Greg Lilleyman’s position as Rio Tinto Group executive of technology and innovation, gives him a leading role in shifting paradigms within the mining industry. The 25-year Rio Tinto veteran took questions from CIM Magazine at the CIM Convention in May, which he attended as a plenary speaker.
CIM: What do you like most about your job?
Lilleyman: I’ve got a range of activities. Part of my role is to be accountable for global construction activity; all of that is very much here and now. At the other end of the spectrum, we are thinking up some of this new “Mine of the Future” work. At the end of the day, I’m motivated by building on the competitive advantage Rio Tinto holds over others in our sector through our focus on productivity and innovation.
CIM: What makes you suited to the diverse work you do?
Lilleyman: I like to be challenged. I don’t want to get up in the morning and know exactly what the day is going to bring. Knowing that one day I could be dealing with the start-up of a smelter in British Columbia and the next day I’m thinking about the use of technologies to address fatigue for truck drivers in our coal mines, having such a wide ranging role really appeals to me.
I also think my engineering background gives me a broad interest in lots of areas. I’m a construction engineer by training, not a mining engineer, so it isn’t just the specific technical mining aspects that interest me about working with Rio Tinto.
CIM: When did you first know that you wanted to devote your time to improving operations, as opposed to running them?
Lilleyman: The more I started working on some of the automation programs, the more I could see that we were missing opportunities in the broader Rio Tinto sense. Over the last five or seven years, more and more of my work has been around finding new and better ways of running our operations.
CIM: You have a data analytics centre in Pune, India – what do you hope to accomplish with this facility?
Lilleyman: There are a lot of data points on our processing equipment, trucks and mobile equipment. But we haven’t used them all, historically. We’ve got in the order of 900 haul trucks across a range of operations around the world. Each of those haul trucks has 200 or so sensors. Most of the maintenance activities on those sorts of equipment are the same sorts of checks and inspections that we’ve done for 10, 30, 40 years. For us it’s about reducing the number of manual inspections, but, then equally, capturing what’s going on with a wheel strut or wheel motor or whatever the case might be. You can find out before it fails and make an intervention, especially if you can find out a few months ahead. Equally, you can extend the lives of equipment that don’t need to be changed yet.
CIM: What is the first task of the data scientists working there?
Lilleyman: We’ve got a couple of our asset management experts out of my group working in India with the data scientists and with equipment manufacturers like General Electric, Komatsu and Cummins to start fine-tuning how can we go about doing this.
They’re going back over the last few years’ data. Some of those trucks might have had an engine failure. They’re going back in time to see if there is a common thread of things that might have occurred one week, one month, three months out in advance. They can then test any patterns in real time to see if we can monitor those challenges.
The data scientists don’t come with the skillset to know what to look for. You’ve got to have your maintenance professionals, your asset management team, who are the experts, who are asking the right questions and probing in the right area with the data scientists, who then bring the analytical skills and the data manipulation skillsets to find things that even a good smart engineer with a spreadsheet wouldn’t be able to see.
CIM: Where do you expect the data-driven approach to predictive maintenance to have the most value?
Lilleyman: Predictive analytics focuses on our iron ore operations in the Pilbara and the Kennecott Utah copper site. But in each case that just happens to be where we decided to start the pilot, rather than that there’s anything unique about those sites compared to others. It’s fair to say that our iron ore business is where we’ve put most of our early innovation focus, and it’s mainly because that’s where the greatest leverage is.
CIM: At the plenary discussion, you and others made the point that employees at every level need to be informed and respected when change happens. How will employees at the level of the operation interact with this data centre? What decisions will they be making on the basis of the information that’s coming out of it?
Lilleyman: We’ve got data scientists working with maintenance engineers and professionals who also then have to see the reality on the ground. They’re working very much with the management and supervision and maintenance technicians on the site. They are asking, for example, “What can we monitor online that means this or that data point will give us the same information as the inspection that you do physically on the truck?”
So, there are lots of interactions, whether it’s that or the operations centre that we deployed in Western Australia. We’ve got people a thousand miles away connecting with operators remotely by two-way radios, giving them instructions. Those two groups have to both respect and understand each other’s role. I think in all of those areas, you’re doomed if you’re not engaging with your workforce on the way through.
CIM: What are the most fundamental changes happening in the roles played by on-site operators?
Lilleyman: Operators who are still sitting within an automated operation have become more supervisors of the process. If you look at the excavator operator, they are now the conductor of the orchestra, which is the trucks interacting with their digger, and they are now supervising what’s going on. The operator is positioning trucks where they want them; they are defining the way that the mining process happens a lot more.
As a result, operators are able to sit back more objectively and look at efficiencies for the whole system. In the past they may have just focused on their own performance in driving a single truck. Now they can look at the movement of a group of trucks and the excavator to pick out opportunities they might not have been looking for before. Now they see a pattern that trucks aren’t getting back under the excavator as fast as they possibly could and maybe there’s a software change from the OEM that could fix this.
CIM: One of the projects you shared in your plenary talk was the wind farm at the Diavik mine, which was conceived and implemented largely by local manager Liezl Van Wyk and other staff at the operation. How do you encourage independent thinking and leadership at operations?
Lilleyman: We certainly expect that the local leadership at an operation will know their part of that business better than anyone else. So, of course they’re most likely to be coming up with the smartest ideas about what can improve their part of the business. This is why we encourage them to act and make decisions like they own the business themselves. By giving people that level of freedom and responsibility they come up with some wonderful ideas.