Right Data at the Right Time for the Right Decisions
It’s one thing to know you’re sedentary, but it’s another thing altogether when your Apple Watch tells you that you’ve only taken 900 steps today.
Data can be a huge motivator for change — but not by itself. You need to get the right data in front of the right people to get things moving.
In this episode of Conquering Chaos, we interviewed Brian Piotrowski, Vice President & General Manager – Pipeline & Industrial Materials at CMC Materials, about using data during change management.
What we talked about:
– Strategy and tips for making data mean something
– Change management should focus on people and culture
– Advice: Don’t track more than 10 data points
– Decision making processes based on fact vs. emotion
Check out the full episode below:
[00:00:14] Josh Santo: Welcome to Conquering Chaos, the show for manufacturing leaders. In each episode, we’re connecting you to the manufacturing leaders of today who are driving the innovations needed to future proof the operations of tomorrow. If you feel like your time is spent fighting fires and trying to control the everyday chaos, this show is the show for you. My name is Josh Santo. I’ll be your host.
Welcome to the show. We’ve got a great topic to explore today with our guest who has spent his career in manufacturing, serving industry giants like Archer-Daniels-Midland, PepsiCo, Kraft Heinz, and General Mills with roles like process engineer, maintenance engineering team lead, packaging systems engineer, plant technical manager, before transitioning to more senior roles like plant manager and site manager at Cabot Microelectronics.
His successes there enabled him to serve FXI as their director of manufacturing optimization, after which she brought his expertise to McKinsey as a senior expert in digital manufacturing and implementation. He’s returned to the world of production to lead Flowchem, a subsidiary of Cabot Microelectronics as their vice president and GM. Please, welcome to the show, Brian Piotrowski. Brian, thank you for joining us today on Conquering Chaos.
[00:01:38] Brian: Thanks for having me, Josh.
[00:01:40] Josh: Well, it’s a pleasure to have you. I’ve enjoyed the conversations we’ve had previously. Brian, speaking at some of the roundtables, you’ve brought up some pretty interesting topics and I thought this was someone we had to get on the show because I know the audience would greatly, greatly benefit from your knowledge and expertise. First, typically, I like to start the conversation with what’s a day in your life look like?
[00:02:05] Brian: Yes. So, a day in my life. Okay. I live in Dallas, but my office is in Houston. I’ll start with the week is I drive 300 miles back and forth every week to my office in Houston where our operations [chuckles] is headquartered. We have two sites there in our main office, but a normal day–
Obviously, what has happened in the Flowchem was a really small entrepreneurial company. Then, when Cabot Microelectronics bought it, Cabot Microelectronics has a lot of structure. I spent a lot of my time is, first thing in the morning, just seeing how the previous day went, production-wise, sales-wise.
Then, throughout the day, I’m very engaged. I’m more of a people person, so I like to have conversations and set up pretty standard meetings through the day, as well through the week to have the different functions and, I think we’ll touch on it later, is what do we use in those meetings. I’m a data freak. We review data as much as we can. Then, the initial part, we don’t have that much, but now, we’ve grown.
So, just going through some key metrics. Then, just saying how things are flowing together. My job on a daily basis is to get everyone together, working from the same page, rowing in the same direction, and just giving guidance. I have a lot of conversations. I love to talk. I love to talk to people, so I have a lot of spontaneous walk-in [chuckles] to offices, walking on the site floor, dealing with operators, and the like. Then, wrap up since I am alone during the week [chuckles] in my apartment, do more of the tactical work at night while listening to music, cooking dinner, and taking it easy, working at night. That’s pretty much what my life has been over the last 10 years with traveling for work and the consulting and now, this job.
[00:04:13] Josh: Practical and getting the job done. Three hundred miles, that is quite the journey to take. Those of you who aren’t from Texas or don’t have much experience with the trip between Dallas and Houston, it’s not a very exciting road trip.
[00:04:29] Brian: No. Texas isn’t known for it, the big mountains and rolling hills. Pretty flat but the farther you get closer to the coast, the greener it gets. So, it’s pretty nice to for a year-round. I’m from Chicago, obviously, originally. The seasons aren’t the same as in Chicago.
[00:04:49] Josh: That’s right. Again, for those who aren’t from Texas, Texas typically talks about not really having too many seasons other than hot and less hot.
Actually, we recently had snow and I was talking with Brian about it. It’s nothing compared to this snow that Chicago gets, but it was enough for this Texas boy to feel like he could strap on some skis and go sliding down the neighborhood.
It sounds like a lot of your time is spent on alignment. You’re leveraging data to help understand key components of the business like what has happened, figuring out what needs to happen, and then, getting the team on the same page to make sure that that comes across.
Well, data is exactly what we want to talk about today. Our topic is getting the right data to the right people at the right time to make the right decisions. This is a topic that Brian brought to us and I think it’s pretty fascinating. First of all, before getting in too deep to the topic, let’s just define it. Brian, talk to us at a high level, what does that mean to you?
[00:05:57] Brian: At a high level, obviously, data is important, but data needs to mean something. You got to have the right data, something that is actionable, something that is meaningful to people in your organization, and you can collect a lot of data. You hear the buzzwords of data lakes and all that. You got to make sure that the data is something that you think is going to add value or improve a process or improve knowledge within the group at that point. The right data is a starting block. That’s why it says we need the right data.
You walk through the process, right data to the right people. You want to push. Every organization that I’ve worked for and what I’ve witnessed is the more you can push decision making down to the lower lowest levels, it just makes things more efficient. You got to have– you got certain people in certain tasks, a maintenance technician or an operator on the line or a supervisor that’s running an area. A maintenance technician doesn’t really care about what’s the overtime for [chuckles] a certain area.
You need to point that data that’s meaningful to the right person. If you have a lot of maintenance data, send it to the maintenance technician or maintenance manager at the right time. The better you can get that information in near real-time, the better. One of the things I struggle with is we get data four to six weeks later. That doesn’t really help change things because it’s stale.
You want data to be as fresh as possible so that you are making adjustments. You’re making decisions that coincide with the situation at hand and then, might make the right decision. That’s the last part of it.
A lot of people, there’s a lot of tribal knowledge. There’s a lot of emotion when you’re working in a [chuckles] manufacturing plant. There’s a lot of things going on, a lot of fires to be put out. People do get emotional. A lot of it goes to more of a subjective type feeling.
Data really helps make it more objective. Everyone can rally around real data and it’s hard to argue real data. It makes people rally around that data and make a decision that tends most of the time to be a better decision than just going off emotions. I’m trying to framework those four statements at a higher level.
[00:08:42] Josh: Absolutely. We’re going to get into each one. Some of the key points that I heard with what you said. When you’re talking about the right data, we’ll get into this more, that data has to mean something. Not only does it have to mean something, but it has to mean something to the person who is consuming the data. There’s a specific scope when you’re talking about who’s the right people to consume this data and that right time.
What you described, that four to six weeks before you’re getting some sort of data about what happened and what occurred, that’s quite the lead time to understand what’s going on, especially as you consider the different challenges in the current day environment with some of these well-known industry trends that are facing organizations, in manufacturing, in particular, there’s a need to adapt quickly and to be more agile.
How can you do that if it’s taking a significant amount of time to get the data you need to drive those decisions? Speaking of decisions, there’s a lot of, like you said, tribal knowledge, “I’ve got this experience doing this. I think this is the best way.” How do you take that subjectivity, that ego out of the conversation and use that? “Here are the cold hard facts.” Those cold hard facts are just the evidence that leads us to the truth. I think that this is going to be something good to explore. There’s a lot to break down with each of these topics. Before we do that, I’m just curious, what is it about this topic that resonates with you in particular?
[00:10:13] Brian: Ever since I was a young engineer, I always enjoyed pulling data and putting it together. I’m a pretty competitive person. To be able to measure performance and visualize it– I’m a very visual person as well. I think a lot of people are in the same boat. If you see it, you can react to it easier.
Being able to get the data and visualizing it in a way to show how things are going, how things are performing, I think it engages folks through action. If they see data, you can point to and it’s concrete. Then, you could say, “Hey, I did this action and this metric.” This number goes up or it goes down, positive or negatively. It drives actions. It drives emotion. It drives accountability and engagement.
I saw that early on in my career as an engineer. I’ve always been interested in how do I influence people, whether it be in an operator or manager, a co-worker, to change their behavior. I’ve had a lot of experience and seen a lot of successes when you show something, when you show data, and you visualize it in a way that people can easily understand it, it tends to move people in the direction that you’re trying to go. That’s always been something that I’ve leveraged [chuckles] throughout my career.
[00:11:42] Josh: Why do you think that being able to visualize the data is so helpful in changing behavior?
[00:11:49] Brian: I think trends are a big thing. If you can show like, “Hey, we’re operating here.” It’s good if you want to improve something, you can put a goal. You can draw a line and say, “Hey, we want to get this data from where it’s at to this point.” You can anchor around this data to gather input from all levels of an organization to say, “Yes. This is why this is happening. I think if we do X, Y, and Z. We can make it move.”
It puts a framework about how you’re going to attack this particular issue or opportunity to drive in a certain way. That data, again, it’s that objective view is, again, you take the emotion out of. Well, I don’t think that’s going to work. Well, [chuckles] you can try something and you can see how the system reacts, whether positively or negatively, and then, your group can, again, anchor on that information to further make other actions moving forward. I think people being able to see it, and it’s not just talk, it’s actually something concrete that you can anchor on to to drive your teams.
[00:13:05] Josh: There were two key ideas that I took away from what you were saying, which is how the data and visualizing this data helps with goal setting. You talked about trends. Trends are identifying what are those problems that we need to fix or what are those places that we need to innovate? Here’s our goal. You didn’t say this explicitly, but I picked up on this is how do we leverage the team, the expertise of the people who are responsible for solving this to actually be a contributing member of how we’d make an impact.
It almost sounds like leveraging data and building this picture and rallying around a common problem and empowering these people to do something about it and to see their impact is also impacting the culture with regard to change management and that spirit of competitiveness. Well, great.
[00:13:58] Brian: Yes. While I was consulting, obviously, that was my job was to influence folks with not much authority. [chuckles] That was something that was really powerful to be able to come, even an operator in a steel mill. You can show them, “This is where we’re at. You got any ideas?” They interact. We enact some of their suggestions. It really does drive by a lot faster than just talk. Again, it’s that anchor of the approach.
[00:14:27] Josh: Well, people at all levels want to be heard, and they want to feel like what they’re doing makes an impact. It doesn’t matter how old you are because that attribute is typically associated with Gen Z and Millennials, but that’s something that seems to be pretty true as everyone wants to make an impact.
[00:14:46] Brian: Yes. The one analogy I think it’s pretty strong that I share in some meetings is how do we use that today. Fitbit or Apple watches, it’s a thing that’s a pretty simple object, but it’s very visual. It tells that, “How many steps did I do today? What’s my heart rate?”
It drives this data and the visualization of that data at the right time at the right place to the right person. It motivates them to make action, “Hey, I got to get up and walk,” or, “I got to slow down. My heart rate is–” It applies to the analogy I used to try to apply to folks in the organization to say, “This is a good method to drive change.”
[00:15:30] Josh: What a great point. It’s not a way that I thought about it before. I’ve got an Apple Watch strapped in my wrist. I get the notifications on the hour of stand-up. I didn’t even think to consider how that’s leveraging a data point to say, “You need to do this to make it happen.” Even the weekly summaries that I get, “You accomplish this. If you just do X, Y, and Z, you’ll accomplish this, and you’ll get a little bit better.” That’s such an interesting way of bringing that picture together.
Now, let’s get into this concept of right data, right people, right time, for right decisions, focusing on right data. This is something that I think it’s going to be pretty important to make sure that people understand because there’s tons of data points out there, especially when you’re looking at the improvements to sensors, the internet of things, technology that can just gather more data points. When we’re talking about right data, there is a real need for guidance. How do you go about determining what is the right data?
[00:16:37] Brian: I guess, first, you look at the issues at hand. What is the main things that you want to work on? What levers on that object or that issue, what levers will drive difference, different performance than that? Once you lay that out, and everyone agrees on, these are the functional levers, what data is attached to those levers? Do you have that data? Do you have too much of that data? Because like you said, with PLCs, and the new edge computing though that you can slap on any PLC, you can get a lot more data than you could even five years ago.
It’s really putting a focus on what are the few key measurable that you have, that you feel confident enough are going to be able to be manipulated to get where you want to go.
Obviously, it takes trial and error. You may take a subset of data and then, let it run for a couple of weeks. Try to put in some step changes, or try to adjust what you’re doing and see how they react. Then, once you gain confidence in these measures or this data is actually correlating to what you think or it can be totally opposite and they can be the opposite of what you thought happened. You’re able to change that signal. Then, you can focus on, “Okay. These are the metrics that we know that will give us that signal if we’re doing something different, if we’re improving, or getting farther away from our goal.”
[00:18:34] Josh: It sounds like there’s a decent amount of work involved with even figuring out what’s the right data for the problem you’re tackling, the organization you’re running, the people involved. When we think about that, I know in my experience, people want something where you can just put it up, set it up. It’s already doing exactly what I needed to do. I’m done at that point. That doesn’t sound like that’s a realistic expectation from what you’re saying.
[00:19:05] Brian: Yes. I think you might hit the obvious few by doing, “This is our process,” or, “This is our program and we have these three main things,” but usually, when you lift the hood on the [chuckles] car, there’s a lot more that the field engineers or whatever. They have tons of data. It’s just bringing that to the team, as a group, maybe the processes, explain what that data is, where it’s coming from. Then, there are some statistics involved there. You got to have someone that’s able to, “Let’s take this data. Do a quick correlation. Study. We change this and this is what occurred.” It doesn’t have to be only mechanical-type of stuff. It could be, personal processes as well as back-office effectiveness, quoting, sales quotes, and stuff like that. Being able to take the time and understand that data is going to be manipululated– you can manipulate that data. Then, you all agree that, “Let’s start with this set.” As you go, there’s always, “Hey, if we knew this or if we knew that, that would give us a better view of the issue or our ability to change.” Then, you start to broaden out because then, it’s valid.
Once you get that starting, 5, 10 things that you want to track because you don’t want to track a hundred things. There’s just– it’s not possible. People get confused. People go off in different directions.
I would say you start with 5 to 10 items to measure on any project, any group, any process. Then, once you go and you start to understand that better, that may take a month or two, and then people– I’ll talk about performance management cadence of how often do you meet, you review the data. How do you track the actions?
As you move along and mature as a group that people start to get smarter about what’s going on of, like, “Hey, maybe we need to add data. Maybe we need a new instrument or maybe we need to add these two data points.” Some things about this– we thought this was important and now, it’s not. We’ve proven it really doesn’t matter. Then, you take it out because, in my opinion, in my experience, anything more than 10 items to track starts to lose value.
[00:21:37] Josh: Well, a couple of key components of what you described is there is a need for a strategy where you got to have some idea of what that end goal is and that iterative approach. It’s setting that expectation that– Look, you got to start with something and you may not be starting with everything right. Maybe you find that none of it’s right, but you have to start somewhere and constantly review and iterate so that you can get closer down the road to what that ideal state of the right data is.
Now, in those situations when you’re talking about what are the different data points that you might need, how often have you found that you’ve had to bring in a new process or a new tool to capture those data points?
[00:22:26] Brian: As of late, the last five, eight years, it’s been more– the accessibility to data is becoming more easily attainable. With that, it’s almost always you come up with, “Hey, I wish I knew the humidity in this room,” or, “I wish I knew vibration on this piece of equipment,” or, “I wish I knew how long people taking on a phone call,” if we get back-office approach.
There’s so many options now. It’s so easy to get that information that I would suggest having someone that I was going to bring this up later, but having a champion of the team of just data technology because technology is evolving so fast that what you thought was there three years ago was the only thing. Now, there’s 10 more ways to do it.
I’m a big proponent of trying new things. Currently, with my current job, we’re trying wi-fi access points where we’re dropping it in the process and tracking it through the system. Through wi-fi , new flow, measurements, and stuff like that. That’s relatively inexpensive when you look at the grand scheme of what you spend in a large facility.
I always encourage trying new things and learning from it. With the price of getting a data point now is becoming cheaper and cheaper with wi-fi or cellular networks. It’s a lot easier now. I definitely would have a component of the project to try new things, to get data that you don’t have at this point.
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Now, back to the show. –of what that new thing is. Now, I know we haven’t gotten through the whole stage of getting down to the right decisions but, at that point, it sounds you just have a hypothesis of what the impact would be. How do you balance bringing in this technology to serve this purpose with the cost of that?
[00:26:29] Brian: Any project, obviously, you have to come up with what’s the entitlement. What is the end game? What could we possibly get to? Put some value to it. Is it X amount of dollars or X amount of time? Everything eventually leads to dollars.
Then, you have to justify what we’re doing, what’s it going to buy us. That’s going to be some chunk of change or time. It’s a pretty typical engineering management [chuckles] conversation of, “Yes. We don’t want to spend 10 times what the return will be.”
Yes. You fall back on traditional hurdle rates. Again, I’m a little bit more of a risk taker. I would say, “Hey, let’s gamble a little and put this much of the project against this new technology,” because once you prove it, this is something that we might talk about later is starting with a pilot. It may be a great tool and then you can leverage it. If it works out, then, you can leverage it across the whole organization.
You’ve already helped out. You’ve gone through the process of vetting it and understanding how it works. Then, that adds a lot of value to the bigger organization. Going from pilot to [chuckles] full mode across several sites, that’s where the savings comes in. You have to articulate that to maybe [chuckles] the higher levels of, “Hey, if we spend this, we’re investing in not just this project, but potentially, the whole corporation can benefit from this at some point.”
I would say most companies, in my experience, they’re usually doing the same thing. Either they’re all making chemicals or all making food. Your technology should be leverageable across the bigger organization.
[00:28:37] Josh: Speaking of the bigger organization, we’re talking about people at that point. Let’s get into this idea of we’ve talked about the right data, the right people in determining who is that group of right people, and how do we make sure to service their needs? How does that then impacts the right data that we just talked about?
[00:28:59] Brian: Typically, I’ll say most of the time, it’s more downwards. You want an operation or a process to improve or to change for us. The right people make is where the area that you’re targeting. Depending on the project. Is it a particular line or is it a particular department? Those are the right people. You don’t, again, like I said earlier, you have all this data, can you funnel it? Can you cut it down, slice and dice it, and then, present it in a way that those people, the right people that are going to enact the decisions that you make as a project team or management team, they see that data in the form that they most best will absorb it. The right people are the people in the organization or the sliver of where you want change to happen.
[00:30:09] Josh: When delivering the data to the people, is there any clear-cut way in which that data should be presented, or are you talking about raw data?
[00:30:21] Brian: No. I would say it’s data that’s been put in a form that they will understand. If I’m a maintenance tech for line A and line A does this certain procedure, that data has to be relatable to them. They have to understand, yes, I’m seeing this data and I understand what it means and how that data is affecting my process.
I used the influence model of McKinsey. It was one of the things we always go to is you got to tell people why this is important. “Why is this data important to me? Do I know what to do with that data once that happens? Do I have the capability of making that change, moving that data to the way I’m being instructed to do, and then, I see others doing it?” It’s fed into you have to package this in a way that people can understand it, act on it, and feel comfortable that what they’re doing is they’re having the power and the ability to change that in the direction that they’ve been asked to do it.
[00:31:32] Josh: There’s a component not just of determining what that right data is and who the right people are, but making sure that it’s something that they can then understand and action off of it, which does have implications for the rollout of something like this, which is how do you keep people up-to-speed and make sure that they understand why you’re doing it, what that means for them, and how they go to interact with that? Any best practices from what you’ve done?
[00:32:00] Brian: Yes. A big thing that I’ve used over the last several years is we call it performance management cascade. Very structured meetings that go from the lowest levels to the highest levels of the organization. We keep what data is going to be kept and reported on and visualized, depending on what that track is. Each track would have– this program team will have a track, the project will have a track, operations has a track. Then, very scripted, “This is what we do. This is what we say. This is what we review.” Then, the big thing is action tracking.
If you’re going to do this, don’t have meetings just to have meetings. Don’t show data just to show data. The whole purpose of this is to make better decisions that will affect the outcome of what you’re searching quicker, more efficiently, and have a bigger impact. It’s very structured, very succinct. This is what we do. This is what we look at. This what we say. Then, the ideas come out of that. Everyone has, “Yes, let’s do this. Let’s do that.” Well, the important thing is what is it, who owns it, and when you’re going to get it done. Then, the next day, “Did you get it done? No? Okay. [chuckles] When are you going to get it done?”
It’s that constant cycle of people start to know, “Hey, I’ve got to answer the question. What am I doing?” Then, it tends to drive people to action. If you’re held accountable, what you inspect gets done versus expect. It doesn’t have to be very difficult or complex.
Most of these meanings are 5 to 15 minutes. It starts on the floor and it goes supervisors, the managers, the regional manager, then all the way up to the VPs of the companies, and then, back down. We have to push that data. What are we seeing? Are we seeing the improvements? What’s it mean? “Hey, we saved X amount of dollars,” or, “Hey, our product is performing this much better and we want some business.”
That information has to go up and down, but in that track, using the same data, everyone’s looking at the same metrics, everyone’s looking at the same process, the same action items.
[00:34:42] Josh: What I appreciate about what you called out is the prescriptive nature of this approach. It’s not just, “Here’s some data and you’re the people that need to see it.” It’s, “Here’s a form or a framework, I should say, for how you can make sure that you were successfully interacting with this data and getting closer to that end state,” which we’re going to transition to in a second of right time and right decisions, but that key call out of you’re not just dropping something and saying, “Figure it out for yourself.” “Here’s how we expect this to be useful. Here’s the framework in which you could start to get value out of it.”
Now, one thing that’s pretty critical is that data must be not just the right data in the sense that it’s relevant, but the right data in the sense that it’s timely, especially if you’re talking about having this be almost a daily meeting. Let’s talk about that idea of right time.
[00:35:34] Brian: Yes. Data gets stale. Okay. I learned something happened three months ago. I can’t affect that or if I’m investing, if I find out, “Oh, a new IP is coming out,” but it’s three months old, I can’t go invest in it. [chuckles] You missed the boat. Basically, you’re missing the boat.
You have to keep that data as fresh as possible so that it’s in the moment. It’s still in someone’s mind because if you go, “Oh, we had a quality issue,” and it happened six weeks ago, people forget. How do you make things better if you don’t remember the specifics and then, people maybe will not makeup, but they’re like, “Hey, I think this is what happened.”
That information then drives the wrong decision because maybe that problem’s already fixed, maybe it was a one-time abnormality in the process. Being in the here and the now and having that fresh data, you can actually– Everyone’s attuned to, “This is what’s going on. They’re hard facts. There’s hard data. We can make hard decisions.” You’re not always going to make the right decisions, but I think the expectation of success is going to be a lot higher the closer you can get to that real-time, almost nothing as real-time data, but we call it near real-time. If it’s fresh within hours to a day, that’s a lot better than three weeks to three to six weeks.
That’s where the right time comes in because if you’re not there with the right data at the right time, people will make their emotional decisions. They make their tribal decisions. That may or may not be the right decision. You’re opening up the window of percentage of error goes up when you don’t have that data as soon as possible. If you can get it down to the minute, obviously, you can get really good at controlling– In my mind, I’m thinking more processes, but you can apply this to anything.
[00:38:00] Josh: If I were to really just simplify the right time, it sounds like the right time is right now.
[00:38:09] Brian: Right. That’s how [crosstalk]
[00:38:10] Josh: You’re impacted by what is available. The state of technology doesn’t necessarily offer exact real-time, but there are ways of capturing data and having that visualized or having that analyzed within a few hours or a day, which is much better than some of the current response times that you can see, which can be a week. It could be six weeks, from the example that you had earlier.
When you’re thinking about delivering that in the right time, there’s got to be a couple of different concepts that come to that. One is presenting the data. Then, two, is also capturing the data. It sounds like that really has to inform some of your decision of how do you classify the right data, which is when do we need this data by? Because I would imagine that there are some data points that while it’s preferred to have it now that it is okay, if it’s end of day, end of week, is there any perspective on identifying the times to tie that to the data point?
[00:39:20] Brian: I would say that it’s the amount that can be at risk with delaying the time that you get the data. If something goes awry and within an hour, you make a million dollars’ worth of off-spec product, you want to do everything you can to get data as close to real-time as possible. Workflow, if we get the data tomorrow, it’s really not going to cost us any more than if we got it in a minute. That’s a cost-benefit analysis of how much do you want to spend to get that data as close to real-time as possible. I was working in a steel mill. We tried everything. We were using data straight off the line. We’re sending APIs off through our software to visualize it in the control room. We did everything. We started out it was every five minutes. Within that five minutes, a lot was going on on this line. We’re like, “How can we make it faster? I want a three. I want a two.”
We spent a lot of time, energy, and money, to get that data back to the line as fast as technologically possible. Then, there are some like a rail car, we’re looking at railcar inventories where stuff is moving from switchyards. That wasn’t as important because they have flexibility in scheduling but, yes, if we got data closer, we can schedule better. It wasn’t paramount that we needed it every minute. That was more, “Hey, can we get it from every week to you every few days?” We worked with the railroads and stuff like that. How do we build systems? How do we build ways to get that information back and forth in a more timely fashion?
That saved us in production, meeting orders, and better on-time shipments, and stuff like that. Again, short answer, cost-benefit analysis of what’s the risk of not having that data in real-time and then slide it from there.
[00:41:46] Josh: That’s such a good point. You’re really having to ask yourself, “How long can you stomach this problem or how long can you afford this problem to continue?” If it’s something that you can’t afford, that’s going to drive the urgency, but if it’s something it’s not a big cost until this point. It’s such a great lens to apply when talking about data points because, in my position, I’ve frequently had the conversations where people are wanting to know it now. We do have to have this conversation of, “What are you going to do with that data?”
[00:42:19] Brian: Right. As I told an old CFO of mine, “Data cost money. Data isn’t free.” In order to get it, how urgent do you need that data? How fast you need that data? How accurate do you need that all those levers drive the price? You have to balance how much are you willing to invest in that data. What’s the benefits of that data?
[00:42:46] Josh: What’s the benefits. That’s where we’re coming to at the end of this topic of the right decisions because that’s where the benefit’s going to come from. Let’s talk about those right decisions. When you’re talking about this idea of right decisions, previously, let’s take subjectivity out of it, let’s take the various degrees of expertise that often come into play, and let’s use the data behind it.
How do you go about coaching your organization to make these right decisions and empowering people who may have not previously been able to make these decisions to actually make these decisions?
[00:43:25] Brian: I think it’s important. What I’ve seen is if you do it in a group setting and you build– you’ve got to build a circle of trust with the folks who are involved.
By having the frequent cadence and the consistency of what is shared in those rooms, it helps build that trust that, “Hey, let’s talk. Let’s try.” Everyone hears the same message, everyone hears the same story, and everyone understands. We all may have ideas, but at the end of the day, someone, whoever makes the decision, makes a decision and everyone agrees, “Hey, this is what we’re going to do going forward.”
Then, basically, I want to say, “Sit and wait,” but you see what the outcome happens. It all happens in the open. There’s no hiding. In my experience, it becomes very, like I said, very easy to communicate, very easy to understand. Yes. We tried this and then work. Hopefully, the leader of that group uses the methodology of it’s okay to fail. Hopefully, not everyone is like that, but hopefully, you can build that, like, “It’s okay to fail because you learn from failure and you learn from winning, too.”
That’s the tough part. The culture is [chuckles] the toughest part of change. If you can get to that level of some trust, it goes a long way a lot faster. It takes special people. I’ll be honest. That’s why you’ve got to find a champion in each of these little groups that has that skillset. If you don’t, then you may have to borrow people to come in and help coach people. I’ve done it a hundred times. I was the coach.
Then, [chuckles] you train people as to follow the facts. Here’s the process. This is what we look at. This is how we make decisions that’s very objective. Once it’s objective, I think people understand like, ‘It’s okay. It’s not on me. It’s not on you. It’s on the group.” Once group think, group decisions, tend to be a lot less emotional in both the good, the bad, and it moves
[00:45:58] Josh: That idea that you brought up of it being okay to fail, that is something I hear frequently, but something I see less frequently, what in your experience gives an organization the ability to say, “Yes. Not only are we saying we’re okay with failure, but to actually adopt that approach of being okay with failure”?
[00:46:23] Brian: You’ve got to have leadership. You got to have– and that leadership can be– that doesn’t have to be a manager. It can be a team lead. You do have to have someone that is willing to foster that, that being right. That’s where as high up or as low as it goes, if you’re in these projects, you got to find those people that have the ability and the willingness to do this. It’s okay to fail. You just have to frame it, “Hey, guys and gals, we’re going in. We may win, we may lose, but we need to improve, and we need to learn.” Learning is to everyone’s benefit.
It’s tough. I’m not going to lie to you. It isn’t everywhere. If you’re fortunate enough to have some people that have that framework, leverage it as much as you can. Personally, go and find those people and bring them into the meetings, have them be a guest, show up last questions and prod them, prepare them at the beginning of the meeting, “Hey, can you make a statement about–? It’s okay. I support your actions, whether it’s good or bad. I know at the end of the day, we’re all trying to improve.” Nothing’s a straight line. We’d love it to be a straight line, but it’s not. You go up. You go down.
Overall, hopefully, the trend is positive. If it’s not, you learn from it, you maybe retool, and maybe you go into a different direction, or you pick a different project. It’s all positive. You’re trying to minimize wasting limited resources, as we all know. Things are getting tighter and tighter. You got to maximize the tools you have.
[00:48:13] Josh: Hey, I love the positive spirit you bring with that idea of failure and how failure should be okay and it should be empowered so that the organization can learn, can adapt, can innovate, and make progress.
Well, I do want to make sure prior to wrapping, we talked about, from your perspective, an organization that is not on this journey of getting the right data to the right people at the right time for the right decisions, where would you coach that organization to start? How would you see them developing?
[00:48:44] Brian: If it’s important to the organization, I think that’s– if you want to start the journey, I’ve had to do this a few times, you want to go to a place in your organization that’s in the middle, a lot of things are, “Hey, let’s go to the worst-performing area and attack it there,” or, “Now, let’s give it to the best group because they’re already successful and they’ll adopt.”
When you go on the ends, the worse and the best, you limit yourself and you may be setting yourself up for not getting the most benefit on the program. We tended to go somewhere in the middle so that things aren’t going okay, but small attention can drive bigger impact.
That’s why you want to spark that match. You want to strike that match to ignite. I call it the activation energy. I’m a chemical engineer. Every chemical reaction needs a little push. You need something to push that activation energy up so then it goes into freefall because that’s when things really start to become fun and you get a lot of impact, but it takes a lot of energy and it’s easier for you to go into something that’s working pretty well, but not great because then, you can make an impact and show that, “Hey, this process works.” Then, you go from there. You take the middle and then you can extend down to something that’s probably a little worse off for a little better off, and then, you grow the momentum in the organization that way. I would start in the middle and I would find the best change management people you have and people who are really interested in data and working with tools to visualize it, the Power BI, the Azures. They’re easy stuff to learn, but you need a couple strong people in those roles to drive it.
[00:50:46] Josh: That’s great. I love that. First of all, first and foremost is to start, get started, start somewhere. Ideally, it’s not the best place, it’s not the worst place, it’s right there in the middle. I love that idea of the activation energy. You start that chain reaction that’s just going to take you across, but the biggest focus being on the people, who are the people that can champion this, who are the people that can really own this, and who are the people that can empower your organization, and others to do the exact same, which sounds like it’s part of that, that spark that then takes over.
Brian, I appreciate your time, I want to make sure I open this up to you. Is there anything else that you want to leave with our listeners or do you feel like we did a pretty good job covering the topic today?
[00:51:30] Brian: Yes. I mean, I think we covered it pretty in-depth. Again, like you said, just start, do something. There’s a lot of obvious great technology out there. It’s become very cheap, very easy to get to, very, I’ll say, user friendly, too. So, go out and do, just start somewhere, and go from there.
[00:51:51] Josh: Great. Brian, thank you so much.
[00:51:55] Brian: Thanks for having me, Josh.
[00:51:59] Josh: That’s the show. Thank you so, so much for joining us today. Conquering Chaos is brought to you by Parsable. If you’re a fan of these conversations, subscribe to the show and leave us a rating on Apple Podcast, just half the number of stars you think the show deserves. As always, feel free to share what’s top of mind for you and who you think we should talk to next. Until then, talk soon. Take care. Stay safe and bye-bye.
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