Evaluating New Technology for Industry 4.0
Everything in manufacturing is in the process of moving from bits into bytes as part of the digital transformation.
Welcome to Industry 4.0 — an era in which operations are getting digitized and net new revenue streams are being generated off of this influx of new data.
Don Meier , Deloitte Catalyst Innovation Manager at Deloitte Consulting , joins the show to share his thoughts on how all of this new technology supports day-to-day operations and how his team tracks emerging trends.
- The digital transformation and Industry 4.0
- Getting out of pilot purgatory
- Tracking emerging technology
- Building vs buying a solution
Are you ready to start your digital transformation journey? Request a demo today.
Check out the full episode below:
[00:00:00] Don Meier: There’s this intersection of technology and they’re making these amazing things that exist in the manufacturing space that I think is a little bit of a hidden gem and we’re starting to see it come around a little bit more as these technologies emerge, as things mature and people start to realize that this is actually real.
[00:00:19] Josh Santo: Welcome to Conquering Chaos, the show for manufacturing leaders. In each episode, we’re connecting you to the manufacturing leaders of today who are driving the innovations needed to future-proof the operations of tomorrow. If you feel like your time is spent fighting fires and trying to control the everyday chaos, this show is the show for you. My name is Josh Santo. I’ll be your host.
Hey, y’all it’s Josh. Before we get into this episode, I wanted to put this into your ear. If you like the types of conversations we’re having, you’ll enjoy the content that we share through our mailing list. Go to parsable.com/podcasts, scroll to the bottom of the page, and sign up to get more insightful content delivered directly to your inbox. Okay, on to the show.
Welcome to the show. Our guest today is bringing us the know-how on bringing in new technology to support day-to-day operations within manufacturing, based on his experience building and working with, you guessed it, new technology to support day-to-day operations within manufacturing. With family roots in manufacturing, he spent his career serving manufacturers of all sizes and even founded Line Shift, which brought modern manufacturing tools to small and medium-sized manufacturers. Currently leading the catalyst, energy, resources, and industrials team at Deloitte, please welcome to the show, Don Meier. Don, thank you so much for joining us today.
[00:01:51] Don Meier: Thanks so much for having me. It’s great to be here
[00:01:53] Josh: It’s great to have you here. For those of you listening, just a little bit of backstory, Don and I connected a few months ago and we actually recorded an episode and my audio was terrible. I said, I don’t care, let’s release it anyway and my producers ended up reaching out to me and saying, look, Josh, you sound terrible. Don sounds great, but we can not do that to your listeners. Really appreciate you Don being here for a second time and a recording with us.
[00:02:22] Don: It’s great to be back. We’ll see how much we’ve covered the same stuff or who knows where we’ll go.
[00:02:26] Josh: Each conversation has a life of its own. Really looking forward to surprising you with a couple of curveballs later on, but we like to start each episode with the same question. Tell us a little bit about what your day-to-day looks like in your role.
[00:02:41] Don: Absolutely. Like you shared, I’m currently with Deloitte consulting with our catalyst group. With the current state of the pandemic, still working from home. What that typically means my day with is one of two ways. Number one, starting on an indoor run because it’s the summertime here in Texas and it’s too hot, or the part that I enjoy more often of the other two is spending time with my dog Clifton, taking him for a walk in the morning and then start getting to the work side of the house.
My day-to-day focus within the Deloitte catalyst group is really all about three angles. How are we thinking about the technology innovation landscape? How are we thinking about how to bring that landscape together in the form of an ecosystem? Then also, how can we go out and test new solutions, whether that’s bringing together ecosystem partners, building that new solution, or doing something else innovative in the space, specifically, usually within energy resources and industrials?
On a day-to-day level, that’s everything from connecting with startups to learn about their solutions, connecting with a startup and an innovative player, to learn more about what potential opportunities do they see in the market. Do we think that there’s an opportunity to go out and partner on something? There’s a lot of discussions internally trying to identify where we’re going to head, what’s our next big area that we think that we can grow a bit net new business in. Then, of course, working with our team down in the nitty-gritty as we’re building out these partnerships and net new solutions, it’s everything from partnering with our development team and scrum team around the world and partnering with my team here in the US to try to build out those net new businesses that we’ve identified as priorities.
[00:04:21] Josh: You’re building an expertise of the tech that is emerging and essentially identifying the ways that tech can make an impact for different groups across energy resources and industrials.
[00:04:35] Don: Absolutely, and I think maybe I led with the tech there because it’s the fun part to talk about. The one thing that the team is really grounded in across the entire catalyst organization is being problem-centric and making sure that we’re focusing on problems that are not only big and meaty that we know when we solve that there’s going to be a big prize at the end of, but also that we can break down into really meaningful chunks, go out and test these problems and solutions in a very lean startup manner and be the firm’s startup and net new business creator.
[00:05:09] Josh: Got it. You’re also identifying those problems that need to be solved. You’re not just making up problems for tech to “solve” at that point, in addition to monitoring that emerging tech and identifying the strategies and the ways to apply that tech, which is super important. That’s going to make up the core of our episode today but before we do, I think you’ve got a pretty interesting background, especially when we think about what’s going on in manufacturing right now.
One of the topics that comes up consistently is the next generation of manufacturing. While that term often applies to smart manufacturing, smart factories, next-gen manufacturing, what I’m referring to here is the workforce. Manufacturing is struggling to attract younger talent and you have a family history of manufacturing, but working within a factory wasn’t for you. Could you tell us a little bit about your history with manufacturing?
[00:06:04] Don: Absolutely. I grew up in central Pennsylvania in a town of about 10,000 people called St. Mary’s Pennsylvania, also lovingly known as the powdered metal capital the world. A significant amount of industry, something like 75% of the entire workforce is employed within the manufacturing industry space. My grandfather was in industry. My father was as well. He was a machinist and just retired from the frontline workforce a couple of years ago. I’ve grown up around this really blue-collar space and have just a familiarity and at least the familiarity with what manufacturing in the past was.
I continued to stay in the space throughout undergrad. I studied industrial engineering and the most traditional play to go into an industrial engineering is go into the manufacturing engineering optimization. I spent a significant amount of time there, everything from building operations decision support systems to traditional time and motion studies with a stopwatch and a clipboard out on the manufacturing floor. I think there was a moment in undergrad near the end of that time where I felt like I just wanted to do something different. It wasn’t so much an intentional move out of manufacturing, it was more of a move to look for something different.
That’s what I ended up actually interning with and then joining Deloitte Consulting much more than a technology consulting capability doing, for the most part, product management of back-office solutions for some of our largest clients across the DC area. I really enjoyed that. It was a great learning experience. It was a great way to round out the engineering skillset in a very different way and learn how to solve problems in a new and different way.
The really interesting thing about that product management space is I was able to live the entire life cycle of the product, everything from ideation and sales, up through development, testing, and delivery, and then bringing that back around. When I was able to do that, I really loved the ideation and sales component of it. That’s what moved me toward the opportunity. I have the opportunity to go get my MBA. One thing that I think that I’m really good at is this ideation, sales, this net new business creation. I’m also going to work on coming up with, if there’s a meaningful problem I can solve, to go out and try to start my own business.
I went to get my MBA at the Kellogg School of Management and at the same time started a company which you referred to in the opening called Line Shift. Line Shift really from day one, focused on how can we enable the next generation manufacturing worker and the way that it manifested itself, it transitioned four or five times throughout. Everything from big data analytics to more of a pure consulting play, but the one thing that that thread throughout was there was always some workforce element, workforce training. How do we advance the workforce component to that work?
Like you said, it’s a very difficult time to attract talent to the workforce. One thing that I think that you see consistently, and what really brought me back in, is the manufacturing arena, the facility itself, and the work is not 30 doll [unintelligible 00:09:11] dangerous any longer, right? All that work’s been automated or it’s been changed in some significant way.
When you go into these facilities, this state-of-the-art technology that you’re consistently working with, you’re actually making things so there’s that huge element of having something that’s tangible that I feel like a huge part of society really relies on as part of their skill, their core skill set, but it isn’t able to get that from work because most of us who are sitting in front of a computer or a desk don’t have that capability to go out and do something more tangible.
In reality, there’s this intersection of technology and they’re making these amazing things that exist in the manufacturing space that I think is a little bit of a hidden gem and we’re starting to see it come around a little bit more as these technologies emerge, as things mature and people start to realize that this is actually real. That’s part of the exciting work that you and I do, bringing new technologies to the manufacturing space and helping make it more real for manufacturers and everyone around the world.
[00:10:09] Josh: There’s a lot that you said there that I think really highlights why you are uniquely qualified to talk on this subject. You’ve got that family history within manufacturing, growing up in a town that was really built around manufacturing. You even mentioned I think you said it was your grandfather recently retired from being a machinist. Was that correct?
[00:10:31] Don: My dad.
[00:10:32] Josh: Your dad. Your dad, so, direct ties to manufacturing, and then you studied. You went to school and you were in it. You took that and you moved into a technology space where you learned how to build products, to take an idea and turn it into something that can actually be used. This gets us into your team. The Deloitte catalyst team, where you’re looking at different companies, the tech, and the problems that can be solved and you, in particular, have an eye of how can this make an impact for these industrial organizations and how can you vet and really understand what phase the technology is in?
I’d like to hear a little bit more about this subject in particular because one thing that comes up pretty consistently is digital transformation. That’s a buzzword at this point. If you do some research on it, you can find a lot of differing and sometimes even conflicting opinions on what digital transformation is going to look like. One thing that is clear is that the path to digital transformation is paved with strategy and with technology. This sounds like exactly where your team comes in. Could you talk to us about digital transformation and what you’re seeing in your work with companies?
[00:11:54] Don: Absolutely. To throw some maybe slightly less buzzier words into it. When I think digital transformation in manufacturing, I do typically think of, and this as an even buzzier word, industry 4.0. When we make the connection of digital transformation and industry 4.0, I think of those two things as the transformation of companies that make things and the digital transformation component of that is everything currently in manufacturing is in bits, moving into bytes. How can we not only digitize the actual operations that are taking place but the top-line revenue growth? How can we turn not only insight into the operations that we’re building into something that lowers costs, but also create net new revenue streams based on this now digitized data that’s created?
In summary, I define industry 4.0 as the digital transformation of companies that make things both in the form of top-line growth or revenue and in the form of bottom-line growth, which I think is more traditionally what we hear about and the ability to increase the bottom line. Maybe some examples of that would be helpful just in general. If we think about bottom-line growth, I think that that’s an incredible opportunity where operational efficiency can be improved. Whether that’s through having better communication with your workers through a connected worker platform or having the ability to leverage sensor data, to understand that my pallet is now full and somebody needs to come pick it up so that we can continue the flow through the facility. That’s going to increase your utilization, increase your throughput, and your OEE.
Then if we also think about the top line, I get really excited in this space too. There’s so much untapped potential in the future of industry 4.0 manufacturing innovation in the top-line space. Whether that’s a company that can integrate sensors into their solution that understands putting in repairmen and repair women, putting in genuine OEM parts into my machine. If so, I can dynamically update and extend the warranty of the machine and charge in a very different model where I’m actually charging for amount of uptime, or I’m actually able to guarantee service for that machine within a certain amount of time, because I know there are a certain type of part within that machine.
There’s the revenue growth potential that comes from crazy things like 3D printing houses and the opportunities that opens up, whether that’s printing a house that can be lower cost and much more affordable to printing houses on Mars and the moon. It’s an incredible aperture of opportunity, both in the revenue and the cost side. I think that’s what’s the most exciting about it. That’s probably why everyone’s confused about it because it has such a big definition.
[00:14:43] Josh: Those are two very, very different examples and, I’m going to throw my own perception on there and I would love for you to either confirm it or reject it and explain to us why this is wrong. When I hear and think about that idea of bottom-line growth with regard to how can you transform your operations or really, how can you use these digital technologies in such a way that it provides bottom-line growth? As far as the improvements, it seems to be things that people are already seeking to improve current day. You mentioned in particular with operational efficiency, you’re looking at utilization, throughput, OEE.
Would it be fair to say that when thinking about bottom-line growth, the improvements are really focused on known areas of opportunity, whereas top-line growth, maybe that requires a little bit more outside-the-box thinking, little approach to ideas that couldn’t have been considered before?
[00:15:43] Don: Yes, I think on the top-line side, the sky is the limit. On the bottom-line side, I think that it’s almost like if you think of the cost reduction curve as an exponential approaching an asymptote, the way that we think about it is when you digitize, you actually go from that asymptote itself and you just shifted upward. Now you’re not only leveraging traditional means to cut costs and looking for all those different cost-cutting opportunities, you’ve actually shifted the line in a meaningful way that you’re no longer constrained to the same asymptote.
What that unlocks is net new things like, can we in real-time based on say, video data or something like that, identify when something is going to be a recall, prevent that recall in real-time. That is just massive bottom-line savings that we can never even have dreamt of in the past. That’s a big, bold, ingenuitive stuff on the bottom line as well, but there is a lot of opportunity in the traditional space. Of course, from the traditional pure-play industrial engineering, which is the base of this.
[00:16:55] Josh: That’s something that in my experience working with clients, particularly on that connected worker piece, we talk a lot about how the buy-in if you will is that move to digital. We focus a lot on, look, if you’re using paper, you may not realize that there are a lot of inefficiencies that are allowed to exist because you haven’t put the right tools in place to successfully hand off information, because that’s really what we’re talking about. The quicker that information can either be identified or received and acted upon, that’s where you move to that real-time experience.
That example that you gave, the video catches the problem prevents a massive recall. It’s really just different ways of capturing information, sharing that information, and then automating the follow-up action to that. There’s a lot of different breakdowns that we could talk about, but I love exploring this idea. This is a challenging concept that we revisit over and over again which is the way that you are doing this process or this activity, is this the ideal way or are you compensating for a lack of a tool that’s going to let you do it that ideal way?
A lot of times you have to break down that traditional thinking and that idea of this is the way we do it and really explore is there a more efficient and effective way, especially with some of these emerging technologies?
[00:18:22] Don: Absolutely. We try to leverage a lot of design thinking, how might we statements type concepts, which has a lot of what you’re sharing that you’re trying to do with your clients as well. I think a lot of times, you and I take for granted that we sit in front of a machine all day that allows us to constantly communicate, digitize information rapidly, and at scale. When you have to walk half a mile across the facility to let somebody know that the machine is down when a mechanic is needed, and then we need to go out and identify that mechanic that can work on that specific machine. It’s not necessarily easy for me to come up with the fact that it’s possible for me to never even be in the loop in that discussion.
[00:19:07] Josh: That’s such a great example. I can’t tell you how many times I’ve had to have this exact conversation. It’s particularly with regard to that idea of safety versus efficiency. There’s been number of people that I’ve worked with where I’ve had to ask the question, do they realize that they did have to make a sacrifice for safety, they had to make a sacrifice to efficiency for safety.
Now, safety is paramount. By no means do I want someone to take- for the takeaway to be that Josh is saying you shouldn’t put safety first, but in that example that you provide, there’s been so many examples where we’ve seen the safety process requires a signature of an authorized individual in order for the work to be started. If you can’t find that person, and that could be because the operators and the mechanics they start their shift two hours before this person is on-site and now they can’t get that sign-off so that work is delayed. That’s not efficient and it’s also no longer required when you have these remote collaboration capabilities.
You and I are recording. We happen to be in the same town but we’re not in the same room, but we’re able to share information in real-time. I love that example of just those different types of changes and how you can challenge and redo and revisit the way that you’ve done things, leveraging new technology. Now, in the conversation about new technology, one thing that comes up almost as frequently as digital transformation is the idea of pilot purgatory, and I’d love to hear from you, what is pilot purgatory?
[00:20:53] Don: I think it probably comes up just as frequently if not more frequently now. Pilot purgatory being, we have a great new technology. Maybe we even meaningfully thought about what the problem we’re trying to solve is, we got it out to maybe a couple of work cells, maybe even a line, but we haven’t figured out a meaningful way to scale it in part for a couple of reasons usually. Number one, we’re not seeing quite the return on investment that we expected, and number two, we had somewhat of a strategy ahead of time but we might not have had a full and clear strategy around how do we actually, what does success mean, and when we actually see success in this early pilot, what does it look like at scale?
Because what we’ve seen time and time again for some of the lighthouse type of work that’s been done is, initially, we get a little bit of benefit from optimizing a cell. We get more benefit from optimizing a line. When we start to optimize across lines, then we see a significant benefit. When we start to optimizing across facilities, then the whole supply chain’s involved, and we can see these massive operational improvements that take place.
When you can have a machine insight that takes place halfway across the world, and that you have done such a good job planning your data strategy that you know that that exact machine, when it sends this signal is also going to train my model that is working on a very similar or the same machine halfway across the world, going to make that update and give us the opportunity to keep that OEE up, that throughput up, capture that revenue opportunity, whatever it might be, but it’s like you said, a lot of people are focused on getting that tech in and starting small, but thinking big first and figuring out how to get out of pilot purgatory before you even get in, it is so crucial.
I think that comes down to a couple of different things if you’ll let me dive into that. If I think about what helps take something from pilot to scale. I think we touched on a lot of it. Even in the introduction, we talked about strategy, we talked about picking the right technology which I think we’ll talk about more, but there’s some interesting nuance in addition to the strategy from a data perspective. I think, what I’ve seen the strongest and most innovative players do is do some of the boring stuff really well. How are we tagging data? How are we getting the data structure set up? How are we organizing ourselves, both from an individual customer level, how am I as a customer organizing my data strategy?
Then also for the vendors that I’m working with, what does their data strategy, and what does that look like? That fundamental baseline I feel like has been such a helpful delineator between where I’ve been seeing success in the innovation space and where there might have been a little bit more of a pilot purgatory.
[00:23:55] Josh: When you first broke that down, two top-level components being the return on investment and the strategy, which it sounds like some of that is pretty big even just for starting off. Do you have any thoughts on how do you accomplish that start small, start with something specific that you can solve, but still have that other picture of this is what it looks like at scale because it sounds like without thinking about that first or putting some thought into that first, then you are setting yourself up for failure in a sense of just staying within that pilot purgatory?
[00:24:38] Don: Thinking big at first is a big, I agree that that’s- it feels intimidating. I don’t think it has to be something– Thinking big doesn’t necessarily mean I have to put a hundred-page deck or a hundred-page business plan together around it. I think what it means is who, it really comes down to like stakeholder alignment and depending on who’s listening today, the right level of buy-in for what you think the next level of funding is to get to scale.
If I’m thinking about this from a startup perspective, it’s like, I can have extreme success of this at the plant level, but if the executive buyer doesn’t feel like he or she is seeing value directly from the solution, even if it’s providing value but there’s no value add for that executive buyer, when we get to the next level of scale, that’s going to end up becoming a roadblock. That’s how part of thinking big for the startup. How can we think about how everyone throughout the entire organization needs to buy this, and then the same thing for internal stakeholder alignment.
If I’m trying to bring in an innovative player and I know that there is a solution that’s going to absolutely crush it for me, what are the key scale components I need to think of both from a technical and from a financial perspective and think about how I’m going to get executive alignment ahead of time, how I’m going to do the value capture that’s going to show the story all the way up to whatever level I need to be able to be ready to go out and scale this thing, and then how am I going to think about this at scale? Is this something that we want to be part of? Like I said, that core data backbone that I built, do we want this to be something where the more focus is on being an API that we leverage and pull data from and let them manage the core data backbone and we just make sure that it plugs in with our taxonomy.
[00:26:34] Josh: Hey, we’re going to take a real quick break to hear from our sponsors, stay tuned for more Conquering Chaos.
[00:26:41] Rob: Hey listeners, it’s Rob. I’m one of the producers on Conquering Chaos. I’m right here with you for every episode, working behind the scenes to make sure everything is just right for your listening experience. Whether you’re a new listener, binging content to help you conquer the everyday chaos, or a dedicated fan tuning in for each new episode, there’s one thing to always keep in mind. Information is useless unless you use it. Obvious, right, but it’s so easy to learn, forget and then miss out on the opportunity to make real improvements to day-to-day activities.
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[00:28:49] Josh: I’m thinking about this and I’m trying to put myself in the position of some of the types of people we work with. For example, sometimes we’re working with production managers, maybe it’s a plant manager, sometimes it’s continuous improvement managers and others, and thinking about what you just described versus what I might be looking for. A lot of times, we find a lot of people looking to solve a specific problem, the problem that they’re dealing with, and when I hear some of the advice that you give, it makes absolute sense to me but it adds a little bit to a feeling of man, there is a lot of work that has to go into this.
If we’re thinking about new technology, we got to find the technology, we got to make sure it works and does the thing that we’re wanting to do, and then we got to make sure that our boss and our boss’s boss, and our boss’s boss’s boss would be on board with bringing on this type of technology. Then when you think about like the state of a lot of these companies where everyone has a ton on their plate and the last thing they’re able do at times is take on new responsibilities. I’m just curious, in some of your experience, how are you finding people who are, let’s say, resource-constrained, being successful with this process of finding technology and setting up the technology for them, and then selling that technology internally?
[00:30:19] Don: Yes, I think the biggest thing in this, and you struck an awesome chord there, it’s not only about money, it’s also about time constraints. The really great technologies have freed up even a component of a time for the person that’s implementing it or they’ve allowed them to reallocate a certain amount of that time in a different way from someone on their team or someone else on their team to do that work. I think that can be part of the big picture analysis thinking.
If I’m leveraging a connected worker platform, and now I have very detailed work instruction level data that’s breaking down a task that used to I only have to say like, “This took four hours.” Now I know every single thing that happened in every five minutes, well, I’m probably going to have to have less IE time going out there, and maybe time studying the floor, for example. I’m going to leverage my met new IE time to think about how I can capture value on this new solution that I’m bringing to market. I think that’s where we’re headed.
I’m really excited about in the space, is technologies are maturing. They’re getting maybe simpler isn’t the right word. They’re becoming more consumer-like, where technologies are moving towards a place where they’re actually becoming our friend, they’re saving us time, they’re not only improving the core like I’m moving widgets from this location to this location faster, but I’m actually improving the way that I’m measuring that. I’m improving the way that I’m improving that, which is all allowing me to actually more effectively expand that digital transformation and speed it up.
[00:31:56] Josh: I think that consumer experience comment is so key because a lot of times when I’m talking with people about Parsable or not, there is sometimes a resistance there just automatically because tools were traditionally– Software, I should say, was traditionally built for the system and not necessarily for the user. That makes it hard and clunky and confusing. When things are hard and clunky and confusing, people avoid it. If people avoid it, then you’re not getting that benefit that you were expecting to get out of it, out of a solution.
That design trend actually really helps with that idea of adoption, which helps with now we’re getting the data that we need in order to see that value that you’re talking about. It’s almost another factor is whether or not people like using the tool that you’re considering. It’s something to always think about.
[00:32:49] Don: When we think about scale, what stuff that I get really excited about scaling, this comes back to the first thing we talked about which is workforce. That goes hand in hand with the consumer experience. I’m obviously biased having started a company that took a very workforce first approach and was 100% dedicated to that both because of background and because I knew that that was the key problem and challenge that the manufacturing space needed to address, but what we’re seeing is those workforce first technologies are actually able to scale because of the buy-in at every single level of the organization, whether it’s saving time, it’s saving money and it’s just kind of more natural in the cadence.
As we see some of the different generations of workforce, some will be retiring or leaving the workforce, and as we see some of the newer folks joining, I think that there are significant opportunities. That capability, the ability to create a consumer-like app in the industrial space or consumer-like, we’ll call it experience, it’s not going to go away. I think it’s going to continue to be where we see things win whether it’s a new technology, an integration of ecosystem partners, or even just a project in the manufacturing space.
[00:34:05] Josh: Yes, the tech goes viral after a certain point with that natural adoption and word of mouth and that spreading. There’s a lot of tech out there. Since you’re dealing with a lot of different technical solutions, I’d be curious to hear if there is a certain type of technology that you see a lot of buzz around but not a whole lot of results at this time?
[00:34:26] Don: Yes, at Catalyst and with my colleagues across the firm, we’re tracking everything from technology that’s 5 to 20 years out, some really exciting stuff that I’m still learning how it works, whether that’s quantum or other items, up through what’s getting implemented today. I think that there’s a lot of buzz around all kinds of tech in especially the industry 4.0 manufacturing space. One thing that I really expected to get a lot more traction throughout the pandemic, and we did see some creative uses of it. I just thought it was going to explode. I haven’t seen the explosion yet, is more of augmented virtual reality capabilities.
I think that specifically on the VR side, I thought that there was going to be more of an explosion there. I still think that there’s an opportunity. I don’t think that necessarily means that it has missed the boat. I just expected a significantly larger amount of growth in the space. I think in the AR space, it’s starting to find its niche. I think we’re getting close to there, but I would say VR is probably the bigger one where we’re still curious. Funny enough, those terms are almost too old now. Now we’re thinking about things like spatial computing and ambient computing.
How does the environment around you automatically update and more intuitively, when we talk about– That’s why I went to not only an app you build but an experience actually. Maybe the right way to interact with a person is actually to give them a little bit of a nudge instead of providing it to them on their HMI and thinking about how you design an entire work cell or experience when you’re no longer just constrained to, “I need to show somebody something through a screen. Maybe I can do it through physical touch, through sound, through a significant amount of other ways that haven’t been tapped into.”
[00:36:19] Josh: I think that’s a very fair point. A lot of buzz still around AR and VR. In my personal life, I recently got a VR headset and I was blown away. Anyone listening to this, virtual reality is the future. Whether or not work, but entertainment. When it seems like you’re sitting courtside at a basketball game from the convenience of your living room, it’s a crazy experience, but one of the things that I’ve seen as well, especially when we’re talking about augmented reality, now, augmented reality isn’t necessarily just wearable devices, because there’s a big AR market for how your phone can let you see things that otherwise aren’t there.
When I look at the state of wearables, in particular, as a consumer, I’m not satisfied with where wearable devices are. I’m in a much more friendly and forgiving environment than wearables in the industrial field. Now, I think that there are some promising solutions. One of the ones that I keep coming back to over and over again is RealWare, but the experiences that I’ve had personally, and it’s been a year at best since I’ve last interacted with it, so I want to make sure to qualify this correctly, it still leaves me with a less than ideal experience for someone like myself who is more friendly to technology and more patient because the environment that I am in isn’t as demanding with regards to the information that I need, when I need it, and how I need to get it, especially when we start talking about motion controls and voice controls with some of these wearable devices.
That’s just my two cents, but I do think AR, VR, very much the future. Personally, if I can go on an even further tangent, you mentioned giving people information without the use of an HMI but I think you’re absolutely right. I think, at some point, devices, they’re not going to be the way that we’re consuming the information. I think that that’s the bottleneck right now because that’s what’s required in order for us to connect to that digital-connected world. At some point, someone’s going to– I don’t know if it’s Elon Musk’s Neuralink or whatever the case is going to be, to provide that information and let you interact with the digital world, but I do see a future at some point without the need to rely on devices.
[00:38:46] Don: Yes, I wasn’t sure if you were going to– I did not think we were going to get the brain-computer interfaces today, but that’s why you have the conversation, right? You never know.
[00:38:54] Josh: Yes, that’s right. That’s right. Speaking of brain-computer interfaces, no, I’m just kidding. I am by no means well-researched on that topic, but there is something that does come up a lot, especially when we’re talking about technical solutions that I would love to get your perspective on. That’s the idea, or I guess the debate I should say, of whether a company should build their own solution or purchase a solution. There’s definitely pros and cons to each.
Certainly, when we talked about how it can be overwhelming, especially when you’re thinking about it’s not just this problem that we need to solve with this technology, but all these other opportunities to improve the bottom line or open up those new top revenue activities. It can be a lot to try to triage and research all of the emerging tech and trial it and experience it and understand if there’s an ROI and does it work for your company. I’d be curious to hear your experience on the topic of build versus buy and how often that comes up.
[00:39:59] Don: Yes, build versus buy is almost a constant as part of our process. When we’re thinking about scanning the landscape and evaluating the ecosystem and how we can bring everything together, there’s a constant analysis of does it make sense even within Deloitte to build out this capability, whether that’s with human capital or actually doing it in a more automated fashion with software? This is core to the work that we do. Does it make sense to build it native, to collaborate with a number of partners, to collaborate with one partner, to only collaborate with the partner and don’t build anything, just go directly to market? It’s a big piece of the work we do.
I think when we’re making these considerations, we think about a couple of things. I’m sure I’m not going to be exhaustive but let me think through a couple. One thing that I think everyone will always go to is a two-parter, the criticality of IP and data, which is the second part. The big emphasis as the data is the new oil, we need to own our data and we need to own everything.
I think that sometimes there’s actually the opportunity to challenge that paradigm and no doubt, the importance of IP and leveraging a data play and how that can be brought together as part of your broader strategy. Absolutely crucial. I’m not saying that this is always the right decision, but if you look at a venture studio model, which a venture studio model is like a mix of consulting and venture capital. They’ll engage someone like say, a corporation who’s trying to solve a problem hasn’t been able to solve it internally.
Instead of trying to start up a new effort or service or product within the company, because it’s been stymied so many times, they’ll actually spin out a new division and the venture studio helps you spin out this brand new literally startup with venture capital funding already set aside.
What that means is the corporation no longer owns 100% of the startup. They maybe own like 40% of it. You’ve already lost 50 plus percent of your IP and 50 plus percent of your ROI. I think the really interesting thing is these venture studio startups are seeing failure rates so much lower than other startups that the return on investment is significant. I would rather own 40% of a massive pie than 100% of something that I keep getting stuck in, in trying to build a product myself, so I think that there’s some innovative opportunity to think about IP.
Then the other two are a little bit more I think general and I don’t have necessarily a super differentiated opinion on it, but huge things that we consider. Number one is how hard is this to actually build, and it’s usually harder than you think, is what I always like to lead towards and share with everybody. There’s a reason why companies exist out in this space. It’s not because- I’m not saying that we can’t build anything, but we also need to consider what’s the opportunity cost here of building this?
Is there enough unique capabilities that they’ve built in that this can be a true accelerator and more of a partnership play and we can give up some of that game that we get by building it ourselves, just from a pure-play can we build it and how much time is it going to take, but then also can we build it for scale? That’s a whole different ballgame. Of course, we can build it for a pilot, or maybe even if it’s not, of course, we can build it for a pilot. We can probably build it for a pilot, but those build considerations are huge.
Then one that has driven decisions that I’ve seen in the past that I think is such a key consideration is do we have the people to actually do this? We can drive in a certain direction, and know that it’s the right decision to do it, but the talent and pool is very tight out there, especially for certain data science ML-type roles that a lot of this work demands. Can we actually pull those resources in right or is this a unique way to tap into a shared resource pool that’s already building these capabilities?
Then the final thing is, we’re doing it at the smart factory at Wichita which we’re really excited to talk to you more about as well, but it’s do the test. Work with the technologies and don’t just make the decision without having some real-life examples. We talked about it at the beginning of the call, manufacturing is so tactile and there are real products moving through real assembly lines, going through real supply chains. It’s not all digital, we’re trying to digitize as much of it as we can but there’s a certain element to this, there are actual bits that need to be tracked and understood. Does this work with our process and getting out there and testing it, and of course, having the bigger vision before you go do that test for what success looks like but I think those are the big four big ones that we try to think through.
[00:45:02] Josh: I think those are all critical points and the one that really stood out to me was how hard do you think it’s going to be? It’s going to be harder than that because it’s a lot to build a solution, let alone factor in that idea of scale, it takes a lot of resources. In my experience, I haven’t met many groups other than IT organizations that are really advocating for, we can build it ourselves. That’s been wise. I don’t know if that’s the same for you, but that’s certainly what I’ve seen and I’ve also seen a lot of conflict between business users as well as whoever owns the solution internally with we need it to change, we need it to adapt to XYZ. We’re trying to use it but it’s not working for us. We need it to be able to adapt.
There’s just like you were calling out there not only there’s pros and cons, but there’s things to think about, especially that sustaining side of things and how quick can you get it? How can you bring it to life at that point? because building takes resources, infrastructure, and time to get that out. Some of this seems to be fueled by perception of startups or emerging tech companies. I’d like to hear from you, are there any common misconceptions that some of the more established companies you work with have about working with startup or emerging tech companies?
[00:46:28] Don: If we think about broad, maybe lessons learned that I’ve seen out in the space, I think the biggest one is it has nothing to do with technology itself. The biggest one is actually much more focused on if you’re going to be working with a startup or an innovative technology player, maybe that’s a lab, maybe that’s something that’s coming out with really net new cutting edge stuff, they can’t be treated like a fellow Fortune 500 vendor. It’s not because they don’t want to be able to have the same level of engagement as a Fortune 500 vendor but they’re focused on a very different way of creating a big business. That is how can I go out and create a really huge pie and get that pie as quickly as I can? I need to learn so rapidly to build that new and ever-changing business model and product and whatever it might be.
I think a lot of times there’s a misconception around, well, we’ll just put them through the normal procurement process. We’ll just evaluate them like any other technology. I think there’s so much opportunity to think about this a little differently to try to get whether it’s simpler agreements in place, just so you can get started and think a startup yourself within the broader organization so that you can actually infuse more of this technology into this space, whether it’s thinking about it from a business development perspective.
They might not be able to take 100 leads at once because we’re trying to think about we need to really focus in on one or two, get those big wins, build the momentum on both sides because they’re also reporting to a board quarterly as well. It might not be a publicly traded, it might not be a board of investors for a company that’s publicly traded, but they still have to answer to their venture capitalists every quarter for how they’re going to approach and close those next round of sales to build towards whatever the next series A, B, C, D E might be. I think those are the quick ones.
Then the other thing that I thought of when you shared the question is, it goes back to the point about earlier is like and have an optimism about technology, but also know that these are earlier stage concepts and have an understanding of what level of risk, and we’ll call it and experimentation that you want to be taking on. Do I want to be working with an earlier stage company that product is not built out the whole way, but I’m really excited about the IP and my goal is really just to see like we think we could transform our business if we use this.
If we get in early, there’s a huge first-mover advantage. That’s our play. Okay. That’s great but we need to acknowledge that as what we’re doing, not as we’re going to bring in this early technology. Oh, wow, the product’s not built out the whole way and now we’re in a position where I have to go answer to the board or to the C-suite about how, in reality, 90% of the work that was being done was someone in a remote location actually clicking through when we thought it was an algorithm that was doing it. That level of risk tolerance and knowing where we’re heading into ahead of time, I feel like is so critical and needs to be a consideration.
[00:49:44] Josh: I think that’s a great point, really understanding not just the stage, but how you can grow together, even if you want to grow together because especially with these emerging tech, it’s a brand new idea that’s been brought into life and it’s got some growing up to do. Well, let’s start to wrap up here. Let’s talk about the catalyst energy resources and industrials team at Deloitte. I would love to hear how the team can help. If listeners are interested in learning more or working with you, talk to us about how you can help?
[00:50:16] Don: Absolutely. Like I shared earlier, we’re focused on the big challenges and problems that our clients in the energy resource and industrial space are facing. We always love perspectives from anyone in the market. If there’s ever an opportunity, whether you’re an innovative player, a startup, or a corporation, hearing about a problem or a challenge that you’re facing.
Another really great opportunity, we love to keep a beat on the landscape and the ecosystem like I shared earlier. If you have something innovative that you’re working on, please drop us a line. Even if it’s not us, we’ll try to make sure that it’s communicated to the right people. At a minimum, we know when a net new opportunity arises that might make sense the right people are at a minimum aware of it.
That’s how I would say we feel like we can help the most and where the opportunities lie, but there’s also that last piece of it which is thinking about how we can create those net new solutions that are going to help solve those problems, which also gets me really excited. Across the board, everything from problem to solution early stage to late stage to Fortune 500, we want to hear about it and we want to be at the cornerstone of that and innovation in this space.
[00:51:36] Josh: What’s the best way to reach out to your team or to yourself?
[00:51:40] Don: The best way to get ahold of us is to write to firstname.lastname@example.org.
[00:51:46] Josh: All right. We’ll make sure to have that in the show notes as well for easy access, email@example.com. Well, I think that that’s been a lot of great takeaways in this episode and a lot of great perspective, especially on that idea of finding technology, finding the fit, and how to make sure that it sticks around. Is there anything that I didn’t ask you that you wish we would have brought up during the course of our conversation?
[00:52:12] Don: I think so, we got to do it though. I think I wrote down your last Taco place, so I got to ask you again, in case you share a new one. What’s your favorite taco spot in Austin?
[00:52:23] Josh: Oh my goodness. For those of you who are listening outside of Austin, there is I would say a war in the city over what is the best taco place, and you’ll find different opinions all over. There’s of course classics like Torchy’s Tacos and Tacodeli. I’m going to exclude those from the list because they’re popular chains and we’ll add Velvet Taco to that as well. I will say though that those three places offer very delicious tacos. Right now, there’s a place right next to my house that is really doing it for me lately called Veracruz tacos. They’ve got a couple of places in the Austin area, but they’re, I can’t remember the name of the taco but it’s got everything that I would want in a breakfast taco. By the way, I’m thinking breakfast tacos so that’s where my mind goes. What about you?
[00:53:18] Don: I love it. I love it. I would say Cuantos tacos for dinner tacos. Always get the daily special if they go through their entire menu. It’s quite a spot, just incredible, meat savory, and just super well done classic style tacos.
[00:53:39] Josh: Well, maybe we’ll have a special episode where you and I go and review tacos around Austin.
[00:53:45] Don: I Love it. Let’s do it.
[00:53:47] Josh: Okay, great. All right, Don, I appreciate your time. Thank you.
[00:53:50] Don: Thank you.
[00:53:57] Walter: Hey, you all it’s Walter. I’m another producer for Conquering Chaos. Before you go, if you’re not ready to try Parsable to help you get rid of paper, why not watch a quick video instead? Check the show notes for a link to a demonstration Josh put together to show frontline workers, what it’s like to use a dynamic digital experience to get work done. In it, Josh shows you how using a modern-day app enables you to connect to people, information, systems, and machines, just like the apps you use in your personal lives. Take a look and let us know what you think.
[00:54:31] Josh: That’s the show. Thank you so, so much for joining us today. Conquering Chaos is brought to you by Parsable. If you’re a fan of these conversations, subscribe to the show and leave us a rating on Apple Podcasts. Just tap the number of stars you think the show deserves. As always, feel free to share what’s top of mind for you and who you think we should talk to next. Until then, talk soon, take care, stay safe and bye-bye.Listen to find out how you can evaluate technology for industry 4.0