Bill Bellows, who has spent 30 years applying Dr. Deming's ideas, discusses testing for understanding transformation with Andrew. They talk about little tests to measure learning impact and share amusing anecdotes. Topics include effective questioning, incentives in motivation, and fostering comprehension during transformation processes.

Bill Bellows shares his experience of implementing Dr. Deming's ideas with a small group at a large company. They discuss transparency, decision-making, and the importance of open communication to drive organizational change.

Can a 4th grade class decide on an operational definition of "joy in learning"? In part 4 of this series, educator John Dues and host Andrew Stotz discuss a real-world example of applying Deming in a classroom. This episode covers the first part of the story, with more to come in future episodes! 0:00:02.3 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 John Dues, who is part of the new generation of educators striving to apply Dr. Deming's principles to unleash student joy in learning. This is episode four about goal setting through a Deming lens. John, take it away.   0:00:22.6 John Dues: Good to be back, Andrew. Yeah, we've been talking about organizational goal setting last few episodes. A couple episodes ago, we talked about those four conditions that organizations should understand prior to setting a goal. Then we sort of introduced this idea of trying to stay away from arbitrary and capricious education goals. And then we got into these 10 lessons for data analysis. And so what I thought we could do now is we've got that foundation in place is that we could take a look at an applied example in real classrooms of those 10 key lessons in action to kind of bring those alive. And I ran this project a few years ago with a teacher named Jessica Cutler. She's a fourth grade science teacher in our network. And she was going through something we call a Continual Improvement Fellowship. So we do this sort of internal fellowship where people can learn that sort of way of thinking, the tools, techniques, the theories related to the science of improvement. And then they actually take that right away and apply it to a problem in their classroom or their department or their school, depending on who it is.   0:01:55.0 JD: And so what Jessica was doing, what her project ended up being was she was trying to improve the joy in learning in her fourth grade science class. So it's interesting to see how that sort of project evolved. So I thought we could revisit each of the 10 lessons and how that lesson was applied in Jessica's improvement project. And we'll maybe get through three or four of the lessons today. And then over the course of the next few episodes, kind of get to all 10 lessons and think through how they were... How that went in her improvement project.   0:02:08.1 AS: Sounds like a good plan, practical application.   0:02:12.0 JD: Yeah. I mean, it was interesting too, because she didn't initially sort of consider joy as a possibility. She was thinking like, I'm gonna work on improving test scores or something like that was sort of her initial brainstorm. And then sort of pivoted to this when we kind of talked through what was possible from the Deming philosophy type of standpoint. So it's interesting to see how things evolve. But just to kind of revisit, so we talked through these 10 lessons. Lesson one was "data has no meaning apart from their context." So we talked about these questions that are important, like who collected the data? How was it collected? When was it collected? Where was the data collected? What are the values themselves represent? What's that operational definition for the concept under measurement? Have there been any changes to that operational definition as the project unfolds? And so even with a project with a teacher and her students, all of those questions are relevant. They're still important just because you're dealing with students that doesn't mean anything changes on that front. So it was important for her to sort of think through all of those things as she thought through the start of her project.   0:03:28.9 JD: And what her and her students came up with after they sort of decided that they were gonna focus on joy, they focused on this problem statement. And they were like, well, what do we want science class to look like? 'Cause that was sort of their starting point. And what her and her students...Oh sorry go ahead.   0:03:45.9 AS: One thing you started off talking about her, now you're talking about her students. So she got her students involved in this process. Is that what you're saying?   0:03:56.2 JD: Yeah. So they were working together from the very outset even...   0:04:02.0 AS: As opposed to a teacher talking through this with a principal or something in a faculty room and then thinking of how do I... Okay.   0:04:09.2 JD: Yep. That's right. Yeah. And so what they came up with is the sort of desired future state of science classes. "We are able to stay focused through science, enjoy science class and remain engaged." And so to give some context, what was happening is that she taught science and social studies and it was sort of like a back-to-back class period. And they would do science second. And so by the time they were doing science, sometimes the students were getting off task, disengaged. They weren't as engaged as either the students wanted to be or the teacher wanted them to be in that second lesson. So they, they came up with that as the thing they were gonna focus on. And then because they were gonna focus on joy in learning, they had to define what that meant. So what did joy in learning mean to that fourth grade science class? And what they came up with as a definition, which I really like, is "we wanna have fun learning, finding things we like to learn and have fun completing classwork and activities." So they came up with this operational definition. And keep in mind, these are fourth graders and Jessica's having these conversations like, what's the operational definition? That's not probably typical language you're gonna use with fourth graders. But if you walk them through these things, they actually pick up on it pretty quickly.   0:05:26.1 JD: It's actually pretty cool to see.   0:05:28.1 AS: And to them, a more simpler word sounds like was fun.   0:05:35.0 JD: Yeah, right. They wanted that to be a part of the science learning process. So basically, once they had the operational definition, they had to think through, well, how are we going to measure that concept that we've defined? And what they did was they just developed a simple survey. Jessica did it in Google Forms. just had, really just had two questions. The first question was, on a scale of 1 to 10, how much did you enjoy science class today? And then there was a second open-ended question that said, what made you enjoy or not enjoy class today? So it was fresh in the kids' minds. So basically, at the end, each kid has a Chromebook in Jessica's science class. She would just sort of share the link to the survey, and the kids would complete that as the closing activity for the lesson. So she would get two things out of it. So 1 to 10, just a real quick sort of numerical quantified value, how much the kids enjoyed science class that day. And then, because it had just happened, the students could say what they did and didn't like about the lesson. Oh, we haven't used computers in a few days. Or it'd be nice if I had a video to help bring this concept alive. Or there's a few words that you use that I don't know the definitions to. Could you add those definitions to the glossary? So just things like that, simple things like that.   0:06:55.9 JD: Right away. And then what Jessica could then do is take that information and actually adjust her lessons as she planned maybe for the next week, she could make those adjustments based on this feedback she was getting from the students. So that's sort of the application of lesson number one. So what are we measuring? How are we gonna measure it? When are we collecting this data? That type of thing. Lesson two, if you remember back from when we covered the lessons was "we don't manage or control the data. The data is the voice of the process," right?   0:07:28.9 JD: So we talked about this ideas that while we don't control the data, we do manage the system and the processes from which the data come, right? So, and this is really key conception of the system's view. You, you say you're going to improve this particular classroom. So that's the system. So you're not necessarily controlling the data. You're not controlling how the kids are evaluating, the numbers that they're putting one through 10 to assess joy in learning, but what the teacher and then the students, because of this project do have control over are the learning processes that are happening throughout science class, right? And so back to your point about you switch from talking about Jessica, the teacher to the students. And then you said "we" that's also a key conception of taking this approach, right?   0:08:24.4 JD: So what I think Deming would say is that when you're going to improve an organization, you have to sort of combine sort of three critical pieces. One thing is you need someone from the outside, from outside the system that has Profound Knowledge. And then that person or persons has to be collaborating with the people working in the system. So those are the students, they're working in the science class system. And, then you that third group or that third person is the manager or managers have that have the authority to work on the system. So in this case, Jessica has the authority to change what's happening in her science class.   0:09:10.2 JD: The students are the workers working in that science system. And then that third part is that person that has the sort of understanding of the System of Profound Knowledge and it's sort of bringing all of these parts together that really is how you begin to transition sort of conventional classrooms to those guided by the Deming quality learning principles, right?   0:09:33.1 JD: So in in the case of Jessica's project, that person that was, that had a System of Profound Knowledge lens was me. So I was sort of acting as an, the outsider, 'cause I'm outside of the science system. But I have this understanding of the System of Profound Knowledge. And I'm working with Jessica as she's working with her students, to sort of bring that lens to the projects.   0:10:00.4 AS: And what's the point of doing all that if she doesn't have the ability to make the changes necessary to test, if you're gonna if we change this, it's gonna result in something why go and do all this if you're just stuck in a system that you simply cannot change because of government regulation or whatever, maybe.   0:10:17.5 JD: Right. Yeah. So it's bringing all those pieces together. But what I found thinking about the three parts of a team that's working toward organizational improvement, what I've found in the past is, in my experience, whether it's a school improvement team or a district based improvement team, most of them are devoid of at least one parts of one of those components, usually two of those components, 'cause usually students aren't involved.   0:10:45.9 JD: And then in most school systems, there's no one with this outside knowledge, the System of Profound Knowledge lens, right. And I think it's what we're really doing is the students can identify the waste, the inefficiency, the things that aren't going well from their perspective, but we don't often ask them. Or if we do, we do it in a way where it's an end of year survey or an end of semester survey, but this is collecting that feedback in real time and then acting on it. We're not planning to do something next year with this feedback, we're actually planning to do something the next day, or maybe the next week, to adjust the science lessons.   0:11:24.8 AS: And it's one of those two things that come into my mind, what, how do you handle the idea that what's causing the impact on joy in learning could be that the student had a bad night, the night before. And I guess by doing many samples that starts to kind of wash out. And then the other question is since the students know that the teacher could likely make an adjustment, is there any possibility that they could be gaming or playing the system.   0:11:57.1 JD: Well, that's interesting, because I think, well on the first point. I think pretty quickly, my experience with this and David Langford I know you've talked to has echoed this sentiment is you know, he was working with high school students, this is an elementary project but either way. You may get some students that don't take this seriously. At first. And you may get some kind of crazy answers crazy brainstorms or crazy survey submissions, although I don't think Jessica got much of that.   0:12:30.2 JD: But in other projects I've gotten some stuff at the outset that was a little bit off the wall. But like David said to me when I first started this and then it's been my experience since is that kids, once they realize that you're actually gonna act on the feedback, as long as the feedback is in good faith. They actually start to take it seriously, pretty, pretty quickly. And so I think pretty quickly, those sort of types of worries go by the wayside. Now, I will say I did say that the...   0:13:01.1 JD: One of the components that has to be on this improvement team is the person that has the authority to change the system. So at the end of the day, even though we're gathering this input, Jessica's really the person as the teacher of that classroom that has the authority to make the changes to the system based on her judgment or, her professional judgment as a science teacher of what should happen. And so the students certainly offer feedback and inform that process, but ultimately it's Jessica that's gonna determine the changes to the system.   0:13:34.0 AS: I hear David in my ears saying, you know what, Andrew? You don't trust the students? They probably have a more honest, view of what's going on than most adults do. So yes, I hear the voice of David Langford.   0:13:49.2 JD: Yeah. Well, and interestingly, and we'll get into this towards the end, not today, but when we get to some of the other lessons, interestingly, not to give away the story, but, one of the things that was getting kids off track was a lot of noise during class, kids making noises. And they actually came up with this system where they were kind of penalizing each other. This was their own idea. And so, kids know exactly what's going on in class. And so it was interesting to see how they came up with some ideas to rectify that. But yeah, so it was really just bringing together, these three groups or, the group of students and then Jessica and then myself. It's that combination that's really where the power for improvement lies. And again, I, that type of partnership is just not typical in school improvement situations.   0:14:45.7 JD: So that's lesson two, applied. Lesson number three is "plot the dots for any data that incurs in time order." Right? So we've talked about this a lot. The idea behind the primary point of "plot the dots" is that plotting data over time helps us understand variation, and that in turn leads us to take more appropriate action. I think that what we decided to do with Jessica's project is, start plotting the points on a run chart and connect those points with a line, and then it becomes pretty intuitive as we're looking at that data, what joy in learning looks like in this science class. And then once we have enough data, we can turn that run part chart into a process behavior chart and actually add the limits.   0:15:40.2 JD: So, like I said, Jessica, once her and the class determined that what they were going to improve was join in learning, and they defined that concept operationally and created the survey, right away they started gathering this survey data as a part of the project, and usually they would gather the data maybe, two or three times a week across the course of this particular improvement project. So maybe I'll share my screen just so you can see what that initial run chart looked like. So, you have this run chart, and I left this in the spreadsheet so you could see the actual data. So as she began administering these surveys, she would send me the data and then I would create it the run chart for her, start plotting that data so that both of us could sort of see the variation in that survey data over time. And then she could actually take this, she would put this run chart on a slide, and every week or so she would actually show the students what the data looked like.   0:16:49.7 AS: And just to be clear, we've got a chart for those that are listening, we've got a chart that has a blue line and it's going up and down kind of around the level of about 79. So they've got points that are based, that are days. Some days are below that 79 some days are above. But also I'm assuming that those points are the output of all the surveys. So the average answer on that day from the survey as different from the average or median of all the day's output, correct?   0:17:31.1 JD: Yeah, that's right. So this is, the run chart from Jessica's class that's displaying the survey results. And what they're measuring is joy in science class as assessed by the students.   0:17:44.3 AS: On the first day, the students basically said, 75% of the respondents said that they had joy in science.   0:17:51.9 JD: That's right. So in this particular school year, which was two years ago, so we had done some of the project planning before kids went on winter break, and then when they came back from winter break, they were ready to start administering the survey. And we started plotting the dots, charting the data over time. So the X axis for those who are listening are the dates.   0:18:16.2 JD: The, Y axis is the joy in learning, percent of kids that the rating of the kids from one to 10. And then I just turn it into a percent. And so you have the green line, the central line running through is the median. We're using the median 'cause that's fairly typical for a run chart because typically run charts don't have as much data as a process behavior chart. And so, outliers can have a greater impact. So we're using the median to sort of control for that. Although this data's fairly tight. So on day one, like you said on January 4th of this school year 75, the kids sort of rated the joy in learning of that particular lesson as a 75% of 100. And then you sort of see it bounce around.   0:19:04.7 JD: That median of 79. And so what I'm showing is the data from the first 10 surveys that Jessica administered at the end of class. So over the course of 20 days from January 4th through January 24th, she administered that this survey 10 different times. So about two to three times a week. And so we see a high of about 83% joy in learning and a low of 67% joy in learning. And you have about half the points above the median, about half the points below the median. So even though it's only 10 data points, Jessica and her class, and then myself, we were starting to learn about what did joy in learning, joy in science class actually look like? Now that we have this definition and we're measuring it with these surveys and then plotting these data points. So again, she's actually putting this up on, on the, up on the screen so kids can actually see this. And what she said was after the 5th or 6th survey, and she's plotted this and put this up on a screen a few times, the kids are actually getting excited. And they're wanting to see their data. They're wanting to see what the results look like for each survey as she started plotting this.   0:20:30.6 AS: It's funny because I, when I was a loading supervisor at Pepsi, I started putting up the percent correct for each of the loaders in the warehouse. And I didn't make any comment or anything, I just put it up there. And yeah, people are interested when they start seeing numbers, they start thinking, they start asking questions.   0:20:54.3 JD: Yeah, and you can see too at a school, in a fourth grade science classroom, you can see all types of lessons, you can sort of build up this reading graphs, calculating percentages, using when do you use line graphs for some other type of graph?   0:21:10.2 AS: And why use median versus mean? Because a small amount of data could be distorted if you have a huge outlier.   0:21:18.8 JD: Yep, all kinds of practical lessons. So this brings us to sort of the last lesson for this particular episode. I think lesson four is two or three data points are not a trend, right? So, we've said that you should start plotting the dots as soon as you've decided to collect some type of data that occurs over time. And really when people ask me what type of data can you put on a run chart or a process behavior chart, there's almost any data that you're interested in improving in schools unfolds over time, almost all of it. Whether that's a daily cadence, a weekly cadence, monthly, quarterly, yearly, whatever it is, right? But the problem is the vast majority of data that we look at as educators and really probably most people, it's typically two or maybe three data points. But that doesn't tell you anything about how the data is varying naturally. So when we start thinking about this particular data, we start learning quite a bit. For one, as a teacher, I would have no idea how my kids would evaluate their joy in my classroom.   0:22:29.9 JD: And so I think if I was Jessica, I'd be pretty happy off of that, that the sort of average or the median rating is close to 80%, basically the rating each lesson has an eight out of 10. Right. I think a second thing is let's say we were a school district and we did systematically give our kids some type of survey that assess their satisfaction with the school. Right. Maybe they do it twice a year or annually. Right. And so after at the end of the year, you have two data points, but you don't really have any idea for what to do with that data. You have no idea if you collected three or four or five data points, what that would look like. And here she is in just 20 calendar days and a couple of school weeks. She's got 10 data points to work with already. So she's building that baseline of data. So I think what this is to me is just a very different approach to school improvement.   0:23:39.4 JD: And the tools are relatively simple. The ideas are relatively simple. But I think overall this really, the takeaway I want for folks is that this project really illustrates a very different approach to school improvement, guided by these sound sort of Deming principles for how to use data, to how to understand variation, to how to include the people working in the system, right?. We've talked about these arbitrary targets throughout this series, and you could see that when Jessica and her class would go to maybe set a goal for joy after collecting some of this data, that goal would be tied to something real. It's tied to actual data from the classroom. And you can sort of avoid goal setting as an act of desperation when you take this type of approach.   0:24:38.4 AS: Joy in the joy of bringing joy in science.   0:24:44.2 JD: Yeah, it's really all about this process, right? It's the kids getting into this process, that's the psychological part. They're involved in their educational process. And so that is completely different than what's happening in the typical classroom, I think, in the United States.   0:25:00.5 AS: You can imagine somebody not wanting to do this because they're afraid of what they're going to see.   0:25:07.0 JD: Certainly [laughter]   0:25:08.3 AS: Yeah.   0:25:08.7 JD: Certainly. Yeah. Hopefully they would be open to sort of collecting the data and being reflective as a professional. But I could see, maybe that's not, tha's not always the case. And another question, I kind of shared this project with some folks, in different settings, and one of the questions I typically get is, well, what about the science test scores? Like, this is great if kids have joy, I guess is kind of the reaction. But what... How does that impact the academics?   0:25:44.0 JD: And my response is, well if kids don't find joy in their learning and they're not engaged, what kind of results are you gonna get? [laughter] To me this is sort of like a part of the process that leads to academic outcomes, when you enjoy the things that you're doing, when you feel like you have some control over a process, maybe not the full control, but when you have some control, when you have input into something that you're doing all day long, you're gonna have more investment because, you know, because you're seeing that your input has meaning. That's really that psychological component.   0:26:18.2 AS: It's obvious, but maybe not proven.   0:26:21.9 JD: Yeah, I think so. I think so.   0:26:28.4 AS: Okay.   [SILENCE]   0:26:30.9 JD: Yeah. I think that's a pretty good spot to wrap up this opener with the... We covered those first four lessons and started to look at how this project unfolded in, Jessica's classroom. And I think, next we can kind of see as she gathered more data, what this looked like over time. And then as she sort of had that baseline in place, then the next thing we'll look at is: what did she do as a change idea or an intervention to try to make these rates go higher in her classroom?   0:27:08.4 AS: That's interesting. I mean, in my wrap up of this, I think how lucky, is Jessica to have someone from the outside? I think a lot of teachers and a lot of people in business, they don't really have anybody to go to. And the company's not providing that type of stuff or the school is not providing that. And so you just kind of make it up as you go along. And I think that's, that's one of the things, 'cause I'm, I think like probably other listeners and viewers who are, listening to this, they're thinking, I wonder if John could help me do that in my area? The idea of, we all know there's places that we could improve that we may not be. And if a school system can provide that, wow, that's a big... That's exciting.   0:27:56.6 JD: Yeah. I'd be happy to. And it was like a, it was definitely a mutual effort. Jessica put a lot of work into sort of, 'cause she has gone through that fellowship, she had to sort of learn all of these tools and then actually, turn around and put them into practice in her classroom. And she found ways to do this in a way where, she could still do the things she was required to do, like delivering the lessons that she was required to deliver and those types of things. But then she found ways to sort of incorporate what she learned in the fellowship to make her classroom better. Seeing that, seeing her openness to feedback that really made this like a, I think a, mutually beneficial experience. And I think the kids enjoyed it too.   0:28:39.2 AS: And the purpose of this series too is, the idea of how can you do this at home and how can you start doing it in your own school, in your own classroom, in your own life? And so I think I'm looking forward to the next session where we're gonna go deeper into... I've already got, a series of questions and things that I'm wondering, and then I saw some tabs in your, in your worksheet that I thought, okay, there's gonna be some more interesting stuff. So I think we're all gonna see you in that next section.   0:29:09.9 AS: And on behalf of everyone at the Deming Institute, I want to thank you again for this discussion and, taking the time to go through these steps with us. And for listeners, remember to go to deming.org to continue your journey. You can find John's book Win-Win, W Edwards Deming, the System of Profound Knowledge and the Science of Improving Schools on amazon.com. This is your host, Andrew Stotz. And I'll leave you with one of my favorite quotes from Dr. Deming, and it's particularly apropos, people are entitled to joy in work.

Dr. Deming developed his philosophy over time and in conversations with others, not in isolation. As learners, we tend to forget that context, but it's important to remember because no one implements Deming in isolation, either. In this conversation, Bill Bellows and host Andrew Stotz discuss how there's no such thing as a purely Deming organization and why that's good. 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 discussions with Bill Bellows, who has spent 30 years helping people apply Dr. Deming's ideas to become aware of how their thinking is holding them back from their biggest opportunities. Today is episode 20, entitled, System of Profound Wisdom. Bill, take it Away.   0:00:31.6 Bill Bellows: But not just for 30 years. I forgot to say I started when I was 12.   0:00:36.6 AS: Yes. [laughter] Yes. And you've got the hair to prove it.   [laughter]   0:00:43.7 BB: All right. Now, actually, I was thinking the proposal and the title, I thought... I mean, System of Profound Wisdom is cool, System of Profound Questions. Either one of those is good. Let's see which title comes out.   0:00:57.6 AS: Yeah. And I think we'll have to also understand that may some listeners that may not even know what System of Profound Knowledge means, they've been listening. They do. But if today's their first episode, we also gotta break that down, just briefly.   0:01:10.9 BB: Yeah. Okay, let's do that. All right. Well, let me give an opening a quote from Dr. Deming from chapter three, and then we can explain this SoPK, System of Profound Knowledge, thing. But in chapter three of Dr. Deming's last book, The New Economics, the last edition, edition three, came out in 2018. And chapter three, Dr. Deming says, "We saw in the last chapter, we are living under the tyranny of the prevailing style of management. Most people imagine that this style has always existed. It is a fixture. Actually, it is a modern invention a trap that has led us into decline. Transformation is required. Education and government, along with industry, are also in need of transformation. The System of Profound Knowledge to be introduced in the next chapter is a theory for transformation." So you wanna...   0:02:15.4 AS: That's good.   0:02:16.7 BB: So let's say something. Let's just say something about SoPK. How would you explain that?   0:02:23.1 AS: Yeah. Well, actually, I wanna talk very briefly about what you just said, because it's just...   0:02:27.1 BB: Oh, sure.   0:02:29.6 AS: At one point, I thought, "It's a system of knowledge." But he just said it was a system of transformation.   0:02:38.7 BB: It's a theory for transformation.   0:02:40.1 AS: A theory for transformation. Okay, got it. I see. And one of the things that I... I look at Toyota so much just 'cause it's so fascinating and how they've survived all these years, the continuity in the business, the continuity and the profitability of the business, the continued march to become the number one auto producer in the world, and having faced all the ups and downs and survived. And I just think that what they have is a learning organization. No matter what the challenge is, they're trying to apply learning tools, like System of Profound Knowledge, like PDSA, to try to figure out how to solve this problem. And I think that many companies, including at times my companies, [chuckle] we sometimes will scramble and we'll lose knowledge and we won't gain knowledge. And so the System of Profound Knowledge, to me, is all about the idea of how do we build a base of knowledge in our business and then build upon that base of knowledge rather than destroy it when the new management comes in or when a new management idea comes in.   0:04:00.7 AS: And that's something I've just been thinking about a lot. Because I do know a company that I've been doing some work with, and they basically threw away a huge amount of work that they did on System of Profound Knowledge and stuff to go with the prevailing system of management, is like going back. And now, they just produced a loss in the first quarter, and I just think, "Interesting. Interesting."   0:04:27.6 BB: Well, a couple things come to mind based on what you said. One is I would propose that Toyota, I'm in agreement of "Toyota's a learning organization." And that'll come up later. I've got some other thoughts on learning organizations. And we know that they were influenced by Dr. Deming. To what degree, I'm not sure of. Shoichiro Toyoda, who is one of the sons of the founder of the Toyota Motor Car Company, was honored with a Deming prize in 1990. And I believe it came from JUSE, as opposed to the American Society for Quality. One or the other. He was honored with a Deming Prize.   0:05:32.0 AS: Yep.   0:05:33.5 BB: Again, I don't know if it's Deming Prize or Deming Medal. But I know he was honored. What's most important, the point I wanna make is, upon receiving it he said, "There is not a day that goes by that I don't think about the impact of Dr. Deming on Toyota." But, if I was to look at the Toyota Production System website, Toyota's Toyota Production System website, which I've done numerous times, I'd be hard-pressed to find anything on that page that I could say, "You see this word, Andrew? You see this sentence, Andrew? You see this sentiment? That's Deming." Not at all. Not at all. It's Taiichi Ohno. It's Shigeo Shingo. I'm not saying it's not good, but all those ideas predate Deming going to Japan in 1950. Taiichi Ohno joined Toyota right out of college as an industrial engineer in 1933, I believe. The Japanese Army, I mentioned in a previous episode, in 1942, wanted him to move from Toyota's loom works for making cloth to their automobile works for making Jeeps. This comes from a book that I would highly recommend. Last time we were talking about books. I wanted to read a book, I don't know, maybe 10 years ago. I wanted to read a book about Toyota, but not one written by someone at MIT or university. I didn't wanna read a book written by an academic. I've done that.   0:07:15.1 BB: I wanted to read a book by somebody inside Toyota, get that perspective, that viewpoint. And the book, Against All Odds, the... Wait I'll get the complete title. Against All Odds: The Story of the Toyota Motor Corporation and the Family That Changed it. The first author, Yukiyasu Togo, T-O-G-O, and William Wartman. I have a friend who worked there. Worked... Let me back up. [chuckle] Togo, Mr. Togo, born and raised in Japan, worked for Toyota in Japan, came to the States in the '60s and opened the doors to Toyota Motors, USA. So, he was the first person running that operation in Los Angeles. And it was here for years. I think it's now in Texas. My late friend, Bill Cummings, worked there in marketing. And my friend, Bill, was part of the team that was working on a proposal for a Lexus. And he has amazing stories of Togo. He said, "Any executive... " And I don't know how high that... What range, from factory manager, VPs. But he said the executives there had their use, free use, they had a company car. And he said Togo drove a Celica. Not a Celica. He drove a... What's their base model? Not a...   0:08:56.2 AS: A Corolla?   0:08:57.7 BB: Corolla. Yes, yes, yes. Thank you. He drove a Corolla. He didn't drive... And I said, "Why did he drive a Corolla?" Because it was their biggest selling car, and he wanted to know what most people were experiencing. He could have been driving the highest level cars they had at the time. Again, this is before a Lexus. And so in this book, it talks about the history of Toyota, Taiichi Ohno coming in, Shigeo Shingo's contributions, and the influence of Dr. Deming. And there's a really fascinating account how in 1950, a young manager, Shoichiro Toyoda, was confronted with a challenge that they couldn't repair the cars as fast as they could sell them. This is post-war Japan. They found a car with phenomenal market success. Prior to that, they were trying to sell taxicabs, 'cause people could not... I mean, buying a car as a family was not an option. But by 1950, it was beginning to be the case. And the challenge that Shoichiro Toyoda faced was improving the quality, 'cause they couldn't fix them as fast as they could sell them. And yet, so I have no doubt that that young manager, who would go on to become the chairman, whatever the titles are, no doubt he was influenced by Dr. Deming. But I don't know what that means.   0:10:23.4 BB: That does not... The Toyota Production System is not Deming. And that's as evidenced by this talk about eliminating waste. And those are not Deming concepts. But I believe, back to your point, that his work helped create a foundation for learning. But I would also propose, Andrew, that everything I've read and studied quite a bit about the Toyota Production System, Lean, The Machine That Changed The World, nothing in there explains reliability. To me, reliability is how parts come together, work together. 'Cause as we've talked, a bunch of parts that meet print and meet print all over the place could have different levels of reliability, because meeting requirements, as we've talked in earlier episodes, ain't all it's cracked up to be. So I firmly believe... And I also mentioned to you, I sat for 14 hours flying home from Japan with a young engineer who worked for Toyota, and they do manage variation as Dr. Taguchi proposed. That is not revealed. But there's definitely something going on. But I would also say that I think the trouble they ran into was trying to be the number one car maker, and now they're back to the model of, "If we are good at what we do, then that will follow."   0:11:56.8 BB: And I'm gonna talk later about Tom Johnson's book, just to reinforce that, 'cause Tom, a former professor of management at Portland State University, has visited Toyota plants numerous times back before people found out how popular it was. But what I want to get into is... What we've been talking about the last couple episodes is Dr. Deming uses this term, transformation. And as I shared an article last time by John Kotter, the classic leadership professor, former, he's retired, at the University... Oh, sorry, Harvard Business School. And what he's talking about for transformation is, I don't think, [chuckle] maybe a little bit of crossover with what Dr. Deming is talking about. What we talked about last time is, Deming's transformation is a personal thing that we hear the world differently, see the world differently. We ask different questions. And that's not what Kotter is talking about. And it's not to dismiss all that what Kotter is talking about, but just because we're talking about transformation doesn't mean we mean the same thing.   0:13:10.6 BB: And likewise, we can talk about a Deming organization and a non-Deming organization. What teamwork means in both is different. In a Deming organization, we understand performance is caused by the system, not the workers taken individually. And as a result of that, we're not going to see performance appraisals, which are measures of individuals. Whereas in a non-Deming organization, we're going to see performance appraisals, KPIs flow down to individuals. [chuckle] The other thing I had in my notes is, are there really two types of organizations? No, that's just a model. [chuckle] So, really, it's a continuum of organizations. And going back to George Box, all models are wrong, some are useful. But we talked earlier, you mentioned the learning organization. Well, I'm sure, Andrew, that we have both worked in non-Deming organizations, and we have seen, and we have seen people as learners in a non-Deming organization, but what are they learning? [chuckle] It could be learning to tell the boss what they want to hear. They could be learning to hide information that could cause pain. [chuckle] Those organizations are filled with learners, but it's about learning that makes things worse. It's like digging the pit deeper. What Deming is talking about is learning that improves how the organization operates, and as a result, improves profit. In a non-Deming organization, that learning is actually destroying profit.   0:14:51.8 BB: All right. And early, spoke... Russ, Russ and Dr. Deming spoke for about three hours in 1992. It got condensed down to a volume 21 of The Deming Library, for which our viewers, if you're a subscriber to DemingNEXT, you can watch it in its entirety. All the Deming videos produced by Clare Crawford-Mason are in that. You can see excerpts of volume 21, which is... Believe is theory of a system of education, and it's Russ Ackoff and Dr. Deming for a half hour. So you can find excerpts of that on The Deming Institute's YouTube channel.   0:15:37.0 BB: And what I wanted to bring up is in there, Russ explains to Dr. Deming the DIKUW model that we've spoken about in previous episodes, where D is data. That's raw numbers, Russ would say. I is information. When we turn those raw numbers into distances and times and weights, Russ would say that information is what the newspaper writer writes, who did what to whom. Knowledge, the K, could be someone's explanation as to how these things happened. U, understanding. Understanding is when you step back and look at the container. Russ would say that knowledge, knowledge is what you're using in developing to take apart a car or to take apart a washing machine and see how all these things work together. But understanding is needed to explain why the driver sits on the left versus the right, why the car is designed for a family of four, why the washing machine is designed for a factor of four. That's not inside it. That's the understanding looking outward piece that Russ would also refer to as synthesis. And then the W, that's the wisdom piece. What do I do with all this stuff? And what Russ is talking about is part of wisdom is doing the right things right. So, I wanted to touch upon in this episode is why did Dr. Deming refer to his system as the System of Profound Knowledge? Why not the System of Profound Understanding? Why not the System of Profound Wisdom? And I think, had he lived longer, maybe he would have expanded. Maybe he would have had...   0:17:28.4 BB: And I think that's the case. I think it's... 'Cause I just think... And this is what's so interesting, is, if you look at Dr. Deming's work in isolation and not go off and look at other's work, such as Tom Johnson or Russ, you can start asking questions like this.   0:17:45.7 AS: One thing I was going to interject is that I took my first Deming seminar in 1989, I believe, or 1990. And then I took my second one with Dr. Deming in 1992. And then soon after that, I moved to Thailand and kind of went into a different life, teaching finance and then working in the stock market. And then we set up our factory here for coffee business. But it wasn't until another 10 years, maybe 15 years, that I reignited my flame for what Dr. Deming was doing. And that's when I wrote my book about Transform Your Business with Dr. Deming's 14 Points. And what I, so, I was revisiting the material that had impacted me so much. And I found this new topic called System of Profound Knowledge. I never heard of that. And I realized that, it really fully fledged came out in 1993, The New Economics, which I didn't get. I only had Out of the Crisis.   0:18:49.9 BB: '93.   0:18:49.9 AS: Yeah. And so that just was fascinating to go back to what was already, the oldest teacher I ever had in my life at '92, leave it, come back 10, 15 years later and find out, wait a minute, he added on even more in his final book.   0:19:10.4 BB: Well, Joyce Orsini, who was recruited by Fordham University at the encouragement of Dr. Deming, or the suggestion of Dr. Deming to lead their Deming Scholars MBA program in 1990. Professor Marta Mooney, professor of accounting, who I had the great fortune of meeting several times, was very inspired by Dr. Deming's work. And was able to get his permission to have an MBA program in his name called the Deming Scholars MBA program. And when she asked him for a recommendation, "Who should lead this program?" It was Joyce Orsini, who at the time I think was a vice president at a bank in New York. I'm not sure, possibly in human resources, but I know she was in New York as a vice president.   0:20:10.0 BB: And I believe she had finished her PhD under Dr. Deming at NYU by that time. And the reason I bring up Joyce's name, I met her after Dr. Deming had died. Nancy Mann, who is running a company called Quality Enhancement Seminars with, a, at the beginning one product, Dr. Deming's 4-Day seminar, when Dr. Deming died, and I had mentioned, I was at his last seminar in December '93, she continued offering 4-day seminars. And I met her later that year when she was paired with Ron Moen and they were together presenting it, and others were paired presenting it. And at one point, as I got to know Joyce, she said, "His last five years were borrowed time." I said, "What do you mean?" She said, "He started working on the book in 19'" evidently the '87, '88 timeframe, he started to articulate these words, Profound Knowledge.   0:21:11.0 BB: And I know he had, on a regular basis, he had dinner engagements with friends including Claire Crawford-Mason and her husband. And Claire has some amazing stories of Deming coming by with these ideas. And she said, once she said, "What is this?" And he is, she took out a napkin, a discretely, wrote down the, "an understanding of the difference between intrinsic motivation and extrinsic motivation. Difference between understanding special causes versus common causes." And she just wrote all this stuff down, typed it up. When he showed up the next week, she greeted him at the door and said, and she said, he said, This is Claire. And Claire said, he said, "What's that?" He says, "Well, I took notes last week."   0:21:54.2 BB: And he says, "I can do better." [chuckle] And so week by week by week. And as he interacted with the people around him, he whittled it down. And I'm guessing it put it into some, there's a technique for grouping things, you, where on post-it notes and you come up with four categories and these things all go over here. There's one of the elements of that, one of the 16 had to, or 18 or so, had to do with Dr. Taguchi's loss function. So that could have gone into the, maybe the variation piece, maybe the systems piece. But Joyce said, basically he was frustrated that the 14 Points were essentially kind of a cookbook where you saw things like, "cease dependence on inspection" interpreted as "get rid of the inspectors." And so he knew and I’d say, guided by his own production of a system mindset, he knew that what he was articulating and the feedback were inconsistent.   0:23:01.9 BB: And I've gotta keep trying. And she said, "His last five years on borrowed time as he was dying of cancer, was just trying to get this message out." So I first got exposed to it 19, spring of '90 when I saw him speaking in Connecticut. And I was all about Taguchi expecting him to, I didn't know what to expect, but I knew what I was seeing and hearing from Dr. Taguchi when I heard Dr. Deming talk about Red Beads. I don't know anything about that, common cause and special cause, I didn't know anything about that. And so for me, it was just a bunch of stuff, and I just tucked it away. But when the book came out in '93, then it really made sense. But I just had to see a lot of the prevailing style of management in the role I had as an improvement specialist, become, [chuckle] a firefighter or a fireman helping people out.   0:24:01.5 AS: I noticed as I've gotten older that, I do start to connect the pieces together of various disciplines and various bits of knowledge to realize, so for instance, in my case, I'm teaching a corporate strategy course right now at the university. Tonight's, in fact, the last night of this particular intake. And my area of expertise is in finance, but now I see the connection between strategy and finance, and how a good strategy is going to be reflected in superior financial performance relative to peers. And of course, I know how to measure that very well. So I can synthesize more and more different areas of things that I know things about, that I just couldn't do when I was younger. So I can see, and he was always learning, obviously. So I can see how he, and also I can also see the idea of, I need bigger principles. I need bigger as you said, theory for transformation. I need, I need to be able to put this into a framework that brings all that together. And I'm still feeling frustrated about some of that, where I'm at with some of that, because I'm kind of halfway in my progress on that. But I definitely can see the idea of that coming later in life as I approach the big 6-0.   0:25:37.3 BB: The big 6-0, [chuckle] Well, but a big part, I mean, based on what you're talking about, it ended up... Previously we spoke about Richard Rumelt's work, Good Strategy/Bad Strategy, and I mentioned that I use a lecture by Richard Rumelt, I think it was 2011 or so. It was right after his book, Good Strategy/Bad Strategy came out. He spoke at the London School of Economics, and our listeners can find it if you just did a Google search for Richard Rumelt, that's R-U-M... One M. E-L-T. Good Strategy/Bad Strategy. LSE, London School of Economics. Brilliant, brilliant lecture. And I've seen it numerous times for one of my university courses. And he is like Deming, he doesn't suffer fools. And, it finally dawned on me, Deming organizations, if we can use this simple Deming versus non-Deming or Red Pen versus Blue Pen, and as, George Box would say, all models are wrong, some are useful. If we can use that model, I think it's easy to see that what frustrates Rumelt is you've got all these non-Deming companies coming up with strategies without a method.   0:27:00.0 BB: What Rumelt also talks about is not only do you need a method, but you have to be honest on what's in the way of us achieving this? Again, Dr. Deming would say, if you didn't need a method, why don't you're already achieving the results? And so it just dawned on me thinking the reason he's so frustrated, and I think that's one word you can use to describe him, but if he is talking to senior staff lacking this, an understanding of Deming's work, then he is getting a lot of bad strategies. And organizations that would understand what Dr. Deming's talking about, would greatly benefit from Rumelt's work. And they would be one, they'd have the benefit of having an organization that is beginning or is understanding what a transformation guided by Dr. Deming's work is about. And then you could look up and you're naturally inclined to have good or better strategy than worser strategies.   0:28:02.2 BB: And then you have the benefit of, profit's not the reason, profit is the result of all that. And, but next thing I wanna point out is, and I think we talked about it last time, but I just wanted to make sure it was up here, is I've come across recently and I'm not sure talking with who, but there's this what's in vogue today? Data-driven decisions. And again, whenever I hear the word data, I think backed in Ackoff's DIKUW model, I think data-driven. Well, first Dr. Deming would say, the most important numbers are unknown and unknowable. So if you're doing things on a data-driven way, then you're missing the rest of Dr. Deming's theory of management. But why not knowledge-driven decisions, why not understanding-driven decisions And beyond that, why not, right? How long... [laughter] I guess we can... Part of the reason we're doing these Andrew is that we'd like to believe we're helping people move in the direction from data-driven decisions to wisdom-driven decisions, right?   0:29:13.1 AS: Yeah. In fact, you even had the gall to name this episode the System of Profound Wisdom.   0:29:24.0 BB: And that's the title.   0:29:24.9 AS: There it is.   0:29:28.9 BB: But in terms of, I'll give you a fun story from Rocketdyne years ago, and I was talking with a manager in the quality organization and he says, "you know what the problem is, you know what the problem is?" I said, "what?" He says, "the problem is the executives are not getting the data fast enough." And I said, "what data?" He says "the scrap and rework data, they're just not getting it fast enough." So I said, "no matter how fast they get it, it's already happened."   [laughter]   0:30:00.0 BB: But it was just, and I just couldn't get through to him that, that if we're being reactive and talking about scrap and rework, it's already happened. By the time the... If the executives hear it a second later, it's already happened. It's still old news.   0:30:14.7 AS: And if that executive would've been thinking he would've said, but Bill, I want to be on the cutting edge of history.   0:30:23.1 BB: Yeah, it's like...   0:30:24.6 AS: I don't want information, I don't want old information, really old. I just want it as new as it can be, but still old.   0:30:32.9 BB: Well, it reminds me of an Ackoff quote is, instead of... It's "Change or be changed." Ackoff talked about organizations that instead of them being ready for what happens, they create what's gonna happen, which would be more of a Deming organizational approach. Anyway, we talked about books last time and I thought it'd be neat to share a couple books as one as I've shared the Against All Odds Book about Toyota.   0:31:08.8 AS: Which I'll say is on Amazon, but it's only looks like it's a used book and it's priced at about 70 bucks. So I've just...   0:31:16.2 BB: How much?   0:31:16.8 AS: Got that one down? 70 bucks? Because I think it's, you're buying it from someone who has it as a their own edition or something. I don't know.   0:31:23.8 BB: It's not uncommon. This is a, insider used book thing. It's not uncommon that you'll see books on Amazon for 70, but if you go to ThriftBooks or Abe Books, you can, I have found multi-$100 books elsewhere. I don't know how that happens, but it does. Anyway, another book I wanted to reference in today's episode is Profit Beyond Measure subtitle, Extraordinary Results through Attention to Work and People, published in 2000. You can... I don't know if you can get that new, you definitely get it old or used, written by, H. Thomas Johnson. H is for Howard, he goes by Tom, Tom Johnson. Brilliant, brilliant mind. He visited Rocketdyne a few times.   0:32:17.1 BB: On the inside cover page, Tom wrote, "This book is dedicated to the memory of Dr. W. Edwards Deming, 1900-1993. May the seventh generation after us know a world shaped by his thinking." And in the book, you'll find this quote, and I've used it in a previous episode, but for those who may be hearing it first here and Tom's a deep thinker. He's, and as well as his wife Elaine, they're two very deep thinkers. They've both spoke at Rocketdyne numerous times. But one of my favorite quotes from Tom is, "How the world we perceive works depends on how we think. The world we perceive is the world we bring forth through our thinking." And again, it goes back to, we don't see the world as it is. We see the world as we are. We hear the world as we are. I wrote a blog for The Deming Institute. If our listeners would like to find it, if you just do a search for Deming blog, Bellows and Johnson, you'll find the blog. And the blog is about the book Profit Beyond Measure. And in there, I said, “In keeping with Myron Tribus' observation that what you see depends upon what you thought before you looked, Johnson's background as a cost accountant, guided by seminars and conversations with Dr. Deming, prepared him to see Toyota as a living system,” right? You talk about Toyota.   0:33:53.9 BB: He saw it as a living system, not a value stream of independent parts. And that was, that's me talking. I mean, Tom talked about Toyota's living system. And then I put in there with the Toyota Production System, people talk about value streams. Well, in those value streams, they have a defect, good part, bad part model that the parts are handed off, handed off, handed off. That is ostensibly a value stream of independent parts 'cause the quality model of the Toyota Production System, if you study it anywhere, is not Genichi Taguchi. It's the classic good parts and bad parts. And if we're handing off good parts, they are not interdependent. They are independent. And then I close with, "instead of seeing a focus on the elimination of waste and non-value added efforts, Johnson saw self-organization, interdependence, and diversity, the three, as the three primary principles of his approach, which he called Management By Means." And so what's neat, Andrew, is he, Tom was as a student of Deming's work, attending Dr. Deming seminars, hearing about SoPK, System of Profound Knowledge, and he in parallel developed his own model that he calls Management By Means. But what's neat is if you compare the two, there's three principles. So he says self-organization.   0:35:31.0 BB: Well, that's kind of like psychology and people. So we can self-organize interdependence, the other self-organized, but we're connected with one another. So that's, that's kind of a systems perspective there as well. And the third one, diversity. So when I think of diversity, I think of variation. I can also think in terms of people. So that what I don't see in there explicitly is Theory of Knowledge. But Tom's developing this model in parallel with Dr. Deming's work, probably beginning in the early '80s. And part of what Tom had in mind, I believe, by calling it Management By Means, is juxtaposing it with that other management by, right? You know the other one, Andrew, management by?   0:36:33.8 AS: You mean the bad one or the good one, Management By Objective?   0:36:37.8 BB: Or Management By Results. Or Dr. Deming once said, MBIR, Management by Imposition of Results. But what's neat is, and this is what I cover and with my online courses, Tom is really, it's just such insight. Tom believes that treating the means as the ends in the making. So he's saying that the ends are what happen when we focus on the means, which is like, if you focus on the process, you get the result. But no, MBIR, as we focus on the result, we throw the process out the window. And so when I've asked students in one of my classes is, why does Tom Johnson believe that treating the means as an ends in the making is a much surer route to stable and satisfactory financial performance than to continue as most companies do? You ready, Andrew? To chase targets as if the means do not matter. Does that resonate with you, Andrew?   0:37:44.1 AS: Yes. They're tampering.   0:37:46.8 BB: Yeah. I also want to quote, I met Tom in 1997. I'm not sure if this... Actually, this article is online and I'll try to remember to post a link to it. If I forget, our listeners can contact me on LinkedIn and I'll send you a link to find the paper. This is when I first got exposed to Tom. It just blew me away. I still remember there at a Deming conference in 1997, hearing Tom talk. I thought, wow, this is different. So, Tom's paper that I'm referencing is A Different Perspective on Quality, the subtitle, Bringing Management to Life. Can you imagine? “Bringing Management to Life.” And it was in Washington, DC, the 1997 conference. And then Tom says, this is the opening. And so when Tom and his wife would speak at Rocketdyne or other conferences I organized.   0:38:44.0 BB: Tom read from a lectern. So he needed a box to get up there and he read, whereas Elaine, his wife, is all extemporaneous. Both deeply profound, two different styles. So what Tom wrote here is he says, "despite the impression given by my title, Professor of Quality Management, I do not speak to you as a trained or a certified authority on the subject of quality management. I adopted that title more or less casually after giving a presentation to an audience of Oregon business executives just over six years ago. That presentation described how my thinking had changed in the last five years since I co-authored the 1987 book, Relevance Lost, the Rise and Fall of Management Accounting, and the talk which presaged my 1992 book, Relevance Regained." And this is when he... After he wrote, Relevance Lost, he went on the lecture circuit, he met the likes of Peter Scholtes and Brian Joiner, got pulled into the Deming community.   0:39:45.4 BB: And then he wrote this scathing book called Relevance Regained and the subtitle is... I think our audience will love it, From Top-Down Control to Bottom-Up Empowerment. Then he goes on to say, "in that I told how I had come to believe that management accounting, a subject that I had pursued and practiced for over 30 years." Over 30 years, sounds familiar. Then he says, "could no longer provide useful tools for management. I said in essence that instead of managing by results, instead of driving people with quantitative financial targets, it's time for people in business..." And this is 30 years ago, Andrew. "It's time for people in business to shift their attention to how they organize work and how they relate to each other as human beings. I suggested that if companies organize work and build relationships properly, then the results that accountants keep track of will what? Take care of themselves."   0:40:50.8 AS: It's so true, it's so true.   0:40:54.1 BB: Yeah, it sounds so literally Tom was writing that in 1999, 2000. Well, actually no, that was 1997, that was 1997, but the same sentiment.   0:41:03.4 AS: It just makes me think of the diagram that we see and that Deming had about the flow through a business, it's the same thing as of the flow from activity to result.   0:41:20.6 BB: Yes.   0:41:21.9 AS: And when we focus on the result and work backwards, it's a mess from a long-term perspective, but you can get to the result. It's not to say you can't get to the result, but you're not building a system that can replicate that. But when you start with the beginning of that process of how do we set this up right to get to that result, then you have a repeatable process that can deliver value. In other words, you've invested a large amount in the origination of that process that then can produce for a much longer time. Um, I have to mention that the worst part of this whole time that we talk is when I have to tell you that we're almost out of time 'cause there's so much to talk about. So we do need to wrap it up, but, yeah.   0:42:09.3 BB: All right. I got a couple of closing thoughts from Tom and then we'll pick this up in episode 21.   0:42:21.3 AS: Yep.   0:42:22.9 BB: Let me also say, for those who are really... If you really wanna know... I'd say, before you read The New Economics... I'm sorry, before you read Profit Beyond Measure, one is the article I just referenced, “Bringing Quality to Life” is a good start. I'd also encourage our readers to do a search. I do this routinely. It shouldn't be that hard to find, but look for an article written by Art Kleiner, Art as in Arthur, Kleiner, K-L-E-I-N-E-R. And the article is entitled, Measures... The Measures That Matter. I think it might be What Are The Measures That Matter? And that article brilliantly written by Kleiner who I don't think knows all that much about Deming, but he knows a whole lot about Tom Johnson and Robert Kaplan, who together co-authored "Relevance Lost" and then moved apart. And Tom became more and more Deming and Kaplan became more and more non and finally wrote this article.   0:43:35.6 AS: Is this article coming out in 2002, "What Are The Measures That Matter? A 10-year Debate Between Two Feuding Gurus Shed Some Light on a Vexing Business Question?"   0:43:46.4 BB: That's it.   0:43:47.2 AS: There it is and it's on the...   0:43:47.4 BB: And it is riveting.   0:43:50.8 AS: Okay.   0:43:50.8 BB: Absolutely riveting. Is it put out by...   0:43:54.0 AS: PwC, it looks like and it's under strategy...   0:43:58.5 BB: Pricewaterhouse...   0:43:58.8 AS: Yeah, strategy and business.   0:44:00.2 BB: PricewaterhouseCooper? Yeah.   0:44:01.3 AS: Yeah.   0:44:03.1 BB: And 'cause what's in there is Kleiner explaining that what Tom's talking about might take some time. You can go out tomorrow, Andrew, and slash and burn and cut and show instant results. Now what you're not looking at is what are the consequences? And so... But... And then... But Kleiner I think does a brilliant job of juxtaposing and trying to talk about what makes Kaplan's work, the Balanced Scorecard, so popular. Why is Tom so anti that?   0:44:37.9 BB: And to a degree, it could be for some a leap of faith to go over there, but we'll talk about that later. Let me just close with this and this comes from my blog on The Deming Institute about Profit Beyond Measure and I said, "for those who are willing and able to discern the dramatic differences between the prevailing focus of systems that aim to produce better parts with less waste and reductions to non-value-added efforts," that's my poke at Lean and Six Sigma, "and those systems that capitalize on a systemic connection between parts. Tom's book, Profit Beyond Measure, offers abundant food for thought. The difference also represents a shifting from profit as the sole reason for a business to profit as the result of extraordinary attention to working people, a most fitting subtitle to this book."   0:45:35.9 AS: Well, Bill, on behalf of everyone at The Deming Institute, I want to thank you again for the discussion and for listeners, remember to go to deming.org to continue your journey. If you wanna keep in touch with Bill, just find him on LinkedIn. 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" and I hope you are enjoying your work.

Explore the transformation of thought processes through Dr. Deming's principles, with insights on vision improvement and system thinking. Discover the role of laughter in the workplace and pitfalls of designing for averages. Delve into Deming's principles for transformative thinking and systemic problem-solving, with references to influential experts and resources for further exploration.

In part 3 of this series, John Dues and host Andrew Stotz talk about the final 5 lessons for data analysis in education. Dive into this discussion to learn more about why data analysis is essential and how to do it right. TRANSCRIPT 0:00:02.4 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 John Dues who is part of the new generation of educators striving to apply Dr. Deming's principles to unleash student joy in learning. This is episode 23 and we're talking about goal setting through a Deming lens. John, take it away.  0:00:30.8 John Dues: It's good to be back, Andrew. Yeah, in this first episode of this four-part series, we talked about why goal setting is often an act of desperation. And if you remember early on, I sort of proposed those four conditions that organizations should understand about their systems prior to ever setting a goal. Those four were capability, variation, stability, and then by what method are you going to improve your system? And then in the last episode, I introduced the first five lessons of the 10 key lessons for data analysis. And remember, these lessons were set up to avoid what I call these arbitrary and capricious education goals, which are basically unreasonable goals without consideration of those four things, the system capability, variation, and stability, and then not having a method. So, it might be helpful just to recap those first five lessons. I'll just list them out and folks that want to hear the details can listen to the last episode.   0:01:31.8 JD: But lesson one was data have no meaning apart from their context. So, we've got to contextualize the data. Lesson two was we don't manage or control the data. The data is the voice of the process. So, it's sort of, you know, the data over time shows us what's happening and we don't really have control over that data. We do have control under that underlying process. Lesson three was plot the dots for any data that occurs in time order. So, take it out of a two-point comparison or take it out of a spreadsheet and put it on a line chart that shows the data over time. Lesson four was two or three data points are not a trend. So again, get beyond the typical two-point limited comparison this month and last month, this year and last year, this same month, last year, those types of things, this week, last week.   0:02:25.6 JD: And then lesson five was, show enough data in your baseline to illustrate the previous level of variation. So, we want to get a sense of how the data is changing over time and we need a baseline amount of data, whether that's 12 points, 15 points, 20 points, there's sort of different takes on that. But somewhere in that 12-to-20-point range is really the amount of data we want to have in our baseline. So, we understand how it's moving up and down over time sort of naturally. Sort of at the outset of those two episodes, we also talked about centering the process behavior charts, like the ones we viewed in many of our episodes. And we put those in the center because it's a great tool for looking at data over time, just like we've been talking about.   0:03:11.4 JD: And I think when we use this methodology, and when you start to fully grasp the methodology, you start to be able to understand messages that are actually contained in the data. You can differentiate between those actual special events, those special causes, and just those everyday up and downs, what we've called common causes. And in so doing, we can understand the difference between reacting to noise and understanding actual signals of significance in that data. And so, I think that's a sort of a good primer to then get into lessons six through 10.   0:03:51.2 AS: Can't wait.   0:03:53.3 JD: Cool. We'll jump in then.   0:03:56.1 AS: Yeah. I'm just thinking about my goal setting and how much this helps me think about how to improve my goal setting. And I think one of the biggest ones that's missing that we talked about before is by what method. And many people think that they're setting strategy, when in fact, they're just setting stretch targets with nothing under it. And they achieve it by luck or are baffled why they don't achieve it. And then they lash out at their employees.   0:04:31.4 JD: Yeah, there was really... I mean, that goes back to one of those four conditions of setting goal capability. You have to understand how capable your system is before you can set, it's fine to set a stretch goal, but it has to be within the bounds of the system. Otherwise, it's just maybe not an uncertainty, but a mathematical improbability. That's not good. Like you're saying, it's not a good way to operate if you're a worker in that system. So, lesson six then, to continue the lessons.   0:05:06.8 JD: So, lesson six is "the goal of data analysis in schools is not just to look at past results, but also, and perhaps more importantly, to look forward and predict what is likely to occur in the future," right? So that's why centering the process behavior charts is so important, because they allow you to interpret data that takes variation into account, allows you to classify the data into the routine or common cause variation or the exceptional, that's the special cause variation, and allows us to turn our focus to that underlying or the behavior of the underlying system that produced the results. And it's this focus on the system and its processes that's then the basis for working towards continual improvement.   0:06:00.6 AS: And I was just thinking about number six, the goal is to predict what is likely to occur in the future. And I was just thinking, and what's likely to occur in the future is exactly what's happening now, or the trend that's happening, unless we change something in the system, I guess.   0:06:16.4 JD: Yeah. And that's why just setting the stretch goal is often disconnected from any type of reality, because we have this idea that somehow something magical is going to happen in the future that didn't happen in the past. And nothing magical is going to happen unless we are intentional about doing something differently to bring about that change.   0:06:39.5 AS: And that's a great lesson for the listeners and the viewers. It's like, have you been just setting stretch targets and pushing people to achieve these stretch targets? And not really understanding that your role is to understand that you're going to get the same result unless you start to look at how do we improve the method, the system, that type of thing.   0:07:05.0 JD: Yeah. And usually when you have those stretch goals, you've looked at what happened last year, and then you base the stretch goal on last year. But perhaps, you're seeing, for the last three or four years, the data has been steadily decreasing, right? And you can't realize that if you haven't charted that over the last three or four years, hopefully beyond that. So, you have no idea or it could have been trending positively, and you may under shoot your stretch goal because you missed a trend that was already in motion because of something that happened in the past.   0:07:44.8 AS: You made a chart for me, a run chart on my intake for my Valuation Masterclass Bootcamp. And we've been working on our marketing, and I presented it to the team and we talked about that's the capability of our system based upon for me to say, I want 500 students when we've been only getting 50 is just ridiculous. And that helped us all to see that if we are going to go to the next level of where we want to be, we've got to change what we're doing, the method that we're getting there, the system that we're running and what we're operating to get there or else we're going to continue to get this output. And so if the goal is to predict what is likely to occur in the future, if we don't make any changes, it's probably going to continue to be like it is in that control chart.   0:08:42.8 JD: Yeah. And that example is, in a nutshell, the System of Profound Knowledge in action in an organization where you're understanding variation in something that's important to you, enrollment in your course. You're doing that analysis with the team. So, there's the psychological component and you're saying, well, what's our theory of knowledge? So, what's our theory for how we're going to bring about some type of improvement? And so, now you're going to run probably something like a PDSA. And so now you have all those lenses of the System of Profound Knowledge that you're bringing together to work on that problem. And that's all it is really in a nutshell.   0:09:22.2 AS: Yeah. And the solution's not necessarily right there. Sometimes it is, but sometimes it's not. And we've got to iterate. Okay. Should we be doing marketing in-house or should we be doing it out using an outsourced service? What if we improve and increase the volume of our marketing? What effect would that have? What if we decrease the... What if we change to this method or that method? Those are all things that we are in the process of testing. I think the hardest thing in business, in my opinion, with this is to test one thing at a time.   0:09:58.5 JD: Yeah.   0:09:58.7 AS: I just, we I want to test everything.   0:10:00.4 JD: Yeah. Yeah. I read in the Toyota Kata that I think we've talked about before here, which talks about Toyota's improvement process. I read this in the book, I don't know if this is totally always true, but basically they focus on single factor experiments for that reason, even in a place as complex and as full of engineers as Toyota, they largely focus on single factor experiments. They can actually tell what it is that brought about the change. I mean, I'm sure they do other more complicated things. They would have to write a design of experiments and those types of things, but by and large, their improvement process, the Toyota Kata, is focused on single factor experiments for that reason.   0:10:48.1 AS: And what's that movie, the sniper movie where they say, slow is smooth and smooth is fast or something like that, like slow down to speed up. I want to go fast and do all of these tests, but the fact is I'm not learning as much from that. And by slowing down and doing single factor experiment to try to think, how do we influence the future is fascinating.   0:11:20.9 JD: Yeah, absolutely.   0:11:22.4 AS: All right. What about seven?   0:11:23.2 JD: Lesson seven. So "the improvement approach depends on the stability of the system under study," and there's really two parts to this. But what approach am I going to take if the system is producing predictable results and it's performing pretty consistently, it's stable, there's only common cause variation. And then what happens if you have an unpredictable system? So two different approaches, depending on what type of system you're looking at in terms of stability. So you know the one thing to recognize in thinking about something like single factor experiments, it's a waste of time to explain noise or explain common cause variation in this stable system, because there's no simple single root cause for that type of variation. There's thousands or tens of thousands of variables that are impacting almost any metric. And you can't really isolate that down to a single cause.   0:12:17.5 JD: So instead we don't, we don't try to do that in a common cause system that needs improvement. Instead, if the results are unsatisfactory, what we do is work on improvements and changes to the system, right? We don't try to identify a single factor that's the problem. So what we do then is we work to improve a common cause processor system by working on the design of that actual system including inputs, throughputs that are a part of that. And to your point, you sort of have to, based on your content knowledge of that area, or maybe you have to bring in a subject matter expert and you sort of start to think about what's going to make the biggest difference. And then you start testing those things one at a time, basically. That's sort of the approach. And then if you're working in an unpredictable system and that unpredictable system is unpredictable because it has special causes in your data, then it's really a waste of time to try to improve that particular system until it's stable again. And so the way you do that is at that point, there is something so different about the special cause data that you try to identify that single cause or two of those data points. And then when you've identified, you study it, and then you try to remove that specific special cause. And if you've identified the right thing, what happens then is it becomes a stable system at that point, right?   0:13:51.9 AS: I was thinking that it's no sense in trying to race your boat if you've got a hole in it. You got to fix the special cause, the hole, and then focus on, okay, how do we improve the speed of this boat?   0:14:06.5 JD: And the key is recognizing the difference between these two roadmaps towards improvement. And I think in education for sure, there's a lot of confusion, a lot of wasted effort, because there's really no knowledge of this approach to data analysis. And so people do their own things. There's a mismatch between the type of variation that's present and the type of improvement effort that's trying to be undertaken. I think the most typical thing is there's a common cause system, and people think they can identify a single thing to improve. And then they spend a lot of time and money on that thing. And then it doesn't get better over time because it was the wrong approach in the first place.   0:14:55.9 AS: Number eight.   0:14:57.6 JD: Number eight. So, number eight is, "more timely data is better for improvement purposes." So we've talked about state testing data a lot. It's only available once per year. Results often come after students have gone on summer vacation. So, it's not super helpful. So, we really want more frequent data so that we can understand if some type of intervention that we're putting in place has an effect. I think what the most important thing is, the frequency of the data collection needs to be in sync with the improvement context. So, it's not always that you need daily data or weekly data or monthly data, or quarterly data, whatever it is. It's just it has to be in sync with the type of improvement context you're trying to bring about. And no matter what that frequency of collection, the other big thing to keep in mind is don't overreact to any single data point, which is, again, I see that over and over again in my work. I think ultimately the data allows us to understand the variation and the trends within our system, whether that system is stable or unstable, and then what type of improvement effort would be most effective. And, again, in my experience, just those simple things are almost never happening in schools. Probably in most sectors.   0:16:25.9 AS: Can you explain a little bit more about in sync with the improvement process? Like, maybe you have an example of that so people can understand.   0:16:34.2 JD: Well, yeah. So, you mean the frequency of data collection?   0:16:39.0 AS: Yeah. And you're saying, yeah, this idea of like, what would be out of sync?   0:16:44.7 JD: Well, one, you need to... A lot of times what happens is there might be a system in place for collecting some type of data. Let's say, like, attendance. They report attendance, student attendance on the annual school report card. So, you get that attendance rate, but that's like the state test scores. Like, it's not that helpful to get that on the report card after the year has concluded. But the data is actually available to us in our student information system. And so, we could actually pull that in a different frequency and chart it ourselves and not wait on the state testing date or the state attendance report card has attendance...   0:17:27.5 AS: Because attendance is happening on a daily basis.   0:17:31.0 JD: Happening on a daily basis. So, if we wanted to, daily would be pretty frequent, but if we did collect the data daily, we certainly can do that. We could see, that could help us see patterns in data on certain days of the week. That could be something that goes into our theory for why our attendance is lower than we'd want it to. You could do it weekly if the daily collection is too onerous on whoever's being tasked with doing that. I think weekly data pretty quickly, would take you 12 weeks. But in 12 weeks, you have a pretty good baseline of what attendance is looking like across this particular school year. So I think when you're talking about improvement efforts, I think something daily, something weekly, I think that's the target so that you can actually try some interventions along the way. And...   0:18:29.3 AS: And get feedback.   0:18:31.1 JD: And get feedback. Yeah, yeah. And you could also peg it to something that's further out. And you could see over time if those interventions that are impacting more short-term data collection are actually impacting stuff on the longer term as well.   0:18:49.1 AS: And I guess it depends also on what is the priority of this. Let's say that attendance is not a big issue at your particular school. Therefore, we look at it on a monthly basis and we look to see if something's significance happening. But otherwise, we've got to focus over on another idea. And if, if, if attendance becomes an issue, we may go back to daily and say, is it a particular day of the week? Or is it something, what can we learn from that data?   0:19:20.0 JD: Yep, that's exactly right. And then the next step would be in lesson nine, you then, and this is why the charts are so important, then you can clearly label the start date for an intervention directly on the chart. So, what you want to do is, once you've chosen an intervention or a change idea, you clearly mark that in your process behavior chart. I just use a dashed vertical line on the date the intervention is started and also put a simple label that captures the essence of that intervention. So, that's right on the chart. So, I can remember what I tried or started on that particular day. And then that allows the team to easily see, because you're going to continue adding your data points, the stuff that comes after the dotted line, it becomes pretty apparent based on the trends you're seeing in the data, if that intervention is then working, right?   0:20:21.2 JD: If it's attendance, I may try, I do a weekly call to parents to tell them what their individual child's attendance rate is. And then we can see once we started making those weekly calls over the next few weeks, does that seem to be having an impact on attendance rates? And then I can actually see too, we've talked about the patterns in the data, there's certain patterns I'm looking for to see if there's a significant enough change in that pattern to say, yeah, this is a signal that this thing is actually working. So, it's not just because it increased, that attendance rate could go up, but that in and of itself isn't enough. I want to see a signal. And by signal, I mean a specific pattern in the data, a point outside the limits.   0:21:17.3 JD: I want to see eight points in a row in the case of attendance above the central line or I want to see three out of four that are closer to a limit, the upper limit, than they are to that central line. And again, we've talked about this before, those patterns are so mathematically improbable that I can be pretty reasonably assured if you see them that an actual change has occurred in my data. And because I've drawn this dotted line, I can tie the time period of the change back within that dataset to determine if something positive happened after I tried that intervention.   0:21:56.7 AS: It's just, you just think about how many times, how many cycles of improvement and interventions that you can do in a system and how far you will be a year later.   0:22:12.3 JD: Yes, yeah. And "cycles" is exactly the right word because really what you're doing, I didn't mention it here, but really what you were doing at the point you draw that vertical line when you're going to run an intervention, you're going to do that through the PDSA cycle, the Plan-Do-Study-Act cycle. So that's your experiment where you're testing one thing to see what impact it has on the data. So if I was going to boil continual improvement per Dr. Deming down to two things is, put your data on a process behavior chart, combine it with a PDSA to see how to improve that data. And that's continual improvement in a nutshell, basically, those two tools.   0:22:51.7 AS: Gold, that's gold. All right. Number 10.   0:22:55.3 JD: Last one, lesson 10, "the purpose of data analysis is insight." So this comes from Dr. Donald Wheeler, but he basically just teaches us that the best analysis is the simplest analysis, which provides the needed insight. But what he would say is plot the dots first on a run chart. Once you have enough data, turn it into a process behavior chart. And that's the most straightforward method for understanding how our data is performing over time. And so this approach, I think it's much more intuitive than if we store the data in tables and then the patterns become much more apparent because we're using these time sequence charts. And again, I know I've said this before, but I keep repeating it because I think it's the essence of continual improvement to do those two things. Yeah.   0:23:47.1 AS: And what's the promise of this? If we can implement these 10 points that you've highlighted in relation to goal setting, what do you think is going to change for me? I mean, sometimes I look at what you've outlined and I feel a little bit overwhelmed, like, God, that's a lot of work. I mean, can I just set the freaking goal and people just do it?   0:24:13.2 JD: Yeah. Well, I think, this is, in essence, a better way. I mean, this is really the wrap up here is that, well, one, when you understand the variation in your chart, you actually understand the story, the true story that's being told by your data. And so many people don't understand the true story. They sort of make up, that's too strong, but they don't have the tools to see what's actually happening in their system. So if you really want to see what's happening in your system, this is the way to do it. That's one thing. I think it also... I tried many, many things before I discovered this approach, but I didn't have any way to determine if something I was trying was working or not.   0:25:07.1 JD: I didn't have any way to tie the intervention back to my data. So what most people then do is tell the story that this thing is working if you like it. And if you don't want to do it anymore, you tell the story that it's not working, but none of its actually tied to like scientific thinking where I tie the specific point I try something to my data. So that's another thing. I can actually tell if interventions are working or not or can have a... I always try to use, not use definitive language. Scientifically, I have a much better likelihood of knowing that an intervention is working or not.   0:25:47.7 JD: So I think especially the process behavior chart, I think, and the way of thinking that goes with the chart is probably the single most powerful tool that we can utilize to improve schools. And we can teach this to teachers. We can teach this to administrators. We can teach this to students, can learn how to do this.   0:26:07.1 AS: Yeah. And I think one of the things I was thinking about is start where you have data.   0:26:12.3 JD: Yeah. Start where you have data.   0:26:14.2 AS: Don't feel like you've got to go out there and go through a whole process of collecting all this data and all that. Start where you have data. And even if attendance is not your major issue, let's say, but you had good attendance data, it's a good way to start to learn. And I suspect that you're going to learn a lot as you start to dig deeper into that. And then that feeds into, I wonder if we could get data on this and that to understand better what's happening.   0:26:41.4 JD: There are so many applications, so many applications. I mean, even just today, we were talking about, we get a hundred percent of our students qualify for free and reduced lunch because we have a school-wide lunch or breakfast and lunch program. And so we get reimbursed for the number of meals that are distributed. And sometimes there's a mismatch between the number that are distributed and the number we order just because of attendance and transportation issues and things like that. But the federal government only reimburses us for the meals we actually distribute to kids. And so if we over order, we have to pay out of our general fund for those meals that we don't get reimbursed for. And so, I'm just bringing this up because we were looking at some of that data just today, that mismatch, and even an area as simple as that is ripe for an improvement project.   0:27:40.7 JD: Why is there a mismatch? What is happening? And prior, I would just say, prior to having this mindset, this philosophy, I would say, well, they just need to figure out how to get the numbers closer together. But you actually have to go there, watch what's happening, come up with a theory for why we're ordering more breakfasts and lunches than we're passing out. It could be super, super simple. No one ever told the person distributing the lunches that we get reimbursed this way. And so they didn't know it was a big deal. I don't know that that's the case or not right, that's purely speculation. Or it could be, oh, we want to make sure every kid eats so we significantly over order each day. Well, that's a good mindset, but maybe we could back that off to make sure we never... We're always going to have enough food for kids to eat, but we're also not going to spend lots of extra money paying for lunches that don't get eaten. So there's all different things, even something like that operationally is ripe for improvement project. And the great thing is, is if you can study that problem and figure out how to save that money, which could by the end of the year, you know, be thousands of dollars, you could reallocate that to field trips or class supplies or to books for the library or art supplies, whatever, you know? So that's why I think this methodology is so powerful.   0:29:02.1 AS: Fantastic. That's a great breakdown of these 10 points. So John, on behalf of everyone at the Deming Institute, I want to thank you again for this discussion and for listeners, remember to go to deming.org to continue your journey. And you can find John's book, Win-Win, W. Edwards Deming, The System of Profound Knowledge and the Science of Improving Schools on Amazon.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."

Bill Bellows, an expert in the teachings of Dr. W. Edwards Deming, discusses seeing organizations as holograms and using the System of Profound Knowledge to identify transformation opportunities. Topics include organizational culture shifts, strategic KPI approaches for innovation, measuring progress through small indicators, and promoting teamwork through a holistic view of organizations.

Do you struggle to meet your goals or targets? Find out how you can change your thinking about goals and your process for setting them so you can keep moving forward. In this episode, John Dues and host Andrew Stotz discuss the first five of John's 10 Key Lessons for Data Analysis. TRANSCRIPT 0:00:03.0 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 John Dues, who is part of the new generation of educators striving to apply Dr. Deming's principles to unleash student joy in learning. This is episode two of four in a mini-series on why goal setting is often an act of desperation. John, take it away.   0:00:32.3 John Dues: Hey, Andrew, it's good to be back. Yeah, in that last episode, that first episode in this mini-series, we talked about why goal setting is often an act of desperation and I basically proposed these four conditions that organizations should understand prior to setting a goal. So it's not the goals in and of themselves that are bad, but it's with this important understanding that's often lacking. So those four things that organizations should understand, one, what's the capability of a system under study? So that's the first thing, how capable is the system or the process? The second thing is what's the variation within that system or process under study? So that's the second thing we talked about last time. The third thing is understanding if that system or process is stable. And then the fourth thing was, if we know all of those things, by what method are we going to approach improvement after we set the goal, basically? So you gotta have those four things, understanding the capability of the system, the variation of the system, the stability of the system, and then by what method, prior to setting a goal. And so I think I've mentioned this before, but absent of an understanding of those conditions, what I see is goals that are, what I call it, arbitrary and capricious.   0:01:48.8 JD: That's a legal characterization. You look that up in the law dictionary. And it basically says that an "arbitrary and capricious law is willful and unreasonable action without consideration or in disregard of facts or law." So I'm just now taking that same characterization from a legal world and applying it to educational organizations and accountability systems, and I just switched it to "a willful and unreasonable goal without consideration or in disregard of system capability, variability, and/or stability." And we see these all over the place for education organizations, for schools, school districts, teachers, that type of thing.   0:02:31.6 JD: And so what I tried to do in the book and tried to do here in my work in Columbus is develop some sort of countermeasures to that type of goal setting and develop the 10 key lessons for data analysis. An antidote to the arbitrary and capricious goals seen throughout our sector. And this process behavior chart tool, looking at data in that format is central to these lessons. So what I thought we would do in this episode and the next is outline those 10 key lessons. So five today and then do another five in the next episode. And in the fourth episode of the series, what we would do is then apply those examples to a real life improvement project from one of our schools. It's helpful, I think too, to sort of, to understand the origin of the key lessons. So there's the lessons that I'll outline are really derived from three primary sources.   0:03:36.0 JD: So the first two come from Dr. Donald Wheeler, who I've mentioned on here before, a lot of Deming folks will, of course, have heard of Dr. Wheeler, who's a statistician in Tennessee, a colleague of Dr. Deming when Dr. Deming was alive and then has carried on that work to this day. The two books, two really great books that he wrote, one is called Understanding Variation, a thin little book, a good primer, a good place to start. And then he's got a thicker textbook called Making Sense of Data, where you get in really into the technical side of using process behavior charts. So I'd highly recommend those. And then the third resource is a book from a gentleman, an engineer named Mark Graban called Measures of Success. And I really like his book because he has applied it, the process behavior chart methodology, to his work and he's really done it in a very contemporary way. So he's got some really nice color-coded charts in the Measures of Success book and I think they're really easy to understand with modern examples, like traffic on my website, for example, in a process behavior chart, really easy to understand modern example. But all three of the books, all three of the resources are built on the foundation of Dr. Deming's work. They're, you know, Graban and Wheeler are fairly similar and I think Graban would say he's a student of Wheeler.   0:05:00.4 JD: He learned of this mindset, this approach to data analysis by finding a Donald Wheeler book on his own dad's bookshelf when he was in college and starting down that path as a young engineer to study this stuff. And basically what I've done is take the information from those three resources and make some modifications so they can be understood by educators, basically. I think it's also worth noting again that process behavior chart methodology is right in the center of this, really for three reasons. One, when you plot your data that way, you can start to understand messages in your data, I think that's really important. Second, you can then start to differentiate between special and common causes, special and common causes, translate that into regular language. I can translate between something that I should pay attention to and something that's not significant basically in my data. And then in so doing, I know the difference between when I'm reacting to noise versus when I'm reacting to signals in my data, so I think that's really important. So the process behavior chart is at the center of all this. So we'll go through five of these lessons, one by one, I'll outline the lesson and then give a little context for why I think that particular lesson is important.   0:06:25.4 AS: That sounds like a plan. So capability, variation, stability and method. You've talked about Donald Wheeler, excellent book on Understanding Variation, that's the one I've seen. And of course, Mark Graban's book, Measures of Success, very well rated on Amazon and a podcaster himself, too.   0:06:49.6 JD: Yeah. And if I was a person studying this and wanting to get into process behavior charts and really knowing how to look at data the right way, I would read Understanding Variation first because it's a good primer, but it's fairly easy to understand. And then I would read Measures of Success 'cause it's got those practical applications now that I have a little bit of a baseline, and then if I wanna go deep into the technical stuff, the Making Sense of Data, that's the textbook that drives everything home. Yeah. So we'll dive into the lesson then.   0:07:19.5 AS: Let's do it.   0:07:20.0 JD: Yeah. Okay. So the first lesson, and I've talked about this in various episodes before, but lesson one, the very first lesson is, "data have no meaning apart from their context." So this seems commonsensical, but I see this all the time where these things aren't taken care of. And what I'm talking about is answering some basic questions. So for anyone looking at my data, they should be able to answer some basic questions, very simply, anybody that looks at my data. First thing is who collected the data? That should be apparent. How were the data collected? When were the data collected? Where were the data collected? And then what do these values represent? So oftentimes I see data either in a chart or in some type of visualization and almost none of those things are known from looking at the data, all important questions.   0:08:18.6 JD: The second question would be, well, that first set goes together. The second question is what's the operational definition of the concept being measured? So we have to be on the same page about what it is exactly being measured in this data that I've collected. I also wanna know how were the values of any computed data derived from the raw inputs? That's important. And then the last thing is, have there been any changes made over time that impact the data set? For example, perhaps the operational definition has changed over time for some reason. Maybe there's been a change in formula being used to compute the data.   0:09:05.4 JD: So an example would be, from my world, high school graduation rates. You know, 20 years ago there was one definition of how you calculated a high school graduation rate, now there's a different definition. So when you compare those two sets of data, you've gotta be careful because you're actually, you're actually working from different definitions and I think that happens all the time. More recently here in Ohio, what it means to be proficient on a state test, that definition changed about 10 years ago. And so if you look at test results from 2024 and try to compare them to 2014, you're really comparing apples and oranges 'cause there's two different definitions of proficiency, but no one remembers those things a decade later. So you have...   0:09:52.3 AS: And then a chart will be presented where the different methodologies are shown as one line that says...   0:10:00.8 JD: Yes.   0:10:00.8 AS: That no one's differentiated the fact that at this point it changed.   0:10:04.6 JD: Yeah, at this point it changed. So first lesson, data have no meaning apart from their context. Second lesson is we don't manage or control the data, the data is the voice of the process. What we control is the system and the processes from which the data come. There's a difference there. Right? So I think this is one of the key conceptions of that system's view, that system's thinking in an organization. When we wanna make improvements in our schools, we need a few things in place. We need the people working in the system. So that would be the students for us, they're working in the system, people that have the authority to work on the system, so that'd be teachers if we're talking about an individual classroom, at the school building level, maybe we're talking about the principal. And those two things are, at least the teacher principal thing is usually in place, the students being a part of improvement projects, definitely less so, but maybe there are places where that's happening. But the third thing is someone with an understanding of the System of Profound Knowledge, I'd say that's almost always lacking in the education sector, at least. And I think the reason the System of Profound Knowledge becomes important, 'cause that's really the theoretical foundation for all the things that we're talking about when we're looking at data in this way.   0:11:38.8 JD: If you lack that conception, then it's hard to bring about any improvement, because you don't understand how to look at that data, how to interpret that data, you don't understand how to run a plan-do-study-act cycle. Because what you're gonna ultimately have to do is change some process in your system and there's some knowledge that you're gonna need to be able to do that, and that's, that third component of an improvement team has to be in place to do that. But I think the most important thing is that we're not in control of the data, we're in control of the processes that ultimately lead to the data. It's a distinction, maybe a fine distinction, but I think it's an important one.   0:12:17.5 AS: The idea of the System of Profound Knowledge and understanding what to do with the data and really understanding the whole thing, I was just thinking what would... An analogy I was thinking about is rain. Everybody understands rain as it comes out of the sky, but not everybody understands how to use that to make a pond, to make an aqueduct, to feed a farm, to, whatever that is. And so having that big picture is key, so, okay. So number...   0:12:57.8 JD: Yeah. Well, and a part of that is something really simple is constantly understanding data is the voice of the process. And so when you're looking at data, what often happens is I'm gonna walk into a meeting with my boss, and I'm looking for some data point, maybe we just got some type of performance data back or survey results or something. I'm gonna pick one of those items where the plot, where the dot from last time has improved when we look at it this time, and I take that and say, "Look how we've improved in this thing." And you need someone to say, "Well, wait a second, while there is a difference between those data points, if I look at the last 12, things are just moving up and down." And there's gotta be someone in the room that constantly points back to that, constantly. And that's where that person with the Profound Knowledge is helpful in improvement work.   0:13:54.5 AS: So the voice of the process is a great way of phrasing it that's been used for a while now and I think it's really good. I remember when I worked at Pepsi as a young supervisor, I saw some problem on the production line and I raised it to the maintenance guys. And they kept coming and fixing it and it would break and they'd fix it and it would break, and I basically got mad at him and I was like, "What the hell?" And he's like, "Bosses won't pay for the things that I need to fix this permanently, so get used to it constantly breaking down."   0:14:33.4 JD: And that's the best I can do.   0:14:34.0 AS: That's the voice of the system, here's what I can produce with what you've given me to produce.   0:14:40.8 JD: Yep. Yep. Yeah. Those guys had a very keen understanding of the system, no doubt in that example. Yeah. Yeah. And that kind of thing happens all the time, I think. That was lesson two. Lesson three is plot the dots for any data that incurs in time order. So a lot of people in this world know Dr. Donald Berwick, he started the Institute for Healthcare Improvement. He was a student of Dr. Deming's, he's done a lot of work in this area. He has a great quote where he says, "Plotting measurements over time turns out, in my view, to be one of the most powerful things we have for systemic learning." And that's what really plot the dots is all about, it's all about turning your data into a visualization that you can learn from. And the National Health Service in England has this #plotthedots. And I think the whole point is that plotting the dots, plotting the data over time helps us understand variation and it leads us to take more appropriate action when we do that. So whether it's a run chart or a process behavior chart, just connecting the consecutive data points with a line makes analysis far more intuitive than if we store that data in a table.   0:16:03.6 AS: Yeah. And I was thinking about if you're a runner and you wanna compete in a marathon, plotting the dots like that is so valuable because you can see when changes happen. For instance, let's just say one night you didn't eat and then you ran the next morning and then your performance was better. Was it just a noise variation or is there something that we can learn from that? And then just watching things over time just give you ideas about what... Of potential impacts of what something could do to change that.   0:16:42.0 JD: Yeah. And we can start with a simple run chart, it doesn't have the limits, it's just a line chart. And then once we have enough of the data collected, enough plotted dots, then we can turn it into the process behavior chart.   0:16:56.3 AS: Some people don't even want to see that, John, like when we looked at your weight chart, remember that?   0:17:03.0 JD: I do remember that. Yeah.   0:17:04.0 AS: So for the people out there that really wanna let's say, control your weight, put a dot plot chart on your wall and measure it each day and just the awareness of doing that is huge.   0:17:18.7 JD: Yep. It is huge. It really is huge. And that works for any data that occurs over time, so almost everything that we're interested in improving occurs in some type of time order, time sequence. So these charts are appropriate for a wide array of data. But the bottom line is that... Oh, yeah, sorry, go ahead.   0:17:33.5 AS: The bottom line?   0:17:35.0 JD: Well, I was just saying the bottom line, whether you're using a run chart or a process behavior chart, it's always gonna tell us more than a list or a table of numbers, basically.   0:17:44.5 AS: I was gonna explain this, a situation I had when I was head of research at a research firm, a broker here in Thailand. I, my goal was to get more output from the analysts, they needed to write more and we needed to get more out. So what I did and I had already learned so much about Deming and stuff at that time. So what I did is I just made a chart showing each person's, what each person wrote each week, and it was a run chart in that sense where people could see over time what they wrote and they could see what other people were writing. And I purposely made no comments on this chart and I'd never really discussed it, I just put it up and updated it every week. And one of the staff that worked for me, an analyst, a really smart Thai woman asked, she said, she went to... She said, "I wanna see you in your office." I was like, "Oh, shit, I'm in trouble." And so she came to my office and said, "You know I went to, so this was maybe six months after I had put that chart up, she said, "I went out to lunch with my counterpart, my competitor, and she's writing research just like me on the same sector, and she asked me how many research reports do you write in a week, and I told her my number, and she was like, "Oh my God, that's a huge number."   0:19:16.6 AS: And she said, "Oh, I didn't really even think about it. But okay." And then she says, "What is Andrew's goal or target for you?" And she had naturally had thought that I had set a target of that amount, that's where she said, "I think I really figured you out." And I was like, "Well, what do you mean?" She said, "You just put that chart up there and you didn't give us any goal, but you knew that we were looking at it, and then it would provide us information and incentive and excitement, and the fact that you said nothing about it, got us to probably a higher level of production than if you had said, "I want everybody to read my reports."   0:19:57.9 JD: Right. Yeah, that's great.   0:20:01.0 AS: The magic of data. What's number four?   0:20:02.4 JD: The magic of data. Number four, so two or three data points are not a trend. So the first thing is, as soon as you've decided to collect some set of data, plot the dots, that should start right away. And again, this really includes all data that we're interested in improving in schools. And I know before I understood this way of thinking, this way of data analysis, I often relied on just comparing two points, that's the most common form of data analysis. What did last month look like, what does month look like? What did last year look like, what does this year look like? What did last week look like, what does this week look like? So that limited comparison is the most typical form of data analysis, especially when you're talking about something like management reports or board reports, revenue over time, those types of things. What was revenue last January? That type of thing. But the problem with looking at just two or three data points is that it tells you nothing about trends, it also doesn't tell you anything about how the data varies naturally.   0:21:17.5 JD: I remember looking at attendance data at one of our schools, and they had up... Last month was 92%, and then had gone up to 94%, but then I just said, well, what did it look like... January is 92, February is 94 in this particular school year, and I just said, well, what did it look like before, and then when you plotted it, what saw very quickly is there was no improvement, the data was literally going like this, up, down, up, down, up, down, up down, right? But no one had that picture, because all you could see was, Here's January and here's February, just numbers written in percentage form, that's almost all the data that I see in schools is in a similar format.   0:22:02.7 AS: On this one, in the stock market, my area of expertise. People always see the up data, the people who have made a lot of money in the stock market, and they see that as evidence that they could make money in the stock market, or they attribute that to skill of that particular person as we want to, with Warren Buffett as an example. And I have, in fact in my class, I asked the students, "Do you think that Warren Buffet outperformed, underperformed, or performed in line with the market over the last 20 years?" And the answer to that is, he performed in line with the market, and I proved that by doing a demonstration through a website that I can do that with, but it was shocking because obviously he's gonna end up with the most amount of money because he let his money compound, and he made huge gains in the beginning years, which compounded over many years.   0:23:02.0 AS: And still he's doing very well, but the point is, is that... The reason why I say this, I also tell the story of, if you had 10,000 people in a stadium and you flipped coins, and asked them if they flipped heads consecutively or tails consecutively to remain standing, and you're gonna end up with 10 people at the end of 10 flips with 10,000, and if you've got a million, you can end up with 20 or 30 or 40 flips that could potentially be heads consecutively or tails consecutively. So my question is, given that long streaks can happen through just plain probability, what if two to three data points are not a trend, can we definitively say, what is a trend?   0:23:50.9 JD: Well, not with certainty, but what this type of data analysis does is it gives you some patterns in the data to look for that are so mathematically improbable that you can be reasonably assured that some changes happened.   0:24:09.4 AS: Right so this is enough of a trend that I'm gonna go with the assumption that there's something significant here.   0:24:21.9 JD: Yeah. I mean it's...well, think back to that attendance example that I just used, so if I went from... If I'm writing this up on, let's say a whiteboard that's in a teacher work room, it says, this month and next month, or last month and this month, and I write those attendance rates up and remember, it's a dry erase board, and I'm gonna erase the last month to put this month's up and so I'm not gonna be able to see that one anymore, I'll have two data points and I'll erase the old one, and so in that example, I used where they went from 92%. It was actually like 92.4% to 94.1%. So it wasn't even two full percentage points. And then you celebrate that as a win, as an improvement, but like I said, you didn't know what happened before, and then you didn't chart after, so you don't really know how things are just bouncing around naturally versus if you had it on a run chart and you did see, let's say, eight points in a row that are above the average attendance for that school, that's one of the patterns that suggest that something different has happened. So you just have increased mathematical probability that there has been meaningful improvement.   0:25:39.4 AS: So it sounds like what you mean in this number four is a little bit more on the end of, Hey, just a couple of data points doesn't have anything, you need to get more rather than somebody looking at a lot of data and trying to understand what is a trend or not?   0:25:56.9 JD: That's exactly right. That's exactly right. And that actually is a segue to Lesson 5, which is "show enough data in your baseline to illustrate the previous level of variation," basically. So this is gonna get a little technical for a second, but the non-technical thing is, we talked about when you have a run chart when you're starting and you have, let's say, three or four or five, six data points at a certain point, you can now have a process behavior chart, which is the addition of that upper and lower natural process limit that defines the bounds of the system, so the limits are not a part of the run chart.   0:26:34.9 JD: In making sense of data what Donald Wheeler basically says is that if you're using an average line, the mean for your central line, then those limits, you begin to have limits that solidify when you have 17 or more values, and then if you're using a median for that central line, that solidification starts to happen when you have 23 or more values. So there's a mathematical theory behind that. But the point is, at a certain point, you start to get enough data to be able to add the limits and feel confident that those limits actually represent the bounds of your current system. But that's getting fairly technical and what Wheeler does go on to say is that, in real life we often have fewer data points to work with.   0:27:31.4 JD: So you can actually compute limits with as few as five or six values, and they can still be meaningful, now they're gonna be not a solid, meaning that each individual data point for a while that you add could potentially shift those limits more than you'd like, because there are a few data points that the limits are based on. But once you get to 17, 18, 19, 20 points, they start to solidify pretty good unless there's some significant change, like one of those patterns I talked about in your data. But an important thing to keep in mind is, is we're using a process behavior chart for continual improvement, so we're taking improvement measurements, not accountability measurements. I'm not trying to paint a certain picture of what my system looks like, I'm not trying to write a fiction about what's happening in my system, I'm actually trying to improve, so I don't really care what the data looks like. I'm not worried about being judged or rated or ranked, it's not an accountability thing, it's an improvement thing. And so I'm just trying to represent the system accurately so that I actually know that what I'm trying is working or not working. It's a completely different mindset. That whole sort of like trying to look better is completely removed from the picture through this type of mindset.   0:28:55.7 AS: I'm just picturing some sort of process where there's a measurement of temperature and the temperature keeps rising, but the worker says, "Boss there's a fire." And the boss said, "There's not enough data yet to confirm that." It only seems like a small fire right now, so I need more data points. Well, sometimes you have to act without thinking about the data and make an assumption that you may be wrong. You turn on the fire sprinklers, boom, and it wasn't a fire, but the damage of letting that go for long and saying I need more data doesn't make sense.   0:29:34.1 JD: Yeah, yeah, that doesn't really work. But the idea with the baseline is, basically, if you wanna improve something, the first thing you do is before you try anything, just gather some baseline data first so you can understand the current conditions. And in that attendance as an example, maybe you don't wanna wait for monthly attendance data, maybe wanna look at daily attendance day, what you have in a school, and just plot that over 12 days, 15 days, two or three weeks, and you can start to get a sense for what this looks like on a daily basis, and then you could try to improve it and see if that improvement has an impact on the data over time.   0:30:15.6 AS: Good, well, let me summarize this, but I have to start off with... My grammar is not particularly great, and since you're more of a school teacher than I am, I may need help with what you said. I think what I got correctly was data have no meaning apart from their context.   0:30:33.6 JD: Yeah, what did I say? Let me see.   0:30:38.5 AS: I always get confused if data is plural or singular.   0:30:41.8 JD: Yeah. Well, it can be either. So in this case, I was using data as a plural, so that's my point. I think technically the singular of data is actually datum. Obviously, nobody uses that 'cause it sounds really weird, but data can be plural, I think so.   0:30:57.4 AS: That sounds awfully Latin of you, alright. Number two, the data is the voice of the process, and that we control the process, not the data, and number 3 we plot the data in time order. Number four, two or three data points are not a trend. And number five is show enough data to illustrate the baseline. Anything you need to say to wrap all this up.   0:31:20.4 JD: Yeah, I just think that... I've mentioned this multiple times. I think when you're talking about continual improvement, primary tool is that process behavior chart, it allows you to visualize your data in a way that makes sense, and then the skill set that you have to learn is how to interpret the process behavior chart. How to use them effectively, how to create useful charts and then underlying... Understanding that underlying logic of process behavior charts. There's other tools, obviously in the improvement tool kit, but I actually think that that particular chart is the most important in my view. And I think with those charts, that tool in hand, we can avoid then those arbitrary and capricious goals that are so pervasive in our sector, basically.   0:32:10.6 AS: Well, that's exciting, and I'm excited for our next session when we talk about the final five. So John, 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 John's book, Win-Win: W. Edwards Deming, the System of Profound Knowledge and the Science of Improving Schools on Amazon.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."

In The New Economics, Deming said “The individual, transformed, will perceive new meaning to his life…” (3rd edition, page 63) But are we ever completely transformed? Discover why Bill Bellows believes that transformation is an ongoing process and how you can keep your learning journey going. 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 Bill Bellows, who has spent 30 years helping people apply Dr. Deming's ideas to become aware of how their thinking is holding them back from their biggest opportunities. And the topic for today is, in this episode 17, Diffusion from a Point Source. Bill, take it away.   0:00:29.6 Bill Bellows: And the title coincidentally, was the focus of my Master's thesis. We'll look at that later.   0:00:37.1 AS: It wasn't a rock and roll song. Yes, correct.   0:00:39.9 BB: No, not a rock and roll. [chuckle] Actually, Diffusion from a Point Source. Was that Mick Jagger or Keith Richards? Maybe it was Taylor. Maybe it was Taylor Swift. Okay. So some opening remarks, and then we'll get to today's feature. And I mentioned in the past, I go back and listen to the podcast, read through the transcripts, and it's very much like “Production Viewed as a System” - is to talk with people that have listened to it, listened to it myself and ask, have I... Are there holes in the explanation? Can I add some more clarity to it? The process I use for these podcasts is, some title comes to mind. I've got a long list that we started with at the very beginning, and then some other topics come up for any of a variety of reasons.   0:01:35.3 BB: And we'll have a title, have an outline, but then as we get involved in the conversation, something I say leads to something that you say leads to something that's not on the list. And sometimes some of those ad-libs, I go back and listen to and say, "Well, I don't know that sounded right. I just wanna add a little bit more clarity". Another thing I wanna say at the outset for those listening, is [chuckle] there is... Somebody posted somewhere on social media that one of the sessions was a total waste of time to listen to which I think is unfortunate. But what I like to say is, where I'm coming from to support The Deming Institute, as your ambition is as well, is to help individuals in respective organizations learn about Dr. Deming's ideas, try to apply them, deepen their understanding, explain them to others, and that's the target audience.   0:02:48.0 BB: So, for those who find that boring, well maybe this is not the podcast for you. And so, and the other thing I wanna say along those lines is, for the majority of my time at Rocketdyne, I had the responsibility of being a transformation agent or transformation person was part of my job. Now, I was brought in, I didn't have that job to begin with. The job I had to begin with was to lead the effort to provide training, facilitation of applications of Dr. Taguchi's ideas. And what I've shared in these podcasts is a lot of what I was doing early on was helping people put out fires.   0:03:38.2 BB: And that's not what Dr. Taguchi's ideas are about. His ideas are about improving the robustness of the performance of a product or service. Whereby what robustness Dr. Taguchi means is "it performs as an athlete incredibly well in spite of differing weather conditions." So the ability of a marathoner to run very consistent fast times in spite of the weather, in spite of the altitude. And so you're getting consistently high, or consistently faster and faster times. That's what Dr. Taguchi meant by, means by, his work means by "robustness."   0:04:16.2 BB: And what I was doing was using tools and techniques associated with his ideas to fight fires. And then, I got frustrated by that. And that led me to Dr. Deming's work, led me to revisit Dr. Deming's work. I had met him in 1990 and The New Economics came out in '93, and I had a couple of years of this frustration. The exciting thing was solving, getting involved, working with some really exciting people, and solving some very high visibility issues. But it wasn't breaking in as much as I would've liked into the, into the robustness piece. And when I came across Deming's work, I started to understand, it gave me a lot of food for thought as to why that might be the case. Now what is meant by transformation? And Dr. Deming uses that term, an individual transformed.   0:05:07.8 BB: And I had asked people that were close to him like, what is his operational definition of transformation? And when I explained it to them, I said, this is what I think he means this. And typically people say that's, they agree with that. And so my simple explanation of what I think Deming meant by transformation is as simple as, me saying to you, the professor to the student, “Andrew, how did you do on the exam?” Whereas I've said in the past, that makes me an observer of your learning to changing the question to how are, how did we do on the exam, where I become a participant? So I look at, so to me, the transformation Deming's talking about is that I no longer look around at things and see myself as separate from them. I look at myself as connected to them, and others being transformed or likewise seeing themselves as integral to what's going on, not watching it go by. Another reason I wanna bring that transformation agent piece up is part of my job, not part of my job, so I went from being mostly about Taguchi's work to mostly about Deming's work because I felt it was far more vital to focus on what Deming's talking about, the transform, how the organization and transform how the individuals operate. Another thing I wanna say there is what I think is interesting, if you look at the forward to Out of the Crisis and The New Economics.   0:06:48.1 BB: In Out of the Crisis, which I think was 1986 or so timeframe, Deming talked about the aim of this book is to help transform organizations. And then in The New Economics, he talks about the aim of this book is to help transform individuals. So he went through, he's shifted his focus from I'm trying to help organizations to I'm trying to help individuals. And that's what I'm hoping to do, interacting with you in these podcasts. So, on the one hand, I'd say to those listening, I don't know what your role is. If you're a transformation agent, that's one role. You may be an individual contributor, a senior software person, a marketing person, which means your job title does not include transforming the organization.   0:07:37.8 BB: So, what does that mean? It means some of what we're talking about may not apply to you. You may be personally excited about the Trip Report and, but it may not be your job to hold seminars within your respective organizations and go off and explain that to people. You may alienate people who think that's their job. So, I just wanna say, ask people, to be careful about what your role is in your organization. I've mentored many people and I'm used to going in and being the transformation person. And, one person I was working with, and she was all excited to wanna go share the Me-We Trip Report with her peers in this company doing software. And I said, "You can't do that". And she's like, "Well, why?" I said, "It's not your job". I said, "One is if you call a meeting to talk about transformation of the organization, or you get into that territory. I said, you're stepping on the toes of people who have that responsibility, perhaps. Or somebody's gonna say, wait, I thought we paid you to be a software engineer. Now you're over here. So, now you sound like you're astray, you're a loose cannon".   0:08:56.8 BB: Now I said, to this person, I said, now if you... There may be a place for you to say, "Hey, I wanna show you this neat solution.” If you think they're interested, ideally they ask you to show you how you did that. So, I think there's a difference when it comes to implementing these ideas, I would just advise some caution to people to not overstep their bounds and what it means to bring these ideas to the organization. So, I just wanted to say that.   0:09:32.4 AS: Yep. I just wanted to highlight the word transformation for a second. And the dictionary definition says, "transformation is a thorough or dramatic change in form or appearance. A transformation is an extreme radical change." And that's interesting, 'cause they say in form or appearance that you could have someone do a facelift that dramatically changes their face and the way they appear. But, has it been an internal transformation? Maybe, maybe not.   0:10:10.9 BB: Well, what's funny is, I mentioned that in previous podcasts, 'cause once a month for 17 years, I hosted an Ongoing Discussion where there'd be... I could have you on as a Thought Leader on a topic near and dear to you. And we send the announcement out and people would call in and it took a few years for Russ to agree to do it. And then, he eventually did, and he did it every January. Typically people would, every month be somebody different. But once I saw Russ's excitement by it, then I said, "Russ, every January we're gonna have you", we did it for four years, and every January I'd fly out to Philadelphia and be with him. So, the last time I did it with him, we were in his apartment. We were sitting pretty close together over the small desk. And in the sessions, the term transformation came up. So, the last session ends, we did four one-hour sessions over two days. The last session ends. And I turned off my recorder. And I said, "Russ, it just dawned on me that you and Deming, you and Dr. Deming both talk about transformation".   0:11:26.8 BB: And I said, "Dr. Deming talks about a personal transformation - I see the world differently.” And Russ looks at transformation as an attribute of a solution. That “we used to do it this way, now we do it this way.” And so, his is not transformation of an individual, but transformation of a solution. And I said, I just... I threw it out as I just, "You both used the word, but you use it differently". And I said something like, now I was waiting to see what he would say with that. And he looks at me and he says, "I see no value in that conversation", which followed by "let's go get lunch."   [laughter]   0:12:22.8 AS: Exactly.   0:12:24.0 BB: And so I thought, oh, I was really looking forward to exploring that space with him. And I shared that conversation with one of his peers later that night. And he said, "He said that?" I said, "Not only did he say that", he said, "You know what? I really wasn't surprised". 'Cause Russ was... It seemed to be a little bit too abstract for him. Anyway, but it's, but he would've put it, "What is this transformation stuff?"   0:12:51.0 AS: That, it's interesting because sometimes we talk about the why isn't Deming more widely accepted and that type of thing. And I think one of the things is that he's driving for transformation versus I think majority of people are providing information and here's how you do Lean, here's how you do this, here's how you do statistics or whatever, and here's all the information. And then you use that to to make better decisions. I think Dr. Deming was never about being better in our decisions but about how do we transform the way we think.   0:13:33.9 BB: Yes.   0:13:34.8 AS: And also the second part is that the idea of shifting from transforming an organization to transforming an individual. I guess an organization doesn't transform unless the leadership has already transformed or is in a process of transformation. So, therefore targeting the individuals for trying to help them get a transformation ended up being the most important or first step, I'm guessing.   0:14:00.2 BB: Oh yeah. No, I thought it was just so neat to see that shift. I don't know if we've talked that much in these podcasts about transformation. I'll have to go back and check. But what we were doing within Rocketdyne to help differentiate, 'cause language is so important. What do we mean by transform? Because it's a very casually used term and I was trying to, you know, with colleagues at Rocketdyne trying to differentiate what Deming's use of that term. 'Cause we liked the term but the challenge became if we used it did it adopt a meaning that he didn't have in mind in which case we're off to the Milky Way.   0:14:48.8 BB: But what we did was try to differentiate physical change from mental, a physical shift from a mental shift. I guess to me a big part of what he is talking about is going from seeing parts to seeing systems to seeing things as being connected to start thinking about as Edgar Schein would as Peter Senge quoted Peter Schein, Peter Senge quoted Edgar Schein, "Culture are the assumptions we cannot see".   0:15:21.5 BB: And, so I was focusing on is we talk about, there's culture, culture comes from the assumptions. The assumptions come from beliefs and that's associated with our thinking. And that's the space that I think has... is the space to be to really believe, to really implement what Dr. Deming's talking about for all those benefits we've been talking about. And so the word, so in the training we were doing in our InThinking Roadmap, we differentiated reforming and we said "reforming is a physical change. Giving things a new name, adding more steps to the process. It's change you can, it's rearranging the deck chairs on the Titanic." And there's nothing wrong. You can move people together to be closer physically but that doesn't move them together mentally. So, there's a sense of we want everyone to be in the same room physically but they're... But you can hear they're in separate rooms mentally.   0:16:22.7 BB: And we've talked about this in a Me Organization I hand off something which is good to you and if it's not good, you give it back to me. If it is good, you say thank you and I'm separated. I am physically and mentally separated and there's nothing wrong with being physically separated I have to hand off to you. But how about an environment where I am mentally, we are mentally connected because we're thinking together. So if you come back to me and say, "Bill I'm having trouble getting these things together". And I say, "Well, hey I can, I..." not only do I understand that I caused that but I can possibly do something about that. That's the mental transformation piece. So there's... I look at it as there's nothing wrong. I look at it as there's a place for transforming, reforming, moving things to be closer, minimizing number of steps. Nothing wrong with that. But that's not what Deming was talking about. He was talking about transforming which is a change of how we see the world. How we hear the world.   0:17:25.3 AS: Yeah. And when I look at the System of Profound Knowledge and we look at Appreciation for a System, look at Knowledge about Variation and Understanding the Theory of Knowledge and then Psychology, I would say the one you mentioned about Appreciation of a System is the one that brings true transformation because we are taught to look so narrowly. And when we start to look at the bigger system it just blows your mind.   0:17:58.9 BB: Well, it's...it... No, I absolutely agree. I can remember in the early ‘90s I had met Dr. Deming once and I thought that's fascinating. And, I put it aside and got buried in the Taguchi stuff and then began to see the issues as I had mentioned in previous podcasts as well as today. And I started thinking there's, there's something missing. And, in the Taguchi school it was, we need more tools, more advanced tools. That's not about transformation. There's nothing in Taguchi's work that was about the transformation that Deming's talking about. And I'm not aware of that mindset. Well, I've not come across that mindset in many places. I don't see it in all the...a lot of the traditional improvement techniques whether it's Lean or Six Sigma or Operational Excellence. I don't see that, that focus. I agree.   0:19:07.6 AS: And, I bought this book Guide to Quality Control by Kaoru Ishikawa.   0:19:11.2 BB: Yep, yep.   0:19:12.6 AS: I got it in 1990. And, but it's a great example of, the objective wasn't a transformation. The objective was understand these tools and maybe that leads to a transformation, maybe not. That wasn't what he was aiming for. He was saying, "Here's the tools and here's how you can apply them".   0:19:32.2 BB: Well, I used to debate with some co-workers and his, one co-worker in particular. And his mindset was, focus on the tools, and the language, in the conversation we're having, his theory was, "Get people to apply the tools and the transformation will eventually happen". I had the same thought.   0:20:00.1 AS: If that was the case, we'd all be transformed already because we're all applying tools every day.   0:20:04.7 BB: And 'cause we, I had heard a comment, I was at a Taguchi conference and I heard a comment. And as soon as I got back to my office, and this gentleman we're both at work really, really early, we'd go down and get coffee at a quarter to six, go back and sit in his office for a couple hours and just have some great, great, great conversations. And I shared with him, I was at a Taguchi conference and somebody said, the reference was, "You wait for the... " It was something, "The journey begins after the transformation starts". And as soon as I said that, he said, "I think it's the other way around", that the transformation happens after. And I thought to myself, I knew you'd say that, because that was his attitude. Get 'em to use the tools, get 'em to use the tools, get 'em to use the tools. And I kept looking at it as, no, that does not. Yeah. I mean it doesn't mean you don't do it, you don't do something. But I think when you begin to see the world and hear the world differently as we're trying to convey, to me that's when the rubber really begins to hit the road. That's when you move. And again, as we talked, there's nothing wrong with tools and techniques, but tools and techniques are guided by your understanding of the system and the other things. And it's just not enough to be a tool head.   0:21:48.7 BB: Other things I wanted, oh, okay. [laughter] So let's go back the cloud model from number 16. And what I did not reference again, 'cause I went back and looked at it and there's what we shared, but what I wanted to add to it was, one is the idea learned from Barry Bebb that you're an individual contributor trying to get ideas up to the cloud, the cloud being the executives in their meeting space, and the idea of handing off to somebody above you. And then the idea that that transfer is going to take a few times from person to person to get someone in the cloud transformed with an appreciation. And relative to Deming's work, it involved the transformation.   0:22:34.9 BB: If it involves trying to get Dr. Taguchi's up to the cloud, ideas to the cloud, manner involve what we're talking about relative to Deming's work, fine. But the other aspect that I then neglected to mention is what Barry's talking about is, is once it gets to the cloud, then what rains down on the organization is the beginning of, in our case, transforming the organization. That's the raining down. So the cloud is not just that place on high that things get up to, but the idea of a cycle that things then start to flow down. And so, I mentioned, you know, I got back from that very first meeting with Barry and went into my boss's boss's office and that I had had that meeting, and little did I know what I was gonna learn from Barry.   0:23:26.8 BB: And learning from Barry, you either go back to... You have to be in your organization, find somebody higher, and immediately I thought I wanted that person to be Jim for his influence. And so I would meet with him on a regular basis. And, and what I was looking for is, what could he and I do together? Because some things take time and some things can be done tomorrow. So I would go into him once a month with some ideas, give him some status of what's going on. So one time I went in and I had an idea, I'd mentioned to him that after every launch of a rocket with a Rocketdyne engine, there'd be a loud speaker announcement. And the loud speaker announcement might say, "Congratulations to the Space Shuttle Main Engine team for a job well done.” Congratulations to the Delta team for the engines made for the Delta vehicle or to the Atlas program.   0:24:24.3 BB: And what I shared with Jim is that I had mentioned that loud speaker announcement to a friend in facilities who was a manager in facilities. And I said, "How does it feel when you're in facilities and you hear that announcement?" And her comment was, "You get used to it."   [laughter]   0:24:43.9 BB: You get used to being ignored. Well, I mentioned to a friend in HR, and he shared with me every time he would hear that announcement in HR, he said he and the guy on the other side of the cubicle wall would stand up and give each other a high five and say, "Way to go", 'cause they were not in the announcement. So I went in to see Jim and I said, I mentioned the woman's name. I said, she said, "You get used to it." And he looks at me and he says, "I want everyone in this organization to identify with every launch." He said, "I don't care if you're in janitorial services cleaning the restrooms." He said, "I want everyone to identify." Well then I said, "Well, that announcement doesn't." And I said, "Could we change the announcement?" And he was about to write it down and he says, "Well, we can do that right now." I'm thinking, "Oh, baby." [laughter] So he calls up the Director of Communications who sits across the hall from him and says, "Would you mind coming to my office for a minute?" Okay. So the person comes into the office, he says, "Do you know Bill?" And the person said, "Yeah, I know Bill". And Jim says to this person, "Could we change the loudspeaker announcement to say from now on, "Congratulations to Team Rocketdyne?" And she goes, "Sure, Jim, we could do that." [chuckle]   0:26:17.9 BB: And so, I had a Taguchi class later that afternoon, and somehow I mentioned the announcement. I didn't mention what I had done, but I somehow made reference to it. And people were used to that. And I remember saying to them, so what if you aren't on one of those teams? And people just said... This is how we operate. It's part of the culture to celebrate those individual teams. And I remember saying to them something like, "Well, if that announcement ever changes, call me," or something like that. It was something like that. And sure enough, when the announcement was made within a week, but I felt it was something, I was looking for things that I could do to influence the culture. Little things that ideally could be, and you know, I was also appreciative of what could Jim do? Now, several years later, the announcements went back to what they were. I'm not quite sure why, Jim had moved on. For all I know the programs were tired of “Team Rocketdyne” where, Team Rocketdyne, it's Team Space Shuttle Main Engine. And so some of the people complained to me that the announcement had shifted, and I turned to one of them and I said, "You go and fight that battle". I said, "If you want it to change, you go, go let the communications person, you go fight for it". And the thing I'd like to, a couple other things I want to point out before we get into the features is...   0:27:55.9 AS: Just so you know, we only got, we got less than 10 minutes, it's a tight show today.   0:28:00.7 BB: Alright. Let's jump. Let's jump to Diffusion From a Point Source, Andrew.   0:28:04.2 AS: Yep.   0:28:05.4 BB: So my Master's thesis back in the, was right around the time of Three Mile Island, I was writing my Master's thesis. And for those who may not recall, Three Mile Island and somewhere in the hills of Pennsylvania was a nuclear reactor that nearly melted down and diffused. [chuckle] If things had gone worse, it would've diffused a lot of bad radioactivity downstream from a stack, from a point. And so, my Master's thesis was looking at diffusion, how very much like that. And what was funny is I would explain to aunts and uncles and family members, "What is your thesis about?" And I say, "Well, remember Three Mile Island? I said, what I'm trying to do is model how it is, how does that radioactivity spread out downstream? How does it go wider and wider and higher and higher? How does it spread like smoke does if you blow out a match and how does that spread?" That's diffusion from point source. And part of what I had in mind with that topic for the audience is for each of us being a point source on our respective organizations and how are we diffusing what we're aware of within the organization, which in part has to do with being a transformation agent or an agent of, playing a role in the organization.   0:29:32.7 BB: The other thing I wanted to point out is in, in my engineering studies, and the equations that we would use about diffusion, um, has a role here. And if you think of a bathtub, so I imagine you're in the bathtub, you've got hot water coming in and the heat from that water coming in. And I'm trying to think, yeah, imagine the water is lukewarm and you're laying in there and you want it to be warmer, so you crank up the temperature. And then you can begin to feel that hotter water hitting your toes and then spreading it - diffusing. And there are mathematical equations I was studying that have to do with that. And what the equations are about is how does the temperature at any point in the tub change, and how does it change throughout the tub? So there's two aspects of change, at a given point, how is it changing over time? And then how is that change spreading until it starts to fill the entire tub? And so it could be you've got a 100 degree water coming out or 120 degree water, and in time the entire bathtub is 120 degrees, in time, which means the diffusion has stopped because it's all the same.   0:31:04.0 BB: And then, a couple hours later, it's all about the room temperature. Well, the analogy I wanna make is imagine going off to a Deming seminar all excited by what you've learned, and you go into your organization and you try to diffuse these ideas or, or another way of looking at it is, I would be invited into an organization and present Dr. Deming's ideas. It's kind of a point source. And so the ideas come out and people feel that spread across the organization. But what tends to happen is within a week, everything's back to room temperature.   [laughter]   0:31:47.8 BB: And that's, and that's the idea being, Deming's ideas come in or whatever the ideas come in, and then they're spreading in space and in time, and then we're back to where we were before. What I was very excited about, most fortunate about, and what we were doing at Rocketdyne is that what's missing from that equation that I just explained to you is a point source. And so when you're modeling, when you go back to the thermodynamics laws that I was modeling, if in the bathtub, there's a... If you've got a source of heat, you're generating energy in that environment, then the bathtub's going to get hotter and hotter and hotter and hotter and hotter. But without that point source, that source of transformation, which is constantly going on, everything goes back to room temperature. So what we were trying to do at Rocketdyne was, how do we take the ideas we're given, integrate them with Ackoff's ideas and Taguchi's ideas and try to create...   0:33:04.1 BB: Not let things go back to room temperature, but what would it take in conversations amongst ourselves and sharing that with others, that we had a constant source of energy, which gets things hotter and hotter and hotter and hotter and hotter. When it comes to Three Mile Island, the point source was an out-of-control reaction. But what we were trying to do is create a, have an environment where a lot of energy was being created, and that led to rethinking what these ideas are about, bringing others into the room, whether it be Ackoff or others. And I find without that, eventually things just go back to normal. And so what is...   0:33:50.8 AS: And what is that back... The back to normal thing, is that, like if we think about gravity as a law, it's naturally gonna pull things back to Earth.   [chuckle]   0:34:04.7 BB: If you go back to room temperature, you go back to where you were.   0:34:09.3 AS: What is it that brings humans back? Is it the...   0:34:12.2 BB: Well, you end up, you go back to blaming the willing workers for the red beads. You go back to all the things that Dr. Deming's trying to pull us away from, and there's this natural force to pull them back to that, you end up with a change in management. Dr. Deming's 14 points of lack of constancy of purpose. And so what we're talking about with Deming's ideas is a source of ideas, energy to transform. And what we're fighting is, individually that we stop learning, individually we stop sharing, individually we stop doing something with it. And so you just unplug the point source and you'll be back to room temperature pretty quickly.   0:34:55.5 AS: So how would you... What is the main message you wanna get across to the audience about this as we wrap up?   0:35:03.0 BB: Message is, find a peer group that you can discuss these ideas with. And that's what's missing is find people you can discuss, listen to the podcast, pay attention to DemingNEXT, find people to share the ideas with, and out of it will come more energy. And, but the idea is that don't stop learning. Don't stop sharing. I am very fortunate that every day I have conversations with people around the world, and it's causing me to reflect on things that happened. And to me, it's helping me stay engaged, keep rethinking what the ideas we're talking about. And so the idea is that I think without that, then individually we go back to room temperature, we go back to where we were before we started exercising. And, but I think what I would like to think is that people listening to this podcast can find again peers to share it with and on a recurring basis. And so again, I'm talking with people around the world every week, and to me that's, part of this is what we're doing at Rocketdyne with these monthly phone calls is just staying engaged, staying in the game, staying in the game, staying in the game. So that's the diffusion from.   0:36:24.7 AS: And to bring it back to the beginning of our conversation, I think that, I guess transformation is when you don't go back to room temperature.   0:36:36.0 BB: It's an ongoing transformation. And this is... There's very few things Deming said I disagreed with. One of them is, and [chuckle] he said, "An individual transformed will create an example". I don't think there's any such thing as an individual transformed, I would say an individual, once their transformation begins but I don't... But thinking in terms of, "once transformed," and I think I mentioned on the podcast, 'cause I had a student in Northwestern years ago, and they're doing presentations at the end of the course on how the course hass impacted them, taking notes from their daily journals. And there were a group presenting that night. The other group was gonna present the next night. So one was anxious, one was calm, and I went up to one of the calm students and I said, "Yeah, so what's new?" And he turned to me and he said, "I'm fully transformed".   [chuckle]   0:37:34.3 BB: No, what we're talking about Andrew, is there's no such thing as... Because there's, if you understand the point source concept, there's no "fully transformed."   0:37:43.1 AS: Yep, that sounds...   0:37:44.6 BB: So then the question becomes, how do we enter and individually stay in that group?   0:37:51.4 AS: So transformation is an ongoing journey. Bill, on behalf of everyone at The Deming Institute, I wanna thank you again for the discussion. And 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. 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.”

In this episode, John Dues, part of the new generation, discusses healthy goal setting through a Deming lens. He challenges the desperation behind typical goal setting in organizations and emphasizes the importance of understanding process, variation, and stability before setting goals. The conversation delves into the complexities of goal setting in education, system design for improvement, and analyzing data patterns for system stability.