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> Another cause was the unrealistic flight schedules which can subconsciously pressure management into normalizing dangerous deviations. In the case of Challen

by jonahx 24d ago

> Another cause was the unrealistic flight schedules which can subconsciously pressure management into normalizing dangerous deviations. In the case of Challenger, I think it's pretty clear this was the "root cause": > According to testimony by Kilminster and Boisjoly, Mason finally turned to Bob Lund and said, "Take off your engineering hat and put on your management hat." Joe Kilminster wrote out the new recommendation and went back on line with the teleconference. > The new recommendation stated that the cold was still a safety concern, but their people had found that the original data was indeed inconclusive and their "engineering assessment" was that launch was recommended, even though the engineers had no part in writing the new recommendation and refused to sign it. -- https://onlineethics.virginia.edu/cases/engineering-ethics-cases-texas-am/space-shuttle-challenger-disaster?utm_source=chatgpt.com https://onlineethics.virginia.edu/cases/engineering-ethics-c... If you want to take the "system" view here, as is often the case, it is the organizational power structure and incentives therein that comprise the dangerous system. You had engineering experts easily predicting the disaster, but they had no decision making power. That was the problem. But if you set up an organization like that, where the egos of "get it done" managers are allowed to gamble with other people's lives to win their own accolades, the system is doomed from the start.

otterley 24d ago
If you’re doing “five* whys” analyses correctly, you don’t stop at the technical causes. You continue further to analyze the causes that precipitated the technical errors, too. This exposes the business reasons behind them and forces management to face them. * the number five isn’t magical here. You don’t have to stop at five, and you often shouldn’t.
majormajor 23d ago
A deeper cause there, as I understand it, is that the statistical analysis was incomplete/flawed but in a way that none of the technical practitioners caught and called out at the time. The way the data was presented was to the effect of "half of the o-ring damage incidents were in cold temp launches", which made things seem less bad. The way it could have been presented was more like "nearly every cold temp launch has resulted in damage, only like 10% of others do" (I forget the exact numbers) which is far more effective highlighting the impact of the temperature and the magnitude of the increased risk. And that would've let people make a stronger case "hey, we know temps in the 50s cause damage almost every time, and it's way colder today." There's a "system" aspect (outside of power structures and incentives) which is that prob/stats knowledge among almost all engineering disciplines (industrial engineering waves from the sidelines) is exceedingly poor and often viewed as "soft" and "less important" than calculus, linear algebra, etc. And so practitioners are ill-equipped at spotting things like that ("wait, is this the right denominator? what about frequency of incident?").

by jonahx 24d ago

otterley 24d ago

If you’re doing “five* whys” analyses correctly, you don’t stop at the technical causes. You continue further to analyze the causes that precipitated the technical errors, too. This exposes the business reasons behind them and forces management to face them. * the number five isn’t magical here. You don’t have to stop at five, and you often shouldn’t.

majormajor 23d ago

A deeper cause there, as I understand it, is that the statistical analysis was incomplete/flawed but in a way that none of the technical practitioners caught and called out at the time. The way the data was presented was to the effect of "half of the o-ring damage incidents were in cold temp launches", which made things seem less bad. The way it could have been presented was more like "nearly every cold temp launch has resulted in damage, only like 10% of others do" (I forget the exact numbers) which is far more effective highlighting the impact of the temperature and the magnitude of the increased risk. And that would've let people make a stronger case "hey, we know temps in the 50s cause damage almost every time, and it's way colder today." There's a "system" aspect (outside of power structures and incentives) which is that prob/stats knowledge among almost all engineering disciplines (industrial engineering waves from the sidelines) is exceedingly poor and often viewed as "soft" and "less important" than calculus, linear algebra, etc. And so practitioners are ill-equipped at spotting things like that ("wait, is this the right denominator? what about frequency of incident?").