Manufacturers are finding amazing value leveraging technology to accelerate innovation cycles and deliver new products and services. Some such examples have reduced innovation cycle times by as much as 35% and decreased cost of NPI similarly. Imagine company revenue being reduced by as much as 40% due to the impact of the pandemic only to emerge rapidly with new product lines that fill the revenue gap at an even greater profitability prior to the pandemic. Lets discuss how this is being done.
- How to accelerate innovation at your company leveraging technology
- How to reduce cost of innovation leveraging technology
- Learn how other companies have done it
Hello, everyone, welcome to impact summit. My name is Michael Walton. I’m in manufacturing industry executive with Microsoft. I have spoke at this event in person before and I absolutely adore it. Hence, I’ve asked if I could speak with you virtually for this particular year’s event. Sorry, we cannot meet in person, I’m sure you understand. Now, I’d like to talk to you about a very personal topic that I’ve been spending a tremendous amount of time on lately. And that is unlocking innovation, and delivering new products at rapid speed to include associated services. Now, in reality, this is also a top line and a bottom line improvement for most companies. I’ll get into that a little bit later. Let’s take a look. So today, I believe you know that digitization offers lots of new opportunities. Now, I see manufacturing being transformed all the time, the fast followers in the early adopters pretty much have made their dent, in the end, leveraging the technologies and now are pushing ever closer to the digital maturity. With that said, ask yourself where you are in this life cycle. For instance, do you use technology to accelerate the product lifecycle, whether it be new product introduction, or it be potentially a new adjustment to a formulation. And from there are you digitally enabling the design and the production process such that while you’re going through the design, you can run over many, many simulations with high performance compute power at rapid speeds at a very low cost that allow you to eliminate the least likely experiments so that the team is really focused on the optimal ones with the optimal outcomes. And also, of course, redefining the value chain, because technology really gives you that opportunity it, it truly allows you to look at things in a different way. I have a manufacturer that I work closely with, that’s a Microsoft customer, who, due to COVID, and the impacts on the economy, they’ve lost 3435 to 40% of their top line revenue. For most companies, that would be well, frankly, very tragic, and very difficult to recover from. But because they’d invested in technology with us to look at rapid innovation cycles to speed up design cycles in in house processes, such that they were putting critical engineering resources on the right experiments to deliver the right outcomes and products, they were able to not only recover, but quickly change to incorporate new products they’d never once made to meet the demand in the market because of COVID in things such as PP, and I’m happy to say they’ve since recovered their drop in top line revenue. And in fact, it looks like they’re going to exceed their projections. So let’s take a look a little further. Right. So historically, automation has moved manufacturing along the way, quite a bit. I mean, I, I have to admit that I’m my backgrounds a lot in process automation. And Mom, you know, manufacturing operations management. And so I have to say that plus in the US historically for many years, and you know, as a huge advocate, but the connected pneus of the product, the plants, the limbs systems, it just all the way from marketing insights through the value chain has been well, it’s been extremely transformational. So, in that message, I’d like to say those investments have really plateaued in I did a survey two and a half years ago. It’s a little dated, of course, but in that survey, it showed a dramatic decline. In the return on either Kaizen exercises that companies were doing for improvement, or in the process improvement area, to be able to mitigate things, whether it be if you’re a lean organization in the weight waste side of it in the mood or in the quality side for reducing scrap they’re just the the hard labor of finding those opportunities, and really yielding a large ROI, you know, just wasn’t there it plateaued. Now,
this really helped me to face the new reality of that we’ve got to move past this. Now I knew that before we did that survey, I just wanted to check back into the market to be sure I was keeping the pulse of the market of our the manufacturers that I see whether it be small, medium, large or monolithic. This is true across the board, they plateaued the the low hanging fruit is not there as much. So it really wanted them to take a look at how it question how they go to market. And so when I’m working with senior executives on that kind of a strategy, you know, are you are you best in class, or at least on pace with your peers, with an accelerated speed to market, you know, the consumer and the b2b to see in those markets as far as you being a supplier, potentially to another customer. I mean, the reality is, is that they know technology transforms how fast you can either reiterate the product, improve the product or produce the product. And the harsh reality is, is that it’s incumbent upon us as manufacturers to produce that product even faster. I can tell you, there’s a lot of research that shows the first to quote and the first to be able to meet the demand with a reasonable price with a reasonable set of of features that meets the customer’s need more often than Win win in that particular scenario. And so let’s What about the rising customer expectations I mentioned or the shifts in product demand, the benefits of risking automation, I got to tell you, the risks, the benefits and the risks of automation, automation investments going up. COVID has really put the squeeze on, in some ways how we use potentially our human capital, and making sure we protect that human capital from risk. And so some automation has come into play to be able to help with that. And then of course, real time data driven insights, I can’t say it enough over and over and over. If data is the new oil, then the data insights is in fact, the new water. I’ve heard that recently. Everyone needs water, everyone needs data insights to be able to drive their business at scale. Now let’s take a look here a little bit further. An example of that is I have some customers to talk about later. One of which is an auto manufacturer, you know, your digitization can accelerate time to market, even in a situation where with like automotive, how clay models and winter tunnels, all of those things, believe it or not, we can actually duplicate those in 3d. With a digital twin. We can gain all kinds of insights through simulation and high performance compute power, so that we can predict what the outcomes will be for the best scenarios to produce what you’re trying to make. It doesn’t have to be a car, it could be formulation of food, it could be ingredients, it could in fact be anything in process and discrete manufacturing.
Now, in the product lifecycle management, many of us have either PLM or even just simply CAD CAD files. And we have formulation of limbs, we have many of these different products. But I got to tell you that by digitizing those assets, connecting to them, bringing that data in with insights and and simulating it in terms of a digital twin, and you’d ask well what is a digital twin. In this particular case, a product digital twin would be an actual digital 3d twin with all that all the finite details, all the testing data associated to it, all of the CAD data associated to it in a PLM. Whatever it may be, even if it’s a set of molecules around the formulation of a product that is a say a food, you can then begin to richly and quickly produce all kinds of outcomes very rapidly cutting the time to market dramatically. In some cases, I’ve witnessed some discrete manufacturers being able to cut their time to market as much as 40 45% and process manufacturers 30 to 35%. I’ll get into that a little bit later. Now on the right, you see about enabling predictive or condition based maintenance, for optimization of that of those assets and the utilization of those assets. Yes, those actually play a part in the lifecycle of this product development. Because those assets, they’re going to tell us all these insights on when the with the machines producing the product and the people along with the actual testing at the end and the quality and all the different parameters associated so that if we need to tighten those, or maybe we need to redefine those from a cost perspective, we can do that and we can rapidly do it. We don’t need to whole army of people in Excel anymore. Now let’s take a look further. So unlocking these opportunities that are connected products and processes, you might say, Well, how do I actually get insights to say, a process function such as foods or drinks? Well, there’s actually the user experience you can connect to now arguing that you can get the insights from different devices on the consumer experience. And the feedback they provide at that, too, creates a body of data around a connectedness to feed into this development cycle. Not only that, but your your marketing insights and how buyers are perceiving the product, the feedback on the product in the market. And furthermore, like on bing.com, all that data is being driven with insights, that marketing can now feed into your development cycle. Now, this whole digital feedback loop from generating that IoT data, and then the converging that data with insights, all the way around to how iterating that design and using that as a as a part of an input, and how to monitor how that produces with your PLM your design all the way through manufacturing and feedback from the customer and performance in the field. And then of course, that will help you to glean insights into what kind of new lines of revenue, I’m working with a customer now, that has had some revenue drop off, they’re not essential. But they’ve now been able to identify with us to new lines of revenue, that were already had the capability inside their company, they just didn’t realize it. But when they were able to look at the data in that body of data, we were able to together through what we call a envisioning session, identify, hey, look, what these are three or four opportunities you have, and they to identify those. And now can you bring those to market. And the fact is, they can bring on the market, arguably even faster than the competition is doing today, in a market that they’re just starting to actually dip their toes in. So how do we plan around this right? How do we think about it in a, say a three horizon plan? McKinsey, of course, has coined that phrase many times. And I love that phrase. So I thought I would share it with you and three unique horizons. Come with me on this journey, write a piece of paper and start jotting down horizon one what you think and two and three as an example, so that you can better articulate for yourself ease. So as an example, in horizon one, how do I connect my product or the user experience and the feedback that I get? You know, by transforming your product in this way, you can truly understand how your customers use printers, if you make printers, and the Imaging Products associated to them, how to see new lines of revenue from those, and that you could capitalize on and meeting new market demands based on insights that never presented themselves before, or potentially others in competition I have yet to even reveal. Furthermore, how do you take all that data and accelerate those cycles of product innovation, enable simulation, drive the convergence of them all into a digital twin, making this digital thread? You know, it’s very possible and I got to tell
you that people are doing it, it is not now, early adopters, I would argue we are past those, and they are going into a sense of digital maturity. They’re not there yet. They’re at the predictive that they are getting into more of the cognitive. Um, so I would ask yourself, what are you going to do? Are you going to connect your products? If you have already? Are you going to accelerate inside of engineering, and really take this by hold? beat your competition, improve your cost by 30%? Time to market by 35%? That’s what I challenge you to. If you don’t believe me, ping me on LinkedIn. And I’ll prove it to you. Now, these are four buckets in this horizon one. So let’s take a look first, as how do we get connected while we get connected, and then immediately start looking at that data and start thinking about expanding that with new services. I have an industrial appliance company that has long been selling industrial appliances in arguably a commodity space. They have differentiated themselves by not only connecting and remotely monitoring the product, but now they’ve added being able to get services and parts rapidly. Depending on that SLA the service agreement that you sign up for Furthermore, they have worked with us to be able to put IoT devices on other existing products they didn’t even manufacturer at the customer site, and being able to monetize those than a similar family of industrial products. Hence, they have created a tremendous new line of revenue and connected services to the tune of last quarter 42% growth. So one of those I’ll be talking about later. So what are they doing? Well, they’re looking at the health of the equipment, how the asset is being utilized, or consumed, potentially say your food or how you’re used in a process. If in fact, you’re like water in the Ecolab example, where they’re helping to people to conserve water, which is please take a look at what they do because it’s phenomenal Ecolab. But how do you then do the connected field service? very real? What do you how do you do it even with COVID? You know, because of COVID, a lot of field service teams cannot go on site to actually do the service. So now or to inspect a product when it’s received. So now we help them to be able to do it remotely through augmented reality or through other visual terms. So they could connect with the customer, and be able to help them to provide that service with work instructions assistance, or to inspect the product they just received. As well as that not only customer service, being able to notify the customer pro actively before a problem comes. Now a lot of this has been around for a while. And so I’m, I’m assuming many of you actually know this, but I want to be able to share that story. The journey starts for those who may not know. Now let’s look at those intelligent extensions. That’s where we go from remote monitoring and insights of how you’re using it and where there may be some anomaly or problems that you can start actively trying to mitigate for your customers or within your engineering processes or your facilities. Now we can create a digital twin, it’s been around a while there are many different twins, there’s no one answer to every problem. As an example, I could have a process twin for my manufacturing process or my engineering process. That’s the key. Also, we get a product twin on the product itself, say like a cat, a tractor, how field moving earthmoving tractor, how do I create that product. And then from that between the process found naked, and the process of engineering it and designing it, and then the digital twin of the product itself, I create the digital thread. Now, these models are driving costs down and speed to market all over. How do we do this what we do it a lot with predictive analytics, machine learning and high performance compute. We also use a lot of AI, which a lot of it is, I don’t say the majority, but a good amount is already built out there. So speed and delivering these things for your companies is really there. It’s not as a heavy lift as it once was. Also mixed reality and interacting with these, this digital thread in the digital twin is really making a huge difference. Take a look at Unilever, Unilever type this in your in your bing.com search box or google and type in Unilever. Microsoft makes a difference or a Unilever Microsoft plant digital twin, and you will see videos that will show you how they have created a digital twin of the facility of the plants and the machines that make their product. Furthermore, the process creating a digital thread. They are real time remotely any manager can see any time and any point oh II product utilization or machine utilization. And if there’s a maintenance outage, are there any predictive maintenance outages coming? What such schedule and all of this with predictive insights that better yet advise them when to look and when to check it out? When a decision needs to be made, not necessarily going to Excel to try to figure out what decision to make. The business applications involved Of course, typical ones like CRM VRP PLM, me as CAD CAM, that’s not new. But using cloud to enable them is very new. The speed the elasticity, the volume that you can deal with in large measure allows you to create these very complex complex models rapidly and be able to mine the data involved in them. very rapid. Bewdley. So let’s take a look at horizon two on the innovation foundation. As you know yourself on the left, is that a fictitious picture? Is that real? Well, I’m here to tell you that’s actually very real. ABB as an example, can do this very easily. And they do it for customers now in volume. So let me ask you this during the digital innovation period, does your factory participate in the actual design process? Some do, and I love it when I hear it, some don’t. And the Unilever example, now they do
visualizing real time factory, your product performance from anywhere, well, you can do that now and do it from my phone, my laptop or tablet, I can do it from any of those, and I can share it globally universally, I can collaborate on it. Furthermore, I can simulate and replicate the physical products through a digital twin the product twin, or the process twin. And I can then interact with that, to ensure optimization and inputs from my design inputs from my laboratory experiments that I’ve done for 50 years and inputs from all of the different marketing insights and rapidly create products, I have to tell you that I got the unique opportunity to participate in a digital tasting through virtual reality. Think about it, I actually was able to taste some ingredients from a customer, using Microsoft Teams, and legal and then augmented reality. And then I was able to give my experience feedback to them verbally, they captured that with my facial expressions, and very rapidly were able to come to a conclusion of likes, dislikes on a scale of one to 10 and feedback directly on how to be able to improve it. How long did I you know, respond to it was so much analytics taken, arguably, from a very, very inexpensive platform. So it allowed them to be able to do this in a way that because it COVID, it couldn’t be in front of the customer. That’s very cool. And then the last rate, the iteration, I participated in the iteration on this. And I watched them be able to take my inputs and other inputs and feed it into the product they were creating, and then rapidly create three outcome experiments that were the highest probability out of 1000s of them, that it ran within minutes to say these are the highest probability to create a plant a palette for the taste for that product that it costs point, I would likely buy it at. Just fantastic. So innovation, I told you, it’s shorter, you can do it a lot faster. What is HPC it’s called high performance compute, or we can call it us big compute power. There’s a few terms for it. But HPC is the one I’ve landed on. And I think is the industry pretty much the standard. Now. We have partners as well as Microsoft ourselves that have built out a collaborative design process, a simulation process of visualization, whether it be through augmented reality, virtual reality, immersive, it could be just through a simple of visual UI through interaction through teams. And then of course, getting that digital validation about making sure that the accuracy and feeling comfortable with the models, making sure that we have defined the outcomes. Now, on this slide, I want to underscore for your three horizons, make sure you are defining the outcomes for each the business outcomes, I want you to know that that is the most important thing, the technology works. The technology works. The reality is that I think often the biggest hurdle to this being adopted by customers is that they don’t believe it works. And it does, then when they try it, there’s usually a change management problem, because the customer themselves weren’t ready for it to work. So all of a sudden, now there’s opportunity everywhere. And it can be daunting at times. Because now you know, management knows it works. You know, it works. Why don’t we take this on and changes always? Well, let’s just say changes sometimes not always. Are the people ready for change? But it does and they do. It just takes some time. Sometimes Sometimes it’s immediate. And then the last reason that it often we have barriers to it is simply because of so much
crap online. So many people espousing their points of view all the time, heck, you probably need and think that about mine at the moment. I hope not. But the point is, is that there’s a lot of static and noise out there. And I can tell you that just sticking to these basic Six work, drive the outcomes, ensure you got a three horizon approach, use those boxes I’ve told you, and then start getting a PLC in hand. Now, accelerating the innovation on this line, here are some key pieces of the technology you’ll need. You’ll also need people and you’ll need process but I bet you already have people in process pretty much under control. So look down below on the cloud workstation rendering the simulation and analysis and deep learning in AI training. I put those words in there specifically, why didn’t someone else actually did it, but I’ve, I’ve helped codified them. Because you could go out and search those online to help you with your education. Also, there’s a lot of education for free. The company I work for Microsoft right now, all the education is free. If you’re not getting it, ping me on LinkedIn, and I will get you the links for you to go. It’s all over the place. on LinkedIn, there’s lots of articles, if you search Microsoft, cloud, like Microsoft, Azure, Microsoft IoT, Microsoft Cloud rendering, Microsoft Cloud workstation, you search that for free and free training with it, you’ll find tons of it out there, the long line is this is the technology platform and how you would do it. Now, a little bit more in detail, let’s talk about some scenarios, engineering from anywhere. Well, you might think that that’s often not affordable, but it’s actually very affordable. And arguably, I would say for many cheaper than using the laptop without cloud workstation. Furthermore, PLM in the cloud, we do it all the time, that’s very reasonable, being able to then collaborate with CAD CAM designs. In fact, even through simulation, identifying industrial control changes and downloading them remotely to machines, through your your security barrier and into the plant is a very real thing. It’s in fact done. I just talked to a customer this morning, who’s been running that quite successfully. And so this allows you to engineer from anywhere with COVID very important in with stakeholders all around the world. Plus, it allows you to innovate more efficiently. And being able to work and collaborate, say with marketing or with your customers. An improves the product quality over time, because it’s enabling efficient collaboration and speed to simulate and design quality into the product at the onset. Now transforming and prototyping, so being able to transform prototyping with cloud rendering happens all the time, even with process goods, whether it be clothing or creating plastics, or how we are able to simulate machines and the use of the process of how water is used. Like I said, with Eagle lab, it works and you’re able to compare the designs at rapid speed with outcomes, designs and costs. So that these simulations to a lot of the work for you, you unlock tremendous value and speed. The most some of the most expensive resources you’ve got are engineers, right? And so being able to drive down project costs, drive up project control, imagine doing a Kaizen event, right, with engineering in the plant and designing out a cost and improving quality. And doing this through rapid prototyping. And in simulated insights with a digital twin for the factory with identified outcomes. It’s very possible. sounds expensive, sounds radical. It’s not. And it’s not it will be the mainstay. Think of it this way. Remember, when I was in an engineering school, we went out and employed a CAD operator, and we had an engineer, would you actually do that today? No, you would expect your engineers to understand CAD same as engineers the future, we’re going to understand rapid prototyping and cloud rendering. So let’s move forward a little bit. Now accelerating time to market speed to market decreasing cost of that getting to market event new product or of the new features within that product connecting with the customer ensuring the quality’s there and the products. Now, what about marketing marketing plan? Oh my gosh, they play a key role. Right? So this these marketing’s your partner in crime, right?
marketing. they too can assist with the cloud rendering. Imagine like I told you that digital feedback loop insights to either the product use it customer experience or consumption, and then what customers are saying feedback or insights at marketing’s gleaning from the trends and new met new emerging trends tied to your innovation cycle, you can immediately control and inside your project cost because you know they’ve got cost points are always button pricer, you can partner with purchasing on commonality and rapidly speed up commonality in your product. or common process. If you have a process twin, you can now use start using that to drive out those costs. And then being able to rinse and repeat and reiterate very rapidly, much smaller team much more agile, faster sprints, you can compare your designs faster. In fact, your customers can participate in this and give you insights very quickly as well. So you’re completing that entire feedback loop. And then of course, empowering the voice of the customer providing consumers the ability to use that simulation you yourself are using. I’ve witnessed gamification of these simulations, were now new lines of revenue. And suppliers who historically had some quality problems, now being able to participate in this. And of course, connecting to their factories, or getting connected to the supply chain with them. In collaborating, I can now mitigate that supplier risk, if you will, to my supply chain and creating a greater degree of resiliency. But it all starts with being able to do this in a collaborative way. With speed and cost. That’s efficient. You remove your on prem constraints, security is better than on prem, run more campus, invent more compute intensive simulations with high performance compute power on demand, you don’t have to wait allocate, you get it far cheaper, always up to date, hundreds of titles of simulation, CAD CAM, and PLM solutions, and then iterate and optimize that design super fast, super fast. I gotta tell you that it delivers performance, accelerated innovation, this does it. Engineering, productivity engineers, though, like I said, is some of the most expensive resources in your company, greater year on year engineering productivity, also, like search, just finding what they’re looking for. So they can be able to duplicate us spraying or leverage a common product. And now this takes care of that with cognitive AI being able to rapidly find, you know, there’s been some surveys that have reported up to 44 or 48%, somewhere in there, of engineering time wasted, just trying to find the digital document they’re looking for. We’ll fix that for you. The other thing is, is that a higher rate of return versus industry, laggards look at that second bullet of 58%. My gosh, that’s just revenue for the taking, in many ways. I’m not trying to upsell you, you can, perhaps you’re doing this already. But I gotta tell you that if I’m a senior executive, I’m looking at this and I’m thinking it’s mature, it’s available, why are we not doing it? Now, I’ve also got a white paper for you. At the end, I’ll talk about briefly it’s free to you, I made sure I got access to it, had it posted for you for free, so you don’t have to pay for it. And so now let’s take a look a little bit further, it’s going to help you to understand this slide deck even better. Now we got each one, we got h2 Now let’s talk about horizon three in the cognitive product. Now cognitives at that highest level of maturity. So if you’ve done one, and you’re into let’s look at from predictive to cognitive. How can we do this, this is where it’s able to come back to you and predict for you new products and services based on trends that the models are seeing without you actually asking kind of scary, super cool. I’ve seen this done with bonsai brain. And I gotta tell you, I have been duly surprised and
elated. On rescale. I’ve worked with them on Azure many times. I know that I’m working with like ANSYS. This is something that we’ve been pushing towards with product design and simulation at a cognitive level. And I believe they’ve achieved it. So take a look at that it will be able to the six bullets, you can read them yourselves. But at the end of the day, you got connected and you had insights. And you’re able to drive those insights through your innovation cycle to drive down costs, drive up efficiencies, identify all kinds of compatibility, commonality, met new products, new insights, drive, you know all kinds Have efficiency even in the plants. Now, the second arise that talked about is more predictive. And being able to do a digital twin inside of a digital thread. And being able to now predict which will rapidly drive down costs, drive up speed to market, and including the simulation layer of all those products. Now, let’s look at the role of the digital twin one more time that I talked to you about track and trace the products all over the entire life cycle. Yes, we can use blockchain with this, if that’s what you’re asking. I have done it, I’ve seen it, we designed it on the out at the very front so that it’s already just a part of it as its life moves through the digital twin and then the digital thread. Now the digital twin is a single source of truth, which enables accelerated innovation cycles through simulation. And think about it if I have a digital twin, again of say, a earthmover, I could see it in three dimensions all the way down to the final measurements, I can simulate its performance from data in the field, I can also simulate, I can look at all the inputs that designed it from CAD, CAD CAM, all the testing all the supplier data. And I can run that through simulations. And I can actually predict more around usage patterns and performance that that product is going to have to drive all kinds of problems out of it right. And then it also helps to this thing called a digital thread, which tracks the changes to the configuration over time, whether it be through the product, twin to the plant all the way back through the design and to the customer in that complete digital feedback loop. Now here, again, as I talked to you about no surprise attractor, so on the left, consumers are using it. And they’ve got all kinds of different physical products that are in the market, we have a digital twin from a connected tractor in the middle. And not only is it connected with insights and predictions, but we can see it three dimensionally anywhere remotely in the world from any kind of digital platform. And we can connect to that customer understanding on the right, how it’s what other features are performing proactively, if there’s going to be a problem, contacting the customer ensuring repair or parts. So there’s no downtime. I’m collaborate with suppliers on that to ensure that any insights we have they’re predicting of any quality deviation performance problem, you name it, including cost, I want to make sure that you know that I’m a big fan of driving down cost. And getting that with the supplier and the supplier participating in all kinds of utilizations around quality and how its operated to include also how it was manufactured, right. So this is the way manufacturing will be done in the future. Straight up, it’s so much more cost efficient, so much more faster time to market, so much more intimate with the customer, that it’s just an imperative, it’s not going to go away.
A little bit more around the threat, I thought this might help you to understand it a little bit more, feel free to take a look at this deeper, I do have notes inside the slides so that you can dive down to this a little bit deeper. Um, I gotta tell you that I’m a big fan of all these utilities. The one that I’ve been using the most lately is the cognitive services. Pretty cool in terms of search and cognitive search or custom vision and being able to use AI cognitive services to enhance both the product and the twin, very impressive, along with enhancing PLM. So I’ll get to that’s just something it’s a sign I’m sorry, but take a look at those notes. You’ll like it. And then the connected product innovation, from ideation around insights from marketing and customers to product design to manufacturing and sales and service all the way through to the time. Well, frankly, it’s sunsetted. And we have disposal. Yes, sustainability is a key part of this. Being able to design sustainable products, design out waste, design out inefficiencies, that leads to less energy, less raw material consumption, and at the end, ensuring a proper disposal and if at all a fully recovered 100% goal of being able to remanufacturer or have a reuse repurpose for that product. Yes, we can do that. And we do do that a little bit more deeper on next gen PLM. This is a bit of an eye chart, little technical, but I leave that to you to see and my apologies for such an eye chart but these are buckets you can easily start tracking to on your three horizon plan. Then bringing it together. Of course, having a roadmap is key, knowing the outcomes you want, put it across the three horizons, and then start talking about how you’re going to map that out. And then drive products through it. Now, don’t try to solve world hunger, and hit entire families of products start with a singular product or product family. That’s what I recommend, then when you exercise this through all three horizons, you’ll gain velocity through those horizons, then once you do, being able to repeat it and repeat it, the ROI will be very clear, getting senior executive alignment to buy often this, I mean, it’s in some cases, I see ROI in the four month range, sometimes under 90 days, I think the longest that I’ve ever witnessed to 17 months, 18 months. And that one was for a brand new product that was a very high cost price point. So still under two years. So customer case studies real quick, ABB awesome company. They’re using this all the way they’re not quite at the cognitive, but they’re in it. And then high performance compute in the cloud, they wanted to design better electrical Transformers for a piece of dry material, and be able to do this in a right a reasonable amount of time. I don’t know if you know it, but like ABB leads in the can in the battery space and charging, chargers to charge your electrical car and just amazing what they’re doing. But they’re been using the connectedness connecting the product, the connected user experience insights, the market connected all on their design and PLM along with some of the insights to the plant, to now be able to rapidly design and simulate using high performance compute. They done this in a way that contains on Azure, the simulation a way that reduced the computational time that it took to get to market traumatically. Also, the technology used in account cloud based solutions, far more cost expense cost affordable than the in house solutions that they were. So this was a real a real epiphany for ABB who’s already a technology leader. But taking that next step into their maturity, and getting closer to cognitive. Now ANSYS big fan of ANSYS have worked with them off and on for many years, their engineering simulation with Azure and Microsoft, we wanted to take that together to an all new level, getting to that cognitive level, you know, working all the way back from insights from marketing, to the proof of concept and being able to bring that to market superclass fast is it’s just like I said earlier, it’s a reality. ANSYS needed a way to perform complex,
multi physics analysis, being able to involve computation, fluid dynamics, through their simulations, they were able to do that and pull in legacy data to help support that to get even more data and create simulation data to drive that. So they able to run a proof of concept very rapidly. That allowed them to start identifying what were the best scalable results possible that they needed the teams to focus on. So it’s quite impressive. I also want to talk about BMW, one of my favorite sports cars in the world, I think are arguably the most beautiful convertible in the world. They’ve been able to look at their process in simulation, in design and features within the customer features within the vehicle and take that user experiences the connectedness of the car, all the way from feedback and driving new cognitive features, ai features that they were able to rapidly design and deploy. And furthermore, as they refine them through simulation and feedback and getting increased quality or net new features, deploy them at scale, very cost effectively, efficiently. And with a tremendous amount of control and security. That is rapid innovation.
Now, if you’d like ping me at Michael Walton on LinkedIn, just search Michael Walton net, Microsoft. And also there’s the connected product innovation white paper I’ve been able to get for you for free. And with that, I want to say I hope that you stay well. I hope your families well and have a great day. Thank you.
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