Shayne Daughenbaugh  00:20

Hello and welcome to another edition of the Lean Solutions podcast. I’ll be your host today, Shane Dothbaugh. Very excited here with the topic that we have today. I’m going to just jump right into it, but but I want you to think about this before we get started here, because I have a question for you, the listening and viewing audience. Today’s topic is going to be one of the main reasons problem solving fails organizations, even organizations that have great people and great tools. So give a quick pause to this and ask yourself this question: What might be one of the main reasons why problem solving fails? Like, come up with a list of maybe two or three different things you can think of. I’ll wait. Okay, so you got it. Fantastic. There’s your list. Let’s see how accurate we’re with the guest that we have on today. I’m very excited to be bringing to the stage Seamus McGuire. And sir, Seamus McGuire is a VP of Operations. Sorry, Seamus McGuire is a VP of Operational Excellence and Lean Systems, and a Lean Systems leader with over 20 years of experience in the medical device manufacturing industry. A Lean System leader, Master Black Belt, a Six Sigma Black Belt, Seamus is also the author of the recently published Finding Fact Before Fixing, a book focused on helping professionals avoid false assumptions with solving problems. Now that might have given away the answer, but welcome to the show, Seamus. Very excited to have you here.

Seamus Maguire  01:54

Thank you very much for having me, Shane. Delighted to be here.

Shayne Daughenbaugh  01:57

So, what is it like to be a newly minted author? Like, did did things change? Did people asking your autograph, anything like that?

Seamus Maguire  02:06

I got maybe one or two additional messages on LinkedIn. Yeah, but nothing nothing major has changed. But I guess the process of writing the book really consolidated my thinking around the topic, which which is was a really nice bonus. Which

Shayne Daughenbaugh  02:23

is yeah, that’s that’s awesome. Kind of surprising though, because you’ve been working in this field for over 20 years dealing with this. So having spent all of this time around problem solving and particularly around just a nature of you know law of averages, problem solving that doesn’t work, right? Like, can can you walk us through an example? I mean, it doesn’t have to be, but maybe even one of your personal examples of of where you from your own work there, where a team was so sure they had the root cause, but found out that they were wrong. Yeah,

Seamus Maguire  02:56

I mean, I have a lot of examples to pull from. I’m other people, of

Shayne Daughenbaugh  03:01

course, in faraway places. I’m not going to

Seamus Maguire  03:03

name names or companies or go into too much details on products, but yeah, I guess a good example would be you know we were making a widget or whatever, and it’s it’s multi layer or multi component, and a good example was where you know we were having a defect, and it was a little brown line that we didn’t want. And you know, the immediate reaction was, “Hey, that’s either mishandling, so operator error, or you know, could be the raw material from our supplier. And so, so most factories have a kind of a go-to cause that people jump to. So in this case, it was this this particular site had raw materials from the the supplier was kind of their one of their go-to causes. So they jumped on this straight away, you know, and it was it was qualified people that that were saying, hey, this it could be this, and immediately started saying, “Okay, let’s swap out new a new batch of raw material. And then it took about five days, and we realized that hasn’t really helped. So the next thing they did was let’s swap in another batch of raw material. That should fix it. And it’s like, and it didn’t help, you know. So,

Shayne Daughenbaugh  04:20

so another another delay. So you’re seven to 10 days.

Seamus Maguire  04:24

Yeah, yeah. So it’s so it’s costly. So you’re you know to to react fast actually just pushed the cost to to further down the road. So you know it was it finished up well in the end. So a supervisor, she she had been an operator for a while as well, you know, had a few thoughts, and one of them was that it could actually be not even be the raw material or even the the thing we thought was ripped. And she said it could be something else. Maybe it’s too much glue. So, so I said, hey, go go make a defect, you know. So I came in the next day, and she said, hey, Seamus, I’ve got. Some good news and some bad news, and I said, “Hey, well, what’s the bad news? Yeah, yeah, I’ll swallow that pill first. And she said, “I made 10 parts, and eight of them failed. And I said, “Okay, well, what’s the good news? And she goes, “I made 10 parts, and eight of them failed. Yes, you know. So, so the thing was, we knew then she had done her experiment, and she knew for a fact that it was the glue. And then we had to dig into that further and solve it. But but it just highlights, you know, the doing an experiment is a lot of effort compared to just trying to think your way through it. You know,

Shayne Daughenbaugh  05:37

right. But so is waiting seven to 10 days before you solve,

Seamus Maguire  05:42

exactly. Yeah, yeah, and I, I think you know when it was swapped, we, we were so sure that that was that the change in raw material was dissolution that we felt we’d fixed it as soon as we swapped it in. You know, it was just a matter ready to move

Shayne Daughenbaugh  05:57

on to whatever was next.

Seamus Maguire  05:59

Yeah, yeah, you know, and then, and sometimes, sometimes it does seem to go away, but that’s just you know yourself natural variation, and then it comes back to bite you, sure

Seamus Maguire  06:08

later on. Yeah. So,

Shayne Daughenbaugh  06:10

so this, well, I’m hearing kind of a a common theme here of either you mentioned just trying to to quickly make changes because I and I get that need to to make those corrections really fast. Figure it out. Hurry up, because you don’t want you don’t want the line down. You don’t want you know the product waiting. You don’t want the client waiting. You know your customer. Right. So you make the case that it’s not the bad tools. You know we have lean tools. Those are great. A lot of companies use them. It’s not bad people. We have great people that have been trained in this. But what’s actually going on, you know? And and I find this very interesting because we’re I think we’re going to go into a little bit of neuroscience, a little bit of the brain that makes us feel like a good guess is that’s fact. Well, I think this is it. So let’s just all eggs in this basket, blow forward and move it. Like what? What’s going on here?

Seamus Maguire  07:06

Yeah, yeah. I mean, it’s a great it’s a great point, and there’s a lot happening. I think in in when we’re at that point in problem solving, and we’re we’re start a guess starts to almost in our minds turn into a fact. So I think the first thing is we’re kind of wired for speed. You know, we we come from a long line of of people who have been making fast, good decisions from a from a survival point of view. So

Shayne Daughenbaugh  07:32

yeah,

Seamus Maguire  07:32

you know, we’re we’re kind of wired to make quick calls. You know, we get rewarded for making fast decision correct decisions. So that’s one part of it. Also, there’s a lot of these, you know, cognitive biases that that are going on. So there’s the anchoring bias. So the first idea that pops into our head, we kind of get anchored to, and that’s like our gut feeling, and and we just simply cannot let go of that. I am

Shayne Daughenbaugh  07:56

such. I I feel this. I feel in in my core.

Seamus Maguire  08:00

Yeah, yeah, and I get it too. You know, it’s a great idea to let go of it, and then you know, that’s kind of overconfidence. Then the overconfidence bias kind of magnifies that as well, or amplifies it. You know, and there’s the Dunning Kruger study that was done, if if you’ve heard of that, and in their study they found that 70 to 90% of drivers are above average. So,

Shayne Daughenbaugh  08:30

right, right. I am one of them. I am one of them. Absolutely,

Seamus Maguire  08:34

yeah, yeah. I’m I’m I’m only a little bit above average, but it’s I think that that kind of highlights, you know, when we have these gut feelings, then we’re really super confident, and we’re kind of wired to trust our gut more than we should, and then we pile on a ton of other things like confirmation bias. We see, you know, maybe things that we exactly reinforce it. We we disregard things that that go against it. Then we have you know there’s the sunk cost fallacy. So you’re you know you’re you’re trying to to try these different raw materials. Two weeks later, you know you don’t want to turn around and say actually I’m I was completely wrong. I I have gotten no further with this problem. You know so so there’s a lot of things happening there, and all of it’s subconscious, so you know it doesn’t feel like you’re guessing either. It feels like you’re reasoning through it. You know. Okay,

Shayne Daughenbaugh  09:29

thank you.

Seamus Maguire  09:30

You’re not all bad. Yeah, yeah, yeah. Exactly. And so I suppose when the group think comes in, and and especially if it’s like an engineer or someone of authority that that is is coming up with these good feelings and and driving them forward, then the groupthink takes over. Everyone agrees, and suddenly consensus, you know, kind of feels like evidence, and that so that’s kind of a trap that that we all follow.

Shayne Daughenbaugh  09:58

Like it’s all of those. Things you mentioned are so easy to do, and then when you just allow it, because this is how we’ve done it, you combine it. It makes this not of things just never get done. How did you? At what point did you realize, hey, this? There’s some other things going on here. What point did did you become aware of some of these biases and the need to counteract them.

Seamus Maguire  10:25

Yeah, yeah, yeah. Good question. So you know, I’ve been doing a lot of problem solving, whether it was Six Sigma, A three, and actually a lot of them were not giving the results that you know, according to the textbooks and and everything they should be, you know. We’re trying to solve problems, and we’re just getting nowhere. And problems aren’t going away either. And I think at some point, you know, you know, we were doing all the right things. We were getting cross-functional teams together at the Gemba, and we still had it. We were thinking, is it just is it just us? You know, maybe it’s just yeah. We we need we need someone better doing it. But but I started kind of observing the the problem solving and how people were behaving in problem solving, and that’s when I really started to see it. You know that

Shayne Daughenbaugh  11:16

fascinating

Seamus Maguire  11:17

yeah that people were just catching on to something. There was zero experimentation being done, and you know I’m I’m a scientist originally. I’m a chemist, and and so everything was done through experimentation and the scientific method. And it was kind of clear to me that we’re just not experimenting here at all. We’re not confer. You know we’re not verifying our assumptions. So as we started doing that, and it it was a little bit it was a lot more work actually, but as we started doing that, we started just getting really good results. So that really really nailed it for me.

Shayne Daughenbaugh  11:52

So so why why do you feel like or why did you find in in your research and and in your experience, why did that experimentation part get pushed out? Because as as you describe it, and I’m thinking, you know, as I’m I’m just kind of taking all this in and thinking, well, shoot, you know, one of the simplest and first things we teach in in Lean is the Plan Do Check Act or the Plan Do Study Act, and there’s part a huge part of that is the experimentation. Yeah, like what happened? That what happens to us that when we’re in a situation, even when we know all of these things, we still fall into hey, we have to hurry this along, or we just you know I don’t know. Do we dismiss experimentation? What what what what have you found?

Seamus Maguire  12:37

Yeah, so I I think in problem solving, you know every other part of problem solving, except for the experimentation, kind of uses the knowledge you have already. You know, and it’s it’s in that regard, it’s quite easy. Experimentation is the only part of the problem solving where you have to generate new knowledge, and that’s pretty. That can be hard, you know. So I even like we used to go down to the Gemba, look at the problem, and then go into problem solving. Well, also, you know, get going to the Gemba can be a can be a hypothesis test. So the only gap was we didn’t have the right question. So when we started, actually, really quick one for for your listeners as well. Like, and the fishbone analysis, a big change we did was we went from just listing words on the fishbone analysis to saying, “Well, okay, you have temperature written here, but what what do we mean by that? What’s your hypothesis? Is it that there’s a high temperature does X or a low temperature is Y? Because right now it’s just words that we’re putting on, and they’re not. We can’t ask a question, you know. So, so that really helped, actually. Oh,

Shayne Daughenbaugh  13:48

fascinating. So, so what? What I’m hearing you saying? Correct me, you know. Bring me back in here. Yeah. You know, in those when we’re talking about the fishbone diagram, in in those like the categories we have, man, machine. You know, all whatever whatever you choose, you’re saying instead of just having words or categories, have a hypothesis, or are you saying have a category and a hypothesis? Yeah, yeah,

Seamus Maguire  14:14

exactly. So you pick a few things on each category, but instead of just putting single words, you know, at least have a hypothesis. Otherwise, you’re not really brainstorming. You’re just brain dumping. You know, words right related to that category. You know, so so that that takes a little bit of practice. I think also experimentation takes time, and in a high-pressure environment, when we’re trying to get rid of a defect, you know, a lot of people don’t. You know, you’re trying to solve this as quickly as possible, and and in in the best intention, with the best intention, you’re trying to trying to get this done. Um, the last thing you want to do is go and spend you know two days trying to gather. Input to output data at the unit level, because very very often it doesn’t yield anything. But if you do that enough, you will yield you know some good information.

Shayne Daughenbaugh  15:13

So I’m curious, you know, in talking about high pressure, like I just I want to understand the people side of this because we’re working with people here, right? All of this has to do with people, and they’re good people. Absolutely. How how did you? Let’s see. How do you encourage people to take that time? So we have the pressure of shoot lines down, or we have this defect. We can’t stop the line. You know. How do you encourage them to hold on, let’s take a breath, let’s pause, and let’s do this the right way, so we don’t have to keep doing it, trying to find what that right way is. Like, how did you communicate that, and and how well was that received?

Seamus Maguire  15:53

Yeah, I think I I think really it started with a kind of a reframe from the leadership, you know, and I mean, leaders want to be data driven. They want to solve problems. They want to, you know, use the scientific method. And at the same time, I’d often hear phrases like, “Hey, what’s the root cause? or “Have we got have we got to root cause yet? Which puts everybody on the defensive, and you know people start just. One person referred to it as putting lipstick on a pig. You know they they they get that gut assumption they’ve made, and they just start dressing it up and saying, you know, okay, well I’m pretty sure it’s this, and then that pushes them further down that avenue of of trying to prove that they know what’s going on, and so the reframe that that we used was instead of the leader saying, “Hey, what’s the root cause, or have you got to cause yet? We we flipped that and we said we started saying, “What what have you ruled out so far? Okay.

Shayne Daughenbaugh  16:56

Oh, okay.

Seamus Maguire  16:57

Yeah, and that really is a game changer because suddenly instead of having this pressure to have an answer, you know you’re pushing for the right process, and people getting no, you know, because chances are you’re going to rule everything out until until you get to the cause. So you are actually making progress when you’re ruling things out, and by having the leader ask, “What have you ruled out? It’s it’s still you’re creating that sense of urgency, but it’s on going using the right process. Right, so that that worked really well. And another one was I’d often hear leaders say, “Hey, you know, what what a so what data have we got? You know, do do we need more data? Okay, you’d often hear that, and and again, it’s with the best intention. But a big issue I see with problem solving, where experimentation or hypothesis testing is skipped, is when people try and use the data they already have. Okay, so it’s not we need more data. Data is useless, right? Unless you have a question. So you gotta have a question first.

Shayne Daughenbaugh  18:07

Interesting. Yeah.

Seamus Maguire  18:08

Leaders can. We started saying, “What question do you want answered? And then, okay, well, what data do we need to generate? Because the data has created for something else, not for this problem. And very often it’s averaged and it’s batched, and we don’t have it on the unit level, and that’s really what you need. If I change, if I take this one input and change it by this amount, it will have an output on, it will have this output on this unit, and then you you get loads of that data, and you can do all kinds of magic. Then you know,

Shayne Daughenbaugh  18:41

right? Okay, so so here’s here’s the two things that I’ve heard so far. One, it’s it’s the reframe comes in the question. It’s not finding the right answer. It’s what are you eliminating, right? That’s what I hear. Which I’ve I find that what are you eliminating? Like that encourages me to dig a little bit deeper, yes. Because once once I think I find the right answer, I’m done. But if I continue to look for things that aren’t working, yes, then that keeps me digging a little bit deeper. And then let’s see, what was the the I just got distracted by what else? Oh yeah, the data. Like there’s we’re drowning in data. Exactly. Most at least most companies are like. If you’re if you’re really trying to improve, then data is one of the resources you have. But like, I love how you you said that because data could be completely meaningless. Data is not information. Data is just it’s content that then has to be filtered. It has to be. It has to answer that question. Yeah. Yeah. You know, and and and how you how you pointed it out that you know one of the flaws could be in data. It wasn’t the data we’ve collected wasn’t produced or collected for this particular answer. It might be narrowed down. It might be too small. It might be too big. But now that we have a question, we can then find the appropriate data to ask that question.

Seamus Maguire  20:01

100% 100% and you know the amount. You know yourself in in a factory on a process or a product. The amount of variables are almost infinite, so you’re never going to have enough data if if the variables are infinite, and you can’t just hope that somehow the maybe it might happen the odd time, but it’s not a good strategy to hope the answer will just kind of, you know, develop out of the data. Right. No, and and I think that’s where AI can be really useful, and I think humans need to have the come come to the the game board with the questions and then use AI to answer the questions rather than just “Hey, AI, can you solve this, please? You know,

Shayne Daughenbaugh  20:46

right. So, so if we have, if we’re reframing this, we’ve reframed the question, reframed how we’re trying to approach it. It almost sounds like we’re doing kata, the practice of kata, right? Where here’s our hypothesis. This is a threshold of knowledge. All this data is on this side of the line. We that data. So what’s the experiment? So basically, you’re you’re taking CADA and using it in in a a very fluid way, but using it to problem solve for bigger solutions.

Seamus Maguire  21:17

Exactly. I mean, CADA is based on the scientific method, and that’s exactly what we’re talking about, you know. And and the CADA is great, and you know, kudos to to to Mike Rother, I think it was, that um that that that put that together, and you know the experimental record. I mean, I’d recommend anybody to to read the the CADA books, especially the field, I think it’s the field manual one is super good. That’s

Shayne Daughenbaugh  21:44

that’s what I tell people. Yes, the field manual

Seamus Maguire  21:45

really good, and the experimental record is is is beautiful. And so so that that’s that’s a great tool to use for for your for your listeners. I think yeah, and another comment on data, but I’ve forgotten it now. Oh yeah, another experimental way of experimenting, of course, is going to the gamba and seeing with your own eyes. So you know when when there’s a hypothesis that can be that’s observable, like hey, Johnny’s tightening it too much or something. You know, you know a leader can can role model and lead by example, and say, okay, let’s go down and observe Johnny and see if he’s tightening it too much, and and it’s so fast, you know, and so that’s you know, poor Johnny gets a bad rap when I’m when I’m giving examples, but yeah, yeah. So leaders can really help with that, reframing their questions to drive the right process, and then also you know, going leading by example and going to the Gemba. I have a funny little story for you. We we had an issue in another company with a chiller, and six of us were in in a meeting room talking about this chiller. Really well educated, you know, seasoned professionals, and we were trying to discussing what are we going to do about this chiller, and then I said, “Guys, I have to put my hands up here. I’ve never seen this chiller, and I have an image in my head. Can anyone describe what chiller looks like? Right? Because I’m, I was, I have this image, but I’m probably completely wrong. Nobody had seen the chiller,

Shayne Daughenbaugh  23:21

and we’re trying to solve a problem. So we

Seamus Maguire  23:23

went down, observed it, and then then we knew what it looked like, at least you know. But I think that’s that just highlights, you know, it’s it’s the power of the gimme, you know. Get going to the

Shayne Daughenbaugh  23:32

gimmick, yeah, yeah. So so changing the frame of mind, asking the question, doing more hypothesis, hypothesizing, and and hypothesis testing. Yeah. What what else would you say? Kind of is part of that. Have you helped teams do when you’re with this reframe? You know, for for them to to truly come to a root cause or a maybe root cause is there could be multiple. What what else what else is there? Anything else that could that could also help.

Seamus Maguire  24:02

Yeah, I think yeah, there’s so many things, and I I don’t want to go into too many. I don’t. I don’t want to give the

Shayne Daughenbaugh  24:09

book away either. Yeah, yeah, no, no.

Seamus Maguire  24:11

But there’s so many so many things. So let me let me think of a few. You know, I think I’ll give you a really nice hint. You know, if you have a yield issue where there’s a visual inspection, okay, attribute agreement analysis is is definitely a thing to do. So you can typically get a big bounce in yield with no process change, and I’ll explain why if I can, I’ll try and explain this and verbally. But you know, when you’ve got a visual inspection that somebody does, you have obvious pass fails in the bin. Obvious, sorry, obvious fails in the bin. Obvious good parts in the good.

Shayne Daughenbaugh  24:56

Right, they keep going. Yep, they keep

Seamus Maguire  24:59

going. The problem is the ones in the middle, which are in the gray area, right? However, when you think about it, if I inspect a part that’s in the gray area, I don’t treat it like a gray area. I treat it like a red, like a obvious fail. It goes in the bin.

Shayne Daughenbaugh  25:15

Okay.

Seamus Maguire  25:15

So if you if you can so with attribute agreement analysis, you can measure how big your gray area is, okay? Then there’s there’s and it’s in my book. There’s there’s maybe there’s at least 10 different things you can do to reduce that gray area very quickly, and now you’ve less of you’ve you’ve more obvious fails, and more obvious passes, okay? And then someone might think, “Yeah, but is the net result not the same? And it’s not because the obvious fails, the the new obvious fails. We’re going to go in the bin anyway. Agreed. The new obvious passes, they were going to go in the bin as well. So you get a sudden boost in yield, which is which is really nice, and I’ve done that many times. And you know, it’s I’ll tell you one example that that we had. We had a lot of fails, and we did that. We did that, and we got the accuracy of the visual inspection was 50% So it was like the flip of a coin for the gray area.

Shayne Daughenbaugh  26:20

Okay, and

Seamus Maguire  26:20

then we we found out that we had Tappy charts for you know particle size, and a lot of the people didn’t know how to use them properly. So it was just a matter of teaching them how to use them properly, and the failures just disappeared. You know, so it’s that’s a nice way of. I guess it’s it’s it’s just another thing you can do to try and to try and reduce your your defects. You know.

Shayne Daughenbaugh  26:50

Excellent. Okay. Okay. Good. Good. Good. Good. Good. Good. Not sure if that

Seamus Maguire  26:53

answers your question, but you know. Yeah. No.

Shayne Daughenbaugh  26:54

It it gave it gave a little more. It rounded it out. So it’s not just about hey ask different questions. You know, it’s it’s challenging assumptions. It’s also finding a way to make it easier to answer those questions and to do your hypothesis testing. You’re talking about a gray area that’s bigger. Well, what if we shrunk that down? Now it’s easier for people to process this, easier to determine is this a root cause or not, because we’ve kind of taken some of the variability away by by doing. That’s that’s how I’m understanding. I could be I could be wrong. I’m not the sharpest man in this room.

Seamus Maguire  27:30

Now you’re good. You’re good. Yeah.

Shayne Daughenbaugh  27:33

Okay. Fantastic. So so that’s that’s seems somewhat simple in your experience. If if someone were to try this, like we’re you know a leader is okay, so we want to start challenging how we do. We’re going to improve how we problem solve. How long can they expect some of this from your experience to catch on? Like what what kind of a time frame is is it something that you found that when you explain it, you know properly, people are like, “Yes, of course, that makes sense.

Seamus Maguire  28:06

It’s it immediately after we go through it. Yeah, it totally makes sense, and some people will really latch onto it and get it. You just got to be careful that because this stuff happens. I mean, it happens to me subconsciously, jumping to conclusions and assumptions and so on.

Shayne Daughenbaugh  28:25

Yeah,

Seamus Maguire  28:26

coupled with organizational pressure, you know, you you really have to set. I mean, and it really that’s where the leader really needs to help. And you know, pick one problem and just stick to it, running through your hypothesis, and just feel good every time you rule something out, you know.

Shayne Daughenbaugh  28:43

Right.

Seamus Maguire  28:44

Yeah. Now there, there, you can use the is is not tool, or you know, I describe it in my book like a logic sieve. You you can use that if you’ve got 50 hypotheses, you can put it through that is is not or logic sieve, and get down to maybe three or four hypotheses that you can attempt.

Shayne Daughenbaugh  29:04

Yeah. So

Seamus Maguire  29:05

that that’s really nice, you know. And then you know you can anyone can look that up anywhere how to how to do that. But yeah. So it’s that’s that’s a nice tool as well. But I think just stick with it and and feel. You know, another reframe is we’re making progress when we rule things out. Okay, it’s not as much fun as solving it. You know what? In a way,

Shayne Daughenbaugh  29:28

it’s what we are doing. It’s just we’re just it’s just taking a little bit longer. So, so if this doesn’t land right, like so, let’s just take this just a little bit further. We’ve given our listeners, hey, here’s here’s how you can do it. Here’s what you can expect. If it doesn’t land right right away, maybe you have some older people that have been in this biz for a long time. They know how it works. You know, don’t bother me with all your fancy little asking questions and whatnot. I know that it’s going to be there’s too much damn glue. Okay, so less glue or whatever. If it doesn’t land. First, what can what can leaders do to try to help massage? And again, it’s the people part of things. Like we don’t want to just railroad. We don’t want to do this to them. We want to do this with them. So, what have you found that helps when people seem to be a little bit resistant?

Seamus Maguire  30:15

Yeah, yeah, exactly. And and and you know, it’s sometimes it’s it’s the younger younger engineers as well. You know, because they’re they’ve got a a lot riding on solving a problem as well. Like they’re trying to trying to make a name for themselves or whatever.

Shayne Daughenbaugh  30:30

Sure,

Seamus Maguire  30:31

you know, and and sometimes it’s you know persistence overcomes resistance to to a degree. And I think you know I’m trying to think of some of some solid experiences there. It takes a bit of coaching as well. You know, people will will show their hypothesis. They’ll almost make it look like they’re doing what you’re suggesting, but then the experimentation will be will be weak, you know. A really good example of that, you know. So a watch out is they might show a regression analysis, but the regression on both sides of the regression they could be average data, so they haven’t rolled up their sleeves and gone down to the gemba and measured each part individually, and get the individual result, and do that over and over. They’ve just got the batch result or whatever, and the batch input, and that’s really dangerous because if if you have a strong correlation, the more you average stuff, the less the the correlation becomes. And actually, I do mention in my book. There’s there’s actually cases where that averaging can flip the regression so so much that you get the opposite of what is true because you haven’t taken it at the individual level. So yeah, which is crazy. But so I think I I think make sure that the people doing that that say they’re going to test it are rolling up their sleeves and and really going to the gamba and getting that data. You know, be red red red flag or alarm signals should be going off if people are like, oh yeah, no, we have that data. Well, show it to me. You know, and process confirmation. It’s it’s not that we don’t trust people. It’s that we want to verify and coach them to make sure we’re doing it right.

Shayne Daughenbaugh  32:23

Sure, you know. So, so what what I heard the first one I loved persistence.

Seamus Maguire  32:29

Yeah, yeah, and

Shayne Daughenbaugh  32:30

coaching.

Seamus Maguire  32:31

Yeah,

Shayne Daughenbaugh  32:32

those are those are two things. Keep going because this we need this anyways. Again, you know, in the situation we talked about at the beginning, we could go seven to 10 days trying to figure this out, or we could, you know, roll up our sleeves and really dig in a little bit deeper to go beyond our biases and beyond what we believe to be the problem, and instead do some hypothesis testing, get there in a more robust way and a sustainable way that continues forward. So it’s it sounds like Sheamus. All of this to say, some of us need to read your book. Finding fact before fixing. Yeah. We will put a link in the show notes. Yeah. Is there any? If if people wanted to get a hold of you, they they really appreciated what we were what we were saying. I’m assuming LinkedIn is is a great way to reach out.

Seamus Maguire  33:22

Yeah, that’s the best way. Yeah, I’m I’m on LinkedIn. I don’t post a huge amount, but I’m I’m I’m there and I’m I’m always reading. Okay, okay. Now,

Shayne Daughenbaugh  33:32

if if someone finds you on LinkedIn, is there a link to your book there, or it’s going to need to have it?

Seamus Maguire  33:38

Yeah, I I’ll give it. I’ll send you it, and there is somewhere on one of my posts recently. I did put a link to it, but thanks for the reminder. Maybe I’ll stick a link on my yeah yeah. And again,

Shayne Daughenbaugh  33:51

ladies and gentlemen, finding fact before fixing is is the name of the name of the book. So very excited, Seamus. Thank you so much for coming in and schooling me on some how to do some better problem solving, how to challenge assumptions that, again, as you mentioned at the beginning, I don’t even, I’m not even aware of some of these things. I just like I have so much knowledge that that it’s that confirmation bias. It’s that you know, oh, I I know what I’m doing. I got this. We’ll we’ll we can figure this out.

Seamus Maguire  34:21

Yeah, yeah, yeah, yeah. So this this is

Shayne Daughenbaugh  34:23

this has been a great discussion. Really look forward to it. And ladies and gentlemen, if you have any questions or comments that you want to drop in the show notes or in in the comment section of this podcast or the video you’re watching, we would love to hear back from you and hear what you think, some of your thoughts, some of the questions you have, we might be able to continue this conversation online. You know, answering these questions until we meet again. Thank you, and have a great day.

Seamus Maguire  34:50

Thank you very much. Take care, Shane. Bye bye.

Shayne Daughenbaugh  34:52

Thank you.