Podcasts > Operations > Ep. 021: The Long Road to Digital Transformation - An Interview with Cisco's Maciej Kranz
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Ep. 021: The Long Road to Digital Transformation
An Interview with Cisco's Maciej Kranz
Monday, August 13, 2018

Far from being merely an isolated one-off event, digital transformation needs to be seen as a journey - a long and intensive one. 

In the sixth installment of the "Ventures in Industrial IoT series" brought to you by GE Ventures, we are pleased to have with us Maciej Kranz, VP of corporate strategic innovation at Cisco Systems and author of the book "Building the Internet of Things: Implement New Business Models, Disrupt Competitors, Transform Your Industry" on the show to share with us more about some of the insights in his book and other advice on how firms can successfully embark and sustain their digitalization journey.   



Erik: Welcome to the Industrial IoT Spotlight, your number one spot for insight from industrial IoT thought leaders who are transforming businesses today with your host, Eric Walenza.

This is an episode of the Ventures and Industrial IoT series brought to you by GE Ventures. In the series, we explore success factors and challenges in industrial IoT markets with CEOs, investors and experts.

Welcome back to the Industrial IoT Spotlight. I'm joined today by Maciej Krantz. Maciej is wearing a number of hats. He is the Vice President of Corporate Strategic Innovation Group at Cisco Systems. He's a faculty member at the Singularity University, a senior adviser to [inaudible 00:57], an advisor as well to FogHorn Systems, and a member of the Board of Directors of both IoTecha Corp and StarFlow Networks. He also recently published a book on the topic of IoT and how it's changing businesses called ‘Building the Internet of Things: Implement New Business Models, Disrupt Competitors, Transform Your Industry’.

Maciej, thanks so much for taking the time to talk with us today. Before we kick off into our discussion, anything else that you want to say around your background to frame where you're coming from in the topic?

Maciej: Quite often in IoT, often when I am sort of having this conversation, the first question I get is, oh, Internet of Things, it's about connected fridges and connected toasters and then we get into all kinds of technology discussion. But the reason I wrote the book and the reason I'm sort of on this quest of getting everybody started on IoT journey is that, yes, IoT is sort of at the baseline about technology solutions. But it really is about transforming industries, transforming businesses, transforming roles that we play in our organization. So it's a catalyst for a digital transformation and multiple levels. And that's why I think it's so important that every organization, whether it's small or large regardless of the industry gets started on the IoT journey.

Erik: How would you break down the different aspects that business leaders should consider when transforming their businesses using this set of technologies?

Maciej: If you haven't started an IoT journey, I sort of have an advice in three buckets. The first one is it gets started with a first project and from that perspective, don't reinvent the wheel. There are tens of 1000s of organizations that already started on the IoT journey. and they usually are focusing on four different sets of use cases. The first one is connected operations, the second one is [inaudible 03:12], the third one is predictive analytics, the fourth one is preventive maintenance.

So that's the first one is pick one of these use cases implemented have a success. The second one is learn from your peer’s mistakes. It's a new set of technologies challenges. In the book, I should have a whole chapter dedicated to common mistakes, learn from your peers mistakes. And the third one is as well maximize your chances of success both with the first project but also in the long run throughout the journey.

And then over there, I sort of listed eight areas. First one is building a partner ecosystem and learn to co-develop with them. The second one is attract and train new and existing talent. The third one is focused on solving real problems. The fourth one is prepared for a journey, not a one-time event. The fifth one is integrate technology solution with business processes. Six, start it with low hanging fruit. The seven makes security everybody's top priority. And the last one transform culture and technology. They may sound obvious, but I can assure you so many projects I've seen across the years that have not followed one or more of these ingredients and they failed. So in a nutshell, if you do these things, you are maximizing your chances of short term or long term success.

Erik: But before we get into some practical examples, you call it the IoT journey., a lot of other people around IoT they use this language of this is a journey, this is a long term trend. formation, you don't hear so much the same language with going into the cloud or with, even with big data or something which is also there's a lot of hype, a lot of expectation around it. Why is the set of IoT technologies different in terms of the impact that they may have on organizations?

Maciej: I think for me, it's probably less. So technology is one aspect of it. When you think about internet, the buying centers and the people who have been adopting Internet technologies, including cloud are having primarily IT organizations, service providers, and obviously as consumers, and often in the technology sort of industries. In many ways, IoT is different because IoT technology actually is targeted primarily at the line of business buying center.

So folks that run businesses, they want to hospitals, oil fields, cities, they runs stadiums, agriculture organizations and others. And from the perspective, these folks actually care about business outcomes, they care about performance, they care about quality, they care about top and bottom line. And they look at the technology as a tool to help them transform their businesses rather than to focus on let's deploy the latest technologies or solutions. So that's the first reason.

The second one is, again, if you look at 20 or 25 years ago when internet went mainstream, a lot of these industries that I mentioned earlier were not the massive adopters of Internet technologies. So in some ways, they still have a lot of business structures, for example, around the vertical integration; they have backward approaches from the technology perspective in terms of proprietary systems. The workforce relationships are different as well.Quite often, in some of these industries, you go into a job and you stay in the same job for 10, 20 years, the job doesn't change very much.

So the reason I mentioned all of these is, it's not just the technology. Technology is the catalyst for much broader transformations. And as a result, we don't talk about like with cloud integrated into existing infrastructure: you have to change a lot of things from a technology, from the business, from the culture, from the workforce relationship perspectives in order for you to be successful. And you can't just do it in one swoop journey. So it was one of the main reasons why we talk about a journey because you're changing so many parameters.

Erik: And this is perhaps one of the reasons that IoT has not been adopted as rapidly as the technology has advanced. So you see a lot of new technology that's coming to market that on the face of it appears quite mature enough and it seems to make sense from a business model perspective. But often there are adoption barriers that are unrelated to the technology, related to do we have the competence to implement and to operate this technology? Do we have the competence in our procurement department to analyze and choose the best vendors or the best partners and so forth?

How do you start a conversation with a company that's beginning this journey if there's multiple parameters that they're going to have to consider and you're going to be talking to somebody that maybe they're in the C suite, and they actually do have control indirectly over these, maybe they're below or they actually don't have control over all the relevant parameters, but nonetheless they're essential starting point?

Maciej: And quite often, the conversation starts with, let's see what your peers are doing. Let's go through 10, 20, 30 examples across the industries of how different organizations are using IoT to improve their operations or to start transforming their businesses. And by the way, the fascinating conversations are not necessarily within the same industry but actually sort of getting ideas from examples in different industries. And then once you've sort of settled on okay, I think I'm going to start with remote operations a project.

Then there are some of them are best practices. One of them is have big vision and big architecture, but start small again because of number of complexities, but also, because you are starting on the change management journey and from that perspective you will have naysayers, you will have people that will try to stop you, you will have a lot of silos you have to bridge. So, start with a small project, get the success, clearly articulate the ROI and then ask for permission to do more challenging projects and more transformative as well.

It's so important to actually have a C suite support, again, for the same reasons. You're dealing often with a mission critical environment and you're transforming them. So there probably won't be mistakes. There will be challenges there. Make sure that you have a C suite support and people that stand behind you and then build a virtual team and establish partner ecosystem.

So these are usually the areas that I would have a conversation with how to get started. And then once you build this virtual team, build the ecosystem, get the first project, get the first success under your belt and then you will be off to a journey.

Erik: Is there a case that you could walk us through somebody that you've worked with? And we can anonymize the name of the organization if necessary. But I think it's useful to have kind of a concrete example of a company where they started, what challenges they faced, and then how they walked around this.

Maciej: I can start with one which is around connected operations. So Harley Davidson, it's an iconic brand, obviously, of manufacture. And the whole value proposition is based on a customer. Each of the buyers and drivers want to have a special breed and special configuration of the custom bike. But before IoT, it used to take hardly up to a year and a half, up to 18 months from the time you would want to place an order for a new configuration to when they actually deliver the product.

And the reason for it is that they actually have had operating what I jokingly call [inaudible 11:56] data islands. Even on the plant floor, they had different stations that were not connecting that the data was not flowing. So as a result, the process were very sequential and very manual. And the same goes from for the rest of a value chain from ordering all the way to delivery.

So in this case, a couple of folks from different walks of life in one of the plants in US got together and there was an unusual bunch. So there were folks from operational technology, and IT and logistics and finance. They got together in one room and said, okay, let's fix it, let's improve our operations. And they started by basically connecting all of the devices and processes with one plant on one network, putting the automation and optimization analytic software on top. And then they started expanding beyond that plan.

So the long-story short, when the data started flowing, they were able to start optimizing their processes. They were able to reduce the time for a new configuration to get introduced from, again, as long as 18 months to as little as two weeks. The inner process, they were able to reduce the time to troubleshoot problems on the manufacturing floor from days and weeks to minutes. And probably equally significant is they improve the bottom line by 2-3%. So that's one example.

Erik: You said this was not so much started from board or C suite, but you said this was a group of operational leaders that got together to brainstorm how to solve this?

Maciej: Correct. So there was very much an example of bottoms-up approach. But I can give you maybe another example, in this case was a predictive analytics. Again, in a manufacturing industry, so in this case, General Motors, they use around 30,000 robots mostly from a company called [inaudible 13:54] a Japanese prefecture. And typically, how the robots are installed on the manufacturing floor is that [inaudible 14:01] will come in, they configure the robots and they sort of say, okay, and call us if you need help.

But GM and [inaudible 14:09] and a couple of other companies said okay, what if we start connecting these robots, then we can get some getting real time data and we can start looking at configuration and tradeoffs between use of material and how we use the robots? And they did that. They actually connected roughly half of the robots so far. And as a result of being able to analyze the data, General Motors actually reported that they were able to anticipate and actually reduce the number of stoppages of the production lines by around 100. And, on average, the system-wide basis, each of the line stoppages cost the company $2 million. So, if you can do it, obviously easy math, GM was able to save $200 million by implementing the zero on your downtime solution for product. So this was a top-down instead of a bottom up approach.

Erik: But I'm sure if I knew the producer of these robotics also has interest in that data because that can be fed back into their production and their R&D and so forth, how have you seen? I think this is a very common question right now. There's multiple people who can find use in data. But once you give data away, it's very hard to track how it's actually used and there's proprietary aspects to it. How have you seen this managed so that you can maximize value and minimize risks?

Maciej: There are a lot of cliché statements about data these days. But I think that approaching data strategically is the first aspect of it. And at the end of the day, more and more companies think of themselves as data companies versus a robot or car manufacturers or even all in gas companies.

So, thinking of user data judiciously and to your point, controlling the use and access is key. What I've seen is that, again, the first one is making sure that the data is improperly collected and stored. For example, all companies right now analyze some of their seismic data from 30 years ago because now they have more modern ways of extracting oil. But also, a lot of data is data in motion. It's a streaming data. And how do you actually get insights from the data before the data is basically becomes too old and loses value?

But then, to your point, also, there's a question of how you get access to the data to the vendors? So like, for example, Cisco, we deploy our routers and switches throughout our customers in environments. And many of our customers give us access to the data from routers and switches, not necessarily the payload, but the data that allows us to help troubleshoot and help anticipate problems with the network before they occur, so from the serviceability and from the management perspective.

There are also aspects of data need to be considered from the privacy and security perspective. And not only how you clearly communicate what you will be doing with the data and how the data is used, but also making sure that the best practices are being followed.

Erik: And you said there were two other use cases that you had in mind?

Maciej: Sure. Probably the most popular use case that I've seen is remote operations or remote asset management. Lots of great examples here, because you to come up with a system wide solution; in this case, you can have a surgical solution to solve a particular problem. If you don't mind, I can give you a completely different example outside of the industrial world.

So in this case, it's 150 store ice cream chain in central India. And the problem they had was they had power outages over there and then they sold generators in the stores. And sometimes there's a power outage at night or managers don't turn the generators on and as a result, the company was losing money because the ice cream was melting. But more importantly, they were creating a health hazard by freezing and refreezing ice cream.

So in this case, a very simple solution. They worked with one of their local startups who dropped us self-contained temperature sensors in each of these fridges, connected them to the networks and then connected them to a communication system. So now as temperature starts to go up, the employees and managers are getting messages through loudspeakers and SMS and phone calls, hey, do something, maybe you need to close the door or you need to turn the generator on. If the temperature continues to go up, these messages will get escalated all the way to the CEO.

I get a very, very simple solution. But what I loved about it is it sort of underscores the point of being hyperlocal. Because you actually need to think about the environment you're in what problems you're trying to solve versus let's say plopping a solution developing in Silicon Valley and dropping it in India and hope it works. And the other way was also very powerful, there this company actually saw a 500% return on investment in the first year of operation of this system.

And the last set of most common use cases that I've seen is around preventive maintenance. And we see the preventive maintenance solutions in manufacturing, in utilities, in agriculture. But the example I wanted to give you is from mining industry. I don't know if you've ever been to the open pit mine, but it's formidable site: you're basically looking at the hole in Earth two miles across one mile deep and there are these little specks going around the edges and these packs are these huge holding trucks that basically taller than a human being. And they basically carry minerals to the station and to the ports.

And every time one of these vehicles breaks down, it costs the company on average, a $2 million of lost revenues per day. And by the way, the only way to pull out this vehicle is to use another of these huge holding vehicles which doubles the cost. It may take days or weeks to figure out what the problem is, how to order parts, get the person and fix the problem.

So what the companies did, they installed a bunch of sensors around these vehicles started collecting data, feeding the data into the machine learning systems and then basically providing predictions. And initially, the systems when I got involved, were providing roughly 80% of sort of accuracy three months in advance, meaning 80% of problems were predicted up to three months in advance. But as the ML system started to learn more, in some cases, we can get to high 90% of challenges, meaning these vehicles will always get fixed before they can break down and very rarely they actually break down.

Erik: Let's go back to the third one, because I think this is a really interesting point that a lot of the companies that are driving the economy are small, relatively unsophisticated companies from an IT perspective. In the spot solutions, the one that you mentioned, excellent example of the mentality of let's do something simple to implement high ROI solving a very known problem, and then hopefully generate some data and that data might then be used to ideate and identify additional problems that can be solved and you can build on that as a foundation. I had a question.

I was in a podcast yesterday, but as the interviewer and somebody asked, how can smart cities connect everything? What is the strategy? This is a problem in mentality of why do you want to connect everything? Why not identify what are your long term problems as a city and then say has technology evolved in the past three years that now allows us to address this problem in a new way and let's solve that problem? I'm interested in your perspective on this. How do corporations make the decision between when is the right time to think big and implement a corporate-wide solution, and when's the right time to think fast and implement a spot solution that solves a specific known problem?

Maciej: And going back to the smart cities, it's sort of funny because I work for Cisco and we basically build networks. But I basically tell people stop talking about how many billions of devices we connect. It's not relevant. What's relevant is what business problems we solve. We're not connecting things just for the sake of connecting them. We connect them because basically, they allow the data to be to get generated and connected from the devices and we can analyze this data and turn them into solutions.

So in this case, if you look at smart cities, as an example, but what has been a challenge for smart cities is basically coming up with the use cases that were actually relevant to the cities. And cities are complex from the ownership and the right always perspective and so forth. And in North America, in Western Europe, we basically focus on three set of use cases: it was either parking, second or third most important revenue streams for the city lighting because one of the biggest expenses for the city or trash bins or trash pickup.

But each of those basically had a clear return on investment. And once you deployed a city wide infrastructure and network for one of them, adding an additional use case was relatively easy. If you think about that, again, this journey with the enterprises is I would typically argue that for your first project, you should actually start with something very focused like this ice cream example, because again, you're trying to minimize the factors of variability here. But at the same time, I've also seen a lot of examples of companies, for example, coming up with automation system in one factory and a different automation system in a different factory. Another one, they actually want to build a system across the factories, they actually have to redo things.

So once you've started on your journey, you have a team, you have the established process, you should be able to tackle the company-wide projects and then build the comprehensive architectures and so forth. So to summarize, I think when you pick a very surgical standalone projects, make sure that they don't require you to basically go back and redo these projects two or three years later. So pick the ones that you can then integrate into a broader architectures and ecosystem.

Erik: I was interviewing a gentleman from PTC, ThingWorx, a few months ago and he made the point he advises people in the space to think big but smart small, meaning think first around what system might hypothetically be useful and then you can start to address issues like standardization of automation system across factories, and so forth and then just start one.

‘Competitive Disruption’, this is one of the topics that you bring up in your book. We've been talking a lot so far about solving problems. The problems we've been really focusing on so far are often cost-based. And we've seen now some significant examples of disruption a bit more in the consumer space, the Ubers. What do you see in the industrial space in terms of either today, or in the five year time horizon in terms of the opportunity for competitive disruption of revenue models due to IoT deployments?

Maciej: Let me tell you sort of what I see today, and how we see these things projecting to the next five years. But what I see from the disruption perspective is, first of all, the disruptions of established market structures. So if you think about a lot of industrial world, like for example, industrial automation, traditionally, these types of markets, and industrial automation is roughly $200 billion market, have operated as one company developing the entire solution.

So if you wanted to set up a new factory or you already go set up a new train station, you will go to one vertically integrated conglomerate and they will develop the entire solution from A-Z, using often proprietary or semi-proprietary technologies. But with IoT, and accelerated cycle of technology innovation, we're now seeing customers increasingly asking the question is like, why should I be paying a premium for a custom solution based on yesterday's technology? And these customers are inviting specialists, whether they are horizontal specialists like Cisco or Oracle, or Intel or SAP or others, the vertical specialists as the answer geographic specialists to work together in developing best in class solutions based on open systems, open standards, open architectures, and in some way also future proof as well. So that's one big change.

The other big change I've seen is a balance of power. We traditionally have seen of customers buying technologies and solutions from vendors. But increasingly, what we see is customers actually don't want to just be buyers, they want to co-develop these solutions with you, which actually makes sense. The customer actually knows what problems they have and their environments. And technology providers also are experts in their technology. So by doing things together, by co-innovating, co-developing together, what I call a “co-economy”, we're getting the best of both worlds.

And then there's a business model disruption. A couple of years ago, we did a study. We reached out to some of our leading IoT customers and we asked them about the transition from the product to service-oriented business model. And roughly 886% of them actually said that they were moving towards the service-oriented business model. Why? Because now they can. They have all the data coming from the operations, and they can make an intelligent decision that instead of building a new assembly line, they can just buy the capacity that they need and basically bring the savings down to the bottom line.

So if you think about, that's what's happening today, but what I see happening in the future one is we're already seeing merger of technology industry and transportation industries. But basically every industry is becoming a technology industry and we see some interesting correlation between retail and manufacturing. So that's one trend.

The second trend is new value propositions. When you think about Harley Davidson example, they are a custom bike manufacturer. But increasingly, what you're seeing is the benefits of mass customization, mass personalization. And the cost of these actually being almost the same as your model T approach to products. And we see new industries being created, a combination of IoT and AI and blockchain and Fog Computing, creating industry of enterprise drones, for example. And of course, there's a migration to new business models.

Because as much as I'm sort of invested in the IoT success, I actually hope that five or maybe seven years from now we actually don't talk about IoT anymore. Like with ecommerce 15, 20 years ago, like nobody talks about ecommerce anymore, it's just part of everything that we do. I actually hope that with every company, large and small, in every industry sort of starts adopting IoT as part of their operation, and in five years, when you and I are having this conversation, we just say, remember this discussion about IoT, now it's just part of our process and we take it for granted.

Erik: Although we have IoT in our name, so we might have to rebrand at that point.

Maciej: I'm sure there'll be a new term that we will all can rally around.

Erik: You've participated in many, many deployments, it sounds like a good number of them have been successful, although I think Cisco actually released a report earlier in the year that that estimate what 75% of IoT deployments had failed to meet their objectives?

Maciej: Yet, yes.

Erik: Yet exactly. So what advice would you give an organization that starting this in terms of taking their first steps wisely in order to minimize the risk of failure?

Maciej: I sort of mentioned this at the beginning of a conversation, but I think it's so important that first of all, get started; second, don't be a hero, don't reinvent the wheel. We've discussed these four different use cases that are across the industries, most common use cases that your peers have already using. So connected operations, remote operations, predictive analytics, preventive maintenance, pick one of them and get going.

Thirdly, dream big, but start small, get support from your CXO and build the ecosystem and build a team. Fourthly, learn from your peers’ mistakes, because like with any new effort, there will be mistakes and most likely some of your peers have already made these mistakes. And lastly, we started with the discussion around the recipe for IoT success. Use this recipe because it's based on sweat of many deployments and best practices. And if you do that, you will maximize the chances of success.

And maybe the last point is that this type of journey is a team sport. It's not for loners. It's not for lone innovators in the garage. So, approach it comprehensively, bring your company, your industry, your peers with you on the journey. If you do all of these things, I'm pretty sure your first project will be successful and then you will not look in a rear mirror, you will just charge ahead.

Erik: Thanks so much for taking the time to speak with us, Maciej.

Maciej: Thank you so much, Erik. I really enjoyed the conversations, great question.

Erik: How can people learn more about the work that you're doing at Cisco, learn about your book reached out to you?

Maciej: I would suggest okay, you can obviously read the book, it's on all the major retailers. I also have a website, Maciejkranz.com, and also the LinkedIn group. So we actually have a lot of discussion on the building the Internet of Things LinkedIn group about the best practices and exactly the topics that we discussed here as well. So go read the book, join us at the LinkedIn group and ask for advice and help; we are here to help you be successful.

Erik: Thanks again. Have a great evening.

Maciej: Thank you so much, Erik.

Erik: Thanks for tuning in to another edition of the Industrial IoT Spotlight. Don't forget to follow us on Twitter at IoTONEHQ and to check out our database of case studies on IoTone.com. If you have unique insight or a project deployment story to share, we'd love to feature you on a future edition. Write us at Erik.walenza@Iotone.com

Thank you for joining us for another episode of The Ventures in Industrial IoT series. You can learn how GE Ventures goes beyond funding to support their partners in technology development and commercialization at www.geventures.com.

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