In Their Own Words

In this episode, Bill Bellows and Andrew Stotz explore the intersection of variation and quality through awareness of the "Paradigms of Variation.” In a progression from acceptability to desirability, Bill created this 4-part model to offer economic insights for differentiating “Zero Defect” quality from “Loss Function" quality," with the aim of avoiding confusion between precision and accuracy when desirability is the choice.   Learn how to decide which paradigm your quality management system fits into! TRANSCRIPT 0:00:02.5 Andrew Stotz: My name is Andrew Stotz and I'll be your host as we dive deeper into the teachings of Dr. W. Edwards Deming. Today I'm continuing my discussion with Bill Bellows, who has spent 31 years helping people apply Dr. Deming's ideas to become aware of how their thinking is holding them back from their biggest opportunities. This is episode 7, The Paradigms of Variation. Bill, take it away.   0:00:30.3 Bill Bellows: Thank you, Andrew, and welcome to our listeners, as well as viewers, if you have access to the viewing version. Yeah, so I went back and listened to Episode 6. I'm going out bike riding 2-3 hours a day, so I listened to the podcast, listened to other things, stop and write down. Let me go write that down. And, so, we're going to pick up today on some major themes. And, what I keep coming back to is, is I think the difference between acceptability and desirability is the difference between how most companies operate and how a company inspired by Dr. Deming would operate.   0:01:29.3 BB: And, I just think of, if there was no difference between the two, then... Well, lemme even back up. I mentioned last time we were talking about why my wife and I buy Toyotas. And, yes, we've had one terrible buy, which I continue to talk about. [laughter] And, it's fun because it's just a reminder that even a company like Toyota can deliver a really lousy product, which we were unfortunate to have purchased. And, we're not the only ones that, and they've rebounded and they've apologized, they've had issues. There's no doubt about that. They have issues, but they have notably been inspired by Dr. Deming.   0:02:30.6 BB: The one thing I brought up last time was relative on this thinking of acceptability, desirability, where acceptability is looking at things and saying it's a quality system of good and bad. It's acceptable, which is good and unacceptable is not good. And, that's how most organizations view quality. Again, the focus of this series is Misunderstanding Quality. Our previous series was broadly looking at implications for Dr. Deming's ideas. And, here our focus is quality. And, so what I'm trying to get across here is quality management, traditional quality management.   0:03:17.4 BB: In most organizations, in all organizations I've ever interacted with is acceptability basis, good parts and bad parts. It's a measurement system of it meets requirements, we ship it, if it meets requirements, we buy it. And, I'm not saying there's anything wrong with that, but I don't think a system focused on acceptability can explain... To me, it does not explain the incredible reliability I have personally experienced in Toyota products.   0:03:46.9 BB: Now, I'm working with a graduate student and I wanna pursue that as a research topic in the spring, 'cause for all I know, the reliability of components in all cars has improved. I don't know if it's, I only by Toyota, 'cause so this woman I've met recently and I'm mentoring her and we're working on a research project. And, I thought recently, I'd like... And, I'm not sure how to do this, but I just know, I think I've mentioned I worked at my father's gas station back in the '70s and I remember replacing water pumps and alternators and all this stuff. This was before Japanese cars were everywhere. There were Japanese cars, but not like you see today.   0:04:33.3 BB: And, so I'm just used to all those components being routinely replaced. And, all I know is I don't routinely replace anything but the battery and the tires and change the oil. I think that's about it. Everything else is pretty good. But, I do think the differentiation between Toyota and most other companies is their appreciation of desirability and how to manage desirability. And, that's why I keep coming back to this as a theme for these sessions. And, what I think is a differentiation between a Deming view of quality and all other views of quality. What I tried to say last time is I just give you indications of a focus on acceptability. It's a quality system which looks at things that are good or things that are bad. It's, last time we talked about category thinking. It's black and white thinking. If the parts are good, then the mindset, if they're good, then they fit.   0:05:38.4 BB: Well, with a focus on continuum thinking, then you have the understanding that there's variation in good. And, that leads to variation in fit and variation in performance. And, that's a sense of things are relatively good, not absolutely good, whereas black and white category thinking is acceptability. They're all good. And, if they're all good, then they should all fit. I was, when I was at Rocketdyne, met, and the one thing I wanted to point out is... Again, as I said in the past, so much of what I'm sharing with the audience and people I've met through these podcasts or people I'm mentoring, helping them bring these ideas to their respective organizations or their consultants, whatever it is.   0:06:29.0 BB: And, so I like to provide examples in here for things for them to go off and try. You at the end of each podcast encourage them to reach out to me, a number of them have, and from that I've learned a great deal. And, so one guy was... A guy I was working with at Rocketdyne, he was at a site that did final assembly of rocket engine components. And, so one thing I'd say is the people who... And for those listening, if you wanna find people in your organization that would really value the difference between an acceptability focus and a desirability focus, find the people that do assembly, find the people that put things together. 'Cause the ones that machine the holes, they think all the holes are good. People that make the tubes, all the tubes are good. But, find the people that are trying to put the tubes into the holes. Those are the people I loved working with because they were the ones that felt the difference every day.   0:07:31.1 BB: And, so I was in a workshop for a week or so. And there's two people ahead of me. They came from this final assembly operation of Rocketdyne. And, during a break, I was trying to clarify some of the things I had said and I used, I shared with them an example of how when we focused on not the tubes by themselves or the holes by themselves, that we focused on how well the tubes go into the holes, which has a lot to do with the clearance between them and the idea that nobody owns the clearance. One person owns one part, one owns another. And, what we realized is if we focused on the relationship, what a big difference it made. So I'm explaining it to him and he turns to me and he says, he's like, "Oh, my God," he says, "I've got hundreds of turbine blades and a bunch of turbine wheels and the blades slide into the wheel." And he says, "I can't get the blades onto the wheel."   0:08:31.0 BB: And I said, "But they're all good." He says, "They're all good." But he said, "Well, what you're now explaining to me is why they don't go together. Why I have this headache." So I said, "Well, do you know where the blades come from?" He says, "yeah". And I said, "Do you know where the wheels come from?" He says, "yeah". I said, "Well, why don't you call them up and talk to them?" He says, "There's no reason for a phone call 'cause all they're going to say is, "Why are you calling me? They're all good." So, he just walked away with his head exploding 'cause he's got all these things.   0:09:05.8 BB: And, so I use that for our listeners is if you want to find people that would really resonate with the difference between acceptable and desirable, talk to the people that have to put things together. There you will find... And, so my strategy was, get them smart. Now they have to be patient with the people upstream 'cause the people upstream are not deliberately doing what they're doing to them. So, what you don't want to do is have them get... You want their consciousness to go up but you now wanna use them to talk to the component people. Now you've got a conversation. Otherwise, the component people say, "Why are you talking to me? Everything I do is good."   0:09:51.6 BB: So, I just want to talk at this point, just to reinforce that I think there's something going on with Toyota that is very intentional about managing desirability when it makes sense using acceptability. So, it's a choice. And, so indications of a focus on desirability is when you look at options that are acceptable and you say, "Of all these apples, I want this one. It's the ripest. Of all these donuts, I want this one. It's got the most sprinkles. Of all these parking spots, I want this one. It's a little bit wider than the other. I want this surgeon. I want this professor for this course."   0:10:33.8 BB: All right. So, what we're saying "is of all the choices, I want this one". So, some new ideas I want to get into tonight are the Paradigms of Variation A, B, C, D, and E. Paradigm A we looked at in the past. That's just acceptability. Does it meet requirements or not? The quality focus is achieving zero defects. And tonight I want to get into B and C. The next time we'll look at D and E. In explaining these ideas recently to someone who listened to one of our previous podcasts and were focusing on, he started asking about decision making. And that got me thinking about, of course, I took years ago decision making with Kepner and Tregoe. And there they talk about decisions. We're gonna look, we're gonna go buy a car, go buy a house. We're gonna make a decision.   0:11:29.4 BB: And, once you decide on the decision, you then list the criteria of the decision. And you come up with all the things you want in this decision. And then you look at each of them and you say, "is it a must or a want"? And let's say you're looking at houses. It could be a lot of houses to go look at. What makes this focus on acceptability, it's musts and wants. And must is very much acceptability. So you say: "We're looking for a house that must be one story, it must be in the middle of the block. The house must be in the middle of the block. It must have four bedrooms, must have two bathrooms". So now when you're looking at all these houses, acceptability says "I'm only gonna look at the ones that meet those requirements". And, so now the strategy is to go from hundreds of options down to an order of magnitude less.   0:12:25.1 BB: Now we're going to get it down to maybe 20. Now you look at the wants. So you've got an original list of all the things, the criteria, and you look at each one and say, "is it a must, is it a want"? And what I've just said is the first screening is all the ones that pass the must get into the next category. Well, with the Kepner-Tregoe folks, they talk about must, which is acceptability, and the wants are about desirability.   0:12:51.4 BB: And then here it ties into Dr. Taguchi's mindset, and we'll look at Taguchi in a future session. Taguchi looks at a characteristic of quality, such as the diameter of a hole, the performance of an automobile, miles per gallon. And he says, in terms of desirability, there's three different targets. There is desirability, I want the smallest possible value. So, if you're buying a house, it could be, I want the lowest possible electric bills where zero is the goal. It's not gonna be zero, but I'm looking, of all the ones that pass the must, now I'm looking at all the houses, and I'm saying "I want the lowest possible electric bill". That's a Smaller-is-Best.   0:13:35.9 BB: Larger-is-Best is I want something which is as big as possible. It could be I want the most roof facing the sun, in case I put solar in. That's a Larger-is-Best characteristic, where Taguchi would say the ideal is infinity, but the bigger, the better, as opposed to Smaller-is-Better. And, the other characteristic is what Taguchi calls Nominal-is-Best, is I have an ideal single value in mind. And in each case, the reason I point that out is that desirability is about going past acceptability and saying amongst all the things that are acceptable, I want the smallest, I want the largest, or I want this. It is a preference for one of those.   0:14:19.4 BB: So, I thought... I was using that to explain to this friend the other day, and I thought that would be nice to tie in here. That desirability is a focus on of all the things that meet requirements, now I want to go one step further. That's just not enough. All right, so now let's get into Paradigms B and C. And I want to use an exercise we used in the first series. And, the idea for our audience is imagine a quality characteristic having a lower requirement, a minimum, otherwise known as the lower spec, the lower tolerance. So, there's a minimum value, and then there's a maximum value. And, when I do this in my classes, I say "let's say the quality characteristic is the outer diameter of a tube." And, then so what I'd like the audience to appreciate is we've got a min and a max.   0:15:18.9 BB: And, then imagine your job as listener is to make the decision as to who to buy from. And. let's say we've got two suppliers that are ready to provide us with their product, these tubes that we're gonna buy. And, your job as a listener is to make the decision as to who to buy from. Who are we going to buy from? And, so we go off and we tell them, "Here's the min, here's the max," and they come back. And, they each give us a distribution. And, so what I'd like the audience to think about is a distribution. Just think very simply of two normal distributions, two Gaussian distributions. And, let's say the first distribution goes all the way from the min to the max. It takes up the entire range.   0:16:08.5 AS: So wide and flat.   0:16:12.1 BB: Wide and flat. That's supplier one. And supplier two, let's say is maybe three quarters of the way over. It's incredibly uniform. It uses a very small fraction of the tolerance. So that's tall and narrow. That's distribution two as opposed to wide and flat. So, imagine we've got those two to buy from. But imagine also, and this is a highly idealized scenario. And, I use this and this is why I want to share it with our audience. Because it becomes a great way of diving into what I think is a lot of confusion about meeting requirements. And, so what I want you to imagine is that no matter who you buy from, they both promise that they will deliver at the same price per tube.   0:17:00.8 BB: So, no matter who you buy from, price-wise, they are identical. To which I'd say that's highly idealized, but that's a given. Criteria number two, the delivery rates are the same. So, we cannot differentiate on delivery. We cannot differentiate on price. The third condition we find out is that everything they deliver meets requirements, 100%. So, if there is any scrap and rework, they don't ship that to us. So, everything they deliver meets requirements. And, again, that's highly idealized.   0:17:41.6 BB: Number four is the distributions are in control. And, that means that the processes are predictable and stable. And, that's guaranteed. So, imagine these distributions day by day every order is the same shape, the same average, the same amount of variation. Also, it will never change. It will never change. And, the other thing I want to point out in this fourth point here is that your job as the buyer is to buy these. They are used as is within our organization. , 0:18:15.5 BB: And, the fifth point is that there's a min and a max. And, so I've been using this exercise for, gosh, going back to 1995, and I throw it out there and then I show them the distributions. I say "same price, same schedule, delivery rate, everything meets requirements, distributions never change shape or location. You're going to use as is. And there's the min, there's the max. Who do you buy from?" And, I give people not only do we buy from one or two, but I also say I'll give you a third option.   0:18:51.5 BB: The third option is it doesn't matter. It doesn't matter. So, what I find is that three quarters of the audience will take distribution two, the narrow one. And when I ask them, why do you like distribution two? They say, "because it has less variation". I then say, "From what?" Then they say, "From each other." And, that's what a standard deviation is, variation from each other. So roughly 75% plus and minus...   [overlapping conversation]   0:19:25.8 AS: When you say of each other, you're talking about each other curve or each other item in the...   0:19:31.3 BB: Each other tube.  So, the amount of variation from all the tubes are close together, so the variation from each other.   0:19:38.6 AS: Okay. Each item. Yeah, okay.   0:19:41.8 BB: Standard deviation is the average variation from the average value. So, when I ask them, why do you like two? Okay, and then I asked the ones who take the wide one in the middle, I say, "why do you like that one," and they say because... And, actually, we'll come back to that. This is pretty funny. They will take that, but a very small percent say it doesn't matter, and here's what's interesting, if I didn't show the distributions, if all I did was say there's two suppliers out there, the same price, same schedule, that guarantee zero defects, the results will never change. Here's the min, here's the max, I'm willing to bet if I didn't show the distributions, people would say "it doesn't matter, I'll take either one". But, as soon as I show them the distributions, they want the narrow one. And, I use this for our attendees, this is a great way to show people that they really don't believe in tolerances, 'cause as soon as you go past meeting requirements, what you're really saying is, there's a higher bar.   0:21:05.6 AS: Okay, so requirements would be... Or, tolerances would be the extremes of that flat, wide curve. And, any one of those outcomes meets the tolerance.   0:21:17.5 BB: Yes, and so for companies that are striving to meet requirements, why is it when I give you two distributions that meet requirements... Why is it when I show you the distributions, and I'm willing to bet if I don't show you the distributions and all you know is they're 100% good, then you say "well, it doesn't matter," Well then what changes when I show you the distributions?   0:21:43.6 AS: I know why I'd choose the narrow one.   0:21:48.1 BB: Go ahead.   0:21:49.1 AS: I know how damn hard it is to reduce variation and I forget about any tolerance of anything, if I have two companies that show me a wide distribution, and another one shows me a narrow one, and let's say it's accurate. I'm much more impressed with how a company can do the same exact output as another company, the same product that they're trying to deliver, but they are producing a much more narrow range of outcome, which could be that they just have automation in their production line and the other one has manual.   0:22:27.4 BB: And, I have seen that within Rocketdyne, I've seen processes do that. I have seen the wide become the narrow through automation. Yeah. Okay, so hold that thought then. So, what I do in my graduate classes is I show that... Not only do I give them two options, I give them four options. So, I throw in two other distributions, but really what it comes down to is the wide one versus the narrow one, and then the other two, I throw in there that usually aren't taken, they're distractions. All right, so what I'll do in a graduate class in quality management is to show that and get the results I just showed. If I present the same exercise and then say, "imagine the average value of distribution one, the middle of distribution one, imagine that is the ideal value".   0:23:24.7 AS: That, you're talking about the wide and flat.   0:23:28.4 BB: Yes. So, all I do is I go back to the entire exercise and now I add in a line at the average of the wide distribution, and then go through and ask one more time, who would you take.   0:23:46.3 AS: So, now the dilemma that the listener has is that now we have a, within limits, within tolerances, we have a wide but flat distribution that's centered on the middle point between the upper and lower tolerance.   0:24:06.4 BB: Yeah, yes.   0:24:08.8 AS: And, then we have... Go ahead.   0:24:11.7 BB: Well, yeah, that is distribution one, same as the first part, we went through this, and all I'm doing now is saying, "imagine the average value of the middle is said to be the ideal value".   0:24:29.4 AS: And, now you're gonna tell us that the narrow one is not on that central or ideal value.   0:24:36.2 BB: No, that is still where it is at the three-quarter point, all I've done is now said, this is desirability. I'm now saying "that is the ideal value, that is the target, that is the value we prefer". And, people still take the narrowest distribution number two.   0:24:58.8 AS: I wouldn't take the narrow one because I would think that the company would have to prove to me that they can shift that narrow curve.   0:25:06.6 BB: Well, okay, and I'm glad you brought that up because according to the explanation I gave of equal price, equal schedule, meets requirements. I deliberately put in the criteria that you have to use them as is. So, now I'm forcing people to choose between the narrowest one over there at the three-quarter point, and the wide one on target. And, there's no doubt if I gave them the option of taking the narrow distribution and sliding it over, they would. Every single person would do that. But, when I give you a choice of, okay, now what? So, two things here, one is, is it calling out the ideal of value, 'cause desirability is not just beyond acceptability, it is saying, "I desire this value, I want this parking spot, I want this apple, I want this value". And, that's something we've been alluding to earlier, but that's what I wanna call out today is that...   0:26:13.7 BB: So, in other words, when I presented the exercise of the two distributions, without calling out what's desirable, all I'm doing is saying they're both acceptable, which do you prefer? But, instead of saying it doesn't matter, I'd like the narrowest one, and it may well be what people are doing is exactly what you're saying is the narrowest one seems better and easily could be for what you explained.   0:26:40.8 BB: But, what's interesting is, even when I call out what's desirable as the value, people will take the narrowest distribution, and so now what I wanna add to our prior conversation is Paradigm A, acceptability, the Paradigm A response would be, it doesn't matter. Choosing the narrowest one, otherwise known as precision, we're very precisely hitting that value, small standard deviation, that's what I refer to as Paradigm B, piece-to -piece consistency. Paradigm C is desirability being on the ideal value, that's piece-to-target consistency. And, in Dr. Taguchi's work, what he's talking about is the impact downstream of not just looking at the tubes, but when you look at how the tubes are inserted into a hole, perhaps, then what he's saying is that the reason you would call out the desirable value is what you're saying is how this tube integrates in a bigger system matters, which is why I want this value.   0:27:54.2 AS: Okay, so let's go back, A, meet requirements, that's acceptability. Anything within those tolerances we can accept. B is a narrow distribution, what you called precision or piece-to -piece consistency. And what was C?   0:28:12.8 BB: C is, I'll take the wide distribution where the average value is on target, that's piece- to-target consistency. Otherwise known as accuracy.   0:28:27.3 AS: Okay. Target consistency, otherwise known as accuracy. All right, and then precision around D is precision around the ideal value.   0:28:37.7 BB: Well, for those that want to take the narrowest one and slide it over, what you're now doing is saying, "I'm gonna start with precision, and I'm going to focus on the ideal". Now, what you're doing is saying, "step one is precision, step two is accuracy".   0:28:56.4 AS: Okay. And step three or D?   0:29:00.9 BB: Paradigm D?   0:29:02.7 AS: Yeah.   0:29:02.7 BB: Is that what you're... Yeah. Paradigm D would be the ability to produce, to move the distribution as needed to different locations.   0:29:17.4 AS: The narrow distribution?   0:29:18.9 BB: Yes, and so I'll give you an example in terms of, let's say tennis, Paradigm A in tennis is just to get the ball across the net. I just wanna get it somewhere on the other side of the court, right. Now that may be okay if you and I are neighbors, but that doesn't get us into professional level. Paradigm B, is I can hit it consistently to one place on your side of the court. Now, I can't control that location, but boy, I can get that location every single time. Next thing you know, you know exactly where the ball is going, and that's Paradigm B.  Paradigm C is I can move it to where I want it to go, which you will eventually figure out, so I can control where it goes. Paradigm D is I can consistently hit any side of the court on the fly.   0:30:11.0 BB: So, Paradigm D is I can take that narrow distribution and move it around for different customers, different applications, and Dr. Taguchi refers to that as Technology Development, and what Taguchi is talking about is developing a technology which has incredible precision in providing your sales people the ability to move the next move it to accuracy and to sell that product by tuning it to different customers as you would in sports, move the ball around to the other side of the court. So now you're going to the point that you've got incredible precision, and now you've got “on demand accuracy,” that's Paradigm D.  Paradigm C is I can do one-size-fits-all which is, which may be all you need for the application.   0:31:06.9 AS: I wanna separate the Paradigm B, the narrow distribution and that's precision around some value versus Paradigm D is precision around the ideal value.   0:31:20.7 BB: And, the idea is that desirability is about an ideal value. And, so if we're talking piece-to-piece consistency, that means it's uniform, but I'm not paying attention to... I have a value in mind that I want. And that's the difference between Dr. Taguchi's work, I mean, it's the ability to be precise. Again, accuracy, desirability is I have an ideal value in mind. And acceptability is it doesn't really matter.  Precision is uniformity without accuracy. And so, if you are... What Dr. Taguchi is talking about is, is depending on how what you're delivering integrates, being consistent may cause the person downstream to consistently need a hammer to get the tube into the hole.   0:32:24.2 BB: So, it's consistent, but what you're now saying, what Taguchi is saying is, if you pay attention to where you are within requirement, which is desirability, then you can improve integration. And, that is my explanation for why Toyota's products have incredibly reliability, that they are focusing on integration, not just uniformity and precision by itself.   0:32:49.8 AS: I would love to put this in the context of a dart thrower. The Paradigm A meeting requirability or acceptability, they stand way behind and they throw and they hit the overall dart board.   0:33:04.3 BB: Dart board. It's on the board. Yes.   0:33:07.2 AS: And, the narrow distribution is, well, they hit the same spot over to the left, right towards the edge, they hit that spot consistently. And, then basically, I'm gonna jump to D just because I'm imagining that I'm just gonna ask the guy, Hey, can you just move over just a little bit, and I'mma move them over about a half a foot, and when I do, you're gonna start throwing that dart right at the same location, but over to the right, meaning at the target. The center of the dart...   0:33:43.9 BB: The bull's eye. Yeah. Yeah, well, that's... And you call that C or D?   0:33:47.6 AS: I call that D.   0:33:49.5 BB: No, I would say, let's call that C being on target, meaning that C is, for games of darts where the most points are being on the bull's eye, that's Paradigm C.   0:34:04.0 AS: So accuracy, yeah.   0:34:05.4 BB: Paradigm D would be a game in which the ideal value changes. So now, okay, now I watch the... When I play darts, I'm sure there's lots of darts games, but one game we used to play it in our cellar at home was baseball. So, the dart board is divided into has numbers one, two, three through, and you'd go to... There'd be a wedge number one, a wedge number two, a wedge number three, that's Paradigm D that I could hit the different wedges on demand. But that's what it is. So A is anywhere in. B is consistent, precision, but again, the idea is if you can move that, but now what we're talking about is, is there an impulse to move it or are we happy just being precise? What Taguchi's talking about is the value proposition of desirability is to take precision, take that uniformity and move it to the ideal value, and what you've just done and doing so, you're now focusing on not this characteristic in isolation, you're now focusing on how this characteristic meshes with another characteristic. And, it's not just one thing in isolation, one thing in isolation does not give you a highly reliable automobile.   0:35:38.9 AS: Is there anything you wanna add to that, or are you ready to sum it up?   0:35:45.0 BB: No, that's it. The big summation is, we've been building up to the contrast between acceptable and desirable. I just wanted to add some more fidelity. Desirable is I have a value in mind, which Dr. Taguchi referred to as a target. So, for people at home, in the kitchen, the target value could be exactly one cup of flour. We talked earlier about our daughter, when she worked in a coffee shop and then, and at home she'd give us these recipes for making coffee and it'd be dad, exactly this amount of coffee and exactly that. And, we had a scale, it wasn't just anywhere between. She'd say "dad, you have to get a scale." I mean she was... We started calling her the coffee snob, 'cause it was very, this amount, this amount. So, in the kitchen then it's about precisely one cup. Precisely one this. And that's desirability.   0:36:40.6 AS: And, I was just thinking, the best word for that is bull's eye!   0:36:48.3 BB: Yes.   0:36:48.8 AS: You hit it right on the spot.   0:36:50.6 BB: Yeah.   0:36:51.6 AS: Great. Well, Bill, on behalf of everyone at The Deming Institute, I wanna thank you again for this discussion. It was not only acceptable, it was desirable. For listeners, remember to go to deming.org to continue your journey. And, if you want to keep in touch with Bill, just find him on LinkedIn. He'll reply. This is your host, Andrew Stotz, and I leave you with one of my favorite quotes from Dr. Deming, "people are entitled to joy in work."

Many companies strive to automate by using more technology and fewer humans. But does their productivity really improve? Does it keep them agile? In this episode, Jacob Stoller and Andrew Stotz share stories of companies that improve productivity because they focus on processes instead of tech alone. TRANSCRIPT 0:00:02.3 Andrew Stotz: My name is Andrew Stotz, and I'll be your host as we dive deeper into the teachings of Dr. W. Edwards Deming. Today, I continue my conversation with Jacob Stoller, Shingo Prize-winning author of The Lean CEO and Productivity Reimagined, which explores applying Lean and Deming management principles at the enterprise level. The topic for today is myth number five, the Myth of Tech Omnipotence. Jacob, take it away.   0:00:29.8 Jacob Stoller: Great, Andrew. Thanks. Great to be here again. Yeah. Tech omnipotence. Well, it's quite a myth. We sort of worship technology. We have for a long time, and we tend to think it can solve all our problems, and sometimes we get a little too optimistic about it. What I wanna talk about is in the context of companies adopting technology and go through some of the stories about that and how that relates to productivity. Really, the myth of tech omnipotence is kind of like a corollary to the the myth of segmented success. In other words, people have believed that you can take a chunk of a company. Now we'll take Dr. Deming's pyramid, and we take a chunk out of that and say, oh, well, that fits so and so in the org chart, let's automate that.   0:01:28.1 JS: And they don't consider what happens to the rest of the organization. It's just this idea that you can superimpose automation. So this has a long checkered history. And the way technology gets justified in organizations is generally what it's been, is reducing headcount. And I used to work in a tech firm, and we used to do this. We would do these studies, not really a study, but you do a questionnaire and you figure out if we adopt this, if we automate this workflow, let's just say, I don't know, it's accounts payable. So you automate accounts payable and you say, well, you got so many people involved, we think we could cut this by three people or something like that. So that becomes your business case. Now, they had categories in these little questionnaires where you would try to get other benefits from the technology, but they tended to be what they call soft benefits.   0:02:35.4 JS: And you know what that word means. Soft benefits means, well, okay, nice to have, but it's not going to get budget money or it's not gonna get approved. So anyway that's really been the kind of standard way of getting tech projects justified. And that goes through pretty much any industry. So what would happen is people adopt these technologies without looking at the whole system. And guess what? You put the software in, you start to implement it, and you run into problems. Doesn't quite work. Doesn't work the way it was supposed to. And so the tech people tended and still do tend to blame the company. They say, well, they had user problems. Users weren't really adjusting to it. These people are sort of way behind. We're a tech company. We've automated the same process for 50 different companies, we know what's good for them. We have to educate them, but they don't seem to want to be educated. So that was kind of the way it was. And I'll give you an extreme example. I did some freelance work for research firm, and one of the studies I worked on, I'm not making this up, it was called Aligning the Business with IT. So it was trying to get people to smarten up with their business and align it to what the smart people are doing with IT. So that's how extreme that kind of feeling was.   0:04:17.3 AS: As opposed to maybe aligning with the customer or something like that.   0:04:21.1 JS: Well, yeah, wouldn't that be crazy? Or how about aligning IT with the business? Finding out what the business wants. So anyway, that whole way of thinking has had, it's sort of filtered into manufacturing in the same way. And I found this out really researching Productivity Reimagined as I interviewed Ben Armstrong from MIT Industrial Performance Center. And what I learned from him is the whole history of automation and manufacturing in North America. And really, what he told me is that between 1990 and 2010, there were increases in productivity, but those were always from reducing headcount. They never found ways to actually grow the value of the business by using automation. So around 2010 or leading up to 2010, manufacturing started to change, and we started to transition into what they call a high-mix, low-volume type of markets.   0:05:33.3 JS: And I've talked to manufacturers that have said, 10 years ago, I only had to make two or three variations of this part, now I have to make 50 or 60. So you're getting shorter product cycles, larger mix. And the big buzzword now in manufacturing is agility. You've gotta be agile. So there was a study MIT, I think this Performance Center did a study. And they found that when you actually try to grow productivity, and this is really since 2010, you actually lose agility at the same time. You're kind of caught in that situation because you can't... That you lose agility when you let go of people. But that was the only way they could increase productivity. Does that make sense?   0:06:29.1 AS: Yeah. So I'm thinking about that's interesting because agility means being flexible, being able to accommodate. And when you think about the typical automation, it's about repetitive, repetitive, repetitive.   0:06:46.5 JS: Yeah.   0:06:47.3 AS: And so I can kind of get that picture about the agility versus, let's say automation or repetitive processes.   0:06:56.3 JS: Yeah. And I think that people are longing for this golden age. You go from the 1920s to 1960s, and manufacturers made incredible gains in productivity with automation. You put in these huge welding lines where they just weld. You look at the body welding, say in a plant, and it's at lightning speed. There's no question about that. But they basically ran into a plateau with that. And one of the robotics companies told me, he said, we learned decades ago how to automate these mass production processes, but now we're getting into a different kind of age where as somebody put it, we're moving from the industrial mass production age into what they call the process age, where processes are becoming more and more important. So to...   0:07:50.8 AS: And I'm thinking about the automation. I've seen videos on like online about let's say a fulfillment center with all these little robots going around and picking, putting things on them and packaging them, and all of that. So I'm thinking, well, automation has become definitely more maybe, I don't know if the words agile, but it's definitely, it's gone beyond like just automating one little part of the process.   0:08:21.4 JS: Yeah. It's gone away from the let's replace people type scenario. And so what the fastest growing segment right now in robotics is collaborative robots, which can work with people. So to put it very simply, instead of a human replacement, they're becoming tools. But these things are amazing. A worker online on the shop floor can programming these, and they have to be able to because things are changing so fast. So a worker, a welder can actually hold the robotic arm and guide it through a weld and thereby program it so it can learn how to do that weld. So then you can get the robot doing all the dangerous parts. If they're welding something large where they might have to get up on scaffolds or something, they might be able to get the robot to do some of the more dangerous types of positions. So that's when you get the real benefit.   0:09:27.7 AS: Yeah. I would think like in a paint booth, which we had in factories I worked at, now you can seal it off and have a robot in there, and all of a sudden lung problems and other things like that just go away.   0:09:40.8 JS: Interesting. Well, so anyway, we're still in a, I think in a rough spot generally with manufacturing because between 2010 and present day, at least in North America, productivity's gone down. And it's because people haven't been able to... They've depended on those people to keep their agility, but they haven't learned how to add value.   0:10:08.3 AS: Can you discuss that just for a second about productivity going down? That's a little bit of an odd thing because I think most people think that productivity's probably going up. What is the measure you're talking about, and how long and why is that happening?   0:10:23.5 JS: I think it's basically... At least I'd have to look at the study that they have, but it's basically output in proportion to the number of hours. I think that's pretty well accepted. So they're losing ground as the demands for agility are increasing. And their attempts to automate have been, caused problems. You automate and you lose your people, and then you're gonna have a heck of a time getting them back right now because that's really hard in manufacturing. But yeah, I would have to look at the study in detail to understand how they got that number, but I was taking it on faith that this is from Ben Armstrong, who's the director of the Industrial Performance Center.   0:11:11.8 AS: Yeah. You just mentioned something that I was just recently talking with another person about, and that was, one of the downsides of an aging workforce is that you're losing really senior people and you're replacing 'em with people that may not have the skills. Also, US kind of is notorious in America for a declining education. And with education coming down for the last 30 years or so, it's also hard to find, let's say, engineers and people that... There's not a deep market in some of these places where there's need. So that's a real challenge that businesses are facing.   0:11:55.2 JS: It is. Yeah.   0:11:56.3 JS: Yeah. And now what they're doing is they're looking at manufacturing from that standpoint. They're now acknowledging that the scarce resource is the human. And we have to actually build, if we're gonna automate, we have to build those processes around people. And that's... I'm gonna just read you a description here. There's, I think you heard of Technology 4.0, where they talked about putting sensors all over the place and having smart factories and that kind of thing.   0:12:27.7 AS: Yeah.   0:12:28.3 JS: Well, we now have something called Industry 5.0, and I'm just trying to get the wording here 'cause this has been around for a couple years, but it's on the EU website. It says it's "a vision that places the wellbeing of the worker at the center of the production process and uses new technologies to provide prosperity beyond jobs and growth while respecting the production limits of the planet." So they're really trying to center technology around that so you're not doing your sort of environmental and your DEI and all that independently of your production, it's all integrated part of it, which is I think something I'm sure Dr. Deming would have advocated.   0:13:17.8 AS: I'm still kind of fascinated by the productivity, and I just look at here in Asia, productivity is just rising. Education levels are rising. Engineering skills are rising. Competency in certain areas, specialties is just rising. And I oftentimes, I think that one of the things why this... One of the reasons why this is a good discussion that we're having is because in the West, in particular in the US, there's a new challenge. And that is how do you bring business... How do you bring jobs back to the economy when you're facing a very, very different workforce from when, let's say I left Ohio in 1985, roughly. It's a very different workforce nowadays.   0:14:07.1 JS: Well, yeah. And I think a lot of the offshoring arguments were about, well, we'll keep the smart jobs here 'cause we're all well educated and we'll export the low paying, less skilled jobs abroad, and we'll all win. But now, of course, we're finding that people overseas are getting darn well educated, so you can't have a more expensive labor force and have people that maybe aren't even as well educated.   0:14:40.0 AS: Yeah.   0:14:40.2 JS: So it's... Yeah, I think the West is in a very tight spot right now.   0:14:45.3 AS: Yeah. So speaking of automation and technology, I was just typing as you were speaking, and looking at productivity, it says... I was using ChatGPT and that says, US productivity growth average 2.7 annually from 2000 to 2007, but slowed to 1.4% from 2007 to 2019. There was a brief pickup in 2020, and then it's been slow since then. And they talked about this productivity paradox that I think is what you're referencing what Ben is saying.   0:15:21.3 JS: Solow's paradox? Yeah.   0:15:22.6 AS: Yeah. So that's interesting. Yep.   0:15:25.8 JS: Yeah. Solow's paradox, what does it say, that you can see the impact of technology everywhere except in the productivity numbers. I think that's what he said.   0:15:36.8 AS: Yeah, so he said that...   0:15:37.2 JS: He said that by the way in 1987. So anyway, yeah, maybe we're slow learners or something like that. But no, that's really fascinating. But I think that there's a difference between GDP growth and the growth of productivity in manufacturing. I think probably the ones that Ben Armstrong quoted were a little closer to actual manufacturing. But right now, GDP includes financial intermediation, it includes... If you own a home in North America, they include imputed rent, the rent you would have been paying as part of the GDP. So I think there's a bit of inflation, I guess, in the GDP over the years. So I think we have to take that sometimes with a little bit of a grain of salt and look a little more carefully at what the numbers are telling us.   0:16:32.8 AS: Yeah. The main ways that we typically look at it outside of GDP is like non-farm productivity, like non-farm worker, what's the output? And the other one is total factor of productivity. So yeah, GDP can be quite distorted for sure.   0:16:50.4 JS: Yeah, for sure. And anyway, and also just taking GDP per worker can be a very misleading number.   0:17:00.5 AS: Yeah.   0:17:01.3 JS: But anyway, yeah, it's fascinating. But again, the myth is... This myth that technology will solve everything is all over the place. I think with autonomous vehicles, the idea of being able to replace drivers is a just enormous economic cherry, I guess, that everybody wants to pick. You think about it what that would mean if you could... If you bought a car and then you could rent it out as a taxi at night, or what it would do to Uber if they didn't have to have people driving the cars. It's just enormous. But it's been very, very frustrating to get to that point. And when you look at a lot of the forecasts, it's still a long way away. So I think we have to be more conservative about that and talk about more the benefits really of technology and people working together. And I think the automatic driving features they have on cars now are fantastic. You can make a car a lot safer. You can slow down if you're tailgating somebody, it alerts you of just even the simple things that if there's a car to your left passing on the freeway, you get an alert, and that's... This is all really, really good stuff, but I still think that the self-driving part is maybe longer off than people think.   0:18:39.4 AS: Yeah. I think regulators too get panicked and then people want action when there's an accident or something like that. You also mentioned something about the computing power that's required for some of what this is doing, and that's a fascinating topic because it's funny, it's just amazing how much computing power is really going to be required over the next 10, 20 years.   0:19:05.0 JS: Yeah. I think there's a bell curve around some of this stuff, and I'm just gonna talk and I'm gonna jump to regenerative AI, which everybody is talking about. And they're saying, how long before I can have regenerative AI write a document that we could actually be held liable for? It can write documents, but you can't trust it. So they keep trying to improve it, but it's a kind of an exponential problem here where the wider you make your bell curve, the exponentially more power you need to do that. To the point where Microsoft is talking about buying Three Mile Island nuclear plant and rebuilding it to power all this AI stuff. So it's just phenomenal amount of power. I think that's somewhat... I don't know, relying purely on more computer power seems like it might not be a winning strategy.   0:20:13.3 AS: Yeah. It's the regenerative AI and all that's going on is also... I like to say when proponents talk about it and its strengths, which it definitely has strengths, I'm not arguing against that, I use ChatGPT almost every day. And I can say I used to have an editor sit next to me a lot of times and now I don't need that because I can go back and forth. But what I can say is that when a proponent of AI gets accused of murder and they're innocent and they're gonna go before a judge, is that proponent of AI gonna use purely AI to build their defense or would they prefer to have a lawyer who's using AI as a tool. I think I would argue we're far away from the trust level of being able to walk in there and say, I trust AI to get me out of this situation that I've been accused of murder and I'm innocent and it can get me out. There's no way any of the proponents of AI would take on that I would argue.   0:21:23.3 JS: Yeah. Well, it's interesting. I very recently had to write an affidavit and my lawyer was being a little slow on it, so I tried ChatGPT just for the heck of it and I created what I thought was pretty convincing. I gave it the facts and it gave a pretty convincing sounding affidavit, but then the lawyer did it and I saw what she did and it was so much... She had it... It was almost a human touch to it. It almost looked a little less like an affidavit. It was more of a sort of a document that had some meaning to it. That was an eyeopener for me.   0:22:10.8 AS: Yeah. Yeah. Interesting.   0:22:13.6 JS: But anyway, yeah, I'm wondering if we could jump back to automation and manufacturing because there's a story I wanted to share with you about some of the followers here of Toyota and, of course, company that's strongly dedicated to Deming's principles as well. And this is a company called Parker Hannifin. And what they do, and this is in the Lean tradition, is they're very conservative about adopting robots or any kind of automation. And they realize, when you bring in robots, you're bringing in software, you have to upgrade the software, you have to maintain it, you gotta train people, there's a risk of obsolescence or whatever, there's all that risk. So you really wanna be very, very careful. So what they do at Parker is you have to, but if you're gonna present a business case for a robot, you gotta be able to show that that's the only way that you can get the improvements you want.   0:23:22.3 JS: And by the way, you gotta have a target. You don't just say I wanna automate this, you say I wanna make this process better, here's how. So I got an example from Stephen Moore who's... He's retired now, but he was the VP I think of operations. So he was certainly the top person in terms of all the Lean initiatives that they did. But he told me and gave me an example. He said that somebody came to them, they had a cell with three people and they wanted to use the robot, one, so that they could reduce from three to two because they needed another person in another area. And secondly, there was a safety problem with that cell with loading and unloading the machines. So they came to Stephen and Stephen said, okay, let's divide our team into two groups. One group can sort out, plan the robotic implementation, how it's going to be done. The other group is gonna see if they can achieve the same objectives without a robot. So by the end of the week, the team that was without the robot team was able to achieve both objectives. They were able to reduce it down to two people and they solved the safety problem over the loading. So just by thinking it out by really going deeply into the process, they were able to do everything that people expected the automation to do.   0:24:58.3 JS: So that is a philosophy, I think is a lesson I think to anybody that's automating. 'Cause remember, we've got lots of companies that are just thinking about replacing people, whereas Parker Hannifin is talking about increasing the value of processes. They're concerned about safety here as well as headcount. And very often, they're looking at processes to improve the quality. So we've gotta look with a broader lens.   0:25:29.1 AS: That's fascinating. And for those people that don't know Parker Hannifin, I had mentioned before that was one of my father's big accounts when he was working in DuPont in the old days.   0:25:37.4 JS: Oh yeah.   0:25:38.4 AS: He was living in Cleveland. We were living... I grew up near Cleveland. But Parker Hannifin is about a $77 billion company. It's got a net profit margin of 14% versus the industry average of about 11%, which is already pretty high. And that's pretty impressive. But what's really impressive about Parker Hannifin is that it is the 11th most... If you look at all companies in America and you ask them which has been consecutively producing dividends since 1957, so about 66 years, Parker Hannifin has been producing an annual dividend. And in fact, they've been increasing that dividend ever so slightly every single year for 66 years. That is a very, very impressive feat. And very few companies are out there. In fact, only 10 companies are better than that, that are listed in the stock market. So there's some fun information from a finance guy.   0:26:35.4 JS: Well, of course, and the fact they've... We talked about some of the productivity challenges in the last while and the fact that they've sustained this. We're talking post 2010 when the productivity has been slowing down, and they've clearly kept things going, which is... We've seen that with Toyota and a lot of companies that follow these principles. It's a way of sustainable growth.   0:27:03.3 AS: Yeah. One of the things about Toyota is it's so fascinating is that they're not sold on automation, they're sold on improving processes. And if automation can help that, that's impressive. That do it, but otherwise, fix the process before you automate.   0:27:21.5 JS: Absolutely. And that's again I think this isolation of operations is a sort of a black box of the corporation where people sit in the boardroom and they just say to the operations person, well, that's your problem, solve it. We don't wanna know about it. So they see things outside the box in a sort of a financial lens. I think we talked about that in myth two.   0:27:45.2 AS: Yeah.   0:27:45.8 JS: Whereas the things that go on with process actually defy financial logic. We're improving quality and productivity and timeline very often too, delivery at the same time.   0:28:03.3 AS: Yeah.   0:28:04.2 JS: 'Cause it's a better process. It's simpler, it's better and it's a powerful concept. But I think a lot of people that are not inside process or not inside operations, aren't aware of that.   0:28:17.8 AS: Yeah. So how would you sum up what you want people to take away from this discussion?   0:28:25.3 JS: Okay. Well, I think there are a few, I guess, bullet points I would emphasise. First of all, there's no question that technology has potential to help companies get significant productivity gains. But you shouldn't see it as a technology-only solution, I think again like we were saying, you have to look at it as a way of improving processes and that's where the power of it really is. I think it shouldn't be about replacing people, but it should be combining the strengths of people and the strengths of technology. I think that's where a lot of the high potential is right now. But that means you've got to know how to optimize your process. And that's what Dr. Deming, what the Lean folks all work very hard on. And I kind of think this is a time when companies maybe need to think more seriously about that. And finally, last but not least, I think one of the wonderful things about technology is you can use it to remove the dull, dangerous aspects of work and you can make the jobs more, you know, safer and more human, I guess, more friendly for human workers by using technology. So I think that's a big hope there.   0:29:55.5 AS: Well, that's a great discussion of myth number five, The Myth of Tech Omnipotence. Jacob, on behalf of everyone at the Deming Institute, I wanna thank you again for this discussion. And for listeners, remember to go to deming.org to continue your journey. You can find Jacob's book Productivity Reimagined at jacobstoller.com. This is your host, Andrew Stotz, and I'll leave you with one of my favorite quotes from Dr. Deming and I hope you're living it right now. "People are entitled to joy in work."

Jacob Stoller, a Shingo-Prize winning author, discusses the inadequacies of traditional management's reward and punishment methods. He challenges the myth of 'sticks and carrots,' arguing for intrinsic motivation as a more effective driver of employee engagement. The conversation delves into the importance of creating a supportive work environment, contrasting motivational approaches across cultures, and the vital role of personal growth in boosting productivity. Stoller emphasizes Dr. Deming's human-centric principles, advocating for collaboration over competition in the workplace.

In this episode, Jacob Stoller and Andrew Stotz discuss the myth that managers need to know everything in order to manage. What happens when you ask non-managers for feedback? TRANSCRIPT 0:00:02.2 Andrew Stotz: My name is Andrew Stotz, and I'll be your host as we continue our journey into the teachings of Dr. W Edwards Deming. Today I'm continuing my discussion with Jacob Stoller, a Shingo-Prize-winning author of The Lean CEO and also Productivity Reimagined, which explores how to apply the Lean and Deming management style at the enterprise level. The topic for today is Myth Three: The Top-down Knowledge Myth. Jacob, take it away.   0:00:31.2 Jacob Stoller: Okay. Great to be here again, Andrew. And, yeah, the myth we're gonna talk about is this notion that managers can make their workers and their people more productive by telling them exactly what to do. And that's surprisingly prevalent in the workplace. But I wanna start out by just saying how this relates to the other myths that we were talking about, 'cause we started with this, what Dr. Deming calls the "pyramid," the org-structure type or...   0:01:08.9 AS: Organizational chart.   0:01:09.9 JS: Paradigm idea, yeah, the organizational structure that says that everything is a independent component, right? You got your different departments, they all work independently, we optimize each, and we optimize the whole, right? So, from that, it naturally follows. And we did Myth Number Two that we can follow financial logic, 'cause financial logic fits nicely into that structure. And of course, we saw last time that all the shortcomings and problems you get when you follow that kind of thinking. So, the third myth is we get to top-down knowledge. And again, that follows from the pyramid structure. If it were true that interdependent components weren't interdependent, that everything could act independently, it would certainly follow that you could have knowledge about those components taught in school and that it would all make sense. I think it's the interdependence that really shoots that whole thing down of top-down knowledge. So... Sorry. Yeah.   0:02:16.3 AS: Go ahead.   0:02:18.8 JS: I wanted to start with a bit of a story just to illustrate how prevalent this is. I was doing a workshop with a small excavation company, and we were looking at ways to make them more effective and serve more customers, grow more effectively, and stuff like that. I did an exercise with them, and we looked at where maybe the waste was taking place the most. And they were driving trucks around a lot. This was a rural area, so there was a lot of mileage that was perhaps being wasted. So, we did an exercise with tracking value and non-value mileage. If you're going to a customer, that's adding value. But if you take a detour to have lunch or something, well, that doesn't add value to the customer, right?   0:03:08.8 JS: So, we were exploring those things, and that exercise worked out really well. They made some big changes, and it actually really helped the company grow. They started posting little notes in the trucks talking about, "Remember, value versus non-value." They were tracking it. And it was really interesting. But the success was largely due to one participant. And I'm sure you've seen this, Andrew, in workshops where somebody really seems to get it. And he had all these ideas, a very, very thoughtful guy, and we were just writing down his suggestions. He had a lot to do with that. But after the workshop, I sat down with him when we were chatting, and he told me that he'd been in the construction business for 15 years, and nobody had ever asked him for his opinion about how work was done. Never.   0:04:04.7 AS: Incredible.   0:04:07.1 JS: I was just stunned by that. This guy was so good. [laughter] When you think about that, it's pretty typical. And I think it's really, people are, managers are taught that it's their job to tell people what to do. And often that puts them in a tough spot. Often they have to be in a role where maybe that they're not that comfortable, because maybe they know deep down inside that there's a lot of knowledge out there that they're not aware of.   0:04:41.3 AS: Yeah, it's interesting. It reminds me when I was a first time supervisor at Pepsi, and I worked in the Torrance factory in Los Angeles, in Torrance, California, and then I worked in the Buena Park factory. And at Buena Park, I was given control of the warehouse. In both cases, I was a warehouse supervisor.   0:05:02.9 JS: Right.   0:05:03.1 AS: And I remember I worked with the union workers who were all moving the product all day long. And I just constantly focused on improvement and that type of thing, and talking to them, and trying to figure out how can we do this better, faster, cheaper and with less injury and all of that. And when I left, it was two years, it was maybe a year and a half that I was at that facility. And one of the guys that had been there, he said... He came up to me, he said, "25 years I've been here, and nobody really listened to us the way you did."   0:05:41.0 JS: Oh, wow. Well, that's a hint.   0:05:41.8 AS: And it just made me realize, "How can it be?" Now, I know Pepsi was taking first-time graduates out of school and putting them in this job, and... I don't know. But I just was... I was baffled by that. So, at first blush you would think you'd never hear that. People are always talking, but people aren't always talking. That's not that common.   0:06:03.1 JS: Yeah, for sure. And it's so really deeply entrenched in the system that it's very, very hard to break. One of them, I talked to a couple of companies that actually went through transformations, and this was with Lean, where they transformed their managers as a lot of Lean companies do. And I know Deming companies do this as well, where they changed their role from being someone that tells people what to do, to somebody who actually is a coach and an enabler, and draws people out and uses their knowledge and encourages them to solve their own problems, whether it's PDSA or whatever methods they support. And both of these companies lost half their management team through that transition. But both of the leaders admitted, they were honest enough to admit, that the reason why they lost the managed, they blamed themselves. They said, "It's 'cause we as the top leaders didn't prepare those people for the change." So, that was interesting as well.   0:07:17.6 AS: I want to go back and just revisit... Myth Number One was the myth of segmented success. The idea that, "Hey, we can get the most out of this if we segment everybody and have everybody do the best they can in each of those areas." Dr. Deming often said that we're destroyed by best efforts. And part of that's one of the things he was saying was that it doesn't work. Segmented success doesn't maximize or optimize the output for a system. The second one was the myth of the bottom line, and that was the idea that just measuring financial numbers doesn't tell you about productivity, and just measuring financial numbers doesn't give you success. And then the third one was, that we're talking about now, is the Myth Number Three, is top-down knowledge myth. And so, I'm curious. Tell us a little bit more about what you mean by "top-down knowledge myth."   0:08:17.7 JS: Essentially it's knowledge from outside the workplace being... How do I wanna say it?   0:08:26.0 AS: Pushed down. [laughter]   0:08:28.0 JS: Pushed down, imported, or imported into the workplace, imposed into the workplace. It's really that idea that something from outside can be valid. And it certainly can, to a degree. You can have instructions on how to operate a machine. You can have all kinds of instructions that are determined from outside, but there's a limit to that kind of knowledge. And when you really wanna improve quality, it really does take a lot more input. But I think there are many... This is one of the myths I think that there are very many different sides to. And one of the sides is that what I call the... It's related to variation, but it's really what I call the "granularity problem." And it's the fact that problems are not these nice, big omnibus types of items that a manager can solve. They tend to be hundreds of problems, or thousands.   0:09:37.0 JS: And so, when you've seen transformations, for example, in hospitals, I think that's an environment we can all understand, again, it's because of many, many different improvements that they become better. One example that I was given is, let's suppose you have a medication error problem. That's really, really common in hospitals now, right? But medication error is, it's not one thing. It could be because of the label, labeling on the bottles. It could be the lighting when people are reading the medications. It could be the way they're arranged on trays. It could be the way they're stored. It could be in the supply chains. The really successful healthcare transformations have been by getting thousands of improvements. And I mean literally thousands of improvements from employees who live with those processes every day. Managers can never [chuckle] know all these hundreds and thousands of things, especially, they can't be everywhere. So, really, the answer is that you do need an army of problem solvers to really get the kind of excellence that we want.   0:10:56.0 AS: Dr. Deming had a quote that he said which was, "A system cannot understand itself." And he's talking about, you got to understand... Sometimes it takes someone from outside looking at the system. And that's different from what you're talking about, which is the idea of someone at the top of the organization saying, "I know how to do this, here's what you guys got to do, and here's how you solve it," without really working with the workers and helping understand what's really going on. And I think what you're saying in this too is the idea that people who are empowered at the work level to try to figure out what's the best way to organize this with some support from above, that's management in that sense is a supporting function to give them ideas. If there's a person that understands quality or Lean, or they understand Deming's teachings, then that outside person can also give that team resources and ideas that they may not typically have. But the idea that a senior executive could be sitting up at the top of the company and then being able to look down and say, "Here's how to do each of these areas," is just impractical.   0:12:12.3 JS: Oh, yeah. And I think Dr. Deming was... He was giving managers, I think, a very challenging task to understand systems and to know, 'cause you're responsible for the system if you're management. So, you really have to know when you have to be constantly getting feedback from people who are working in the system and trying to improve their work within the system. So, yeah, it's got to be a definite give and take. And in Lean, they call that "catchball," where there's a constant back and forth between the managers and the workers in terms of the problems they're having and what needs to be done to help them. So, yeah, it's very tuned in to each other.   0:12:55.0 AS: Yeah, and I would say, from my experience in most companies, management's not really trying to help them. Each unit's fending for itself and trying to figure it out, and they're not really getting that much support from management. And so, the idea being that with the proper support and encouragement to learn and improve, the teams that we have in our businesses can achieve amazing things. And this goes back to also to the concept of intrinsic motivation versus extrinsic. And I think what Dr. Deming, what was appealing to me about Dr. Deming when I first started learning about it, was he was talking about "unleash the intrinsic motivation of people, and you will unleash something that is just amazing." And the desire to improve is going to be far better than... And that's why sometimes he would just say, "Throw out your appraisal system," or "Throw out these things, get rid of them," because what you'll find is you're gonna unleash the passions and desires and the intrinsic motivations. And so, that's another thing I'm thinking about when I'm hearing Myth Three: The Top-down Knowledge Myth. It just, it doesn't unleash that intrinsic motivation.   0:14:16.8 JS: Well, it's interesting, this thing was really studied by the Shingo Institute, where they, they, about, as I think you may know, they give out something called the "Shingo Prize for Excellence in Manufacturing." They also give prizes for books too, which I was fortunate to receive. But they had for years been giving the Shingo Prize to excellent manufacturers leading up to 2007 or so. But they found out that most of the people that had got the Shingo Prize had essentially fallen off the ladder. So, they did a very detailed study, interviewed all kinds of organizations: Ones that had fallen off the ladders, so to speak, and ones that had actually maintained the kind of excellence that they had won their prize for.   0:15:20.5 JS: And they found that the ones that had fallen off the ladder had a top-down engineered approach, whereas the ones that had been successful were much more respectful of their people and getting a lot more feedback from the people, the sort of the respect-for-people-type idea that Toyota has. So, really, what they were saying is that the top-down approach, you might be able to fix up your factory and get really good ratings for a while and you have great processes, but in the long run it's not sustainable. So, they changed their criteria so that now, to get a Shingo Prize in manufacturing, you really have to show culture; you have to show how you're listening to your people, the whole thing. So, it's very different now.   0:16:12.0 AS: Yeah. And it's interesting, we have a company in Thailand that the company and its subsidiaries won the Japanese Deming Prize. And there was 11 companies total in this group that won the prize at different years as they implemented throughout the whole organization. And then a couple years later, the CEO resigned. He retired; he reached the end of his time. And the new CEO came in. He wasn't so turned on by the teachings of Dr. Deming, and he saw a new way of doing things. And so, he basically dumped all that.   0:16:57.0 JS: Oh, really?   0:16:57.8 AS: And it's tragic. It's a tragic story. And the lesson that I learned from that is, one of the strengths of a family business is the ability to try to build that constitution or that commitment to "What do we stand for?" Whereas in a publicly listed business where you're getting turnover of CEOs every four, six years, or whatever, in just the case of Starbucks recently, we just saw turnover happen very, very quickly. And the new CEO could go a completely different direction. And so, when I talk to people about Deming's teachings, I say that family businesses have a competitive advantage in implementing it. And I think Toyota is the ultimate family business in Asia, right?   0:17:50.9 JS: Yeah. Yeah. Yeah, yeah, pride in the family name, and that's... Yeah, and a lot of the interviews I did were businesses like that, where there was a desire to do more than make money, to have a purpose, sustain the family name and that kind of thing. So, yeah, for sure.   0:18:10.0 AS: So, let's wrap this up with you giving us a final recap of what we need to be thinking about when it comes to the Myth Number Three: The Top-down Knowledge Myth.   0:18:24.0 JS: Okay. Well, I think essentially people need to understand that there are limits to what a manager can actually know. And I think the healthcare example, this illustrates that very well. I think they also need to understand that what you ultimately want if you wanna maximize productivity is team productivity. It's the productivity of the group. And people are motivated. You were talking about intrinsic motivation. Part of that comes from actually working together as a team. So, you need to create the kind of trust where information flows freely, and where somebody doesn't hoard their own knowledge but is willing to share it with others, because they don't feel they're in competition with each other. So, again, that's related to driving out fear. So, everything's really interrelated. But I think we have to accept knowledge as something part of a shared collaborative work environment, where everybody wins if knowledge flows freely. And people have to be willing to admit that what they've learned in the past, what they've learned in school has limits in how it can be applicable. And those limits have to be respected. And you have to be willing to listen to every employee, not just the ones that have degrees.   0:20:00.8 AS: All right. Well, that's a great recap. And, Jacob, on behalf of everyone at the Deming Institute, I wanna thank you again for this discussion. And for listeners, remember to go to deming.org to continue your journey. And you can find Jacob's book, Productivity Reimagined at jacobstoller.com. This is your host, Andrew Stotz, and I'll leave you with one of my favorite quotes from Dr. Deming: "People are entitled to joy in work."

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