Lean Operations with John Carrier
February 15th, 2023
32 mins 52 secs
About this Episode
Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.
In this episode of the podcast, the topic is "Lean Operations." Our guest is John Carrier, Senior Lecturer of Systems Dynamics at MIT. In this conversation, we talk about the people dynamics that block efficiency in industrial organizations.
If you like this show, subscribe at augmentedpodcast.co. Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.
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The core innovative potential in most organizations remains its people. The people dynamics that block efficiency can be addressed once you know what they are. But there is a hidden factory underneath the factory, which you cannot observe unless you spend time on the floor. And only with this understanding will tech investment and implementation really work. Stabilizing a factory is about simplifying things. That's not always what technology does, although it has the potential if implemented the right way.
TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. And our vision is a world where technology will restore the agility of frontline workers.
In this episode of the podcast, the topic is Lean Operations. Our guest is John Carrier, Senior Lecturer of Systems Dynamics at MIT. In this conversation, we talk about the people dynamics that block efficiency in industrial organizations.
Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.
John, welcome to the show. How are you?
JOHN: Trond, I'm great. And thank you for having me today.
TROND: So we're going to talk about lean operations, which is very different from a lot of things that people imagine around factories. John, you're an engineer, right?
JOHN: I am an engineer, a control engineer by training.
TROND: I saw Michigan in there, your way to MIT and chemical engineering, especially focused on systems dynamics and control. And you also got yourself an MBA. So you have a dual, if not a three-part, perspective on this problem. But tell me a little bit about your background. I've encountered several people here on this podcast, and they talk about growing up in Michigan. I don't think that's a coincidence.
JOHN: Okay, it's not. So I was born and raised in the city of Detroit. We moved out of the city, the deal of oil embargo in 1973. I've had a lot of relatives who grow up and work in the auto industry. So if you grew up in that area, you're just immersed in that culture. And you're also aware of the massive quote, unquote, "business cycles" that companies go through.
What I learned after coming to MIT and having the chance to meet the great Jay Forrester a lot of those business cycles are self-inflicted. What I do is I see a lot of the things that went right and went wrong for the auto industry, and I can help bring that perspective to other companies. [laughs]
TROND: And people have a bunch of assumptions about, I guess, assembly lines in factories. One thing is if you grew up in Michigan, it would seem to me, from previous guests, that you actually have a pretty clear idea of what did go on when you grew up in assembly lines because a lot of people, their parents, were working in manufacturing. They had this conception. Could we start just there? What's going on at assembly lines?
JOHN: I'm going to actually go back to 1975 to a Carrier family picnic. My cousin, who's ten years older than I, his summer job he worked at basically Ford Wayne, one of the assembly plants. He was making $12 an hour in 1975, so he paid his whole college tuition in like a month. But the interesting point was he was talking about his job when all the adults were around, and he goes, "Do you know that when they scratch the paint on the car, they let it go all the way to the end, and they don't fix it till it gets to the parking lot?"
And I'll never forget this. All the adults jumped on him. They're like, "Are you an idiot? Do you know how much it costs to shut the line down?" And if you use finance, that's actually the right answer. You don't stop the line because of a scratch; you fix it later. Keep the line running. It's $10,000 a minute. But actually, in the short term, that's the right decision. In the long term, if you keep doing that, you're building a system that simply makes defects at the same rate it makes product. And it's that type of logic and culture that actually was deeply ingrained in the thinking. And it's something that the Japanese car companies got away from.
It's funny how deeply ingrained that concept of don't stop the line is. And if you do that, you'll make defects at the same rate that you make product. And then, if you look at the Detroit newspapers even today, you'll see billion-dollar recalls every three months. And that's a cycle you've got to get yourself out of.
TROND: You know, it's interesting that we went straight there because it's, I guess, such a truism that the manufacturing assembly line kind of began in Detroit, or at least that's where the lore is. And then you're saying there was something kind of wrong with it from the beginning. What is it that caused this particular fix on keeping everything humming as opposed to, I guess, what we're going to talk about, which is fixing the system around it?
JOHN: There's a lot of work on this. There's my own perspective. There's what I've read. I've talked to people. The best I can come up with is it's the metrics that you pick for your company. So if you think about...the American auto industry basically grew up in a boom time, so every car you made, you made profit on. And their competitive metric was for General Motors to be the number one car company in the world.
And so what that means is you never miss a sale, so we don't have time to stop to fix the problem. We're just going to keep cranking out cars, and we'll fix it later. If you look at the Japanese auto industry, when it arose after World War II, they were under extreme parts shortages. So if one thing were broken or missing, they had to stop. So part of what was built into their culture is make it right the first time. Make a profit on every vehicle versus dominant market share.
TROND: Got it. So this, I guess, obsession with system that you have and that you got, I guess, through your education at MIT and other places, what is it that that does to your perspective on the assembly line? But there were obviously reasons why the Ford or the Detroit assembly lines, like you said, looked like they did, and they prioritized perhaps sales over other things.
When you study systems like this, manufacturing systems, to be very specific, how did you even get to your first grasp of that topic? Because a system, you know, by its very nature, you're talking about complexity. How do you even study a system in the abstract? Because that's very different, I guess, from going into an assembly and trying to fix a system.
JOHN: So it's a great question. And just one thing I want to note for the audience is although we talk about assembly lines, most manufacturing work is actually problem-solving and not simply repetitive. So we need to start changing that mindset about what operations really is in the U.S. We can come to that in the end.
JOHN: I'll tell you, I'm a chemical engineer. Three pieces of advice from a chemical engineer, the first one is never let things stop flowing. And the reason why that's the case in a chemical plant is because if something stops flowing for a minute or two, you'll start to drop things out of solution, and it will gum everything up. You'll reduce the capacity of that system till your next turnaround at least. And what happens you start getting sludge and gunk.
And for every class I was ever in, in chemical engineering, you take classes in heat transfer, thermodynamics, kinetics. I never took a class in sludge, [laughs] or sticky solids, or leftover inventory and blending. And then, when I first went to a real factory after doing my graduate work, I spent four to six years studying Laplace transforms and dynamics. All I saw were people running around. I'm like, that's not in the Laplace table.
And, again, to understand a chemical plant or a refinery, it takes you three to five years. So the question is, how can you actually start making improvement in a week when these systems are so complex? And it's watch the people running around. So that's why I focus a lot on maintenance teams. And I also work with operations when these things called workarounds that grow into hidden factories. So the magic of what I've learned through system dynamics is 80% to 90% of the time, the system's working okay, 10% or 20% it's in this abnormal condition, which is unplanned, unscheduled. I can help with that right away.
TROND: So you mentioned the term hidden factories. Can you enlighten me on how that term came about, what it really means? And in your practical work and consulting work helping people at factories, and operations teams, and maintenance teams, as you said, why is that term relevant, and what does it really do?
JOHN: Great. So I'm going to bring up the origin. So many people on this call recognize the name Armand Feigenbaum because when he was a graduate student at the Sloan School back in the '50s, he was working on a book which has now become like the bible, Total Quality Management or TQM. He's well known for that. He's not as well known for the second concept, which he should be better known for. Right after he graduated, he took a job in Pittsfield, Massachusetts, for one of the GE plastic plants.
Here he comes out of MIT. I'm going to apply linear equations. I'm going to do solving, all these mathematics, operation constraints, all these things. When he gets into that system, he realizes 30% of everything going on is unplanned, unscheduled, chaotic, not repeated. He's like, my mathematical tools just break down here.
So he did something...as important as marketing was as an operational objective, he named these things called hidden factories. And he said, 30% of all that work is in these hidden factories. And it's just dealing with small, little defects that we never ever solve. But over time, they actually erode our productivity of systems that can eat up 10% to 20% of productivity. And then, finally, it's work that I'm doing. It's the precursor to a major accident or disaster. And the good side is if you leave the way the system works alone, the 80%, and just focus on understanding and reducing these hidden factories, you can see a dramatic improvement quickly and only focus on what you need to fix.
TROND: So, for you, you focus on when the system falls apart. So you have the risk angle to this problem.
JOHN: Exactly. And so just two things, I'm like a doctor, and I do diagnosis. So when you go to the doctor, I'm not there to look at your whole system and fix everything. I'm like, here are first three things we got to work at, and, by the way, I use data to do that. And what I realized is if everyone just steps back after this call and thinks about today, right? When you get to the end of the day, what percent of everything in that factory or system happened that was in your schedule?
And you'll start to realize that 30% of the people are chasing symptoms. So you need data to get to that root cause, and that will tell you what data to collect. And second, look for time because what you're doing is these hidden factories are trying to keep the system running because you have a customer. You have your takt time, and so people are scrambling. And if you put that time back into the system, that's going to turn into product.
TROND: John, I'm just curious; when you say data, I mean, there's so much talk of data and big data and all kinds of data. But in manufacturing, apart from the parts that you're producing, I mean, some of this data is hard to come by. When you say data, what data will you even get access to?
JOHN: I come from the Albert Einstein School is. I need a ruler, and I need a stopwatch. Go into any system that you work in, whether it be your factory or your house, and ask the last time someone measured how long something took, and you will find a dearth of that data. And the reason why I love time data is it never lies. Most data I see in databases was collected under some context; I can't use it. So I go right in the floor and start watching 5 or 10 observations and looking at all the variation.
The second point I ask is, what's a minute worth in your system or a second? So if we're in an auto assembly plant, in a chemical plant, if we're in a hospital, in an operating room, those minutes and seconds are hundreds of thousands of dollars. So within about 20 minutes, not only have I measured where there's opportunity, we're already on the way to solving it.
TROND: So, so far, you haven't talked much about the technology aspects. So you work at a business school, but that business school is at MIT. There's a lot of technology there. It strikes me that a lot of times when we talk about improvements, certainly when we talk about efficiencies in factories, people bring up automation machines as the solution to that tool. And I'm sure you're not against machines, but you seem to focus a lot more on time, on organizational factors. How should people think about the technology factor inside of their operations?
JOHN: So, first, you brought up...my nickname is Dr. Don't. And the reason they call me Dr. Don't [laughs] is because they'll go, "Should we invest in this? Can we buy these robots?" I say, "No, you can't do that." And I'm going to tell you why. First is, I was quote, unquote, "fortunate enough" to work in a lot of small and mid-sized machine shops during the 2009 downturn. And I was brought in by the banks because they were in financial trouble.
And the one thing I noticed there was always a million-dollar automation or robot wrapped in plastic. And large companies can get away with overspending on technology, small and mid-sized companies can't. And so what you really want to do is go and watch and see what the problem is, buy just as much technology as you need, and then scale that.
First is, like I just said, I was just in a plant a few weeks ago, and they just implemented several hundred sensors to basically listen to their system. That's all good. It's data we need. Two problems, why'd you put in several hundred and not put in 20? And second, when we inspected it, about 15% were either not plugged in or weren't reading. So what happened was if we would have started with 20 and put the resource in analyzing that data, then when we scaled to the several hundred, we'd have had our systems in place. Instead, we overwhelmed everyone with data, so it really didn't change the way they work. Now we fixed that.
But your question was, why am I skeptical or slow to invest in technology? Technology costs money, and it takes time. If you don't look at the system first and apply the technology to solve the system problem, you're going to end up with a million-dollar piece of equipment wrapped in plastic. If you go the other direction, you will scale successfully. And no one's better at this than Toyota. They only invest in the technology they need. Yet you can argue they're at least as technologically sophisticated as all the rest. And they've never lost money except in 2009 so that is a proof point.
TROND: What are some examples of places you've been in lately, I don't know, individual names of companies? But you said you're working kind of mid-sized companies. Those are...[laughs] the manufacturing sector is mid-sized companies, so that sounds very relevant. But what are some examples in some industries where you have gone in and done this kind of work?
JOHN: I work for large companies and small and mid-sized. And I'm a chemical engineer, but I love machine shops. So I sit on the board of a $25 million machine shop. They make parts for a diesel truck and some military applications. They make flywheels. So one of their big challenges is in the United States and in the world, we're suffering with a problem with castings. We received our castings. Interesting thing is there are void fractions.
One of the things I do want to share is as a systems guy, I'm not an expert in mechanical engineering or any of that, but I can add value by helping look for defects. Let me tell you what their challenge is. So, first of all, more of their castings are bad. Then this surprised me...I learned from asking questions. If you've ever been in a machine shop, one thing I learned about when you're making casting is that there are always bubbles in it. You can't avoid it.
The art of it is can you put the bubbles in the places where they don't hurt? You minimize the bubbles, and you move them to the center. So one is we're getting bad castings, but the second part was when we made some of these castings, and they had a void problem in the center. So that doesn't cause a problem with your flywheel. The customer sent them back because they're becoming oversensitive to the defects that don't count. And it's because they switched out staff.
So I guess what I'm trying to say here is our supply chain is undergoing this new type of stress because we're losing the type of expert system expertise that we've had from people that have worked in this industry 20 to 30 years. That's a really important aspect.
The second is we're in their line balancing all the time. I think a lot of the things you learn in class, you spend one class on load balancing or line balancing, operation and manufacturing, and then you go into a factory, and no one's doing it. So I just wanted to share two points. My one factor is doing that they cut 30% of their time.
Another system I'm working in they have one experienced supervisor managing four new people on four different setups. What I realized is there's not enough of that supervisor to go around. We're like, why don't we shoot videos like the NFL does [laughs] and watch those films of how people do their work? Because when you're an expert, Trond, and you go to do a task, you say, "That has five steps."
But if I sent you or me new, we'd look and go, "There are really about 80 steps in there." And you explained it to me in 15 minutes. How am I going to remember that? So shooting film so people can go back and watch instead of bothering your supervisor all the time, which they won't do. So what I do think, to wrap up on this point, is when you talk about technology, the camera, the video that you have in your pocket, or you can buy for $200, is the best technology you can probably apply in the next three to six months. And I would greatly encourage everyone to do something like that.
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TROND: I wanted to ask you then, derived from this, to what extent can some of these things be taught as skills on a systemic level in a university or in some sort of course, and to what extent? Do you really just have to be working in manufacturing and observing and learning with data on your own? By extension, to what extent can a manager or someone, anyone in the organization, just develop these practices on their own? And to what extent do you need mentorship from the outside to make it happen or see something in the system that is very difficult to see from the inside?
JOHN: So it's interesting you ask that because that's very much the problem I'm dealing with because as good as our universities are, the best place to learn operations in manufacturing is on the factory floor. So how do you simulate that approach? I teach lean operations at MIT Sloan. And what I do with my students is I ask them to pick a routine task, video two minutes of it, and reduce that by 30%. And I've done this two years in a row.
When you look at these projects, the quality of the value streams and the aha moments they had of time that they were losing is stunning. You know what the challenge is? They don't yet always appreciate how valuable that is. And what I want them to realize is if you're washing dishes or running a dishwasher, why is that any different from running a sterilization process for hospital equipment? Why is that any different from when you're actually doing setup so that maintenance can get their work done 30% faster?
I've given them the tools, and hopefully, that will click when they get out into the workspace. But I do have one success point. I had the students...for some classes, they have to run computers and simulations during class. So that means everyone has to have the program set up. They have to have the documentation. So you can imagine 5 to 10 minutes a class, people getting everything working right.
One of my teams basically said we're going to read...it took about five minutes, and they said, we're going to do this in 30 seconds just by writing some automated scripts. They did that for our statistics class, and then they shared it with their other classmates, beautiful value stream, video-d the screens, did it in about four or five hours. The next class they took later I found out they did that for a class project, and they sold the rights to a startup. So first is getting them that example in their own space, and then two, helping them make analogies that improving things in your own house isn't all that much different than the systemic things in a factory.
TROND: Learning by analogy, I love it. I wanted to profit from your experience here on a broader question. It takes a little bit more into the futuristic perspective. But in our pre-conversation, you talked about your notion on industry 4.0, which, to me, it's a very sort of technology, deterministic, certainly tech-heavy perspective anyway.
But you talked about how that for you is related to..., and you used another metaphor and analogy of a global nervous system. What do you think, well, either industry 4.0 or the changes that we're seeing in the industry having to do with new approaches, some of them technology, what is it that we're actually doing with that? And why did you call it a global nervous system?
JOHN: When I graduated from school, and I'm a control systems skilled in the arts, so to speak. And the first thing I did...this is back in the '90s, so we're industry 3.0. When you're in a plant, no one told me I was going to spend most of my time with the I&C or the instrumentation and control techs and engineers. That's because getting a sensor was unbelievably expensive. Two, actually, even harder than getting the budget for it was actually getting the I&C tech's time to actually wire it up. It would take six weeks to get a sensor.
And then three, if it weren't constantly calibrated and taken care of, it would fall apart. And four, you get all those three workings, if no one's collecting or knows how to analyze the data, you're just wasting [laughs] all your money. So what was exciting to me about industry 4.0 was, one, the cost of sensors has dropped precipitously, two, they're wireless with magnets. [laughs] So the time to set it up is literally minutes or hours rather than months and years.
Three, now you can run online algorithms and stuff, so, basically, always check the health of these sensors and also collect the data in the form. So I can go in, and in minutes, I can analyze what happened versus, oh, I got to get to the end of the week. I never looked at that sensor. And four, what excited me most, and this gets to this nervous system, is if you look at the way industries evolved, what always amazes me is we got gigantic boilers and train engines and just massive equipment, physical goods. Yet moving electrons actually turns out to be much more costly in the measurement than actually building the physical device.
So we're just catching up on our nervous system for the factory. If I want to draw an analogy, if you think about leprosy; a lot of people think leprosy is a physical disease; what it is is it's your nerves are damaged, so because your nerves are damaged, you overuse that equipment, and then you wear off your fingers. And if you look at most maintenance problems in factories, it's because they didn't have a good nervous system to realize we're hurting our equipment.
And maintenance people can't go back and say, "Hey, in three months, you're going to ruin this." And the reason I know it is because I have this nervous system because I'm measuring how much you're damaging it rather than just waving it. And now it becomes global because, let's say you and I have three pumps in our plant, and we need to take care of those. They are on the production line, very common. What if we looked at the name of that pump, called the manufacturer who's made tens of thousands of those? There's the global part.
So they can help us interpret that data and help us take care of it. So there's no defect or failure that someone on this planet hasn't seen. It's just we never had the ability to connect with them and send them the data on a platform like we can with a $5,000 pump today. So that's why I look at it, and it's really becoming a global diagnosis.
TROND: It's interesting; I mean, you oscillate between these machine shops, and you had a medical example, but you're in medical settings as well and applying your knowledge there. What is the commonality, I guess, in this activity between machine shops, you know, improving machine shops and improving medical teams' ability to treat disease and operate faster? What is it that is the commonality?
So you've talked about the importance, obviously, of communication and gathering data quicker, so these sensors, obviously, are helping out here. But there's a physical aspect. And, in my head, a machine shop is quite different from an operating room, for example. But I guess the third factor would be human beings, right?
JOHN: I'm going to put an analogy in between the machine shops at the hospital, and that's an F1 pit crew. And the reason I love F1 is it's the only sport where the maintenance people are front and center. So let's now jump to hospitals, so the first thing is if I work in a hospital, I'm talking to doctors or nurses in the medical community. And I start talking about saving time and all that. Hey, we don't make Model Ts. Every scenario we do is different, and we need to put the right amount of time into that surgery, which I completely agree to.
Where we can fix is, did we prepare properly? Are all our toolkits here? Is our staff trained and ready? And you'd think that all those things are worked out. I want to give two examples, one is from the literature, and one is from my own experience. I'd recommend everyone look up California infant mortality rates and crash carts. The state of California basically, by building crash carts for pregnancies and births, cut their infant mortality rate by half just by having that kit ready, complete F1 analogy. I don't want my surgeon walking out to grab a knife [laughs] during surgery.
And then second is, I ran a course with my colleagues at MIT for the local hospitals here in Boston. You know what one of the doctor teams did over the weekend? They built one of these based on our class. They actually built...this is the kit we want. And I was unbelievably surprised how when we used the F1 analogy, the doctors and surgeons loved it, not because we're trying to actually cut their time off. We're trying to put the time into the surgery room by doing better preparations and things like that. So grabbing the right analogy is key, and if you grab the right analogy, these systems lessons work across basically anywhere where time gets extremely valuable.
TROND: As we're rounding off, I wanted to just ask you and come back to the topic of lean. And you, you use the term, and you teach a class on lean operations. Some people, well, I mean, lean means many things. It means something to, you know, in one avenue, I hear this, and then I hear that.
But to what extent would you say that the fundamental aspects of lean that were practiced by Toyota and perhaps still are practiced by Toyota and the focus on waste and efficiency aspects to what extent are those completely still relevant? And what other sort of new complements would you say are perhaps needed to take the factory to the future, to take operational teams in any sector into their most optimal state?
JOHN: As a control engineer, I learned about the Toyota Production System after I was trained as a control system engineer. And I was amazed by the genius of these people because they have fundamentally deep control concepts in what they do. So you hear concepts like, you know, synchronization, observability, continuous improvement. If you have an appreciation for the deep control concepts, you'll realize that those are principles that will never die.
And then you can see, oh, short, fast, negative feedback loops. I want accurate measurements. I always want to be improving my system. With my control background, you can see that this applies to basically any system. So, in fact, I want to make this argument is a lot of people want to go to technology and AI. I think the dominant paradigm for any system is adaptive control. That's a set of timeless principles.
Now, in order to do adaptive control, you need certain technologies that provide you precision analysis, precision measurement, real-time feedback loops. And also, let us include people into the equation, which is how do I train people to do tasks that are highly variable that aren't applying automation is really important. So I think if people understand, start using this paradigm of an adaptive control loop, they'll see that these concepts of lean and the Toyota Production System are not only timeless, but it's easier to explain it to people outside of those industries.
TROND: Are there any lessons finally to learn the way that, I guess, manufacturing and the automotive sector has been called the industry of industries, and people were very inspired by it in other sectors and have been. And then there has been a period where people were saying or have been saying, "Oh, maybe the IT industry is more fascinating," or "The results, you know, certainly the innovations are more exciting there." Are we now at a point where we're coming full circle where there are things to learn again from manufacturing, for example, for knowledge workers?
JOHN: What's driving the whole, whether it be knowledge work or working in a factory...which working in a factory is 50% knowledge work. Just keep that in mind because you're problem-solving. And you know what's driving all this? It is the customer keeps changing their demands. So for a typical shoe, it'll have a few thousand skews for that year. So the reason why manufacturing operations and knowledge work never get stale is the customer needs always keep changing, so that's one.
And I'd like to just end this with a comment from my colleague, Art Byrne. He wrote The Lean Turnaround Action Guide as well as has a history back to the early '80s. And I have him come teach in my course. At his time at Danaher, which was really one of the first U.S. companies to successfully bring in lean and Japanese techniques, they bring in the new students, and the first thing they put them on was six months of operations, then they move to strategy and finance, and all those things.
The first thing that students want to do is let's get through these operations because we want to do strategy and finance and all the marketing, all the important stuff. Then he's basically found that when they come to the end of the six months, those same students are like, "Can we stay another couple of months? We just want to finish this off." I'm just saying I work in the floor because it's the most fun place to work.
And if you have some of these lean skills and know how to use them, you can start contributing to that team quickly. That's what makes it fun. But ultimately, that's why I do it. And I encourage, before people think about it, actually go see what goes on in a factory or system before you start listening to judgments of people who, well, quite frankly, haven't ever done it. So let me just leave it at that. [laughs]
TROND: I got it. I got it. Thank you, John. Spend some time on the floor; that's good advice. Thank you so much. It's been very instructive. I love it. Thank you.
JOHN: My pleasure, Trond, and thanks to everybody.
TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was Lean operations, and our guest was John Carrier, Senior Lecturer of Systems Dynamics at MIT. In this conversation, we talked about the people dynamics that block efficiency in industrial organizations.
My takeaway is that the core innovative potential in most organizations remains its people. The people dynamics that block efficiency can be addressed once you know what they are. But there is a hidden factory underneath the factory, which you cannot observe unless you spend time on the floor. And only with this understanding will tech investment and implementation really work. Stabilizing a factory is about simplifying things. That's not always what technology does, although it has the potential if implemented the right way.
Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like other episodes on the lean topic. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us. We would love to share your thoughts with other listeners.
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