BẢN CHÉP ÂM THANH 🔗
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Hey, everybody. Welcome to the Pre-Accident Investigation Podcast.
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I'm Todd, your host. Howdy, pumpkins.
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I hope you're doing well. So in North America, we're turning into fall.
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And in New Mexico, the leaves are changing.
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There's color in the mountains. Gold, red, green, brown, always brown.
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We got lots of brown. We're good with the brown.
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But yeah, it's a seasonal change, which is a part of it. It's the circle.
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You know, we just keep spinning around the sun and life just keeps happening
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and we get to spend a little bit of time together each and every stinking day.
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So I have so much to talk about this week.
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It's crazy about this week.
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I want to start with the gang at Southwest Airlines.
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And so I should tell you that Chris, who is a remarkable safety person and super
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interesting in really operational reliability.
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I would say those guys pretty much have that going on.
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They opened up a day where I could go down and watch a class that I'll just
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be really honest, I kind of begged to watch.
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So do you guys remember, and even if you don't remember, and I think it's 96,
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I'm going on memory, a better person would to look this up.
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Diane Vaughn, who's a sociologist, we're going to talk a little bit about Diane
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Vaughn, wrote a book about the challenger accident.
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And she coined a phrase, the normalization of deviance.
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And what she talked about was that slow drift into failure.
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That's a term we've heard. Decker's got a book entitled that.
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And we all know if you look at black line, blue line, the blue line corner,
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all this sort of represents this drift into failure.
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And I actually think this notion of the normalization of deviance is pretty important.
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It's interesting because deviation itself is quite normal. It happens all the time.
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When we normalize the deviance, which is why she uses that phrase,
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what's happening is we take slight deviations, you know, minuscule little drifts,
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little paso-y-paso, step-by-step drifts, and they become the new normal.
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Now, you could ask me, and I got asked in a workshop just this week,
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how many times do we have to react to something abnormal before it becomes normal?
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And I actually think that's a really good question.
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And I'm afraid the answer is going to be not very many times.
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But if you're out there doing research, Matthew Hallowell, I'm talking to you. This could go well.
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That would be something really interesting to look at.
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Because we do have this ability to drift into failure. And Diane Vaughn writes
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about it quite elegantly.
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And we've picked up on that over the last 30 years or so.
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And the Challenger event, because there was much to teach us in the Challenger event.
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And we've talked about it. And we've used it. And I've actually think the idea has made us better,
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but that's not what I went to see in Dallas at the Southwest guys,
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cool headquarters that looks like a spaceship or a set for, I don't know,
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Battlestar Galactica. It's pretty cool building, right?
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They have a class called the normalization of excellence.
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Oh, I got your attention right then, didn't I? Yeah, you just perked up.
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Yeah, I saw it happen. Don't act like it didn't happen.
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And I was really curious about that. And one of the things I think I'm going
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to do, and if you think this is a good idea, just tell me, is we can bring some
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of those guys on and talk to them.
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Because what they did was basically
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take the four components that Diane Vaughn introduced in her work,
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that leads to this normalization of deviance and just reversed them and said,
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well, what if we looked at the negative as a positive?
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What if we phrased it as a positive?
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Wouldn't that also then allow us to normalize towards excellence?
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And that's what we went to do. And it was great. I met so many great people.
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There was a gang of folks there.
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We just had an amazing time and I can't thank them enough.
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I also think it's the beginning of yet another conversation that we're going
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to have in the safety world.
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Because I do think this shift towards productive safety is a really important
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shift that's happening.
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And it's
Load full transcript
happening all over, no matter where you are, what you do,
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is this idea that somehow safety's job was to reduce risk is a really important foundation.
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It's something we stand on.
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But I actually think safety's real job is to make work better.
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And more reliable, and more interesting.
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And so, that's what we did. But I mean, it was crazy. Like, the guy in charge
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of maintenance spent the day with us.
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I mean, that's saying something, right? I mean, that's saying the importance of this discussion.
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And it was great. I mean, we just had a lot of discussions. We told stories.
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We learned from stories. We learned from each other.
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Mostly, it's just really good to sort of check in on one another,
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which is kind of what I always think the podcast does anyway.
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I mean, it's a, it's a big part of it.
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Then I got to spend the day with the plant managers for Vista Energy with,
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with Charles Major and the gang.
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And that was just amazing as well. I mean, it was a really good couple of days.
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Then I got to hang out with the propane dudes because you can't spend enough
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time in propane and propane accessories. And that was a super fun day.
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So I would say, you know, all in all.
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I had a pretty productive week and my flights were only like,
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you know, a couple hours late each time because that's what happens to me because
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I think I'm being punished. I don't know.
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This, this idea of the shift in safety that we've been dealing with for the
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last 20 years or so, it's really starting to make more sense operationally.
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And I like it when stuff moves from the academic side into the applied side.
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Into the operational side. And that's kind of what we're having the conversation
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about, is think about the principles of human performance.
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You know, we talk about those all the time.
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Those at one time were sort of heady and very philosophical.
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Now they've been kind of operationalized. And you see people genuinely talk
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about the fact that if we create an environment where we can't talk to each
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other, we're going to squelch learning. And if we squelch learning,
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we're going to absolutely squelch improvement.
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And so that challenge to make safety a productive part of the organization that
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makes doing its mission more effective.
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That's probably exactly the right conversation that we should be having.
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And I think that's a very, very interesting thing to talk about,
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which kind of leads to today's podcast.
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So today, I don't know what to do with this because, you know,
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we did that big discussion on AI and it was so good to talk to Brent and he's
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always interesting. I mean, he's interesting.
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One is he's got a New Zealand accent, so that's just interesting right away.
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But also the struggle with sort of figuring out where AI fits in the world is
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a pretty important struggle.
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And so because we did this pod around this AI thing, something really unusual happened.
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And that is a person kind of anonymous. I don't really even know who did this,
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did an AI and created an interview.
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I'm guessing they used Notebook LM, but I don't know. I mean,
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it sounds like those guys.
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And basically the question they ask, I think, because I listened to it,
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you're going to hear it too,
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is how have we matured on the journey over the last several years in our thinking
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around human performance and its impact on operations?
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Kind of exactly what I went to Southwest Airlines to observe.
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A normalization of excellence.
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Now, I'm not saying I'm excellent or we're excellent. I mean,
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that would be pretty heady and kind of stupid.
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But I do think this idea of asking this question was interesting.
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And somebody thought it was interesting enough to actually produce this little interview.
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So that's what I want to do today is share with you what that interview kind
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of talks about and see what you think, because I'm curious what you think.
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I don't really have a lot to do with this. I mean, I'm not even sure how it happened.
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Well, I am sure I don't know how it happened, but I'm not even sure how you'd make this.
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But I think it's very it's very forward thinking to use AI to address this question.
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And I'm
guessing it's a supportive move towards some uses of AI that don't involve surveillance.
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At least that's what I sort of drew from the invitation that was attached to this file when I got it.
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And I'll grant you, I'm super worried about the surveillance side of this,
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but I'm worried about it for a lot of reasons.
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Safety for sure. Reliability for sure. Justice for sure.
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But I'm also worried about it in kind of everyday life. It just seems like,
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I don't know, people are a little too much in my business, if you know what
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I mean. And I think you do.
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So let's sit back and listen to this and tell me kind of what you think about this interview.
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I'm curious to see if you find any value in it at all. I'm curious.
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I don't know. The jury is definitely out from my side.
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And so I think the very best thing I could do is take it to you and ask you
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guys what you think. So without any further ado, from an anonymous listener
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who clearly is motivated and cares, here's an interview that was created on my journey...
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Over the last several years and how my thinking has changed and matured.
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I'm not sure it has. That's a complete different podcast.
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But let's listen to it and see what you come up with. Here we go.
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Welcome to today's Deep Dive, everyone. We are jumping into some incredibly
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fascinating territory today, really exploring the evolution of workplace safety
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and human reliability. Yeah, it is a huge shift in how we think about work. It really is.
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And to do this, we are pulling from two major works by Dr. Todd Conklin.
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So we have excerpts from his Five Principles of Human Performance and also from Better Questions.
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Right, which are both just, I mean, completely foundational if you are looking
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at this stuff. Absolutely.
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And our mission today is really to trace how Conklin's work has matured over the years.
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You know, going from these sort of early foundational philosophical truths about
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human error all the way to these really mature practical frameworks.
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Frameworks that, frankly, just completely upend how modern organizations respond
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when things go wrong. Exactly.
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And honestly, whether you are managing a huge corporate team or you're designing
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a new process or, I mean, maybe you're just trying to understand why you keep
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making the same mistake in your own personal life.
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This is for you. Yeah, it changes everything.
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It fundamentally changes how you view human error.
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Okay, let's unpack this. So to really understand how far Conklin's work has
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come, you kind of have to look at the baseline he was fighting against in the beginning.
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Right, the old way of doing things. Exactly.
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For decades, the holy grail of reliability in business was this concept called
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behavior-based safety. Oh, yeah.
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And the whole underlying assumption there is that workers are basically just
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like cogs in a perfectly choreographed machine.
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Right. And if a gear slips, the machine breaks. Precisely.
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So the logic for management was that accidents are simply the product of bad choices.
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If we just get workers to behave better, you know, reward the good behavior,
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punish the bad, then we achieve perfect reliability.
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Which is such a seductive illusion, right?
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I mean, it takes this incredibly complex thing, human psychology,
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and just turns it into a simple spreadsheet.
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It really does. But the sources point out that this is so flawed.
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Because if managing human behavior was really as simple as treating people like
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simple machines or like vending machines where you put in a reward and get out a safe outcome.
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We wouldn't have accidents anymore. Exactly. We would have eradicated workplace
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accidents back in the 90s.
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Yeah, because people just aren't machines. And that was the early pivot in Conklin's
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work. He realized that workers, they do not show up to their shifts intending to fail. Right.
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Nobody wants to get hurt. No one. They come to add value.
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And the most crucial takeaway from this early period is that the exact same
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behaviors that create success are often the exact same ones that lead to failure. Oh, wow.
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Wait, explain that a bit more. Well, the only difference is how the system responds
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when a disturbance happens.
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Workers are always improvising and adapting to keep production
moving.
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When that improvisation works, they get a bonus. When it fails,
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they get fired. Right. The behavior didn't change, just the outcome. Exactly.
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So as his thought matured, Conklin really distilled this into his five principles
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of human performance and the bedrock of that. I mean, principle number one is
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just simply that error is normal.
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Which sounds so obvious, but it is revolutionary for a corporation to admit. Totally.
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You can't train or wish or punish error out of the human condition.
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And then principle two is that blame fixes nothing. And principle four is that
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context influences behavior. Context is huge. It is.
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And there's this brilliant analogy in the text, the airport analogy,
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which I just love. Oh, yeah. That is a great one. Right.
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Because you think about a place like LAX or London Heathrow.
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Millions of people. And they are stressed. They're exhausted.
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They are looking at their phones, dragging luggage. It's pure chaos. Total chaos.
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But they rarely bump into each other. And it's not because everyone makes this
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deeply conscious choice not to hit anyone.
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It's not a behavioral choice. No, it's because the context, the actual physical
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environment, is designed to absorb human inattention. They have wide spaces.
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They don't have, like, random unmarked stairs in the middle of the hallway.
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What's fascinating here is that it introduces this concept of local rationality.
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Local rationality. Yeah. Okay, break that down. So local rationality basically
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means that workers do what they do for a reason that makes complete sense to
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them at the time. Oh, okay. Yeah. Given the specific pressures and context they
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are in, their actions are rational.
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So when an accident happens, blaming them retroactively just makes the error
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look like a choice. Which it wasn't. Right.
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Blame just sucks up all the emotional space in the room without offering any
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actual operational value. It doesn't fix the hallway.
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It just punishes the person who tripped. Exactly. But, you know,
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shifting away from that blame is really hard.
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And the text talks about this real-world friction Conklin faced as his theories matured.
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Oh, the resistance was massive. Yeah, it challenged deeply held beliefs.
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There's this story in the sources about a guy known as Captain.
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Captain, right. Yeah, he was this highly respected safety leader.
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A guy who, like, literally walked around hugging his co-workers.
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He cared deeply about his team. A really good guy. Right.
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But when Conklin started saying that we have to stop blaming workers for systemic
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failures, Captain just got furious.
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He stood up in this training session and basically shouted that you just have
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to admit that some people are stupid.
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Wow. Yeah. He said like 10% of workers are just idiots and that's why they get
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hurt. And then he walked out of the room.
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Which is such a defensive reaction, you know? It really is. And it ties into
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this provocative thought experiment from the text about drunk driving.
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Here's where it gets really interesting.
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Is drunk driving a choice or a mistake? That's a heavy question.
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Because if you decide it's a malicious choice, your entire safety strategy relies on punishment.
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You increase fines, build jails, and you anchor everything to the hope that
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fear will force an impaired person to make a good choice.
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Which is completely illogical because an impaired brain literally can't make
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reliable decisions. Exactly.
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But if you view it as a mistake or a system failure, you design interlocks.
00:16:22.759 --> 00:16:25.580
You install breathalyzers that make the vehicle impossible to start.
00:16:26.155 --> 00:16:28.315
You design the failure out of the system.
00:16:28.773 --> 00:16:32.926
And that synthesizes the whole point perfectly. Because the consequence of a
00:16:32.926 --> 00:16:36.825
failure is completely agnostic to the cause. Meaning what exactly?
00:16:37.289 --> 00:16:40.993
Meaning a broken leg doesn't care if the worker was smart or stupid.
00:16:41.254 --> 00:16:45.026
A severed finger doesn't care if it was a genuine mistake or an intentional
00:16:45.026 --> 00:16:47.320
shortcut. The consequence is identical.
00:16:47.819 --> 00:16:53.016
Oh, I see. So calling a worker stupid, like Captain did, is not just mean,
00:16:53.016 --> 00:16:55.708
it's actually a lazy investigation tactic.
00:16:55.993 --> 00:17:00.910
It kills curiosity. Exactly. It stops the organization from learning anything about the system.
00:17:01.177 --> 00:17:04.966
Because the minute you say he was just being stupid, you stop looking at the
00:17:04.966 --> 00:17:06.835
broken machine or the terrible schedule.
00:17:06.947 --> 00:17:09.297
You're off the hook. Management doesn't have to change a thing.
00:17:10.093 --> 00:17:13.906
Which leads us right into how Conklin's work actually transitions from just
00:17:13.906 --> 00:17:17.260
this philosophical theory into real operational practice.
00:17:17.921 --> 00:17:21.196
Right. Because it's no longer just about saying, hey, error is normal.
00:17:21.196 --> 00:17:24.515
It's about how you actually access the knowledge from the floor.
00:17:25.370 --> 00:17:28.984
And this is where we see the shift from traditional investigations to what he
00:17:28.984 --> 00:17:32.510
calls learning teams. Yes, learning teams are crucial.
00:17:33.166 --> 00:17:36.154
Traditional investigations just look backward to assign blame.
00:17:36.154 --> 00:17:39.905
Learning teams look at how work actually happens on a normal day. Right.
00:17:40.399 --> 00:17:43.069
And there's this great analogy in the source about burnt eggs.
00:17:43.824 --> 00:17:47.824
So imagine someone is cooking breakfast and suddenly smoke is everywhere.
00:17:47.824 --> 00:17:50.534
The eggs are completely ruined. A disaster. The breakfast is ruined.
00:17:51.097 --> 00:17:51.956
And you have two choices.
00:17:52.582 --> 00:17:55.904
You can either just throw the pan in the sink and go out to eat at a diner,
00:17:55.904 --> 00:17:58.909
which gives you immediate satisfaction. You get fed right away. Right.
00:17:59.326 --> 00:18:02.774
Or you can take the time to figure out exactly why the eggs burned.
00:18:02.774 --> 00:18:07.239
Was the heat too high? Was the pan bad? Which takes time and effort. Exactly.
00:18:07.692 --> 00:18:11.854
The pressure to just fix the problem by going to the diner is always stronger
00:18:11.854 --> 00:18:14.749
than the pressure to actually learn why it happened.
00:18:14.975 --> 00:18:17.269
But fixing without learning is totally useless.
00:18:17.593 --> 00:18:21.704
Because if you just go to the diner, you're going to burn the eggs again next
00:18:21.704 --> 00:18:24.264
week. Because you never fix the stuff. Exactly.
00:18:24.931 --> 00:18:28.854
And if we connect this to the bigger picture, the text uses this historical
00:18:29.214 --> 00:18:33.528
medical analogy that is just spot on. Oh, the bloodletting one. Yes.
00:18:33.830 --> 00:18:38.009
Hundreds of years ago, doctors used bloodletting to treat everything, right?
00:18:38.538 --> 00:18:41.504
And they did it because they were completely convinced they already knew the
00:18:41.504 --> 00:18:43.994
answer. They thought they had it all figured out. Right.
00:18:44.494 --> 00:18:47.722
And now we look back and realize that their certainty was the problem.
00:18:48.198 --> 00:18:52.557
The mature approach is realizing that not knowing is actually really powerful.
00:18:53.178
--> 00:18:56.667
Conklin says, the answer is the enemy of the question.
00:18:57.079 --> 00:19:01.564
The answer is the enemy of the question. That is so good. It means that by admitting
00:19:01.564 --> 00:19:04.776
we don't actually know how the work is being done on the floor,
00:19:05.287 --> 00:19:07.917
leaders open the door to true organizational wisdom.
00:19:08.340 --> 00:19:11.790
You have to ask the workers. And to really see what that looks like in practice,
00:19:12.090 --> 00:19:16.153
the text gives us this incredible real-world case study from Bob Edwards.
00:19:16.460 --> 00:19:18.092
Oh, the receiving doc story.
00:19:18.440 --> 00:19:21.673
That is a powerful one. It really is. So let's walk through this.
00:19:22.618 --> 00:19:25.649
There's a near-fatal incident on a busy receiving dock at a plant.
00:19:26.119 --> 00:19:30.258
A worker driving a fork truck misses hitting a pedestrian co-worker by literally
00:19:30.258 --> 00:19:32.539
just inches. Terrifying. An absolute near miss.
00:19:33.550 --> 00:19:37.910
And in the old behavior-based model, management would just fire the driver,
00:19:38.084 --> 00:19:40.567
right? Or punish him, tell him to be more careful and move on.
00:19:40.567 --> 00:19:43.453
Yeah, put up a sign that says, watch out for pedestrians. Exactly.
00:19:44.137 --> 00:19:47.370
But instead, this management team used a learning team.
00:19:47.707 --> 00:19:51.077
They brought five workers together around a flip chart, and they just asked
00:19:51.077 --> 00:19:55.555
them to map out their morning routine. No blame. Just tell us how the work actually happens. Right.
00:19:56.267 --> 00:20:01.177
And that was the aha moment. because they discovered that at exactly 9.30 a.m.
00:20:01.177 --> 00:20:05.557
Every day, there were three completely conflicting activities happening at the
00:20:05.557 --> 00:20:08.568
exact same time. A total systemic failure. Right.
00:20:09.223 --> 00:20:13.727
You had plant fork trucks arriving to get supplies. You had external delivery
00:20:13.727 --> 00:20:16.419
trucks trying to unload. And this is the craziest part.
00:20:16.914 --> 00:20:20.787
9.30 was the floor workers' break time. And they had to walk right through the
00:20:20.787 --> 00:20:22.335
dock to get to the break room. Yes.
00:20:22.967 --> 00:20:25.367
To go to the bathroom and the break room, they had to walk right through this
00:20:25.367 --> 00:20:26.723
chaotic zone of heavy machinery.
00:20:26.915 --> 00:20:32.250
So what's amazing there is how this mature approach yielded system-level solutions, right?
00:20:32.598 --> 00:20:37.567
They didn't just give the driver behavioral scolding. No, they actually changed the environment.
00:20:37.724 --> 00:20:41.567
Exactly. They moved the parking lot for the trucks, they painted a protected
00:20:41.567 --> 00:20:45.915
green walkway for the pedestrians, and they staggered the external deliveries.
00:20:46.124 --> 00:20:47.134
They changed the context.
00:20:47.482 --> 00:20:51.777
But the most profound detail of that whole story to me is what the plant manager
00:20:51.777 --> 00:20:53.937
did with the driver. He apologized to him.
00:20:54.204 --> 00:20:58.667
He apologized. The manager apologized to the driver for the terrible,
00:20:58.667 --> 00:21:02.598
dangerous environment they had forced him to work in. Wow. Yeah.
00:21:03.225 --> 00:21:07.027
And because they treated him with that kind of respect, that driver eventually
00:21:07.027 --> 00:21:10.961
became a safety lead for the whole plant. Because they realized he wasn't the problem.
00:21:11.420 --> 00:21:14.089
He was the person trying to solve the problem in a bad system.
00:21:14.379 --> 00:21:18.566
Exactly. And that just perfectly summarizes the whole maturation of Conklin's work.
00:21:19.152 --> 00:21:23.217
Going from that basic understanding that, hey, human error is normal to fighting
00:21:23.217 --> 00:21:25.654
that toxic, you can't fix stupid culture.
00:21:25.938 --> 00:21:29.447
Right. And moving all the way to deploying these learning teams that actually
00:21:29.447 --> 00:21:32.926
co-create safety with the workers. Yeah. The people doing the actual work.
00:21:33.729 --> 00:21:37.096
So what does this all mean for you listening right now?
00:21:38.320 --> 00:21:41.920
I really want to challenge you to evaluate your own response to failure.
00:21:42.562 --> 00:21:46.998
It is a hard habit to break. It really is. When a system breaks down around
00:21:46.998 --> 00:21:48.919
you, do you ask who failed?
00:21:49.645 --> 00:21:54.131
Or do you pause and ask, how did the system fail? You really have two choices.
00:21:54.740 --> 00:21:56.898
You are either getting better or
00:21:56.898 --> 00:22:01.208
you are getting even. Getting better or getting even. You can't do both.
00:22:02.300 --> 00:22:04.535
And I want to leave you with a final thought to ponder today.
00:22:05.144 --> 00:22:10.508
If hazards are not static, permanent things, but rather these fluid variables
00:22:10.508 --> 00:22:13.885
that constantly move in and out of our lives based on context.
00:22:14.100 --> 00:22:15.296
Which they absolutely are.
00:22:15.824 --> 00:22:19.448
Then how much of our daily stress is simply caused by stubbornly trying to manage
00:22:19.448 --> 00:22:23.887
human behavior instead of just redesigning the actual pathway we walk on?
00:22:24.485 --> 00:22:26.943
Think about that the next time you find yourself blaming someone for it.
00:22:35.924 --> 00:22:41.535
Hmm. What do you think? I don't know. I think it's, uh, it's definitely interesting.
00:22:42.162 --> 00:22:44.984
The technology is kind of amazing. It's, they sound like real people.
00:22:44.984 --> 00:22:48.744
Like you could meet them. Like, I don't know that they might like one of them
00:22:48.744 --> 00:22:52.024
might even have bad breath. I mean, that's how realistic it sounds.
00:22:52.634 --> 00:22:56.984
Thank goodness. We don't know that answer, but it's there. And the content seems
00:22:57.344 --> 00:22:59.609
interesting. I mean, I don't know.
00:23:00.102 --> 00:23:05.434
I don't even know if I agree with it, But that's a great little addition that
00:23:05.434 --> 00:23:13.484
somebody sent as a response to the conversation we had about AI just a week ago or so. Excuse me.
00:23:14.364 --> 00:23:21.027
Sorry. I had some incredible enchiladas earlier, and now I'm enjoying them one more time.
00:23:21.653 --> 00:23:25.502
Kids, growing old is not for the weak. Let's just say that.
00:23:25.868 --> 00:23:30.430
So that's the pod for today. I hope you enjoyed it. Have a great, great, great week.
00:23:30.871 --> 00:23:34.323
I hope good things happen to you. Look for me. I'll be on the West Coast saying hi.
00:23:34.989 --> 00:23:38.594
Kissing babies. You know, it's all going on there. Until then,
00:23:38.594 --> 00:23:40.887
learn something new every single day. Hope you did today.
00:23:41.381 --> 00:23:44.393
Have as much fun as you possibly can. Be good to each other. Be kind to each other.
00:23:44.818 --> 00:23:49.067
Take care of each other. Check in on each other, even the least among us.
00:23:49.321 --> 00:23:54.274
I think it matters more now than ever before. Have I got a story for you?
00:23:54.274 --> 00:23:57.112
I'm trying to still think about how to tell this story.
00:23:57.438 --> 00:24:00.254
It's that good of a story, but I don't know how to tell it. I'm going to have
00:24:00.254 --> 00:24:02.330
to figure it out. Until then...