Past Good and Bad Knowledge
Abstract
Chris and Fred discuss the so-called ‘bedrock documents and statistics’ that are used over and over again as if they are universally correct – even though they might have nothing to do with ‘your’ machines or systems. WHY?
Key Points
Join Chris and Fred as they discuss the role that documents that are quoted as if their figures and conclusions they contain are unambiguously correct, for all machines, for all time. Why?
Topics include:
- ‘I want a quick answer and don’t want to think too much about it.’ If this sounds like you … we can’t help. You are the type of person who likes the documents we are talking about. Please seek therapy. If you want to be a designer or an engineer … be a designer or an engineer. Oh … Artificial Intelligence (AI) is a tool. Not a god.
- Give me an example. There is a report authored by Nowlan and Heap that multiple industries use to say things like ’89 % of components never wear out.’ The trouble is that Nowlan and Heap’s report contains reliability curves that don’t make sense (like a straight line going from 100 to 0 %, which can’t happen), based on data that is incomplete as components were being replaced early on in their lives (never allowing them to wear out and produce wear out data), and focused on United Airline’s fleet of aircraft circa 1978 (which isn’t shared, so we can’t interrogate it). Aircraft are around 10 times more reliable today as they were then, so even for today’s airlines these figures would be meaningless. But Nowlan and Heap’s report is frequently cited to this day as if it is just as authoritative on things like power-generating plants and manufacturing facilities.
- But we have been using these figures forever and everything is fine. Really? What does fine mean to you? Making the same mistakes over and over again and having this reality normalized? If you want to be competitive, take the time to understand your machines. This will help you make decisions that would almost certainly drastically improve reliability and availability through things like adjusting maintenance regimes and so on.
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I appreciate the comments about understanding the assumptions. I think too often, especially in Reliability Engineering, the tools presented take significant work to understand the assumptions being made. Those that present the tools can even dismiss the background machinations as too complex or say something along the lines of “we don’t have time to go through it today.” Sometimes the methods are so complex even a bright engineer might struggle comprehending what the inputs to the method are required to be accurate.
Quick easy answers are probably inaccurate. Many good techniques out there, difficult sometimes to sort through what’s applicable.
Thanks Maxamillion … and I concur! There are a small number (I repeat … a small number) of decisions where you genuinely don’t have a lot of time to make (these are distinct from decisions you have had plenty of time to make but left until the last minute). So using those quick, but potentially inaccurate answers might be the best you can do. But unfortunately, we go to the quick answers when we don’t want to find the right answers.
Thanks for your input.