Interested in connecting with Dave? Reach out to him on Linkedin!

About MachineMetrics: MachineMetrics’ intuitive and flexible machine data platform easily collects and analyzes data from any piece of manufacturing equipment to deliver powerful, actionable insights for factory workers in a matter of minutes, driving decisions from anywhere in the world at any time that go right to the bottom line starting from day one.

Key Questions and Topics from this Episode:

(00:59) Intro to Dave Westrom

(02:22) Intro to MachineMetrics

(07:42) Can you share some use cases for MachineMetrics?

(15:29) Have you seen a reluctance to adopt IoT and how do you handle that with potential customers?

(18:13) What differences are you seeing in digital transformation initiatives at the corporate level versus the factory level?

(23:16) How important is corporate buy-in when undergoing these digital transformation initiatives?

(26:10) Do you see companies often trying the DIY track first and, if so, what challenges does that come with?

(31:03) What makes business development in IoT different from other industries? Do you have any advice for companies who are struggling with it?

Transcript:

- [Announcer] You are listening to IoT for All Media Network.

- [Ryan] Hello everyone, and welcome to another episode of the IoT for All podcast on the IoT for All Media Network. I'm your Ryan, Ryan Chacon, one of the co-creators of IoT For All. Now, before we jump into this episode please don't forget to subscribe on your favorite podcast platform, or join our newsletter at IoTforall.com/newsletter to catch all the newest episodes as soon as they come out. Before we get started, does your business waste hours searching for assets like equipment or vehicles and pay full time employees just to manually enter location and status data? You can get real time location and status updates for assets indoors and outdoors at the lowest cost possible with Leverege's end-to-end IoT solutions. To learn more, go to IoTchangeseverything.com that's IoTchangeseverything.com. So, without further ado, please enjoy this episode of the IoT for All podcast. Welcome Dave to the IoT for All show, thanks for being here this week.

- [Dave] Thank you, good to be here.

- [Ryan] Yeah, it's fantastic to have you. Been looking forward to this conversation for a while, ever since MachineMetrics became a partner with us. So, this will be a good one. I wanted to start off by just having you give a quick introduction, talk a little bit more about your background experience. From my understanding, you have a quite unique background, being involved in a lot of different companies across the industry and connected industries. So if you could just elaborate on that and talk a little bit more about who, so our audience knows who they're listening to.

- [Dave] Sure, yeah I've been with MachineMetrics for about two and a half years now. I'm responsible for global business development, which includes our partner ecosystem, strategic accounts, among other things. Prior to MachineMetrics, I was part of the executive team at a company called ThingWorx, which was an early IoT pioneer around application enablement, was acquired by PTC. Prior to that, I was part of the exec team at a company called Lighthammer, which was acquired by SAP and subsequently became their IoT platform. Before that, I have had various roles at companies such as ABB, General Electric, Wonderware, which is became Invensys, become Schneider, now AVEVA. So much of my career has been in the IoT, industrial software, enterprise software and solutions spaces.

- [Ryan] Fantastic, that's awesome. Let's talk a little bit more about the current company MachineMetrics. Tell me a little bit more about what the company does for the audience members who may be unfamiliar, kind of the role you all play in IoT, that sort of thing.

- [Dave] Sure, so MachineMetrics provides a very unique industrial Internet of Things platform. And I think to describe it or frame it would be perhaps to look at what's available or traditionally has been available on the market in comparison, and how MachineMetrics is in that context. You know, there have been a number of statistics quoted by McKinsey, Cisco and others, that talk about a fairly high rate of failure of industrial IoT projects. And we attribute that to the options that have been out there in the past. The first option is really what I would call a package application or a package solution. Customers will typically get value out of the box in that, but have a very limited opportunity to innovate to extend the application, to add their own IP, to build their own applications. The second option is the more horizontal IoT platform, which allows you to build anything you want, but you gotta use the tools of that offering and it typically takes a long time to get to initial value. And oftentimes the projects just sizzle out. The first option you get some value quickly, but you're very limited in your ability to expand, you have to conform your processes to the software, and often those approaches become obsolete. Second option you can do anything and everything, but it takes a long time to get to value. The third options are the customers that have had a traditional MES products, ERP solutions that try to shoehorn those into their discreet manufacturing assets. And oftentimes that is a nightmare. And even if they are able to connect those assets, the cost to maintain the code they write to do that is typically prohibitive. So MachineMetrics really offers the best of all the options. It's unique in that it's a true platform in that it has the ability to allow extensions, expansions, applications to be built, IP to be added, but it also comes with applications that are packaged in the products, so our customers who are discreet manufacturers can get to value very quickly and very rapidly. So it combines both and it really does so in a way that is unique. And that uniqueness lies in the automation of the data transformation or contextualization layer. So what MachineMetrics has done is basically, we connect to a machine asset, we extract the data and we automatically transform or contextualized the data so that it's consumable not only in our packaged applications, but in other applications, and other tools. So again, the more traditional approaches that I mentioned earlier, you typically have to use a tool to model some sort of process, to graphical tool in many cases, then you've got a connector, you extract data. Then you've got to write all the code in between to marry that data into that model. With MachineMetrics, there's a common data structure, there's no graphical modeling. You connect to the machines and you automatically have consumable data. In the challenging environment of COVID that we've had, this has been a huge advantage because 90% of our customers now implement MachineMetrics on their own. We send them an edge device, they're able to hook it up. We can support them remotely, but we don't have to send people into their plants. They can connect it themselves, and they're usually up and running, and focused on continuous improvement and driving value right at the get go. So it's critical, it's been a huge advantage for us and being able to have our customers focus on value and continuous improvement without having to go through that whole process of spending months if not years coding and connecting the various connectors and adapters up into models that they built has made a huge difference.

- [Ryan] That's fantastic. I appreciate you kind of diving really into the details there, that's great. And now when you, if you wouldn't mind, could you elaborate on some of the individual use cases? You don't have to get into the company specific if you don't want to, but just kind of at a high level, some of the use cases and take us through maybe experience that the company originally was having, and then after they kind of installed MachineMetrics and your offerings within their organization, what they started to see from an output perspective that kind of changed their business.

- [Dave] Sure, so we focus on the discrete manufacturing industry. So it's typically companies in areas like medical device manufacturing, automotive, all the tiers, industrial, aerospace, some oil and gas. And the types of manufacturing operations vary. Some are very lower mix, high volume operations, which essentially means they make a lot of the same parts component product in a high volume. Many of those plants are capacity constraints, and so for companies that fit into that bucket, the things like utilization improvements dropped right to their bottom line. So oftentimes when we start with a customer on a industry for a journey, we find that they're collecting data manually from their machine assets. They're writing it down on paper or on a board, and they're measuring, they're capturing metrics, key metrics like utilization or OEE or podcast, whatever's key to drive their business. But that data is not very accurate. So, data captured manually is a, I would say, a very poor foundation for continuous improvement. What we also see is when we start working with these companies and we collect real time data from the machine assets, there's often a surprise of what the baseline for these metrics actually are. There's the perception within the customer is that they're doing a lot better than they really are. So, for example, when we measure utilization, I believe it's 28% is the utilization figure that our customers will start with when they start working with MachineMetrics. They typically think they're doing a lot better than that, and it's similar with the other metrics as well. So we have hundreds of customers, we've connected to thousands of machines. And by having access to that data, we understand what that baseline is and where our company is comparable to others in terms of their performance. So being able to baseline a key metrics such as that with a company along the lines of what I described, that is that a sort of a low hanging fruit for starting a continuous improvement initiative around the manufacturing assets. And we'll typically see a 15 to 20% improvement in a key metric like that for that type of customer within a couple of months. And often that'll pay for the investments in MachineMetrics But that's only the first step in a continuous improvement journey. The other end of the spectrum are companies that are more higher mix and lower volume. They may make many, many different types of components, so many different types of customers. Oftentimes they will price those products based on how long it takes to make them, based on their cycle times, based on their job standards, and those standards are, are typically stored in an ERP system. We find that many of their customers, when we start working with them, don't know where those figures came from, they haven't been updated in a long time, they're not very accurate. And again, once they start collecting real time data from the machine assets, they're able to go in and optimize those. And when you're pricing your products based on that, there's a direct link to the profitability of the product. So, by having that data and being able to put processes in place to improve in those areas, that drives clear benefit and clear results for those customers. And then there are a lot of customers that are in between. And so often they will focus on, again, cycle times, downtime improvements, really depends on on the customer and what their business drivers are. But MachineMetrics is essentially a foundation that allows customers to identify, prioritize and execute on continuous improvement initiatives. And I'll give you one quick example of a customer that we work with, and we've promoted in a white paper some success that they had around, this is a medical device company. They started their journey at one plant, they started using MachineMetrics, they had bottlenecks they couldn't identify. When they started capturing data, they found that they had major downtime issues. Drilling in deeper, they realized that it was their setup times that were killing them. And more specifically was their tooling setup on the machines. So they leveraged that data, they re-engineered their processes for tooling setups, they retrain their operators, they actually added operators. Most of our customers go the other direction. They added operators. They were able to reduce setup times by more than 50%. Then they were able to move their bottlenecks, they then proceeded to a subsequent initiative where they reduced idle scheduling time by 98%. And they kept going. And at some point in this process, they presented this to their executive team who basically said, "This is great, but what about the other plans? How much money we losing because we're not doing this with the other plants?" So from there they proceeded to deploy MachineMetrics to their other plants, I think it was six other plants. And they did that in under six months. And again, they were able to rapidly roll that out because of the things I talked about earlier. The ability to rapidly connect to these machines with little to no friction and create consumable data and the right tools, the dashboards, the diagnostic tools, the reports, to get them up and running and focused on continuous improvement, that's what drives value.

- [Ryan] That's awesome. That's tremendous. What y'all are doing, that sounds fantastic. And I appreciate you kind of diving into the details there, I think it'll help our audience a ton kind of gather, not just what you all are doing, but also the ROI companies are seeing from working with you, which is great. And I wanted to ask, so we've seen this in a lot of other industries, and I'm sure it's pretty apparent in manufacturing as well. But when you work with manufacturers or you speak with companies kind of as you're starting the relationship with them, how reluctant are they to adopt new technologies or adopt something like MachineMetrics into their systems? Does it take a lot of selling? Does it take usually a pilot to kind of get them on board to see the ROI before they invest the time? We've just seen the reluctance to adopt IoT at times can be quite high, and I'm curious how you guys handle that and what you've seen.

- [Dave] Yeah, it really varies with the customer, depending on a lot of factors, including their size, their organization, their culture, their experience. I mean, we've worked with companies that have had negative experiences and have had you know, if I were to tell a story, I just told you, they'll look at me and they'll say, "That's crazy. There's no way you're gonna be able to send me an edge device. I'm gonna be able to hook it up, and then an hour I'm gonna be up and running with data. That's impossible, 'cause we've done it before and it doesn't work." So, and talk is cheap. So in cases like that, we'll look at a pilot, and we'll prove it. We'll actually send a tool, and I'll look it up, and set some objectives and prove it. Other cases there's, again, there may be cultural challenges, there may be organizational challenges, oftentimes you need someone in the organization and within the manufacturing facility to champion the project that can drive continuous improvement, can cut across some of the organizational silos. There are often some challenges, as I'm sure you're aware, between the manufacturing and operation side of the business and the IT organizations. So yeah, it really varies depending on the company, and there are a lot of different factors that drive that.

- [Ryan] So I wanna expand on one thing you were kind of mentioning about just kind of the separation within organizations at times. When you engage with companies, could you talk a little bit more about some examples around digital transformation initiatives at kind of the corporate level versus the small manufacturing initiatives across the manufacturing plants and kind of why there oftentimes is a separation of what's going on the different views, the values that they see, and kind of how you may handle that?

- [Dave] Yeah, and that's a great subject and topic. It, you know, much of what I described earlier and those use cases where we're focused squarely on the manufacturing part of the business, right? The operators, the supervisors, the plant engineers, you know, and again, there's different personas, there's different use cases and different parts of the organization benefit in different ways. And the things I described earlier, and I'll often really what we view as the low hanging fruit. But I think we're when you start looking at the digital thread going across the enterprise, we're in the very early stages of the opportunity and where the benefit is. And we're seeing some incredibly innovative things from our customers in terms of how the machine data is being leveraged across that enterprise. If you think about it, there's really three things that emanate data, so to speak, right? There's the machine and device assets in the plants, there are the people, and then there are the systems. So there's data coming out of all three of those groups. And when you combine it and bring it together and apply different tools and software, there's many things that can be accomplished. So a lot of what we talked about, I gave one example earlier with the ERP system, but some of the more creative things. You know, we have customers that are leveraging the data across their plants, they're tying them into other tools. Our customers don't have to use our applications or our tools, they can use their own. We have customers that have things like Power BI or Tableau, they build dashboards across plants, and they'll tie into their financial systems, and they'll be able to start looking at, and answering questions such as, "When do we sunset a particular piece of equipment and replace it with new equipment? when should we expand?" When a plant manager says, "Hey, I wanna buy a million dollar asset." How do you justify that? Where's the data? Where does it come from? What is your capacity situation? You know, why is it that this particular machine with this operator in this plant performs 20% better than the same machine with a different operator in a different plant? Where does it make the most sense to make a particular product? And we have one customer that has a primary manufacturing facility, and they would get to capacity, they'd get new orders and they couldn't make them. So they'd start calling around to their other plans saying, "Hey, can you make this product? We got this order." And the answer was always, "No, we're busy." Well, now they have visibility with MachineMetrics across all their plants. So now they call the other plants and they say, "You're gonna make this product, we got this order. We know you have capacity available on this line, with these machine assets. We're scheduling it there." They're able to do that. Combining the data with other systems, CMMS systems, for example, realizing the vision of the investment in those systems. We have customers that invested in those systems and they're still maintaining their equipment on a schedule. You know, how do you optimize the maintenance of equipment if you don't know how long the machine has been running or what the load is on the machine? You know, it's hard to do. So pulling the machine asset data from MachineMetrics into those systems enables that optimization. We have customers tying this into their HR systems, optimizing performance reviews, leveraging machine asset data. So, when you look at the the digital thread across the enterprise and the potential use cases and the opportunities to drive all kinds of value. Again, those are just a few examples, but we're just getting started.

- [Ryan] And do you think, when it comes to kind of investing in that digital transformation initiative, that it's important for an organization to, I guess, does it require an organization to have kind of the corporate level buy-in, to have success, or can you kind of do it at a different level and still see success with an organization in order to see progress, see that return on investment on the small manufacturing side?

- [Dave] Yeah, I think you can see, and we see huge value and return on investment just on the manufacturing side. With the examples I gave you earlier, and there's many others I could add to that around just within the four walls of the manufacturing plant. And extending it tends to apply more to the larger companies or the medium sized companies. But it's a pretty wide range, and again, lots of different opportunity. But we see our extra small, small customers, also achieve tremendous amount of value from their investment in MachineMetrics. But again, every customer has different objectives. They have different problems they're trying to solve, different priorities, and it really depends on what their goals are, and what their focus is. But we believe that the manufacturer has deep domain expertise in their processes. And our goal is to enable the data infrastructure, to allow them to leverage that domain expertise to optimize their processes. And we've worked with companies and see companies again that spend inordinate amounts of resources just trying to connect to machine assets and maintain those connections and all this code they wrote. And essentially it would be more efficient and effective if they just use MachineMetrics for that. But the bigger issue is, it's a misallocation of very valuable resources, because those are the people that have the expertise around their own processes that no one else has. And by focusing them on optimizing those processes, that's where they're gonna get the most value. And that's what we believe. So we've tried to create an ecosystem where our partners and our customers can focus on what they do best. And we can we can enable that with a foundation we provide in our platform.

- [Ryan] One thing I wanted to ask about, kind of something you just mentioned, relates to organizations that you may come in to working with that have already attempted something on their own, so kind of like a DIY solution themselves. Obviously from our conversation, it's becoming very apparent that if they started with MachineMetrics, they would have a much easier time, but sometimes that's not often the case. So can you talk a little bit about the challenges that you all see when companies kind of start down the DIY track and then realize in a much later time that they probably should have started with something much more simple like MachineMetrics, and kind of just the challenges that imposes and why now it's probably a better time than ever to kind of skip the DIY process and go to something like MachineMetrics.

- [Dave] Yeah, there are a lot of challenges. And again, technology changes so rapidly that a decision that may have been a good decision five or 10 years ago may not be a good decision now, or perhaps shouldn't be continued now, right? So you know what we see is is that companies will go down this path. And again, I think it's been interesting with COVID is that, many of these companies not only could the vendors not come into the plants anymore, but these companies couldn't send as many of their own people into the plants. And resources were furloughed, laid off. And all of a sudden, you've got all this code that was written over the years to connect to these assets and enable these systems. And you've got different people in different plants that built these applications in different ways, and now they're not there anymore. And how do you maintain that? How do you sustain it? And you've had, you keep adding to this. And then every time there's a change within the operation, if the software on a machine is updated, or another application is updated, you have to go change more software in it. And at some point you've got a whole crew of people, and they're essentially just holding up something that the whole goal is just to keep it from crashing down on the organization and keep it going, but it's not really driving any incremental value. So how and when do you make the decision to restructure that or to go in a different direction? And it's difficult, it's a difficult decision. It's an organizational decision, and companies handle it differently. And it's, we work with manufacturers, we work with machine builders, we work with machine builder distributors, we work with all of these different companies, and in many of those cases, they've either created their own solutions, in some cases they've created their own platforms. In the case of, again, machine builders, many of which of whom are partners of ours, in the past they've had their own monitoring applications or their own platforms. And our focus is, again, is working with companies to enable them to focus on what they do best. So again, we think the manufacturer has deep domain expertise in the processes. We think the machine builders have deep domain expertise on their own machines, and the focus should be on optimizing each of those. And the challenge with machine builders is that, manufacturers are very reluctant to buy a monitoring offering or an IoT platform from a machine builder and put it on another machine builders machine, right? And they want a common data infrastructure across all their machine assets. They want a common user experience across all the assets. They don't wanna have 10 different monitoring packages across different machine types. So again, it makes it prohibitive if you're in that position. So that's really a lot of what we see in the market, and a lot of where we're driving in terms of the direction and the ecosystem and where we see the opportunity.

- [Ryan] That's great. I appreciate all that. This is fantastic conversation so far. I think just kind of extrapolating there, as you've been doing on the, not just the view you have of the market, but the way you're interacting with organizations, what you're seeing with organizations when it comes to the implementation of MachineMetrics offerings, and how they're seeing successes, it's just a great testament to what you have all that going on, so this has been great. One question I wanted to ask you, kind of a little separate from what we've been talking about, and it's a question that we don't talk, or at least get to answer very much, because we don't have too many people involved in the business development side on the podcast enough, is, without giving away any of your secrets, can you talk a little bit about just the business development side of IoT, kind of why it's maybe different or unique from other industries and any best practices, tactics, tips, things you've seen that's led to business development success on your end that may be listeners out there who are struggling with the business development side may be able to kind of take away from it?

- [Dave] Well, business development is defined differently in different companies. I mean, for MachineMetrics, in my experience, specifically with ThingWorx, Lighthammer, or other startups I've been with, I get involved with the strategy piece of it. The partner ecosystem is a primary focus. And it's important to have alignment, but I think with small companies and specifically, it's really important to understand what your focus is, what you have that's differentiated, and the market you're going after, because if you try to do too many things you're not gonna be successful. And it's also key that once you have that focus defined in terms of where your core competence is, that you surround that with complimentary components of software, hardware, services, whatever it may be. Because ultimately from the standpoint of the customer, it's the total solution that they're interested. And if you're providing just a piece, that doesn't get to the value that the customer is looking for. So having an ecosystem that brings all those things together, right? So, MachineMetrics is not a consulting company. We provide a platform and a foundation with data and insights that enables consulting. And now if that consulting comes from the end customer or if it comes from a partner of the customer, or from a third party, we certainly wanna work with those companies, and we wanna supplement our offering with those capabilities, same as system integrators, same with other other players within that ecosystem. We wanna work with them, we don't wanna recreate anything th