Podcast #123: Industry 4.0 Vision

Good day, and welcome to another edition of our digital industry leadership series. Today I’m excited to introduce Baz Khuti, the co-founder and strategic adviser of QiO, a company helping to make the industry 4.0 vision a reality. A technology and business visionary, Baz has held executive leadership positions at GE, CTO Cloud, Emerson Electric as Chief Architect, and Invensys as Chief Architect. Over 30-years he’s brought to market-leading software products from idea to industry standard, with six industrial software patents and several prestigious industry awards across the UK, USA, and Asia. Baz also holds an MSc in computer science.

Baz, welcome to our digital industry leadership podcast.

1:19    

Thanks, Ken, good to be here, and many thanks for the opportunity to share my perspectives and insights.

1:25   

As well, it’s great to feature a truly digital industry pioneer in that regard. So, let’s start off with that as a note, I must say how impressed I am with your overall digital industry leadership journey. What would you say is your red thread, and how has it informed your perspective of the digital industry?

1:45    

That’s a great question, and as I sat back and thought about it if you look at the two core strands which make that red thread, there’s the ability to see conversions of different technologies coming together. So, the dependencies between these technologies have a knock-on effect of those technologies on each other, which is what really, I’m good at and understood from the very beginning, and then presenting that in context to business leaders. So, removing the complexity of it, but simplifying as what this means for a business executive so that we can capitalize and own market share and so on. So, it’s really a convergence of the internet, the cloud, and the cloud to Big Data, now Big Data with AI, and as we go into 5G how all those interconnects and then provide an opportunity from a business context perspective. That’s the first thing.

2:51     

The second thing is, a lot of companies make efforts on innovation and innovation as a process, but really when you have that perspective, technology convergence is occurring and will occur, then you’re really being creative. I really see myself as a creative guy who is able to bring together different technologies at the right time, to allow companies to get the material benefits from it. Creativity is normally associated with musicians, artists, and so on, not with software architects. But if you look at the complexity of what we have to simplify and bring to market, it requires a heck of a lot of creativity to simplify that.

3:43    

And so bringing that message into the market so that the strand can be understood by, and a story brand can be understood by our clients is really-really important. So, stringing those two things together has really brought me the canvas in which to architect, and then the paint is really the software that is making it a reality. I hope those two strands make sense, yeah?

4:08   

They do, I love your analogy, I might just call it ‘Just in time creativity’ in some sense, right. As you say, ‘Painting just in time on that canvas’. It’s rare, at least from my perspective, to find somebody who has held similar roles across three digital industry leaders, especially over one-and-a-half decades, so again Chief Architect at Invensys 2004, Chief Architect at Emerson through 2010, and CTO Cloud GE through 2015. That gives you a unique perspective, what were some of the key trends that you saw emerging over that time, especially relevant to OT?

4:47    

Yes, and digital. Really, there’s no wrong or right way here in my view. All are at the point in time which were trying to do the right thing. In Invensys we were ahead of our time, we were pushing ideas to our customers and the technology wasn’t always there, so pre-cloud in that case and pre a lot of the Big Data technologies we deal with now. So, kind of innovating and bringing ideas to our customers.

5:26    

Emerson was more pragmatic, it was really technology investment that was needed to continue to grow, and growth was a key factor, Emerson in my time there went to a global company. Having the IT infrastructure as well as the various applications into the market, to own market share and grow in certain markets especially Asia, required a lot of investment in technology. The approach that Emerson took is the problem first, what is the problem we’re trying to crack, and put that at the forefront with the customer, and then figure out what technology and options are available, so a very pragmatic approach to moving to a solution selling approach.

6:18    

GE, a short stay there, but in terms of what they were doing was really a visionary approach, to own the future, be the pioneers of the industrial internet, and create the path for others to follow. GE is a great company, they did that, they put it out there and said, ‘Look, this is what the art of the possible is,’ and people like me followed and learned a lot of the things which were done at GE around what we now call industrial internet, or industry 4.0, so I saw them as pioneers and was proud to be part of that.

So hopefully those are the kinds of key trends and behaviors I saw in those companies.

7:07   

You were at GE at a very interesting time, CTO of clouds, so my sense is you were probably there as a bit of a predecessor to Predix, because of course post-2015 that became the major focus, and I must say they truly were a visioner and a leader in this space. We’ve observed in prior podcasts that digital industry is driven in part by the virtualization of OT systems, much as the internet and cloud companies virtualized IT systems over the past two decades. To what degree do you see, heavy industrials especially, moving to the cloud?

7:47    

The OT side, if you look at OT as two dimensions, firstly the systems themselves, so SCADA systems, DCS systems, HMI systems, if you look at the architecture it’s really client-server architecture, and they benefit from virtualization. But then think about them actually working in the cloud, because of latency and bandwidth it’s going to be a tough act to do that, there are some movements in that space.

8:18    

The second aspect of OT is the data, and the data is proprietary, there are I think 160-odd protocols exist, I once had to create a spreadsheet that laid out all the protocols which were in different OT systems. They’re proprietary to hardware vendors, the industrial companies have really created their own proprietary standards out there for themselves and associated with that. So, pulling the data is out is hard, the standardization will take time, there’s no HTTP standard down there which we can all adopt and benefit from like on the internet, so over time that will make sense. But what companies are doing in terms of cloud and seeing it in virtually every industry and industrials, is the movement of that data to the cloud.

9:10    

I see cloud in my vocabulary in three areas, these are what I call puddles, ponds, and lakes if that makes sense.

9:21   

I love it.

9:21    

Because of the latency and bandwidth you’ve really got to have a puddle at some point in the factory or near the equipment so that you can actually make real-time changes with the operator, and then have an aggregation of that at the plant level, what I call the pond, then those two are synchronizing to a lake. So, people are jumping to the lake but forgetting about the pond and the puddle, if that makes sense. So, I see an OT hybrid cloud coming together, the hybrid is these three constructs above.

10:06 

Given what you’ve just described and your unique perspective over time, what do you see as the largest opportunities and perhaps challenges for the future of OT systems?

10:19  

I think the biggest opportunity, and you see it in our day-to-day lives is autonomous. The autonomous technology is in the car, there are semi-autonomous cars out there. I’ve just bought a new car and it’s got a lot of capabilities to help me from a safety perspective that just wasn’t possible. And so those autonomous capabilities in the plant – so think about an autonomous plant of the future, or a semi-autonomous plant in the future being operated.

10:49   

Now in IT, if you look at the IT side of things a lot of automation has occurred both in networks, in data centers, and in applications, and so on, so a lot of automation has been and still continues to occur. Then we created monitoring centers like SOCS (Security Operation Centres), or NOCS (Network Operating Centres), to oversee and manage that. So, if you think about the plant of the future and it’s an autonomous or semi-autonomous plant of the future, where you’ve got human orchestration or machine orchestration occurring, then how do you monitor and manage that from a command center in a remote location, and which the plant is 5G enabled, or a high degree of great wi-fi in there.

11:42  

So if you do that, you’re creating what I think will happen which is – my words again – sorry to be corny here but really what I call a MOC which is a Manufacturing Operations Centre where you are controlling the operation of the plant. And so, the great opportunity is there for manufacturers to move, and we’ve seen that in high-tech already, in China especially it's occurring today, and we’ll see that move into other industries as we go forward. So, I think that’s a great opportunity, moving to autonomous plants.

12:18 

This is probably a good lead in to a company you’ve co-founded, QiO, with stands for Questions Insights Outcomes, you co-founded this in 2015 really pioneering one of the first analytics firms focused on industry 4.0. What problem were you trying to solve and for whom?

12:37  

As you said earlier, decades of working in the industrials and virtually at every area, shop floor to boardroom, design. I said well what’s missing, why isn’t adoption faster in any industry industrial, and I said there’s a key piece missing, we’re forgetting about the operator. The hero at the end of the day is the engineer who makes it all happen, and if you look at what’s happened, the average experience is 20 – 30 years of experience, there has been a reduction in workforces, so the knowledge is concentrated in a few. The attraction of new talent into these industries is not so attractive, it's sexier to go into the start-ups in the Bay area and so on. And so, attracting the talent is an issue, but the knowledge is with the engineer, so how do we make the thumb essentially the engineer? So, we tried to focus on that, and then what we tried to do there is say, ‘Okay, how do we bridge the boardroom reality gap?’ So, the promise of digital in the boardroom with the PowerPoints which we put up there, and the efficiencies which can be gained, and the management, consultants, and the strategic consultants providing their views on what the opportunity is, to the reality and the time it takes to actually generate the value. So how to bridge that gap between the promise of digital, and the value of digital in the plant.

Then the third area was really around what I started to see, but really experienced as building QiO, we hired some of the best data scientists out there, and we were not getting what we needed, what we were getting was experiments, we were getting proof of concepts, we were getting pilots, but we weren’t getting into full-scale production in our early stages. So, we pivoted and changed to say there’s a gap between data-science and operationalizing data-science, and what we call applied AI into the hands of the operator. So QiO’s solutions solve those three things.

15:13  

Essentially, we provide AI applications that are the best friend of the engineer, so think about these as augmented analytics which the operator is interacting with, and it’s his best friend, it’s a personal coach, it’s a mentor – AI is not a threat, it’s a mentor to improve his operational efficiency and on the productivity, but yet improve safety and reduce risk. So, really that’s the target of the personal mentor to the operator, or the engineer, to use the software in the plant or at the edge. So, I think that’s the journey we embarked on, and have done.

15:59 

You must have done something right because you guys have accrued a string of commercial wins across companies like Rolls-Royce, and Lloyds Register, as well as industry accolades from Gartner, ARC Group, Frost & Sullivan. What were some of the use cases that brought you these wins and accolades?

16:19  

In Rolls-Royce which was our first client, and a marquee client, is really the fusion of data across multiple business units and with their customers, both AT and OT data, and then about 72 different data sources which historically had never been pulled together, a lot of that was in PDFs, and the business – coming back to the problem we were fixing, the problem we were fixing was around how pre-approval of warranty and maintenance work at the service centers, has turned around the time of the engines in the service center. So, how do we accelerate that and improve the customer service to our client?

17:09  

And so what that fusion of data did, and was using advanced software techniques, it pre-approved and had a 4X improvement in the business process from the turnaround of their engines and maintenance work completed. Whilst at Lloyds it was looking at the 250-year-old company in marine, it’s got huge amounts of datasets across the world, thousands of customers, it is a trusted brand across its customer base. And so, if you looked at their data sources, again it was indifferent repositories, different silos, it hadn’t been brought together, and the context because they’re in marine of what we did there was to customers obviously fuel is a big cost in the marine. So, if we can take tide external data, weather data, tide data, and as well as vessel data like the movement of the vessel, then we can predict fuel efficiency in terms of understanding the best time for a vessel to leave port because when it leaves port is when it's incurring the highest amounts of fuel cost. So, understanding tide, tide height, and tide wavelengths, and so on allowed us to reduce energy costs or fuel costs for marine operators.

18:33 

In some sense its reminiscent of – I think it was Beth Comstock at the time at GE who came up with this tagline of ‘The power in one and two percent,’ and of course, Bill Rue did a great job of espousing how much value there is in simply saving one percent in energy or two percent. And so, it is these little things that add up over time, and interestingly even the thought about when you leave the dock and how much difference that can make. So yeah pretty cool.

19:03 

I see that you’re often quoted discussing how to scale from concept to full-scale implementation, what have you seen are some of the key success factors for those companies that are fully able to make this transition?

19:18  

Yes, it goes back to that gift or mindset around the canvas and seeing a complex puzzle that needs to be put together, rather than all the detail within each software component. But then understanding that complexity to that puzzle, and pulling that together, really requires how do you know it’s going to work? Because you don’t! It’s a bet at the end of the day, and so the principles I have learnt, brutal lessons learnt in life is really experimentation and experimentation with your customers. I’ve always found customers are willing to co-create if you ask the right question in the right way and can build trust with them, and so customers co-creating and providing their data to you, providing their problems to you, opening up their ideas so you can co-create with them is a fantastic opportunity to validate the puzzles you’re trying to build. Some pieces may fall off and break, which is okay because you’re in experimentation.

20:33  

So, once you’ve done that and perfecting that it’s really ensuring that the glue between this is based on standards, and in going finding those standards, whether the customer can give you an idea of what those standards are in the industry. So, building the foundations of the puzzles and the glue between them, or the creases between them, is based on these standards, industry standards. So, whether it’s taking a bet on 2015 on Cloud-Native which really nobody moved onto at that time, and we said that is the future because it’s going to be a hybrid cloud model as we go, we can’t lock ourselves into any particular cloud provider as a software company, because you’re going to have to operate at the edge.

21:25  

So taking that there in 2015 and saying Cloud-Native found our principles and standards of what we’re going to adopt, was the right thing to do at the time. So, understanding industry standards both on the technology side, software, and so on, but also on the OT side and there are so many standards out there, so we have a digital twin like a Myers-Briggs for example for assets, which we call PARCS. One of the metrics in PARCS is reliability, well if you look at the IEEE it’s done a fantastic job on reliability, the Barta for asset life cycle modeling is just fantastic, it’s all there. So, when we’re building the reliability dimension in our PARCS framework, leveraging the IEEE standard was just the place to go to. Our energy efficiency standard is based on the energy star rating here in the US from the Department of Energy, and so it allows us to align that when somebody is using our software the creases are built on standards, and so that’s really the thing.

22:37  

Then lastly, how to what I call repeatability, and it’s not like scalability, people mix the two up. Repeatability is we all know tech transformation is built on those three pillars of tech, process, and change management, and you’ve got to make sure repeatability is built into a blueprint that allows that to occur. So, you could take it from one plant to another plant, to another plant, to another plant, or one asset to another asset, and so on, seamlessly. So, designing in that puzzle repeatability is foundational in what I saw. So, when you look at whole product design experimentation standards, and really repeatability out of the core capabilities I think are needed.

23:27 

We’ve observed COVID-19 as being a bit of a digital accelerator, what impact have you seen on the space you operate in, and what do you see is the longer-term impacts and perhaps opportunities?

23:42  

It’s been a massive impact obviously everybody in the world, and good to see now the vaccines coming through, and the light in the tunnel occurring and getting back to normality. But the new norm, what will that look like? What have we learned from the immediate impacts?

24:01  

Obviously remote working, and social distancing rules and so on, but some of the things I’ve observed and seen from my clients are not coming out, one of them is how do we have a magic box so that the maintenance engineering team, which can’t come into the plant anymore, can actually maintain the equipment? How do you have (VDI Virtual Desktop Images) to the equipment? People haven’t thought of that and that needs to be innovated, that needs to be created, that magic box needs to be created by the hardware manufacturers as a conveyor belt, or as a CNC machine and it needs maintaining, well it’s being maintained by a third party, and the engineer usually comes onsite to do that. But with COVID and the restrictions with that, that’s not possible, and that causes risk and safety issues.

24:58  

So, some of the things which companies are delving with, another area is people/customers are recognizing the knowledge is concentrated on a very few individuals, and if those individuals got COVID and things were to happen there, then there’s a material impact to the plant’s operations. So, concentration on knowledge is really key, those are some of the immediate impacts I’m seeing.

25:27  

The long-term to me is all about risk for understanding your supply chains and your manufacturing process investment, investment more in maybe semi-autonomous plants, and those manufacturing operation centers/command centers of the future. But it comes down to some of the basics, some of the plants out there have got very poor network connectivity in the plant, or access to the plant, so companies have invested in local area networks in their office buildings and so on, and we all benefit from that when we’re in the office, but when you go in the plant the equipment is not connected, the plant network is not… 10 gigabits or 100-gigabit network put in place, and then the wider area network to the plant is not the best it could be. So, investment in that, investment to implement wi-fi, industrial wi-fi, and 5G I think will be key changes as a result of companies looking at their industrial footprints.

26:43  

When one thinks of the industrial IoT, I’d say the killer use case is remote asset management, and I think you’ve just described it well, and that is why we’ve referred to the last year almost now as this digital accelerator because most of our portfolio companies are focused on that because we only focus on digital industry, so this remote asset management – how do I augment a human out on site, or now how do I effectively do what a human would have done out there? That’s where we’ve been seeing the great take-up, and your remote workers have seen the great take-up in collaboration tools.

27:24 

In our conversations kind of leading up to this, you had mentioned an impending next step, and forgive me if I’m putting you on the spot here but, can you share more about this next step?

27:36  

Yeah absolutely, you’ve seen my journey as an innovator and disruptor here, and I love Carlos Santana the great musician here, he has a saying which says to reinvent himself to stay young and fresh. And really to invent myself going to an area of industry capitalizing on all the knowledge I’ve got in healthcare, I think healthcare is just prime for adopting these technologies. If you think about the two of us, how many healthcare records do we have? Look at how many x-rays you may have had, or blood tests you may have had, can you access that yourself? You can’t. You get a report or a visit to the doctor, but I can’t get my own healthcare records as an individual and be accountable for my own health.

And so Precision Health is a key area I think of the future, and it all revolves around data. So, if I could pick some of the data thinking, and the data approaches around data augmentation and using AI in data to speed up, accelerate these digital journeys in the healthcare sector, I think it’s going to be a great area to explore and work in. I’m going to be advising and working with a company called Modak which has done this, it’s a fantastic company based out of Hyderabad in India, nearly 40 percent of the company is female, great CEO in terms of party, and I really got to know them over the last few months. I think they’ve got great opportunities to help healthcare companies and pharma companies make that data transformation, to help them build those data lakes out, to reduce risk and time.

29:39 

I have to think which podcast it was, somebody had mentioned we have better predictive analytics for our heavy assets than we do for our own bodies!

29:50  

Exactly, I mean it’s crazy, isn’t it?

29:52 

Then you think how many of those patterns are actually over the lake, the quality of the data, the availability of the data, having things in a standard format so you can look at them over multiple sources, so I can clearly see how your journey leads you to Modak, I think it’s a great next step and I think it’s a timely next one. We’ve featured more and more of what I’d just generally mid-tech, if you will, companies, and what you’re seeing is the people that are largely leading those have a foot in, of course, the physical science has been – or medical sciences, and the other type in some type of industrial background, so I think they’re pattern matching if you will, and overlaying those. So, it seems to me that may apply to your situation as well.

30:38  

Absolutely.

30:40 

In closing, can you provide recommendations of people, books, and/or other resources that inspire you?

30:47  

I think people are really coming down to kind of a spiritual side of me, and I’ve got some gurus which I’ve followed over my whole career, which have given me guidance and the eastern way of thinking effectively. So those have been foundational in terms of having faith, to get through the challenges of life. I moved from Kenya as a young child to England, a kind of refugee there, and then studied there, migrated there obviously and lived there, and then moved to America 20 years ago. So, I’ve moved from different continents and I do adjust and make a living essentially. So, a lot of faith is key there.

31:42   

In terms of books, they really reflect that thinking, and two come to mind. One is by a professor at Stanford University on creativity it’s called The Highest Goal, it was given to me by an Emerson executive who actually was leaving as I was joining, so he was in his final days and I was a newbie coming in. I was the first-ever Chief Architect in Emerson’s 130 years or so ever to take that role. He said, ‘Baz you’ve got to read this because it’s going to hold you in good stead,’ and as I’ve been looking at the book over the last 15-odd years I’m still scratching the surface of it. It’s called ‘The Highest Goal’ by Michael Ray, as I say he was a professor at Stanford.

32:30  

The other one was given to me by my wife as she saw me build my start-up, build this start-up called QiO, and the challenges I was experiencing as any leader, and the disappointments, and especially dealing with environments or circumstances I’d never had to deal with; so how do I get to know myself better, and the opposites of me, so what’s my natural nature? Then knowing what the opposite of that is and having that in mind. So, there’s a book called, ‘Leadership Gap’ by Lolly Daskel, it’s a great book, an easy read, it basically paints a picture of what’s your strength as a leader, but then it gives you a completely opposite view around what that strength means. I really didn’t look at the opposite view until I read that book. Hopefully, those give two indications.

33:35 

Those are two great recommendations. I must say that neither has run across my radar, so very original. Highest Goal by Michael Ray, and Leadership Gap by Lolly Daskel. Excellent. Well, Baz thank you for this insightful interview.

33:53  

Thank you very much Ken, and I appreciate the opportunity to share my learnings here, and hopefully, they help others in the digital journey embark as well.

34:03 

And maybe I’ll add spiritual journey, because it sounds like you’ve had one of your own, and you’re very inspirational I think as a result.

34:11  

Thank you.

34:12 

This has been Baz Khuti the co-founder of QiO, and if I may add, the perpetual reinventor of himself.

34:20  

Thanks, Ken.

34:22 

Thank you for listening, and please join us next week for the next episode of our digital industry leadership series. Thank you and have a great day.

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