Welcome to the Executive Data Series.
DXC created this new program to provide advanced insight into the data domain. In a series of conversations, DXC experts will explore data-driven decision making, and offer their perspective about what it takes to be successful in data, information and knowledge activities.
Mohammed 'Khal' Khalid, global advisory director at DXC Leading Edge, will moderate our series. Discussions will draw on research conducted by DXC Leading Edge and upon our executives' experiences working with customers.
In this conversation, Khal welcomes Chief Technologist, Smart Manufacturing Russell Duggan-Rees. We invite you to listen to their full conversation or, if you don’t have time now, to a short extract about finding the right approach to implementing Industry 4.0 technology. You can also find a full transcript of the discussion below.
The conversation
Q. It's my absolute pleasure today to introduce Russell Duggan-Rees to discuss this very interesting subject. Russell, would you like to tell us a bit about yourself?
A. I've been in the IT industry for over 25 years. I specialized in enterprise architecture within manufacturing and the public sector within those years, but during the last eight years I've been specializing in Industry 4.0, and I've had the pleasure to do it at DXC, HP and CSC. And I've had the privilege of working with some excellent customers to improve their processes using Industry 4.0.
Q. Thank you, Russell. So let's get started. Thinking about Industry 4.0, what are you finding that customers are asking for help with?
A. They're asking for help in many different areas, because each of our customers has a different maturity level in their understanding and their use of Industry 4.0. But in terms of the challenges that customers have, they're really asking for the ability to overcome both business and technical challenges. So they're asking us both to help them improve production performance, but also to understand which technologies to use, how to use them, how to integrate them, and how to secure them. We often get a mix of stakeholders that we engage with. If we engage with the business, they want their KPIs improved. If we engage with the IT department, they often want an understanding of the standards, architecture and security that comes with implementing these sorts of technologies.
Q. The terms “Industry 4.0,” “smart factories,” “smart manufacturing”; in a couple of sentences, how would you sum up what these terms really mean?
A. I would say that it is the use of new next generation technology in conjunction with existing technologies that manufacturers have, to improve production, supply chain, logistics and KPIs. So in essence, what has really happened within the industry is that these technologies have become available, commoditized and affordable. And when we bring them into manufacturing processes, they are giving the step change in improvements that the legacy technologies in manufacturing couldn't do; or could do, but for an extraordinarily huge amount of money.
Q. So the technologies have matured, they've commoditized, they're more available, they're at a lower cost point, and are more proven. From a business perspective, what is really driving the agenda for executives?
A. Just to touch on the technologies from a "why now?" perspective. The ability to do this has been around for many years, but the technology has really only become available recently. From a business perspective, the manufacturing industry is going through a hard time. There's a wide skill shortage within the industry, which means a lot of processes that can't be automated are harder to maintain and operate, and of course that comes with more expense. They've got soaring energy costs, there's inflation, they're dealing with increased interest rates, and there's instability in the supply chain. And many of them have not yet recovered from COVID-19, and they’re still recovering from historic crises like the 2008-09 financial downturn. There is a compelling argument within the manufacturing industry that they just cannot maintain their current trajectory with the current tools, infrastructure and capabilities they have to meet all of those challenges. Plus, there is huge demand on the manufacturing industry; there are more people and more demand on manufacturing products. So it's a real perfect storm, if you like, of all these things that are coming together. The fact that we've got this technology that can take them to the next phase in their improvements comes at the perfect time.
Q. You've mentioned a skills shortage, automation, energy costs, inflation, COVID recovery. What I think you're highlighting is that there is unused capacity that can be accessed. So why do these technologies get at that unused capacity, as opposed to more traditional process improvement activity?
A. In effect, the existing legacy – let's call it "Industry 3.0" – technologies gave little information about what was really happening within those processes across the supply chain, production and logistics. And that lack of visibility leads to insufficient decision making. In other words, a lot of the capacity that can be used or is available is either hidden – so they don't know that they've got the extra capacity – or just not used – because the point in time that they learn about it being available is maybe the next day, week or month. For companies that haven't adopted these technologies, they may recognize that a week ago they had 20% more capacity that they could have used, but didn't because they didn't know about it. So, for me, I see this like turning a light on and visually understanding where the business is at any point in time. What I see from Industry 4.0 are information-rich solutions that are turning that light on and giving the managers, operators and procurement leads that extra visibility and ability to make better and faster decisions.
Q. This whole topic of decision-making is dear to my heart. You touch on the timeliness of the insights. What kind of decisions do you see are being enabled?
A. We see many different use cases to improve decisions, but some of the more valuable ones are towards the in-shift production problems that customers have, or supply chain warehouse to production. The manufacturing industry runs on minutes and seconds. Some of them are 24x7 operations, they're getting tons of material in the door, so they're working against the clock all the time, and that leads to an inability, or lack of information, to make an optimal decision. They lose money through wastage, they're inefficient, and the energy – the optimization – is not there, because it gets used producing bad products.
From a decision point of view, it's understanding where the schedule is not being complied with and having that rich information to know the reasons why: Because there is an order that didn't have complete material in it; or, for example, the sugar or flour was contaminated; or the right people weren't available on their shift; or the machine’s going to go down. All of these things take ages – I'm talking sometimes half an hour to an hour's worth of decision making. But not only could they have predicted that there's going to be a problem with the machine, or someone that's unwell and can't come in, or the supply chain has sent bad material, they've got the ability to reschedule; they've got the ability to figure out how to dynamically reschedule this in order to keep to their target. And that's where I'm seeing, particularly in the factories, that information being used to stop unplanned events – which always happen – from happening. With this information and the decisions that they can make in real time, those unplanned problems are removed almost instantly, and that is a massive value to the manufacturing industry.
Q. So what I'm hearing is the step change improvements of Industry 4.0 technologies enable timeliness of insights into the decision making process, and ultimately enable dynamic rescheduling, for example.
A. Industry 4.0 can also automate those decisions, with systems that can analyze the complete production and take in many different parameters, and then give the decision makers options or the optimal decision to make based on these factors: cost, timeliness, amount of skills, the condition of the machines. So it isn't just about giving people better insights – that is just the first phase – but because Industry 4.0 has got such potential, it also leads to us automating those decisions, as well.
Q. That's absolutely fabulous to hear. As you know, I talk about the data metabolism and the Discover-Develop-Defend states. So given the wealth of experience that you and your team bring, how do think people should prioritize what should be done now or in the future? Ultimately, what do you think is the right approach?
A. Vendors out there. I know for a fact that there are over 300 IoT vendors, and the approach that is typically taken is that manufacturers aim to look at the vendors they know and the ones that independent analysts provide as "world class." And they'll go through the process of reviewing those. But then they have to integrate, secure, deploy and scale them. So we find that customers are challenging themselves with trying to build these capabilities and also adopt their architectures.
We're helping customers in accelerating that build and deploy and scale, and we're also helping them use best practice within the IT and OT industry area. That could mean bringing customers out-of-the-box platforms that they don't have to spend time designing, integrating, securing and supporting, which I see as a big, big challenge. I've seen customers take two years to develop that strategy – and that’s just the strategy. So that's the technical side.
Now from a business perspective, there is also a plethora of individual problems across production and distribution that combine to cause factory-level KPI issues. Knowing which problems to tackle first, which ones are having the biggest impact, the root causes of those problems – that is where we see a lot of manufacturing businesses struggle. They really do struggle, because if you imagine how many problems are on the shop floor, how many in the supply chain, how many in distributing the hundreds of thousands of products a day; just trying to find where to start, which of the problems is the biggest, and how they are reflected towards strategic business goals. So DXC is helping manufacturers with a methodology that brings them to a point where they've got all of the problems understood, the priorities of those problems understood – and understood strategically – but also which ones can be improved with Industry 4.0 capabilities.
Q. So as I hear you, gaining situational awareness, understanding the situation, bringing together that business perspective and the knowledge of the technology – particularly the technology platforms – and helping to do that prioritization, I would assume that given the experience that you bring to the sort of things you've seen before, you can identify patterns and then get to the root cause of those items and move further forward.
As you think about that journey, what would you define as the key elements of success?
A. Smart manufacturing / Industry 4.0 is about improving production, logistics and supply chain to produce more, at a higher quality but at a lower cost, ultimately improving the P&L and market share. So that's the ultimate goal. It's a big change when customers do these sorts of transformations; and it's not just a technical change, it's a cultural change. So I see senior management engagement and backing as the most important key to success with smart manufacturing. Those that don't get that senior stakeholder backing will fail. Nine out of ten times I have seen this, and that's because of the change that has to happen in the business to adopt these sorts of technologies, but also to change processes. I did say that there is some automation, but I was referring to areas where there is a human interaction with the information that we provide. When they don't choose to act on the decision or to use the information to change processes, then it fails. So it does need to come from the top.
The second most important key factor, as I just mentioned, is that the workforce needs to be brought in, as well. So you've got senior management backing, but the workforce need to be communicated with properly because this could be affecting their jobs – not just in terms of process change, but it could be threatening their jobs. So we really do help customers focus on this element, on the way they use these technologies. Some of our customers – on the shop floor, for example – have never used an IT system. They don't even have an ID to an IT system, and then we're asking them to use the information to change their work processes, their daily tasks. It's a big change for people. So senior stakeholder backing, workforce engagement, with a massive amount of communication so they understand how this is benefiting the business, how this is going to help them within their daily work.
Another key to success is that you should focus on each factory individually, but address the technology needs centrally. What I mean by that is that each of these factories is different. Very rarely do we see customers that have factories producing the same product that work with the same standards, the same infrastructure, the same maturity. So each factory is an individual entity with different maturity levels, different technologies, different people and different cultures – and you need to address priorities at a factory level.
Then address the technology centrally: don't have silos of technologies, or silos of standards within the technologies or within each factory. That really should be a top-down, centrally governed capability so that you can roll up this information for better decisions. For example: factory-wide reports that show other factories with unused capacity. If one factory is underperforming for a particular work order, you could then get that work order completed at the other factory. That kind of automated decision wouldn't be possible if you had different systems in each of the factories. So it's really important to manage that centrally.
Another key success factor is to work with partners that already have expertise in delivering smart manufacturing transformations. As I said before, it does take time to design in-house, integrate, choose the vendors and secure support. Partners are a key to success, because they can accelerate and lower the risk – not always lower the cost, but it certainly lowers the risk and the impact of failure.
Q. One of things you mentioned also is the cultural change piece, and from my own work, this notion of trust comes up quite a lot: trust in the information, trust in the insights. What are your key experiences around how to move the trust dial forward positively?
A. There's a whole range of reasons why there's a trust problem, and that comes from, yes, the data might not be right from other systems. That's rare, because we know how to deal with that. But if the standards and algorithms aren't correct and don't lead to the information that the business expects, then there's a problem. But also when the information tells them what they don't want to know: where it's been suboptimal, or where they've got huge hidden capacity, as you mentioned before, and management will say, "Well, you've been losing revenue because you've not been planning to the potential of the factory." But that's what Industry 4.0 in the culture piece is about: it doesn't matter, we're here now and we can move forward with that.
So we help with the trust, with ensuring those elements are there, that the data and its integrity are there, are timely and are coming from the right source. The standards that we apply to the data are absolutely key, because otherwise you're going to get questions about that particular KPI. And we've absolutely got to get that right, because if the enterprise doesn't trust the data, then they will stop using the system.
The other aspect of this is that gradual ability not to overexpose where the hidden capacity or the overspend has been. Again, we normally do that at a site level so the site management team can manage that before it gets given to senior management, who then start questioning. That's another reason why, typically, if you do this top down and you're asking the factories for a consolidated KPI dashboard straightaway, they will be nervous; because if they're being exposed, they haven't got the chance to rectify things before that information is seen. So we're helping them with those elements.
We also help with the communication of why change is necessary. Starting with the business case, communicating down to the right stakeholders the reasons for the change and how it's going to help.
We also do a lot of training, because the continual service improvement teams in manufacturing get drip-fed this information today. Without Industry 4.0 it’s drip fed, and then suddenly they get information at the speed of a hosepipe, and they struggle to understand and interpret it, and they struggle with the velocity of that data. So we help those teams interpret the data, and we train them on how to understand the dashboards, to understand and interpret the information so they can then make improvements within their processes.
Q. You touched on the discovery through a variety of data, the velocity data challenges you scale up and implement change, and then the volume of data that's needed to help automate decision making. But what I find fascinating is that this is really a cultural change program. And underpinning all of this is: engaging a culture of learning rather than blaming; the technologies, combined with a focus on the right KPI; bringing together the right people at the right time inside the organization, and with partners.
So with all of that, let me put you on the spot. If you were to meet younger Russell, what advice would you give to younger you?
A. The advice I would give to my younger self would be to take more low-impact risks. And I say that because I don't believe that any company would take high-impact risks towards innovation, so we need to focus on the lower impact risks, but take more of them. I'll give you an example. Working with a company that used to have an ROI of only 12 months, sometimes with the return on investment for a program of work that would improve the factory processes. They actually changed their culture and policies to allow for the possibility of not getting a return on the transformation they were going to do. They decided to do "small and quick" and to look at low-risk opportunities, so that if there isn't a return, they won't be penalized. That is the sort of approach that is needed in order to take innovation into a company. What I typically do see is companies that have got a big return on investment, financial value, or the transformation needs to be fast and in-year, they really do struggle getting these sorts of technologies in, and then they obviously struggle to start the journey. So I would say to myself, take more low-impact risks.
The other advice would be to trust my expertise, even if it's questioned. I have had the privilege of delivering some of the most advanced Industry 4.0 smart manufacturing solutions, and they've achieved their goal. But at the time, because it was pioneering, I did question my expertise in terms of whether it was the right solution, whether I chose the right vendor, whether we were looking at the right data, and whether it was the right use case.
Q. Two wonderful pieces of advice to younger Russell, and it certainly resonates with me, as well. I want to say thank you, Russell, for your time and energy today and for sharing your insights.
Listen to more in the Executive Data Series
The value of procurement data (Session 2)