In this episode of the Futurum Tech Webcast, Interview Series, our conversation today centers on an increasingly relevant topic operational resiliency, operational efficiency, and sustainability.
We all know that organizations today are dealing with a number of disruptions. From the supply chain to staffing, we are seeing an increased need for resiliency, or the ability to adapt to continue meet expectations. We are also seeing an increased need for greater operational efficiency, or the ability to do more with less. As if those pressures weren’t enough, many organizations are beginning to develop sustainability initiatives. They are trying to do more, with less, without impacting the next generation.
My guest today is Mike Denley, Senior Director, Portfolio Management at Siemens Digital Industries Software, who joins me to discuss the challenges facing organizations today, and what Siemens is doing, specifically as it relates to Siemens Industry Software, to help them achieve their goals.
Our conversation centered on the following:
- An overview of what operational resiliency, operational efficiency and sustainability mean for today’s organizations.
- An exploration into how the IIoT fits into all of this.
- The connection between data, the IIoT and resiliency.
- An overview of how IIoT, data nalytics, and digital twins help improve operational efficiencies and sustainability.
- A look into how Siemens is helping customers navigate the challenges in today’s workplace
This was a great conversation and one you don’t want to miss.
If you would like to learn more about Siemens MindSphere, you can get more information here.
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Disclaimer: The Futurum Tech Webcast is for information and entertainment purposes only. Over the course of this webcast, we may talk about companies that are publicly traded and we may even reference that fact and their equity share price, but please do not take anything that we say as a recommendation about what you should do with your investment dollars. We are not investment advisors and we do not ask that you treat us as such.
Transcript:
Shelly Kramer: Hello, and welcome to the Futurum Tech Webcast. I’m your host, Shelly Kramer. I’m a principal analyst and founding partner here at Futurum Research. On today’s show, we are going to talk about a really important topic and one that is top of mind for many people these days, operational resiliency, operational efficiency, and sustainability. My guest today is Mike Denley, who’s a Senior Director, Portfolio Manager at Siemens Digital Industries Software. That’s a big title. Hey, Mike.
So before we dive in, I’ll give a little backstory here. So I think that I’m one of those people who’s always looking for the silver linings that have come out of slogging through a global pandemic, and I think for me, that one of the biggest things is that it really took operational resiliency and business continuity. It took those issues, and it elevated them higher than ever before because organizations realized these things can’t be number four and five on a business strategy list, a business contingency plan list, a business resiliency plan list. They are literally at the top of the list.
So we also know that what organizations are dealing with today is massive disruptions. We’ve got supply chain, we’ve got staffing issues, and we see an increased need for resiliency and the ability to adapt, to continually adapt in order to meet changing expectations. So we’re seeing those things. We’re seeing an increased need for greater operational efficiencies, the ability to do more with less, especially in today’s current macroeconomic conditions.
If all these pressures aren’t enough, many organizations are also beginning to develop, or have already developed, or have already planted a flag in and are working toward sustainability goals that they’ve established for their organization. So they’re trying to do more with less without impacting the next generation. So it’s a lot, and Mike and I are going to be talking today about how all of these topics are interrelated, how they differ, and what Mike and his team at Siemens is doing to help organizations achieve some of those goals. So, without further ado, this is going to be a fantastic conversation. Mike, welcome to the show. I’m so glad to have you.
Mike Denley: First, thank you for inviting me, and extended thanks to our audience for taking the time to listen.
Shelly Kramer: Absolutely, absolutely. Well, we have a great audience, and I’m sure they’re going to love this conversation. So one of the things I love to do as we kick off these shows, I’d love to hear just a little bit about you, Mike, and your career path, and then you can tell us a little bit about your career at Siemens.
Mike Denley: Sure, I’ll be glad to. I’ve been with Siemens for 22 years, so it’s a long period of time. Backdrop is I ran development processes for one of our PLM products within the team’s… They’re called team center within the Siemens portfolio, and within that, also product management, ran the aerospace and defense services business in the America, and then more recently, with our industrial IOT portfolio.
What that brought to the table is the understanding of working with enterprise, large customers in the field as well as small and medium size, but from a development point of view of requirements and development of software products, but also how they have to be implemented and how they have to be placed into the organization to allow them to use them effectively. Then, more recently, with the industrial IOT, being able to collect data on its usage of both the physical product as well as the software product, and that’s what makes us… It makes me a little bit unique, but also Siemens unique because we’re able to provide that whole domain set to our customers.
Shelly Kramer: Right, right. Super important.
Mike Denley: Yeah.
Shelly Kramer: You’re the guy in the team who makes everything work, really.
Mike Denley: Yeah, really.
Shelly Kramer: I mean, when we met, we were having some conversations at the beginning of this show about our reliance on technology, right?
Mike Denley: Yeah. Mm-hmm.
Shelly Kramer: I mean, it drives everything today, but you also have to have the humans working alongside the technology that figures it all out together. So I went over some of these things in the introduction to this show, but I want to set the stage for this conversation.
Mike Denley: Yeah.
Shelly Kramer: So let’s talk a little bit about operational resiliency, operational efficiency, and sustainability. Let’s go a little deeper into those, each one of those. Let’s start with operational resiliency.
Mike Denley: Well, maybe we should start with some definitions because that’s a fancy word [inaudible 00:05:34], right?
Shelly Kramer: There we go.
Mike Denley: As we look at… and you touched up on this. As we look at operational resiliency, it’s really the ability to adapt to disruption, and disruption comes in many forms. Change is one of those. As we experienced disruption in the last three years with COVID lockdowns, with supply chains, with… Now, we have more of the sustainability or ESG trends in the industry. These are all disruptions to a business and more importantly, to a manufacturing as we try to put product out in the area. Along with resiliency, it’s not just the adaption to disruption. It’s also the expectation that the organization will continue to deliver and even continue to deliver with more improvements. Does that make sense? I mean, it’s funny how we get in there, and that’s actually related to the other term that you talked about, operations efficiency, right?
Shelly Kramer: Right.
Mike Denley: You had mentioned doing more with less, and as we look at it, it’s greater productivity, or greater throughput, or using less resources. As we look at a disruption in our operations, typical disruptions like downtime, that causes inefficiencies, and it causes inefficiencies with the product line, with people, with resources of materials, handling, as well as the whole idea of we’re expected to meet a certain product quota a day, and so a manufacturing has to look at that. So operations efficiencies start to look and measure, and how they measure is one of the most common ways is through OEE or Overall Equipment Effectiveness. That’s a set of KPIs and measures that we look for. How much uptime or the availability of our product line, and then also, how much throughput we can make through that system, and of course, quality and looking for how we’re using materials, and time, and resources.
You mentioned sustainability, which is of the name of the trend that’s going on there now. I mean, we’ve always had manufacturing, and we’ve always had operations for hundreds of years, but sustainability is one of the more common. As you said, it’s doing more with less, but it’s really one more step. It’s actually doing more with less without impacting the next generation. Right?
Shelly Kramer: Right.
Mike Denley: That’s really important, and that is related to more of a more recent trend, especially in the financial industry, fiduciary responsibility of ESG, the Environmental, Social, and Governance. Those that are not as familiar with ESG, it’s really an approach to evaluate how companies are measured against the identified social goals. Right? Not necessarily the business goals, but the social goals such as CO2 reduction, right, and emissions, and workforce diversity. Does that make sense? I mean, at a high level. Yeah?
Shelly Kramer: At a high level, yes, and I think that to explain a little bit about the importance of ESG, the way that our team thinks about it is that for the last decade plus, organizations of all sizes have been focused on digital transformation, right? The integration of technology into business operations, technology working alongside people, and processes. It’s a three-pronged thing, right, people, processes, and technology, to streamline business operations, to move from an analog way of doing things to a digital way of doing things.
So what we talk about here and what we believe we see happening now as it relates to ESG is organizations are also adopting, in addition to ongoing digital transformation journeys, which is a journey that never ends. Technology continues to change. We need to continue to change and adapt. The world continues to change. Disruption happens, right? But with regard to ESG, this is particularly important because what we’re seeing pretty much universally and across the globe is organizations planting a flag in responsibility as it relates specifically to sustainability goals and the environment as a whole. So organizations are making promises that by 2030, we will have reduced our carbon footprint by such and such or by 2040, and these are our plans as it relates to sustainability.
This is incredibly important through… This is incredibly important to employees, customers, other stakeholders within the organization. It’s a business reputational thing. I mean, I think there’s very much… We realize collectively that we’re all in this together, and we all have an obligation to do what we can to reduce the impact of business operations on the planet so we have a place for other generations. So when you add in… You’re focused as a business leader in the manufacturing space in this conversation. You’re focused on business resiliency, and you’re focused on operational efficiency, and how you can use technology, and how you can adapt your processes and everything else to be more efficient. But then, adding to that equation is this whole ESG part, the sustainability part of it. So it’s a very full plate.
Mike Denley: It is, and maybe I can share a little bit how some… They’re related, but different, if that makes sense, because that what’s really at the crux of this.
Shelly Kramer: Yeah. Yeah, they are.
Mike Denley: As we talk about operation efficiencies and sustainability, and now this ESG, they’re all managing resources. Right? That’s common, right? How to be more efficient in their use? People, energy, and materials as you said, right? The short-term objective is basically a line. “Let’s use resources wisely and efficiently, and cut waste.” That makes sense, right?
Shelly Kramer: Right.
Mike Denley: We make an analogy into… Let’s say, I have a machine, an asset on the shop floor. Let’s say it’s a motor, right, and it’s in idle mode, right, or not being used, and/or it’s operating hotter or not correctly configured. It could be using more energy. So I want to make sure that I detect that and optimize for that. That makes sense. Right?
Shelly Kramer: Right.
Mike Denley: But if we look at these longer-term objectives, they start diverging through operations, efficiencies, and sustainability, I’ll say, /ESG. What I mean by that is let’s assume that you’re using oil as your energy source. Okay. That makes sense, and let’s assume that you could be using it the most effective and efficient way. Okay. That’s the idea, but the current ESG trend is to use alternative forms of renewable energy like solar, or wind, or hydroelectric, and this is what puts it at odds or differences. Does that make sense?
Shelly Kramer: Yeah.
Mike Denley: So we need to rethink how we’re using natural resources and as we start looking at alternative renewable ways. So we all need to reduce resource consumption, but we also need to be able to understand how to use resources smarter.
Shelly Kramer: Right. Absolutely, and that is something that we will… I believe we’re at the beginning stages of really focusing in on this as it relates to ESG, and I don’t want to confuse anybody. ESG encompasses… Sustainability is part of ESG. Okay?
Mike Denley: Mm-hmm. Yeah.
Shelly Kramer: So they’re not two separate things.
Mike Denley: No.
Shelly Kramer: Your ESG initiatives, your sustainability initiative, they’re the same thing. Okay? So there is something however that plays a large role in an organization’s ability to deliver as it relates to operational resiliency, and operational efficiency, and sustainability. That is the industrial internet of things. So let’s talk about that a little bit. Where does the IIOT fit in all of this?
Mike Denley: Yeah. That’s a great question, and they do have a common denominator, right? The numerator may change, but the denominator is to being able to collect data. Usually, that data is collected via sensors and allow us to measure and predict trends not only for one object, that motor I was talking about or an asset like a motor, a pump, or generator, but really, the whole production line. It starts at the manufacturing, but it could be a whole interconnected system of devices like the manufacturing line or a distribution center, or a more complex like a train or a building. Right? So as we get into this, right?
So this is where we start connecting IT with OT, and this IT-OT convergence. What is key for, let’s say, operational resiliency and efficiency is this ability to collect and measure not only the process data and the device data in near real time, right, and that includes temperature, and status, and humidity, and stuff. But we couple that with material and product flow because part of their resource usage is moving that material, moving that product through the line, right, and ensuring that operations is efficient. So this collected IOT data then can be analyzed. That’s what’s really important, but what’s more important when we collect it, it’s got to be in context. Just to look at data without understanding what’s actually happening on the manufacturing floor, it doesn’t provide the entire picture, so we need to have that data analyzed in context to identify this trend or event.
One example is, to get people’s heads around this, condition monitoring, right? The motor can be on or off. That’s a condition. We can detect that or a process threshold like temperature. If we’re doing resins, maybe the control point is greater than 80 degrees. Yet, that’s another condition, but that may indicate that we need to service the motor or get into the heating element because it’s too hot, if that makes sense. So we are using IOT to optimize the operations and gain efficiencies, but also to reduce this downtime. Does that make sense a little bit?
Shelly Kramer: It absolutely makes sense. Super important. So you touched on this already, but the IIOT generates a boatload, that’s a technical term, by the way, of data.
Mike Denley: Boatload. Yeah. No.
Shelly Kramer: A metric ton. It generates a lot of data, and that presents sometimes a very big challenge for organizations. So let’s talk a little bit, if we can, about data analytics, and data analytics capabilities, and the role that they play here.
Mike Denley: Yeah. Before I start, I’d like to just make one thing. As this data, you’re absolutely right. As we’re collecting data, one of the statements that’s made sometimes is that resources are finite, but data is infinite. Right? We have a tendency to collect data on all aspects, thinking that it’s better to collect data, even though we’re not currently using it.
Shelly Kramer: Right.
Mike Denley: So you’re like, “Why am I saying this? Of course, data is important.” But there is smart data management as well, right?
Shelly Kramer: Right.
Mike Denley: You think about collecting more data than it’s needed. Well, that has to be stored on a disc drive. That means that I’m using materials for that drive and I’m using energy to power it, so just…
Shelly Kramer: More resources.
Mike Denley: More resources. So as we get into analytics, we have to understand why we’re doing the analytics, but also, what data I need to do that analytics, and we have a tendency to use more data. You’re probably more aware of this. Data is growing at this exponential rate. In one of the stats that recently came out, 90% of the data has been generated in the last two years. Isn’t that interesting? Right?
Shelly Kramer: It’s crazy.
Mike Denley: It is crazy, and then the other trend that’s part with this data… and I’ll get to analytics here in a second, but this is important is that the ongoing trend with IOT is the shifting to cloud-based data centers. Right? We have the hyperscalers out there, and companies think that’s part of the sustainability answer, and it is. Why? Because instead of having these siloed data centers at each company by company by company, we are actually able to combine that and have reduced overall IT footprints, which is actually reducing energy in carbon usage and emissions. But at the same time, there’s also a need to think about not putting everything in the cloud, but keeping some of that data analytics at the edge and using other techniques like closed-loop digital twin. I know we’ll probably talk about that a little farther in the conversation here, but let’s talk about…
Shelly Kramer: We will.
Mike Denley: “Gee. Now, I generate all this data. Right? I generate this data. Now, what can I do with that?” Of course, we talked about, “I collect data. I want to measure something,” and what I start doing with analytics, I start trying to understand the behavior of my assets or my motor, or my generator, or a plant line. Even though we’re talking about manufacturing, we could be talking about the operation of a product in the field like a wind generator, a windmill.
Shelly Kramer: Right.
Mike Denley: Right? So even though we talk about manufacturing, it’s easy to understand, but it’s really the complete life cycle here. So we start building these models of behavior, and people will know that as, “Oh, that’s AI, Artificial Intelligence, or ML, Machine Learning.” What we’re trying to do is build simulation of the behavior of that device or that system, and then we use the IOT data to optimize that to create a set of rules or conditions, right, and then we use real live data in that model to predict behavior like a possible breakdown. We already said breakdowns are not good for operations efficiency. They are waste of resource, right?
Shelly Kramer: Right. Right.
Mike Denley: So that data with the analytics can tell us the right time to perform maintenance, which is predictive or optimized maintenance. Right? The same model concept can predict energy usage. Right? Remember, I used that. The motor is running hotter, right?
Shelly Kramer: Right.
Mike Denley: You’ll have a higher temperature or it’s vibrating more. Then, we know the motor is using more energy than necessary. End of story. We know that. So data analytics allows us to generate new insights at much faster rate to improve this efficiency and quality. Of course, we talk about bottlenecks, and manufacturing, and maintenance costs. Right? So.
Shelly Kramer: Yeah, and I think that one of the key things here is that across the board, what we hear from customers, from manufacturing organizations is that that data challenge is a very big challenge. Again, we have massive quantities of data that we’re collecting that’s growing at an exponential rate. It’s going to continue to grow at an exponential rate. We’re collecting data from hundreds and thousands and millions of sensors, right?
Mike Denley: Yeah, yeah. Right.
Shelly Kramer: Maybe not all in one organization, but it really is, and that’s bringing into focus the importance of edge computing and those capabilities. But in reality, knowing what to do with that data, having a data management capability, having the ability to actually use that, the key parts of that data to do exactly what you want to do, that’s the biggest challenge that a lot of organizations face today. So I think that that, to me, knowing the value prop that Siemens brings to the table, that’s a really important part of this equation.
Mike Denley: I agree with you, and this, the use of… the analytics of data, being able to collect data, understand which is the smart data, and the behavior models is whether we’re in an engineering process, or a manufacturing process, or in the field execution operations process, the idea is to shift left the discovery earlier in the cycle. As we know, as we shift left in the cycle, we’re actually reducing the number of steps after that fact. We’re actually reducing resource usage and meeting these sustainability goals at the same time. That’s what’s interesting, right?
Shelly Kramer: Yeah, and super important. Well, that leads us to… I know you touched on it here just a minute ago, but digital twins. Let’s talk about digital twins and why this is so important.
Mike Denley: Yeah. I just mentioned about shift left, right, the whole idea, and what the digital twins play into that shift left earlier discovery for I’m finding out a requirement is not going to be met earlier in the design cycle or I find out that the motor is running hotter, I can fix it earlier than having it break down. So a digital twin by definition, just a high level, is it’s a virtual representation, something in logic or inside the computer of a physical object like a machine, or a motor, or a pump. Right? It has these behaviors, but it could be a more complex, layered system like a system of systems, like a representation. A train would be in this or a building. You can see there are many… In a building, there’s HVAC systems, right?
Shelly Kramer: Right.
Mike Denley: There’s the motor, and locomotion, and energy usage, and so there’s these layered items. So the train itself could be a digital twin, but also, everything within it is also layers of digital twins. So this is what’s interesting, and so you get the system of systems. We talked about these models, these ML models, Machine Learning models, and one way of thinking of a digital twin is a set of related behavior models. They’re just layered up. Some of them interact, some of them don’t. Technically, digital twins have been around for a really long time, even though they become more relevant now than ever before. One could argue that a CAD model is a form of a digital twin because it’s a virtual representation via a geometric software of a physical object. Right? It’s the motor, but it’s a CAD model of that motor. Right?
What is different today than just saying, “Oh, I’ve got it in the CAMO,” is that we’re able to collect this data and measure it in near real time. So this usage data via this industrial IOT, as we’ve been talking about, we can relate it to the digital twin, and now we can have a live virtual representation of that product with its behavior. Not only that, we can actually… Since it’s live data or near real time data, we can actually check the expectations or the assumptions we made on the requirements within that operations, and then start looking at, “Is the product design correct? Are the requirements correct? Was it manufactured correct? Was it service correct?”
That’s what the value of the digital twin allows us. It allows us to collect this data, place it in context, and then apply it back to these behavior models. When we do that, it’s a closed-loop digital twin. Feeding back real live data to optimize the model and predict or to validate your assumptions of requirements or operations is a part of that, and that’s what makes it interesting because now we’re able to predict behavior under a variety of conditions because in a testing world or a real live world, we don’t know when there’s an energy shortage. Right?
Shelly Kramer: Right.
Mike Denley: We don’t know, and so now we can predict that.
Shelly Kramer: Digital twins are a big deal.
Mike Denley: Yeah, I think so, and closed-loop digital twins are even more important from my point of view, right?
Shelly Kramer: Yes. Absolutely. We are on the same page there.
Mike Denley: Yeah.
Shelly Kramer: So let’s then wrap it up and talk about… So how does the IIOT, data analytics, and digital twins help as it relates to… I mean, you might have touched on this a little bit.
Mike Denley: Yeah, a little bit.
Shelly Kramer: With regard to operations, efficiency, and sustainability, we know part of it is about resource management and monitoring and that sort of thing, but any thoughts on that over and above what you’ve already shared?
Mike Denley: Well, again, digital twin is this virtual representation. It has a series of machine learning or models to it. Right? We’re collecting IOT data, but one of the things that we’re also doing is collecting other forms of data from other systems like PLM systems, or ERP systems, or MES systems. So, now, we’re not only able to identify current usage because that’s what we’ve been talking about, but also, other insightful relationships like quality of supplier parts because I don’t want a poor supplier part, right, or an in-field operations. We are predicting trends or outcomes. Again, this closed loop comes back into that.
Shelly Kramer: Right.
Mike Denley: So digital twins are relevant across the entire life cycle, but have specific value in… We’re talking about product engineering, or manufacturing, or operations. The whole idea is to use these closed-loops digital twins to identify non-optimized operations and resource usage. We’ve been focusing on natural resources like water and energy, but it also applies to materials, and time, and people. Right? That’s where the ESG comes in, right, because it’s all encompassing. It’s not just the natural resources. It’s actually looking better ways of doing it. So as we apply closed-loop digital twins and AI/ML within this ESG space, we can predict and track the carbon footprints starting at the design, that’s interesting, even before the first prototype or product components are being manufactured. That’s the difference and…
Shelly Kramer: That’s exciting. I mean, it’s tremendously exciting, right?
Mike Denley: Yeah.
Shelly Kramer: I mean.
Mike Denley: Yeah. Yeah, and then what’s further is that once that first product is built and actually in the field, we can then take what we have planned as part of the engineering. Then, we can actually monitor the real product and track its life across the supply chain and in the field against service life cycle. That is pretty powerful.
Shelly Kramer: It is really powerful, and it’s all about data. Right?
Mike Denley: Yeah.
Shelly Kramer: Yeah.
Mike Denley: Yeah. It’s funny. It’s all about data. You’re right with the…
Shelly Kramer: Yeah.
Mike Denley: Yeah.
Shelly Kramer: It is, but when you can… and that’s really what I think the foundational premises of digital transformation is. Right? I mean, it is data is… being a data-driven organization is the key to success, but also, knowing the role data plays, knowing the role technology plays, the right technology solutions that allow you to gather, and use, and test. All these things we’re talking about all work together, and data is the lifeblood of any organization.
Mike Denley: Yeah. My only qualifier to that, not that you’re wrong, is having the right data.
Shelly Kramer: Yeah. Oh, absolutely. Absolutely.
Mike Denley: Too much data is not the answer and having smart data management along with it, but you’re spot on. Right?
Shelly Kramer: So important. Yeah, so important. So one of the things I always ask my guests on this show is, do you have any case studies or customer examples that you can share with us about how Siemens is helping customers navigate these challenges?
Mike Denley: Yeah. Siemens as a large company has hundreds of examples across our accelerator ecosystem across multiple industries, but let me share a couple that I think would strike a chord with our audience. We’re helping several soft drink bottlers boost resource efficiency collecting more than, I think, over a hundred data points just on energy consumption and almost two-dozen data points and water consumption. Key resources, right?
Shelly Kramer: Uh-huh.
Mike Denley: These are being updated regularly, every minute, and monitored via dashboards and alerts. The whole idea is to reduce energy consumption while improving system maintenance and of course, prevent energy loss, right, in a bottle.
Shelly Kramer: Right.
Mike Denley: We talk about tens of thousands of dollars when that line goes down, right?
Shelly Kramer: Right.
Mike Denley: So there’s a cost to that, and then when we talk about energy, another example is in the wind energy field, we’re working with a large service and maintenance company of wind farms using closed-loop digital twins. Now, wind farms are one of those alternate forms of energy. So important in sustainability, and so we’re using closed-loop digital twins and IOT feedback not only to reduce operating costs because that’s part of sustainability and increase asset availability, but what’s more important is to increase energy yield, why we’re extending the remaining useful life of gearboxes in the field. You hear about a wind turbine. There’s a gearbox that keeps the blades running. Extending that life reduces the carbon footprint, if that makes sense. I don’t need to build another gearbox. I can reuse the one I’ve got, and that’s part of the circular economy. Does that make sense?
Shelly Kramer: Right.
Mike Denley: Which is something we haven’t talked about, and I don’t want to confuse anybody, but yeah.
Shelly Kramer: But an important part of any sustainability initiative.
Mike Denley: Absolutely.
Shelly Kramer: Yeah.
Mike Denley: We have to understand, especially in product design, because the relife now… before we talked about retirement of product, right?
Shelly Kramer: Right.
Mike Denley: How to salvage the product. That’s no longer. Now, it’s a reuse, right?
Shelly Kramer: Right.
Mike Denley: It’s second economy. “How do I take that same product and upgrade it in the field possibly for a new item?” One of the other items I think is interesting in this sustainability is a different type of manufacturing, sustainable fish farming, right, which is supporting the smart food production. We’re still leveraging sensors, and IOT data, and this AI-based analytics, but we’re doing this in this aquaculture area in a closed-loop system of a controlled environment with multiple levels of water treatment. Think about how clean that water has to be and with solar energy. So we get this complex set of systems, systems of systems, and the whole idea of monitoring that in real time is to ensure proper nutrition of the fish, not over feeding them, smart water management by reducing waste and pollution, and of course, the ultimate goal is to have a productive, scalable, environmental-friendly operation.
Shelly Kramer: Right.That’s very cool.
Mike Denley: Yeah.
Shelly Kramer: So as we wrap up what I knew was going to be a great conversation, thank you so much for that, what I want to ask of you is… So for someone watching or listening to this conversation who says, “Okay. Everything that Mike said makes perfect sense. I’m not there yet. Where do I start? What do I do? Where should I go?” So what’s your best advice for that person?
Mike Denley: Yeah. Well, there’s lots of resources, but one of the ones I would start with is at siemens.com, but /sustainability, so siemens.com/sustainability. Now, why there? Because we discuss openly how we are helping our customers, and so other customers can look at that and say, “That’s like me. How do I identify with that?” But more importantly is how Siemens is achieving net neutrality because we’re a manufacturer too, right?
Shelly Kramer: Right.
Mike Denley: So we have a dual responsibility not only to help our customers, but also as we produce product that we’re meeting those ESG and sustainability goals within that. So that’s my first place I would start, and they do a really good job. They talk about what’s happening in the marketplace, what’s happening around the world at the global consoles, and what the commitments are by governments and such.
The other place I would start is… “Gee, I don’t know much about industrial IOT. Where do I go?” There is siemens.com/mindsphere, M-I-N-D-S-P-H-E-R-E. We talk about industrial IOT, and when is it time to do it yourself, and when is it time to buy, and then we look at related solutions. So we’re pretty open about that. Then, another is we have an ecosystem, and part of MindSphere is mindsphere.io. We talk about solutions, and partners, and specific customer use cases.
Shelly Kramer: Right.
Mike Denley: So I think those are three good starts because you need to do your research. Right?
Shelly Kramer: Right.
Mike Denley: I think Siemens is a really good source of information because we believe in openness of our ecosystem and our products. So that’s a good place to start.
Shelly Kramer: Right. Absolutely. Well, great advice. Mike Denley from Siemens, thank you so much for spending time with me today and sharing some of your gray matter. I knew it was going to be a great conversation, and I’m sure our audience will agree, and we’ll have to do it again soon.
Mike Denley: I appreciate your guidance here, and it’s been good. Thank you very much.
Shelly Kramer: Absolutely, absolutely. Well, with that, that’s a wrap for our show. Thanks to our listening audience or our viewing audience as always for being a part of these conversations. I will include some of the links to the references that Mike mentioned in the show notes so that you’ll have those easily accessible. With that, it’s a wrap.
Mike Denley: Thank you very much. Bye-bye.
Author Information
Shelly Kramer is a Principal Analyst and Founding Partner at Futurum Research. A serial entrepreneur with a technology centric focus, she has worked alongside some of the world’s largest brands to embrace disruption and spur innovation, understand and address the realities of the connected customer, and help navigate the process of digital transformation. She brings 20 years' experience as a brand strategist to her work at Futurum, and has deep experience helping global companies with marketing challenges, GTM strategies, messaging development, and driving strategy and digital transformation for B2B brands across multiple verticals. Shelly's coverage areas include Collaboration/CX/SaaS, platforms, ESG, and Cybersecurity, as well as topics and trends related to the Future of Work, the transformation of the workplace and how people and technology are driving that transformation. A transplanted New Yorker, she has learned to love life in the Midwest, and has firsthand experience that some of the most innovative minds and most successful companies in the world also happen to live in “flyover country.”
