This FIRSTHAND: Operational Certainty in Pod podcast with Emerson’s Chris Hamlin is a follow up to our 5 Questions for an Emerson Expert with Chris.
In this podcast, we explore Chris’ thoughts on a presentation he gave recently at a conference in the Netherlands. The presentation highlighted the massive technological shifts occurring that are affecting the very way we organize our businesses and drive performance improvements.
Jim: Hi, this is Jim Cahill and welcome to the FIRSTHAND Operational Certainty in a Pod podcast series. And today I’m joined by Chris Hamlin. Chris is a director for our Operational Certainty Consulting team and he’s based in the U.K. Welcome, Chris.
Chris: Hi, Jim. It’s great to be with you.
Jim: Now, I know you were, just before the holidays at the NexGen 2018 Conference in the Netherlands. I recall that your presentation had a fantastic title, “Tyranny, Heresy, and Confusion: The Job’s Not Done.” Tell us a little bit about your premise that organizations traditionally organize themselves to optimize performance against the most valuable or expensive constraints.
Chris: This idea of constraints is becoming more and more fashionable in terms of understanding how organizations work. And the idea…And it’s actually something that kind of we as control and optimization engineers have understood for a long time. If you’re trying to maximize the performance of a chemical plant, what you need to do is identify what’s the most expensive constraint that exists on that plant. It’s the place where the greatest value is, but it’s also the most expensive thing to get rid of.
Very often, they’ll be something like a big rotating machine. The big rotating machine is the size of these, and it is prohibitively expensive to get rid of it. So, what we do is as controls or optimizations engineers, we try and push as hard as we can against that limit, make sure that machine’s working as hard as it can possibly work, for as long as it will possibly do it. And what we do is that we then organize everything else around the facility to make sure that that machine is exercised as hard as we possibly can. And we know that we do that the chemical plant makes as much chemical as it possibly can.
Now, organizations are exactly the same. In fact, all systems that have got some degree of optimization do exactly that. They look to see what was the biggest obstacle or barrier to them being more successful, whatever success means. And what they do is they then maximize their performance against that limit by organizing themselves to ensure they’re always pushing against that to the greatest possible extent. So, in the case of organizations it may be, in fact, I think we may come on to those in a little while is very often around information and information flow. That’s the most valuable, the most valuable thing that an organization possesses is the information and one of the critical things in terms of an organization being successful is the extent to which information can flow through it to support decision making.
So what we see is organizations forming themselves and structuring themselves to make the best use of the information that’s available. And that’s why we have HR departments because it optimizes their ability to use the HR-related information. It’s why we have production departments and maintenance departments and legal departments because it’s all around efficient, fast, effective use of that information. It’s also why we all gather together in buildings called offices because one of the best ways historically of moving information is to talk to each other and that’s much easier done if we’re in the office or the cube next door than on the other side of the Atlantic.
Jim: Yeah, that’s interesting you say that about information because right at the start of that presentation you described a new era that we’re entering into in human history. Can you share a little bit more on why this is you consider it a new era and what are the implications for organizations?
Chris: Sure. I mean, I think this is really, really profound. So as I say, you know, organizations have optimized their structures to take advantage of information. And you say, “Well, how long’s that been going on?” Well, my view is, that’s been going on ever since we stopped being hunter-gatherers and developed agriculture. At the point in time where we stopped fending for ourselves as independent entities and living in caves and we started growing stuff and growing of surplus that we could then somehow trade for other things, we became a social species and at that point the flow of information was the single biggest determinant in how we organized and structured ourselves.
It’s why we came together as communities, in villages, in towns and cities. It describes and explains the way politically we organize ourselves. It explains how the role of countries and councils and parliaments and senates and Congress and, you know, all that sort of stuff. Everything we do as a human species for the last at least 5,000 years has really been about organizing ourselves around the most efficient and effective use of information. And kind of closely then related off the back of that. What that drives is, it drives us geographically, physically, to be close together, to be proximate because we’re still living today. The best way of conveying information is to actually sit across the room from somebody, share a coffee, share a beer and exchange by talking.
So, we were physically proximate to one another. Now what that does, it introduces a new constraint, it introduces a constraint around creativity. So, when limited because of the physical organization, we’re limited to our ability to access creativity and new ideas. So, that’s kind of a secondary constraint that’s existed and that’s emerged. For 5,000 years this has been the case. We organize ourselves physically close together to exchange information but by virtue of doing that, we then restrict our ability to access new ideas, new talent, and creativity.
So, what’s changed in the last…I don’t know you can argue last five years, last three years, last 18 months even, right. For the first time in human history, now, it’s not often you say that, for the first time in human history, we’ve been able to move any amount of data and information at any speed to any location on the planet at almost zero marginal cost. That’s very recent, very, very recent. That’s really been true. What does that mean? That means that, that information constraint that has been the single overriding factor in how we organize ourselves for 5,000 years, in the last five years, suddenly stopped being a constraint. It’s no longer the thing to organize ourselves around. We can now move data and information wherever we want.
What we also then do is we get access to effectively the entire creative talent pool of the whole planet. And the fact that, you know, Jim, you and I interact quite frequently and most of the time, we’re opposite sides of the Atlantic and we kind of work together as though we’ve been in the office next door for many years. We can do that today. We haven’t been able to do that historically. If that constraint goes away, it means we need to organize and structure ourselves according to something else. What’s the next most valuable constraint? Now, I’ll be honest with you, I don’t profess to necessarily to know what that is. I would like to think it’s something to do with well-being, maybe even happiness, I don’t know, maybe…whatever it is. What’s happening today is that we’re gonna see this dramatic change in how organizations, companies, societies, countries organize and structure themselves.
And then just the last piece of this is, if that wasn’t profound enough is that this secondary issue around creativity and access to creativity. Now we can call on pretty much anybody that we may be aware of anywhere on the planet and contribute collaboratively across continents, across time zones, across oceans. That means that any one of us, the entire global talent, world’s global talent is available. That sounds pretty profound. If I then say, if you think about how education has increased, the part will go, population has increased. I think it’s a reasonably safe claim to make that there are more technologists, scientists, and engineers actively working on the planet today. Right now, at this moment in time, there are more people providing creative input to the way we function as a species. Then the entirety of all of those roles, scientists, engineers, technologists, in the entire history of humankind. So, there’s more active creative talent right now than everything that got us from the caves to where we are today. Now, if that doesn’t blow your mind, nothing will.
Jim: I think you’ve made a pretty solid case for this new era in human history that we find ourselves in. You talked in the presentation that there’s five heresies that kind of lock companies into existing performance levels, let’s take them one-by-one here. The first one was the tyranny of retrospection. Can you tell us a little bit more about that?
Chris: Yeah, sure. I mean, I’ve always been intrigued about the process industries and one of it is that the process industries are very conservative for good reason. You know, the consequences of things going wrong in an oil refinery or chemical plant are pretty significant, pretty serious.
But we’re the same, we still kind of operate the same way as we did when I first started work 30 years ago and actually probably when my parents started work 30 years before that. And there aren’t very many industries that more or less work the same way with the same sorts of processes and the same sorts of business models now as they did two generations ago. In fact, I struggle to think of anything that’s like that. And what is it? What locks us into that? You know, or is it a locking or are we where we just optimize then we still optimize now. Well, you’re pretty clear, I don’t really believe that’s the case.
And as I’ve thought about it as you say, there are kind of these five ideas. The first one is this tyranny of retrospection. One of the things that locks us into the behavior that we have, it stops us, prevents us from seeing the potential future, the potential opportunities, this idea of retrospection. We’re all taught about the planning cycle, this is the idea that you plan something, you re-enact it, you then measure the performance, you review, and then modify your plan. This planning cycle is taught as a best practice, right? At business schools, in general life, and, you know, whenever we do productivity training, we get taught this thing.
And it’s interesting to kind of reflect on that and think about other industries. Other industries have broken away from it. What it means is that you’re stuck always looking over your shoulder, you’re always looking to what happened historically, and then identifying things that went wrong and trying to stop those things from going wrong again in the future. You don’t think about the present you’re currently in. The best analogy to try and get this across is to think about a car racing team for those people, you know, down here in Texas, the circuit of the Americas. Formula One comes here once a year, you know. Think about the behavior of the race team. And think about how that’s different from what it would have been 15 years ago, you know, 15 years ago, 20 years ago, they did this plan, act, measure or review. You know, you would plan to do a race, the race would then take place, the driver would drive on his own following whatever strategy he would’ve been sent. And when they deviated from it, the driver was on their own. After the race, we would analyze performance, think about what had happened with a view to changing our plan for the next race. But actually in the race you’re in, there was precious little the team could do, it was all down to the driver.
That’s fundamentally different from what would happen today. What happens today is you’ve got a whole army of people sitting behind the driver. We’ve got real-time telemetry providing all sorts of data about the driver’s physical condition, about the car’s condition, about the environmental conditions around them, about what the competitors are doing, all feeding into this… But it is a small army of people who are monitoring different aspects of the race continuously updating and changing the plan and the strategy in real time. And when unforeseen events happen, they can respond immediately, so, the race strategy that’s in operation when the race finishes is often fundamentally different from the race strategy that you went into at the start of the race.
We don’t do that in the process industries. In the process industries, we’re still stuck in this mentality of planning the race of tomorrow by looking at the race of yesterday and just sort of let today be whatever it is and just hope against hope that it’s kind of all right. And I think a good friend of mine talks about the fundamental shift that we will make in the process industries over the next five years as a shift from reactivity to proactivity, from reaction to prediction and prescription. We will start running process manufacturing operations in real time, continuously, and we’ll break away from this periodic planning and review cycle.
Jim: Yeah. And it sounds like the technology is there first, and we’re figuring out the workflows and people side of actually taking advantage of the things going on there. The second one you mentioned was the tyranny of now.
Chris: Yeah. And this one’s very specific to optimization and process control. Optimization and process control has huge, huge potential in terms of actually improving the profitability and performance of the process industry. I mean, that’s undisputed, due to the money that’s been spent on it over the last two or three decades kind of pairs that out. But in the vast majority of cases, you know, some extremes of batch processing notwithstanding, the vast majority of cases, optimization is done in the instant. You find that process control strategy, all I think about is now, this point in time. This point in time isn’t a point in time, it has no length, it has minimal duration. And all I do is try and get now as good as now can possibly be.
When we get to real-time optimization, that’s absolutely how we drive closely real-time optimization, is just maximize this moment in time. Sounds like a great idea until you say, “Well, what’s the consequence?” What’s the cost? What’s the price of now? What are you sacrificing? What haven’t you thought about that’s already happening? What haven’t you thought about the implication and impact of the decisions you make now? What about the fact that there’s a ship coming in in a week’s time and you have to get the original time.
What about a decision that you make right now will take the most valuable feedstock and run that feedstock till it’s run out. Then I’ll take the next month’s valuable feedstock and run that till it’s run out without any comprehension of the fact that, at the end of the day you gotta run it all. And actually, maybe there’s a better position, maybe recognizing that over a period of time, you’ve got to use it all. So what’s the best way of using it all? It’s a bit like a kid in a restaurant.
We’ve all seen them do it, you know, put down a pizza in front of a kid in a restaurant, and they take the best bits, they take the best bits first, because that’s optimization in now. And boy, the headache you have to get him to finish that pizza and actually eat the broccolini that may be kind of on the edge that’s left till the end. That’s not the way to do it. We as adults know that actually, the combination is the best solution. In the world of process control optimization, we don’t think that way and we need to change the way we think about time.
Jim: That’s interesting and very true that, you know, you take that, you’re really limiting over a longer time horizon. The next one is fidelity inversion.
Chris: Okay. So, you know, fidelity inversion, this one is the one that I think kind of bemuses me the most, right. And it’s, you know, all throughout my career, it’s puzzled me as to why it exists. So, let me describe what it is. As a process controls engineer and optimization engineer in the early part of my career, we invested phenomenal amounts of time, money and energy and some of the brightest, smartest people in our whole organization dedicated to making the plant run that little bit better.
A huge amount of effort going into that, phenomenally complex, rigorous chemical thermodynamic models, pushing the boundaries on what computer science could do, and what our understanding of modeling and maths and optimization were capable of doing to make that production plan work a little bit better. A few years later, I found myself in a production planning organization. My role there was to actually provide the link between manufacturing and the commercial operations. But all of a sudden, I was working with a different community. I was working with that community that you see on the TV, that are sitting in trading rooms, trading cargoes with crude oil. Yes, it’s those guys that’s out there shouting and screaming with three telephones and kind of apparently simultaneously taking input from 16 different TV screens. Truth of the matter is, they’re making most of it up on the fly, this is all gut instinct stuff.
Now, when you think about the value associated with the decisions that those people are making it’s extraordinary. A bad decision over the purchase of a cargo of crude oil for a refinery or a poor decision on a commitment to provide an unexpected order to a customer on a unit has huge financial implications. One decision. Whereas moving that temperature controller by 0.3 of a degree on the air flowing furnace in over the next 30 minutes is actually relatively small.
What I see is that, you know, those traders, those commercial people, they’re running…They’re basing their decisions on things that are typically even today is still on spreadsheets with information that’s kind of updated because of this tyranny of retrospection issue, maybe once a week, or once a month. Huge, huge, big calls made on pretty crappy models and tools. And then these relatively small calls, what flow should we run in this cooler out? What temperature should be to run this reactor out in terms of the financial impact that they can have over that 30-minute time window? Relatively low-value decisions being made on the basis of phenomenally complex, rigorous detailed models. And that’s the fidelity inversion, that is almost, it’s almost a truism in the process industries that the more valuable the decision, the poorer the quality of the data and the models that are used to support it. Makes no sense to me, it never has and I still don’t really understand it.
Jim: That is interesting. Where the time, effort, energy everything else is not on the value scale of what delivers the most value there. The fourth one you had talked about in that presentation was self-fulfilling prophecy.
Chris: Yeah. So I mean, this is a dynamic or a phenomenon that actually reinforces the first three things I’ve talked about, which is like, the first three sound crazy when you think about them. But you say, “Well, why do they continue to exist?” Well, one of the reasons is because we do use the best models and the best insight and the best information we have to make decisions in real time. So, the decisions we make come from the best models we have. By definition, we don’t have anything better to test it against because if we had some investors to test it against. We wouldn’t be using it to test what we did and develop that what we did. We’d be using it to make the decisions.
So, there’s kind of like no one way of identifying that there is a better way because if there was a way of identifying there was a better way, we would be using it to do that better thing. So, we don’t have any way of understanding how much better we could have done. Coupled with that, and again, I know this from my own personal experience, when we’re trying to fit a theoretical model to the real life on a plant or in a business or on the supply chain, we build the theoretical model out, to rigorous first-principles quite often, right? A lot of detail, a lot of theory. Inevitably, that theoretical model doesn’t match reality.
And what we then do is we put these adjustments in. Actually, one job I had, we actually call them freak factors. We put these freak factors into the theoretical model to make the theoretical model look like reality. What are we then doing? So we’re basically compromising our understanding of what’s out there to make this model match what we observe. Is it then any surprise that when we then go back and use that same model to test out other alternatives and scenarios, it comes back with, if you like, a validation of what we’ve done.
And this is a kind of self-fulfilling prophecy, we built ourselves into this kind of loop where we adjust theory to match reality and then test our understanding of reality against that adjusted theory. All we do is we tell ourselves that we get what we expected to see and what we expected to see is what we get with there’s nothing that’s actually…we can’t break out of that loop to identify that there is something different out there. There’s a different potential. We just don’t have the models to be able to do that. We don’t have the constructs to be able to identify what it is that could have been done better. And what we do is we live in this kind of cozy world of because everything lines up and it’s like and agrees, it, therefore, must be right. Don’t buy that, I don’t think it is necessarily right. It just means we’re self-deluding and as I say, it’s just we live in this self-fulfilling prophecy.
Jim: Yeah. It’s kind of the world we see based on the models that we are operating under. Let’s see, and your fifth and final heresy was that we’re busy chasing noise. Can you expand upon this?
Chris: Yeah. So I mean, this is one that, you know, as control engineers, we understand noise, right? We understand the relationship between the time it takes to act on something and our ability to influence an outcome. If a signal’s very, very noisy, we filter out the noise to get to like the underlying trend and then only try and move things that we’re capable of moving. What we know is if we don’t do that, if we could chase the noise we actually just destabilize the system and we propagate it and amplify it and we make the system much less stable.
We understand that from an automation process controller’s point of view. But when I kind of look at the way businesses operate, I don’t see the same discipline. I don’t even see the same understanding. You know, how many of us have worked for that boss that’s always on your back, chasing stuff much faster than you’re able to actually respond to? As control engineers, we know that on a cascade controller, the master controller, the outside controller needs to be 10 times slower than the inside one. We find it’s true in a management organization, every level of the ladder you need to go, you need to be 10 times slower than the level below you. How many of us would say that we’ve got bosses that are 10 times slower than we are? So, what are we doing? They’re injecting…you know, we’re busy chasing noise, bouncing our control valves, our people around and actually, all we’re doing is amplifying the disturbances. We don’t have the discipline to say there’s nothing we can do about that because it’s too fast but look at the underlying trend, look at the underlying flow.
And I also say the same thing kind of in the way we chase markets, big refineries. It takes, I don’t know, probably two, three, four, five, weeks for material to flow through a refinery from oil being received, being held in storage, being run and processed and developed and converted into the, you know, finished products. The finished products being then held in storage and finally being shipped to a customer. So, you’ve got a four-week, let’s say, a four-week time constant in that manufacturing process. Yet we have people in the commercial sides of those businesses, trying to make decisions based on minute-by-minute moves in the spot price and actually making feedstock purchasing decisions chasing things that may even move far faster than you’re actually gonna be able to do anything with it.
That is exactly even more insidious than that because what I certainly see in some industries is you’ve got one community doing that chasing the markets, but at the same time, we’ve got another community in the, you know, financial risk group who are actually trying to hedge exposure to those same deviations. So, they’re busy putting derivative instruments in place, trying to insulate the business from currency fluctuations, market price fluctuations, even demand fluctuations. At the same time, you got another community out there trying to capitalize on those same moves, almost certainly undermining the best efforts of the other groups. And this is all comes down to fundamentally chasing noise and understanding what you can affect and what you can’t affect and having the discipline to go after the things that you can realistically have an impact on.
Jim: Okay. So, we’ve had this big constraint around the flow of information removed from us. We’re operating under these five heresies as you addressed in that. So, given all that what are the questions organizations can ask to free themselves to move their performance to the next level?
Chris: Okay. So I mean, you touched on a really important point, you know, with this breakdown of the constraints around information, what we will see, I am absolutely convinced in the future is that a lot of these silos breakdown instead of everybody having their own model and their own view that guides them about what they should do, will get much more integrated and ultimately, potentially centralized decision support processes, I suppose. I was going to call them models, but it’s more than that and we will be able to do things in much more consistent ways because we can share information and data much more freely and on a much more consistent basis.
So, as that happens, and as we kind of unlock our concept of how organizations should be structured, I think, as you say, there are a set of questions we should ask ourselves about almost any activity that takes place. The first one for me is where does any particular activity… Well, I suppose the first one is whether any particular activity needs to happen. Kind of linked to that is where does it need to happen? Where does it need to take place? Let me give you an example of machines engineers, right? Historically on big process equipment, anything that was related to the big machines on a process plant needed to be done local to the machine because you needed to be able to look at it, see it, touch it, feel it, to really understand what it was doing.
As a consequence of that, you needed to be close to the machine to be able to actually make an informed decision about what to do with it. In the new feature, the where, as to the where question, where does that any decision about this machine need to be made? Well, actually, in the new feature, we can move all of the data wherever we want it. We’ve got novel types of sensors that tell us everything we need to know. You don’t need to be there at the machine. In fact, there’s a compelling reason to move away from the machine because the machines is in this hazardous, dangerous, dirty, difficult environment. We’ve actually seen…moved that decision making to somewhere remote for no other reason than to stop exposing people to risk. Think about where first, do you need to do it locally or would you prefer to do it remotely? Once you’ve asked the where question, you’ve then got to say, well, okay, now that we’ve moved it, you say, who, who needs to do this?
So again, this goes back to the machines engineer. What we had when I first started work is that every major production site had a machine specialist and because you needed one in every major production site, every company had a small army of machines engineers, all dedicated to a specific location. Because we had a small army of machines engineers, we then had a center of competence around machine engineering, right? I don’t know where it went. If we say now, okay, we want to move that decision making away from the facility and the question about who should then make that decision? Well, do we want to carry on doing that? Does it make sense for me as a chemical manufacturing company to be making decisions about machines? Or would it make more sense for the experts at GE or Pratt & Whitney, or Mitsubishi or whoever runs and owns those machines who have true expertise and excellence around those to be the people to do it?
So first of all, where? Second question is who? What I do find is that an awful lot of the time, maybe not always, but an awful lot of the time when we take this information constraint away, we change our decisions. So, things that we thought were core competencies for us as manufacturers stop being. We actually would much prefer to outsource them to true specialist organizations so that we can focus on our real true core competence which is manufacturing the material. So, we ask where? We ask who? Once we’ve answered those two questions, the third question becomes how?
Historically we’re used to the ANSI/ISA-95 modeling of layer one, layer two, layer three, layer four, layer five, right? And this rigid hierarchy in terms of architectural hierarchy and information flow of hierarchy.
There are two new things, two new kids on the block now, you know, we’ve got the cloud, right? And we’re going beyond that, we’ve got clouds, fogs and lakes and ponds and who knows what all these kind of like distributed somewhat etherial things that we don’t really understand. And we also have an edge, this thing, we’ve discovered this thing called the edge. We have edge applications and edge servers and edge of this and edge of that. And we still have our rigid hierarchy of the purging model that we still operate. So, we’ve got some important decisions around architectures, the how question, but the how follows the where and the who. Clearly, if you’re gonna have third-party experts at some remote location make operational decisions for you, that’s not gonna be on premise. That’s going to be a cloud, never to be a cloud-based application.
Conversely, if you’re taking people off production platforms out in the North Sea, or in the Gulf of Mexico, and you’re expecting the platform to operate autonomously, you’re unlikely to have that intelligence in the cloud. For me, that’s kind of more on edge where autonomy matters is more of an edge type application. But I think that’s still emerging, right? Our understanding about how these different architectures work and play and how they work together, more importantly, is still something we’re learning, we’re still feeling out as an industry. But it’s clearly, clearly gonna be very, very different in the future from what we’ve grown up understanding in the past.
Jim: So, I guess bringing this home, how is it that we as Emerson and our operational certainty consultants can assist manufacturers in their transformation efforts, you know, to take advantage of this new era we’re in?
Chris: Okay. So we spent a long time trying to work that through and think through what we should be doing for our customers and our clients, right?
That we have a responsibility and a duty to our good customers to actually help them work their way through that. And in order to do that we’ve had to work out for ourselves first, what does it mean? What actually is this gonna mean? What we’ve come down to, right, is actually a fairly simple, straightforward process. First thing that we would advocate people should do and we can help people all the way through this process. First thing to do is understand where you are, where are you on that journey? What are your current capabilities? If people talk a lot about digital transformation, so, on that digital transformation journey where are you? And we have a maturity model that we use as the basis for doing that sort of assessment. So, you gotta know where you’re starting from.
The second thing you’ve then got to do is work out what you’re shooting for. In order to be competitive, and differentiated and successful and profitable in the mid to long term future. How are you going to do that? How are you going to maintain your competitiveness? And importantly, what capabilities does that mean you need to have? Now, we’re not saying you should set this out in rigid, rigorous detail. This isn’t an engineering project. This is talking at fairly high level about what sorts of capabilities do you want and do you need to be differentiated in your markets?
It’s kind of like you’re identifying your true north, right? What’s your true north? What’s your direction? What are you shooting for? At fairly conceptual terms, but you need to know where you’re trying to go. What we then say is okay, we know where you’re trying to be and we know where you’re starting from. We don’t need to think about how do you fill that gap? Identify the gaps first I suppose and then how do you fill a gap. And you can’t fill the gaps, all of the gaps, all at once, all in one go. There’s a journey you have to go on. Now, we often talk about helping our customers and clients develop a roadmap, a roadmap for digital transformation, or, you know, about operational excellence or manufacturing excellence or whatever level we wanna build on that. And that’s kind of right, if you know where you’re starting, you know where you’re trying to get to, you can work out the root and navigate and then manage your way on that journey.
But I actually like to use a slightly different analogy. A roadmap is something you do in a stable environment. When the road is there, you’re going to follow it, it isn’t going to change, it’s not gonna change as you’re on it. The digital world right now is very different from that, it’s changing so fast. We’re all learning, new technologies are emerging all the time, new capabilities are being developed all the time.
We’re right in the emerging phase of a new round of innovation. And as we move and progress, the environment changes. So for me, it’s actually rather than thinking of this as a roadmap and a road journey, I kind of think of this as trying to navigate a ship through an ice flow. We know where we’re starting, we know where we’re trying to get to, you need those two points, we know what the next step is, and what the likely steps after that are. But we need to have a strategy that’s flexible enough to allow us to respond to a changing environment and a changing world.
And that’s exactly what we’re trying to do with our operational certainty consulting practice, is provide the support to our customers and our clients to help them navigate through that ice flow. You know, there’s an ice flow we’re going to be in for the next 10 year or 15 years or 20 years. Don’t try and plan the thing out in infinite detail, in perfect, you will bring perfect foresight because you don’t have it, nobody has it. Know where you’re trying to go, know where you’re starting from, understand your next steps and then have people around you that have the expertise and the knowledge and the experience to be able to help you anticipate and predict the future that lies in front of you and navigate successfully to that endpoint.
Jim: Yeah, I think what you said is really important, start where you’re going but don’t design it all out because the further you are out there, the more uncertainty there is. So, understand those steps, plan out the initial steps you’re taking very well with an eye towards what are the ones after that, all with an eye on where is true north on the project. Well, that’s been really fascinating. Where can people go to learn more about operational certainty consulting and everything we’ve discussed today?
Chris: Okay. Well, there’s a number of things you can do. I mean, first up, you know, I’m accessible. You know, you can find me on LinkedIn, you can find me through the Emerson expert sites and pages. I’m always open, you know, to being approached. Beyond that, you know, Jim, you’ve run your own, some experts’ blog. That’s a phenomenal source of ideas and inspiration and gives you some understanding of the depth and the breadth of people that you might call on from Emerson. There’s also the user community, the Emerson Exchange 365 community, great way not only to learn and ask questions of and contribute to other Emerson folks but also to the Emerson customer base in general. You know, there’s a user area where users and customers and clients can learn from one another and exchange. But also then finally, point you to the Emerson website. You know, the Emerson website is full of all sorts of information. If you look there, go to the Emerson website, look for operational certainty consulting that gives a lot of information about who to contact, how to contact people in different parts of the world. And also some of the, you know, the things that we can do and the ways that we engage.
Jim: That’s great. Well, thank you so much for joining us and sharing your ideas. And I hope a lot of manufacturers will take a look at it and see how they can break free and start to get on their journey.
Chris: Well, that’ll be great and I welcome every opportunity to kind of talk to you, Jim, and to the rest of our customer base, you know, learn from them as they can learn from us.
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Learn more about improving operational performance in the Operational Certainty section on Emerson.com. You can also connect and interact with other optimization experts in the Services group in the Emerson Exchange 365 community.