EPISODE THREE: JEFF WINTER - INDUSTRY MANUFACTURING EXECUTIVE

Dave Shook: Welcome to the data breakdown podcast today. I'm here with Jeff Winter who's been newly minted as the most influential industry 4.0 person in the world. So we're really looking forward to a very interesting conversation and educational conversation today. So Jeff, how would you best describe your role within manufacturing and what lies behind this recent crowning?

Jeff Winter: Sure. And I'm happy to be here. So my entire professional career has been in manufacturing. I've worked for several industrial automation companies, you know, focusing on sensors measurement devices. Plc's pneumatics, machine safeguarding. So a lot of industry 3.0 stuff. But recently at Microsoft, I got to focus more on the newest most cutting-edge technology, AI ML Digital Twins, Iot Cloud Edge, you name it. But besides the companies themselves,…

Dave Shook: Right.

Jeff Winter: I've also been in several different roles I've been in sales marketing operations, and strategy. And so I've kind of seen a wide gamut of opportunities challenges and needs, and I've been kind of dedication to the industry 4.0 space for about the past four years.

Dave Shook:  That's really cool.

Jeff Winter: So it's kind of exciting to know that you know I figured out what it was was able to help kind of communicate the message and help advance the the industry and get recognized along the way.

Dave Shook: That's really cool. So So, clearly you have a great deal of excitement an interest in what's going on with transformation of industry. So, We're going through a Really like a technology generational shift right now,…So what are you most excited about these days?

Jeff Winter: One of the things that makes industry 4.0 so exciting for me, is how early we, as a whole society are in this transformation right now. I mean, we're just getting started and scratching the surface and, you know, as an example World Economic form, has had their lighthouse program for about four years now. And they've kind of like traverse the planet looking for factories that are doing industry 4.0 and so far. As of January 2023, they've identified a whopping 132 factories out of evaluating more than like 10,000 out there. Only a hundred and thirty-two. That's a small number, you know, but 132 factories, they're killing it. And they're seeing it massive impact on their organization. So having industrial companies transform. That's, that's what most people focus on the actual company transformation. And that is where I spend most of my time. But I think the real excitement will be witnessing the whole industry transform together. And when that happens, the tide rises for all boats everyone and arguably everything. We will be able to benefit simultaneously, you know, for example.

Jeff Winter: One of the industries once the, you know, the industry starts to, to capture analyze and share data, and insights across the entire value chain. I personally think we'll see all types of transformations that we've never even really predicted before a good example, imagine companies sharing their AI-driven demand forecast and production models with upstream and downstream companies, you know, imagine how much better planning coordination will be across the entire value chain. So it's hard to predict what things will look like. Because, you know, we start collecting and analyzing more and more data and oftentimes data that we never even knew was out there. And once we do this, we might discover totally new insights that can change the path of individual companies or the industry as a whole. So speaking in like a three to five-year time frame. I'm excited to see how the whole industry transforms and what it transforms into. But more practically speaking, what I'm most excited about today. This is simple, it's chat GPT. I mean, this is the newest most disruptive technology out there and most the world didn't even know what it was just a few months ago.

Jeff Winter: And now it's the single most talked about topic across all industries, manufacturing included. And this technology is gonna transform the way that companies operate and the way that they engage with customers, I mean, so many tasks at all levels from entry-level, all the way up to the CEO, will be streamlined and simplified. And basically, anyone who ever goes to the computer to look anything up, or to type something in their job is going to be significant. More productive with chat GPT. so, Sure.

Dave Shook:  So I'd like to dig into that actually for a bit, right? So I mean people have seen You know, a few sorts of news stories about, you know, chat GPT being used, but potentially by students to cheat on it on assignments and there have been a few sort of interesting cover conversations that people have had with it. But what do you see is for the pragmatic?

Dave Shook:  Industrial sort of work applications of chat Gpt that are going to affect people in their daily lives in like these repetitive sorts of tasks that consume people's time. And, you know, generally don't are necessary, but do not them of themselves. Generate a lot of value, right? Like, that's really kind of where we try to, to automate as much as we can. So where do you suggest Gtt, like having a significant impact in the industry?

Jeff Winter: Yeah, happy to talk about it. Keep in mind, I'm gonna use the word chat, CPT kind of from now on is more the common verb to represent generative AI. So I'm not going to break down the difference between the newly, you know, released Gpt4 GPT 3.5 GPT.

Dave Shook: Right. Right. Okay, so this so you

Jeff Winter: I'm gonna be talking about Chat GPT more conceptually rather than the public-facing, You know, interface, everyone sees just to know that because everyone knows the word chat GPT, That's how I'm gonna be talking about it.

Dave Shook:  Right.

Jeff Winter: So, the biggest thing that I've seen out there in these few months that's taken off is around the understanding of what it is, there's gonna be a massive amount of education that's needed to know how to take advantage of this technology, as well, as how to avoid it, actually causing either harm or loss in productivity because the public chat cheap BT is out there.

Dave Shook: Right.

Jeff Winter: Everyone is using it. Whether you're using it to cheat on tests where they're using it to help with your job, whether using it for personal stuff, it doesn't matter what you're using it for, everyone is using it, and once you start

Jeff Winter: Build that into the work environment you could, in theory, lower productivity, because you're using it incorrectly, or you're using it for things that it's not actually helping and you're basing decisions on stuff. It's producing that it can't really produce. I've even done some exercises with companies, where I ask the leadership of companies to go. If you had chat GPT theoretically connected to all your company data, what would you ask it? And It's fascinating to see the questions that come that get come back from these executives because what they generally show is kind of two big things one, they don't actually understand what generative AI is in terms of generating an answer they're either thinking of it in terms of like a search engine or they're thinking it of something that it's not like machine learning because they don't really understand the difference. So, when these questions come back, it's usually you don't understand them. Like it can't really do, that's not what's designed for or the thing you're asking for, we've been able to do for 10 years a generative AI at all, to be to pull that off.

Dave Shook: Yeah.

Jeff Winter: But you as an organization, need to have a chat GPT strategy, so that you can guide every department, every function, every role, how to utilize a properly to actually improve the job rather than, as a fun shiny tool that they're using on the side that they don't know how to use. And it becomes a distraction

Dave Shook: So you should have truly disruptive, so you see it as truly disruptive. Right? Because that's, that's the thing,…

Jeff Winter:  Oh yeah, absolutely.

Dave Shook: You said disruptive technologies are the ones that profoundly change how people work, right? And that initially There ends up being a whole bunch of experimentation with people going. Does it do X? Well does it do y well and then You sort of learn. But there's we see this in the Gartner Hype curve, right? But if there's, this is Larger than any single item in the Gartner hype curve. This is using. This is true. Fundamentally disruptive to, you know, how we work.

Jeff Winter: I think what makes it fundamentally different than other big conceptual disruptive technologies out there, Let's use blockchain as an example is that's disruptive conceptually but to actually is obnoxiously easy to start using today, and the way that the models are trained and Fine-tuned doesn't require a lot of knowledge or understanding about artificial intelligence. So the technology will be adopted and applied quicker than any other technology. I can think of out there because you can use it as an individual or as a company or both and that's something like Blockchain requires a lot of investment to be able to pull off. It is disruptive, but it's a bigger deal to take on chat cheap BT you can start utilizing in hours.

Dave Shook: Yeah. Some of our salespeople are already using it. It extensively to find out all sorts of things. And yeah, I'm reminded of, you know it, You know, the old saying about, you know, we were promised flying cars and we got this, right? Well, this is actually kind of the beginning of computers, you know, fulfilling the flying car promise, right? The early science fiction like the original Star Trek where they could ask a computer a question. And they could get a response. Well, this is the first thing that is actually getting us towards that sort of interaction with computers and it is, you know, profoundly different from, you know, even typing a search question into Google, right?

Jeff Winter: Very true. I mean, that's why it'll fundamentally change society. I think, overall, in every industry, every function as I said, anyone that has to use a computer to look something up or to type anything in it will make your job more efficient and more effective if you utilize it the right way.

Dave Shook: Right now, I'd like to sort of dig into the utilization of it because, you know, for the last several years we have been working well, My whole career we have been working on getting acquiring or acquiring industrial data and organizing industrial data and making it possible to you know, ask questions of that industrial data. You've talked a lot about like the value of data and, if I think about it, chat GPT. There's always the concern of garbage and garbage out, right with it with any software, right? So, it's still going to sit upon a bedrock of the industrial data that is available to it. Correct?

Jeff Winter: Sort of and it depends on how you look at it because I would say sort of because you can think of I'm gonna call the verb conceptually Chat, GPT is kind of like two aspects. One is There's Chat GPT itself which was trained at a point in time of common publicly available. You know, Internet data like all of Wikipedia and all the books that are out there for the purpose of understanding the language that's kind of like it was main purpose to be able to understand what you type and to be able to produce something that you understand as part of it.

Dave Shook:  Right.

Jeff Winter: So look at it like that, there is no external data that you're using there. You can start to tap GPT as the public interface without using internal data.

Dave Shook:  Right.

Jeff Winter: So the garbage and out an example, doesn't really apply there. Now companies like Microsoft that have pulled the models into their company and other companies will come out with their competitive versions. You know, we'll, we'll have kind of the same thing. We're now you can use the chat GPT, all right, whether it's

Jeff Winter: That. Or one of the actual specific sub-models that are behind it to be able to tie it to your data and then yes garbage and garbage out for what you're looking at. But that's where the real transformative aspect comes in because you don't just use it as a helpful personal assistant with a kind of understanding language, but you're using it as a helpful personal assistant on your own data. So yes, there is a component of needing that, and that's where I actually say that. You know, a lot of the conversations I have with people around. This is the actual implementation of the chat GPT functionality, is fairly minor. All right? The bigger lift is actually getting all of your data integrated contextualized, normalized, and properly indexed. So that things like chat GPT can reference, summarize build upon pull the information from the information that there because if you're just gonna load thousands of using the chat GPT, it's not gonna make any sense of it. You have to have something else. You know, a lot of machine learning tools are out there that will actually do the calculations for you. And then you're just using kind of chat GPT as the concept as more the interface so that you don't have to know how to use the software. You don't need to know how to use it. A great example, like supply chain management, you can just have an entry-level truck driver, you know? Type into the chat GPT of the company and goes “What's my route today?” It's not actually determining that it's pulling up the information from the supply chain management system that's already determined. What is route is you know his or her route is for the day, so it's a change in the way that you're accessing the information to allow the learning curve to be dramatically short. That's just one potential use case.

Dave Shook: That's actually a really, really good clarification because it's it is important to point out that this sort of Generative AI piece is about the communication between the human and the data. It's not about the solidity of the data in the background, right? So those of us who have been working for years about, you know, acquiring and properly contextualizing data, That work is still important. Right. Chat GPT and these generative AI tools will not make that problem disappear. They're probably going to make some contextualization easier because it'll probably make it easier for us to inquire about some of the underlying data structures and ways that we can comprehend. But it's not going to take away a bunch of those sorts of rigorous thinking that you have to do in terms of contextualizing data and data quality. Correct?

Jeff Winter: Well, absolutely, because it is a language model at its core. It's good at understanding, you know, parsing out, dissecting, and predicting text is what it is. As soon as you start to have numbers in there you don't really want to rely on the engine behind how it scoured the Internet to determine what to do. So, for example, if you're into, you know, looking to calculate the economic order quantity from my supply chain data and spit that out, it's gonna do whatever it found on, Wikipedia to determine that calculation of which it can do some simple arithmetic. But anything other than that, you're not going to want to depend on it. You'd rather use it as the interface to pull from the data. That's something else. Actually calculated. What that economic order quantity is if that makes sense. So you end up using it more generally speaking as the interface to make it so that it can access things quicker, faster, better and provide context around it so you can even ask it questions around some of the stuff rather. Then doing the actual calculation itself.

Jeff Winter: So one of the great examples is like the best use case for it. In general, summarization, is a capability that it has really, really been able, to do very well. So you be able to take large amounts of text data, whether it's your website data, whether it's policies inside your company, whether it's user manuals for machines, and to be able to sift through thousands and thousands of words, to be able to help summarize information. And spit that out; it's extremely great at that, it's also extremely great at generating.

Dave Shook: And that's extremely helpful.

Jeff Winter: Yes. And that's where companies that are, you know, starting to use. It can benefit tremendously from that stuff, and it causes an incentivization to digitize all your policies and all your users' manuals. And basically, all your contracts, anything typed, should be digitized and loaded in an accessible database. The right way that Chat GPT can have access to it and then you now can, you know, imagine being an employee and just go, I got a question about my policy, or my HR or my employee standing or anything, and they can just they can ask it, and it will be able to pull reference and give you an answer in layman's terms the way that you need to understand it.

Dave Shook: That's fantastic, and it profoundly changes our notion of data in the industrial world because up to now, data has been numbers, right? And data still is numbers, but it's also words and I think as an engineer right the number of engineering reports. I was at a particular company about five years ago, and there's a chronic operational problem at this company they have brought in half a dozen engineering companies over the years to analyze it and it's all generated reports. And you know, these things are pretty big documents and it would be really nice to have someone be able to summarize that for me rather than me have to read through every single one of them, right? So that would be fantastically helpful.

Jeff Winter: Absolutely. Now, imagine having an integrated into things that you use every day, like this call that we're on right now, and having the entire thing transcribed, and then, say, spit out, 150 words summary of what we just talked about that can be done today.

Dave Shook: That's mind-blowing,…That this conversation just completely derails. All the thoughts that I had about what we were gonna be talking about today because I was thinking about the standard sort of,  industry 3.02 4.0 transition around operating data, right? So like what industry is, usually, you know how far how mature is the industry right now in using data to drive decisions, right? And like there have been studies that you've been involved with and you know, you have a great deal of experience in this area. And I guess I'd like to ask you a couple of questions but within the context of disruption that we're going to see with you know, let's call Chat GPT. This is still really important it's just less exciting, perhaps. So you talk like a bit about you know how companies are doing with regard to their, you know, making data accessible and consumable so that something like chat GPT could, you know, get it into people's heads.

Jeff Winter: Sure. I mean and this is where I actually think the topic of the difference between industry 3.0 and 4.0 makes sense here because it will play into how you can leverage and utilize something like a chat GPT to to capture and harness kind of the difference because it's one of my favorite topics is the value of data. And whenever we talk about industry 3.0 we usually think about automation or the reduction of human intervention and processes. And whenever we think about industry 4.0, we should be thinking about cognition or the process of requiring knowledge and understanding and it's really capturing and harnessing, the power of data is what separates industry 3.0 and 4.0 chat. GPT is one of many technologies out there that will help with that. Harnessing the power doesn't really help with the capturing of it. But if you just talk about the sheer amount of data that is generated, this is one of my favorite things. I like to tell people is so Eric Schmidt, the former CEO of Google famously, and 2010 that the amount of data generated since the dawn of civilization up until the year 2003 was estimated to be five, exabytes worth the data and you don't even need to know what an exabyte is. But according to Statista in 2022, an estimated 94 zettabytes worth of data was generated. You don't even need to know what that number is. But what that means is, there was nearly 19,000 times the amount of data generated last year as all of civilization up until 2003.

Dave Shook:  Right.

Jeff Winter: That's a ton of data, that's crazy, but unfortunately according to Splunk an estimated 55% of enterprise data is either unknown or unused by anyone in the company. So, so much of that is wasted value and that's not even used let alone are you taking advantage fully of the other remaining percent but those that are capturing the data and analyzing the data and turning it into insights and taking actions on those insights to both optimize internal processes and provide additional value to customers Ideally through, selling the data, as a form of like a digital service. Those companies will have a huge competitive advantage over their competitors and chat GPT can help with both the internal optimization, as well as the change in the way that value is provided to customers. You know, examples of where best-in-class companies are using their data. These would be things like, you know, inventory management and demand forecast, things that became number one in 2022 with the supply chain disruptions that are out there but before that, the top area was fault prediction and predicted maintenance quality controls another top use case to monitor production processes and identify patterns, or anomalies and inside, like the factory data. Basically, it's becoming used for everything from energy, efficiency, and ESG reporting. And even if you use the engineering data, it's being leveraged to help with product design and on the business side we're seeing price optimization, warranty analysis and of course, sales and marketing analytics. So people are starting to tap into that data but those that do the fastest they're gonna have such an advantage over their competitors.

Dave Shook: Yeah, absolutely. I was lucky enough to start my career working in operations for seven years working in a chemical plant right where we had to worry about cost and quality and all that, and I think back what you're saying to you know, the problems that we were dealing with, in the 90s about, you know, where you know, how we can optimize and just supply chain nightmares, that we had there was one particular occasion where we had to make a batch of product for a particular, very important customer, and we didn't have

Dave Shook:  One of the small quantity raw materials that we needed. So our production scheduler said, How about? We push it out two weeks until that stuff arrives and the president phone them up and said, I don't care if you need the company plane to bring that stuff, you're making it today. Right. So, you know, it would be nice not to have to have that conversation. Right. So yeah. There's there's and and your ability to manage supply chain. and, as you said earlier, your ability to manage the ability for

Dave Shook: Supply chain participants to jointly manage a supply chain. As we move forward, becomes, you know, much more powerful through sharing of data and, you know, bringing resilience into the supply chain because there are, you know, there are when you hand off material, you hand off data and just like you can slip up and handing off material. There's lots of problems in handing off data and in the old days it was things like tickets and bills of waiting in that kind of thing. And That's not a very efficient way of telling people, what's going on compared to proper electronic data interchange. So yeah, I mean, as we Improve the Availability of Data, We can change how we operate within a company and we can change how the industry operates, right? That's really cool. 

Dave Shook: Okay, so, one of the biggest benefits with data-driven culture, is the ability to innovate faster and more frequently. Can you share some examples of where like manufacturing industrial organizations have succeeded in innovation by leveraging that data?

Jeff Winter: So, I love the subject of innovation especially, because of how important it has become. I mean, technology. Today allows companies to be innovative at in accelerated rate, which means failure to react quickly can be catastrophic, but when most people think of the word, innovation, they often confuse it with the word invention and although they're very similar, they're subtle. Differences are quite important. So I wanted to find both of them before we kind of go forward. So, the definition of invention it's really the act of creating of designing or building something that has never existed before and often through research and experimentation and inventions can be patented, you know, to protect the intellectual property of the inventor but innovation is more about the practical implementation of ideas that result in the introduction of new products or services. Or improvements in the way, products and services are offered and kind of add value to the innovator or their customer. You don't necessarily have to invent anything with innovation.

Dave Shook: Okay.

Jeff Winter: And so, for innovation, I like to guide people to Dublin's 10 Types of innovation, to get people thinking about all the areas innovation can occur. They include things like customer engagement in brand and channel and service and the product in terms of the system or the performance processes network, the structure, the company and probably the most important the profit model. Data allows for innovation in all ten of these. And so there's a lot of ways that companies can become innovative internally from an optimization side. But I would rather go over example, like how they how they did innovation from a transformative external side.

Dave Shook: Right. Yeah.

Jeff Winter: The way that they engage with their customers Shelley Group is a great example of this. So Charlie Group is a global company based in Italy that manufactures and services equipment. For dispensing, soft drinks, water and beer. And they have something like 400 employees. And I believe six, different production facilities and they're implementing an iot strategy. That connects them to their end customers and enables new service delivery and revenue models.

Jeff Winter: So they implemented a model-based system with PLM and IOT as the foundation for this transformation which even include the development of this Smart Warranty program, that's completely unique in their industry. Their warranty coverage is actually determined and changed based off of how hard and how, how frequent you use the equipment that's innovative. But on top of that, by leveraging the data, from their IoT connected product, they were able to improve profitability by significantly differentiating their tap and brewing offering. And that resulted in like a 14% greater sell through via enhanced sales and inventory management, a reduction in equipment. Failure, by 13 Percent they've been improved product quality by monitoring temperature, shelf life, and sanitation cycles by something like 27%. And even reduced cost by service cost by like 10% do better predictive maintenance and all this was possible through their development of their digital thread which leveraged IoT and PLM at the heart of it that extended in their case all the way out to their customer. So that shows a bunch of different innovation kind of coming together. All taking advantage of data just by one company.

Dave Shook: That's really cool, and I love how they. They? Really started with wanting to change the company. Built the PLM and IoT platform, and then leverage that data, nine ways from Sunday. Rather than going out at what. We're tend to see it. A lot of IoT stuff is like driven by relatively narrow use case approaches, right? And so the transformative piece is really about thinking about changing the company and then using technology to change the company rather than going well, this new widget will work in this one particular area.

Jeff Winter:  I would agree.

Dave Shook: So the other day we got a call from an oil and gas client looking for guidance on how to Address their Othellic strategies because they're seeing a proliferation of solutions where companies are putting, you know, sensors collecting data, providing Ai/ml analytics. And these are all coming from like OEMs automation companies. All of these different providers with overlapping slightly different offerings and a lot of the vendors are interested in locking in the customer right? Like there's there's this still seems to be a space where, you know, vendors want to lock customers into their ecosystem, and customers are aware of the risks associated with that. So what are some of the things that operate that organizations could do to address those concerns?

Jeff Winter: So that's a good question. It's an interesting question, and the shift in the industry is kind of going away from what you're calling that lock-in. Because if you look at industry 3.0, as, as the market will call it, it's fairly mature, and that means that because it's mature, it allows companies to kind of consolidate and kind of dominate the particular industry.

Dave Shook: Right. Yeah.

Jeff Winter: And when you dominate a particular industry, it allows you to develop a platform that wants to stay within the platform. The whole idea of industry 4.0 In the fact that it's a collection of a bunch of new technologies. Kind of simultaneously coming out that are all working together to disrupt the industry is changing everything. Maybe one day this will occur again, but not for the foreseeable future, in my opinion, because this transformation

Jeff Winter: Technology is not only changing the way that all the industry 3.0 companies have to work but it's allowing a whole bunch of new entrants into the market. New entrance, from both startups, as well as giant players that are out there that are able to get into new spaces because this technology can be leveraged in areas that it traditionally hasn't been able to be leveraged before. So, the biggest shift, I see is more how data will be collected and democratized Traditional systems were more point to point.

Dave Shook:  Right.

Jeff Winter: You know, you had a center connect to a PLC that connected to SCADA, that connected to MES that connected to ERP.

Dave Shook:  Yeah.

Jeff Winter: And the data it kind of had a hierarchy of how it had to flow. But new architectures are more many to many kind of allowing for things like sensor direct to cloud. And this completely changes the way that data is consumed and shared and because of this, it breaks down a lot of the traditional monolithic systems and platforms as it can just honestly be passed which couldn't really have been done 15 years ago. And this is the part of the reason why so many industry 4.0 startups had been so effective because they can leverage a lot of this, especially cloud technology. It's more publicly available and just be able to tap into and harness the power of the cloud by bypassing, you know, potentially systems along the way. So it's it's not only changing the way that manufacturers go about this. It's changing the way that solution providers, go about, providing the value because you need to be able to play in these giant ecosystems and this is something I look and go, the industry 4.0 Market is so large in comparison to the industry 3.0 in terms of it's it's opportunity because if you look at the industry 3.0 market today it's roughly like 250 billion dollars to 300 billion depending on the report that you look at. But if you look at over the next like by 2030. So next seven to eight years, the industry 4.0 market expected to hit like, well, over a trillion dollars, it depends on what technologies you include in it or not. But it's gonna grow at a significantly faster and bigger rate. There isn't one company in the world that can do industry 4.0, Not even close. There's not one company's even close to being able to do it

Dave Shook: Holy crap.

Jeff Winter: Which means that you're reliant more on. Ecosystems of many companies coming together. In order to be able to provide the value of industry. 4.0 versus industry 3.0 could have been done with fewer companies that can claim. I can; I can do all automation.

Dave Shook:  So with regard to the industry 3.0 and it's three 4.0. There's a natural tendency for. Larger industrial companies but, especially the ones that are more driven by an engineering culture, to want to simplify their procurement problem by going to a smaller number of vendors in an ecosystem environment. You may be dealing with more vendors and, you know, I would imagine that, you know, those of us who are like technology enthusiasts are going to be more willing to go to specialist, suppliers of specialist types of equipment, but he kind of changes the procurement idea. I've been telling people that, you know, you have to stop thinking that you're buying an instrument for 25 years and start thinking that you're buying an instrument for five years. Because an IoT instrument may only last five years in an industrial environment and that's fine. Right? That the communication protocol may actually no longer be ideal that the, you know, the cost of it is so low and the engineering cost when you're going direct to cloud, rather than working your way up through that layer cake of, you know, plc SCADA mes, you know, up to the cloud. The engineering costs are so much lower dropping in some Wi-Fi enabled sensor and routing it straight to the cloud that the whole sort of economic thought process that has been built into engineering up to now : it's just profoundly broken. So, this is one of these disruptive things, but you still are going to end up with drives toward standardization, right? And common some sort of commonalities within an organization, at least as well as open standards around things like Lauren and Mqtt and OPC UA correct?

Jeff Winter: Yeah. I mean, I would agree with that and just to further complement on the procurement side, not only is the vendor landscape changing. But the way that you're purchasing and managing what you own is changing, this example we're subscription models completely change everything.

Dave Shook: Right. Right.

Jeff Winter: I don't do they change the way that you pay. They change the accountability of the provider to continually have to make more value. You don't just buy a thing, and then it's the same thing eight years from now. No, you buy a thing, and then you get updates and firmware updates. And over there are updates that change it over the course of your ownership. So you're incentivized to provide different values on top of it. Now to answer your standards side, Yes, there is a need for standardization within the industry, especially when it comes to things like common data, models and connectivity, and the use of technologies. But because of the speed of change, a lot more of this openness is embraced because you're doing a lot more change quickly because it's based on day to day data aggregation. And

Jeff Winter: What you do with it is that you need to be able to quickly move change, switch things out more than you would a physical piece of hardware if that makes sense. So, a lot of open APIs and different things or what companies are massively looking to so they can develop into a ecosystem that they control that they can swap out embrace, a new technology, do whatever they need quickly without completely uprooting or disrupting. What they're doing allows the company to be agile and innovative, which is a big component of what companies want to want to be.

Dave Shook: We're almost out of time. So one last question for you five years from now, when you look back at your digital transformation journey, how will you know you succeeded?

Jeff Winter: So, I assume that this is through the lens of a manufacturer?

Dave Shook: No, this is you personally.

Jeff Winter: Oh, me personally. Well, I would say that is interesting because I went through my digital transformation journey through covid by having to figure out stuff, like leveraging LinkedIn, to build a digital presence and brand. I went through my own personal digital transformation journey, and I would say, Kind of my success with that is kind of rising to be recognized as one of the, the top people in the industry. I mean, I want to 11 industry awards last year and then this newest one is probably the most prestigious in what I what I have so that would probably be my way of answering it…

Jeff Winter: I would love to answer for the manufacturer as well. So like how your digital transformation journey can be looked at as a success and there's really one simple litmus test question. Did your journey actually transform the way that your company fundamentally operates including how you provide value to your customers or the market? If the answer is yes, I think it did. If the answer is no, either your journey failed or was limited to just digital optimization, but it wasn't a transformation. Because if your digital transformation is successful and I mean truly successful, it fundamentally changes the way that your company operates, including how everyone's role within the company changes and so that's probably the question you'd ask yourself, did you fundamentally change your company?

Dave Shook: Fascinating. Thanks again, Jeff for sharing all of this insight. I feel like I've been drinking from a fire hose and thank you very much for listeners. I hope you enjoyed our conversation this week about, you know, Digital transformation at really, the speed of Jeff Winter. We'll see you next time. 

Jeff Winter:  Thanks for having me.