysis Part 3 to learn how to use the FMEA tool to analyze the important variables carried forward from the Cause and Effect matrix, using QuikSigma.




We’ve identified cook’s skill and recipe as being important variables. We’ve looked at cook’s skill and said, okay, how could that hypothetically go wrong? We could get an unskilled cook doing the baking and that might have a very bad outcome because we might somehow get unsanitary product that could make someone ill and according to the anchor points that we have chosen, that would be a severity of nine.

Now we’re going to turn our attention to occurrence and detectability. So we look and we’ve got four boxes right here where we can list causes of this failure mode. Now, if you got five or six, then you just copy the first two items over into these boxes and just keep going and I’ve only shown one here as as an example. There could be other failure modes associated with that variable. So we’ll look back in real life and say, okay, how many of our position descriptions are unclear? We find that some of them are and some of them aren’t. So we can give that a five and so that happens probably more often than we would like. The two that we seem to be in trouble on are inadequate investigation during hiring and not having any ongoing training. Those appear to happen with the too much frequency.

Now, the fact that the prevention column is empty is a warning sign to me. Remember, it’s better to prepare and prevent than it is to repair and repent. Prevention is better than inspection and detection, but the fact that got only inspection and detection options here shows that I may have an opportunity. So what are the chances that our present system, which is identified here and is empty here, what’s the possibility that that’s going to detect a bad condition if that occurs? Well, happens at least reasonably often. Whatever we chose on our anchor points, we’ve given that a four.

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