1 00:00:01,320 --> 00:00:03,960 Speaker 1: Hi everyone, I'm Mark Taylor and you're listening to Switch 2 00:00:04,080 --> 00:00:07,080 Speaker 1: on the BENF podcast. On December second of last year, 3 00:00:07,080 --> 00:00:09,719 Speaker 1: we did an episode on ethical cobalt Danta and I 4 00:00:09,760 --> 00:00:12,200 Speaker 1: interviewed Quacy and POFO about a recent trip he took 5 00:00:12,240 --> 00:00:14,280 Speaker 1: to the Democratic Republic of the Congo to look at 6 00:00:14,320 --> 00:00:17,000 Speaker 1: mines there. He talked about some of the minds he 7 00:00:17,000 --> 00:00:19,040 Speaker 1: saw that we're our teasonal minds where people were still 8 00:00:19,120 --> 00:00:21,759 Speaker 1: using pick axes and shovels to mind cobalt. But he 9 00:00:21,800 --> 00:00:24,639 Speaker 1: also mentioned about halfway through the episode about a mind 10 00:00:24,680 --> 00:00:27,400 Speaker 1: he saw that he called the most advanced mind he'd 11 00:00:27,400 --> 00:00:29,520 Speaker 1: ever seen. Now, today we're going to dig into that 12 00:00:29,560 --> 00:00:32,440 Speaker 1: topic a bit more. Yeah, I know pun intended. We're 13 00:00:32,479 --> 00:00:35,400 Speaker 1: going to talk about digitalization and mining. We'll talk with 14 00:00:35,440 --> 00:00:38,879 Speaker 1: BENF digital industry analyst Daniel lu about a report she 15 00:00:38,920 --> 00:00:44,240 Speaker 1: wrote with Quacy called Digitalization Strategies in Mining. BENIF clients 16 00:00:44,240 --> 00:00:46,240 Speaker 1: can find this report on beanf dot com, the BENF 17 00:00:46,240 --> 00:00:49,360 Speaker 1: mobile app, and the Bloomberg terminal. It has always Beneif 18 00:00:49,400 --> 00:00:51,239 Speaker 1: does not provide investment or strategy advice, and you can 19 00:00:51,280 --> 00:00:53,760 Speaker 1: hear the full disclaimer at the end of the show. Okay, 20 00:00:53,960 --> 00:00:59,120 Speaker 1: let's get to it. Hi, Daniel, him Mark, thanks for 21 00:00:59,120 --> 00:01:02,319 Speaker 1: coming in, Thanks for having me. Can we start off 22 00:01:02,360 --> 00:01:06,280 Speaker 1: today with the real basics. So why would anybody who 23 00:01:06,360 --> 00:01:10,679 Speaker 1: is not a miner care about digital advances in mining? Well, 24 00:01:10,760 --> 00:01:13,360 Speaker 1: I think the core of your question is why should 25 00:01:13,360 --> 00:01:17,200 Speaker 1: anybody who's not a minor right care about mining the industry? Okay, 26 00:01:17,280 --> 00:01:20,039 Speaker 1: So if you care about things, then you should care 27 00:01:20,040 --> 00:01:22,520 Speaker 1: about mining because mining is how you get the material 28 00:01:22,560 --> 00:01:26,759 Speaker 1: to make things. So if we expect economic growth, which 29 00:01:26,840 --> 00:01:30,639 Speaker 1: hopefully we all do, that usually translates into more things, 30 00:01:31,040 --> 00:01:36,720 Speaker 1: more new products, more technology, more material goods, and mining 31 00:01:36,800 --> 00:01:39,440 Speaker 1: is going to have to really boom and take off 32 00:01:39,440 --> 00:01:42,800 Speaker 1: to fuel that demand. So, in essence, if you care 33 00:01:42,800 --> 00:01:45,560 Speaker 1: about things, if you care about economic growth, you have 34 00:01:45,680 --> 00:01:48,080 Speaker 1: to care about mining. So the advances will talk about 35 00:01:48,080 --> 00:01:51,760 Speaker 1: today everybody should maybe be rooting for those, I think so, 36 00:01:52,120 --> 00:01:55,920 Speaker 1: I mean, if you're excited about electric vehicles, if you're 37 00:01:55,960 --> 00:01:59,040 Speaker 1: excited about the new iPhone next year, if you're excited 38 00:01:59,080 --> 00:02:04,720 Speaker 1: about really any new technology or any material good, then yeah, 39 00:02:04,760 --> 00:02:09,080 Speaker 1: you should be excited about these improvements in mining. That said, 40 00:02:09,440 --> 00:02:11,919 Speaker 1: what are the pressures that are facing miners to make 41 00:02:11,960 --> 00:02:14,280 Speaker 1: improvements that we will talk about in a second. Yeah, 42 00:02:14,320 --> 00:02:17,720 Speaker 1: so I think that maybe, um, it's a corollary to 43 00:02:17,840 --> 00:02:20,799 Speaker 1: kind of what's happened in oil and gas. So oil 44 00:02:20,840 --> 00:02:24,560 Speaker 1: prices have tanked and been very volatile the past few years, 45 00:02:24,680 --> 00:02:27,680 Speaker 1: and the same thing has been happening in the commodities markets, 46 00:02:27,680 --> 00:02:31,679 Speaker 1: so for iron, aluminum, cobalt, gold, everything like that. UM, 47 00:02:31,800 --> 00:02:37,960 Speaker 1: commodities have been in wild fluctuation. And obviously commodity prices 48 00:02:38,040 --> 00:02:42,000 Speaker 1: are directly linked to the revenue, the top line revenue 49 00:02:42,040 --> 00:02:45,639 Speaker 1: of all mining companies. So when commodity prices fall as 50 00:02:45,680 --> 00:02:50,280 Speaker 1: they have, that means mining companies lose a lot of revenues. 51 00:02:50,320 --> 00:02:52,520 Speaker 1: So in the past decade or so, if you look 52 00:02:52,560 --> 00:02:56,360 Speaker 1: at commodity indices, they've been swinging a lot and that's 53 00:02:56,440 --> 00:03:00,640 Speaker 1: resulted in up to loss of market cap for a 54 00:03:00,720 --> 00:03:06,200 Speaker 1: lot of major mining companies like Rio Tinto, Haliburton, Glencore, 55 00:03:06,400 --> 00:03:09,079 Speaker 1: a lot of these companies that are big players globally, 56 00:03:09,200 --> 00:03:11,880 Speaker 1: and a few of the ones that we write about 57 00:03:11,880 --> 00:03:14,560 Speaker 1: in our note. In the note, you also mentioned to 58 00:03:14,760 --> 00:03:17,880 Speaker 1: other challenges that are facing miners, a surgeon demand for 59 00:03:17,919 --> 00:03:21,040 Speaker 1: battery medals, and a focus on sustainability. Can you comment 60 00:03:21,120 --> 00:03:24,640 Speaker 1: on those how those are driving minors toick go digital. Yeah, 61 00:03:24,680 --> 00:03:28,680 Speaker 1: so more electric vehicles, more grid scale batteries on the 62 00:03:28,680 --> 00:03:33,280 Speaker 1: grid will require more battery metals like lithium and cobalt. 63 00:03:33,880 --> 00:03:36,360 Speaker 1: A lot of those are mine from artisanal minds today. 64 00:03:36,400 --> 00:03:40,400 Speaker 1: A lot of them involve humanitarian or environmental issues and 65 00:03:40,440 --> 00:03:43,680 Speaker 1: their supply change. So these two things really are linked. 66 00:03:43,720 --> 00:03:46,840 Speaker 1: To get more battery metals and to become more sustainable, 67 00:03:47,520 --> 00:03:50,760 Speaker 1: the mining company or the mining industry should look towards 68 00:03:50,880 --> 00:03:55,120 Speaker 1: technologies that can improve its operations, improve their yields, and 69 00:03:55,160 --> 00:03:59,400 Speaker 1: really improve transparency so that you can help make it 70 00:03:59,440 --> 00:04:03,160 Speaker 1: safer for humans. So going back to sustainability a little bit, 71 00:04:03,160 --> 00:04:05,520 Speaker 1: that's a growing trend all over the place, and it 72 00:04:05,520 --> 00:04:08,040 Speaker 1: seems to be mining is following suit or are they 73 00:04:08,120 --> 00:04:10,560 Speaker 1: kind of affording their own path. I think they're trying 74 00:04:10,600 --> 00:04:13,200 Speaker 1: to follow suit. Yeah, absolutely. I think a lot of 75 00:04:13,560 --> 00:04:17,000 Speaker 1: large investors, especially institutional investors, are paying a lot more 76 00:04:17,040 --> 00:04:23,279 Speaker 1: attention to efficiency, human rights issues, environmental stewardship in their operations. 77 00:04:23,360 --> 00:04:28,200 Speaker 1: And mining is a pretty materially intense industry. It creates 78 00:04:28,200 --> 00:04:30,440 Speaker 1: a lot of voice and it creates a really really 79 00:04:30,480 --> 00:04:34,720 Speaker 1: big impact on the landscape. So, yeah, there is in 80 00:04:34,839 --> 00:04:38,240 Speaker 1: increased E s G pressure across the board. But mining, 81 00:04:38,240 --> 00:04:40,320 Speaker 1: I think has a really big onus to use more 82 00:04:40,320 --> 00:04:45,680 Speaker 1: technology to improve its environmental footprint. So why is digitalization 83 00:04:45,800 --> 00:04:48,360 Speaker 1: the way to go for miners to accomplish these goals 84 00:04:48,839 --> 00:04:51,400 Speaker 1: or is it? Or there are other ways they could advance? Well, 85 00:04:51,440 --> 00:04:54,520 Speaker 1: I think number one, we should look at mining as 86 00:04:54,640 --> 00:04:57,640 Speaker 1: just one of several industries that are becoming interested in 87 00:04:57,720 --> 00:05:04,080 Speaker 1: digital technologies. So may manufacturing, power, utilities, oil and gas, transport, shipping, 88 00:05:04,080 --> 00:05:06,960 Speaker 1: you name it. All of these incumbent heavy industries are 89 00:05:07,000 --> 00:05:10,880 Speaker 1: becoming very, very interested in using new technologies to just 90 00:05:10,960 --> 00:05:14,120 Speaker 1: become leaner and better. And in a lot of regions, 91 00:05:14,240 --> 00:05:17,680 Speaker 1: especially in places like China and Germany, with a lot 92 00:05:17,720 --> 00:05:23,160 Speaker 1: of support for industrial technology, you as a company need 93 00:05:23,279 --> 00:05:26,560 Speaker 1: to adopt more digital technology if you want to compete 94 00:05:26,560 --> 00:05:28,919 Speaker 1: into the future. So for a lot of industries and 95 00:05:28,920 --> 00:05:31,640 Speaker 1: a lot of regions, going digital is no longer a 96 00:05:31,720 --> 00:05:34,560 Speaker 1: question of if or should I It's a question of 97 00:05:34,760 --> 00:05:38,680 Speaker 1: when or how, which I think is the natural next step. 98 00:05:38,920 --> 00:05:42,240 Speaker 1: And the next question, how are they going digital? Yeah? 99 00:05:42,640 --> 00:05:45,160 Speaker 1: So I have a lot of examples from our research, 100 00:05:45,320 --> 00:05:48,440 Speaker 1: but let's maybe start from the basics. So one of 101 00:05:48,480 --> 00:05:54,880 Speaker 1: the most basic technologies in industrial digitalization is sensors. Sensors 102 00:05:54,960 --> 00:05:58,919 Speaker 1: capture signals from the physical world and turn it into 103 00:05:59,279 --> 00:06:03,640 Speaker 1: digital So there's a lot of applications for sensors in mining. 104 00:06:04,600 --> 00:06:07,920 Speaker 1: Um One tidbit that I learned is that for every 105 00:06:08,200 --> 00:06:10,760 Speaker 1: unit of material that you dig out of the ground, 106 00:06:10,920 --> 00:06:14,000 Speaker 1: only one percent of that is usable at the end. 107 00:06:14,600 --> 00:06:18,919 Speaker 1: So that means everything that you dig up and transport 108 00:06:19,000 --> 00:06:22,480 Speaker 1: and use chemicals to refine needs to be taken care 109 00:06:22,520 --> 00:06:26,400 Speaker 1: of somehow so or one of the interesting applications for 110 00:06:26,440 --> 00:06:29,360 Speaker 1: sensors that we looked at is putting sensors on the 111 00:06:29,440 --> 00:06:33,080 Speaker 1: lip of a shovel truck. So when that truck digs 112 00:06:33,120 --> 00:06:37,640 Speaker 1: a new bucket of material from the ground, these sensors 113 00:06:37,680 --> 00:06:42,600 Speaker 1: can send kind of X ray beams into the material 114 00:06:42,640 --> 00:06:44,760 Speaker 1: and the dirt in the bucket, and then it can 115 00:06:44,800 --> 00:06:49,159 Speaker 1: instantaneously show you, the driver or the operator, how much 116 00:06:49,400 --> 00:06:52,559 Speaker 1: of the material in that bucket is, for instance, gold, 117 00:06:52,680 --> 00:06:55,920 Speaker 1: how much is cobalt, how much is aluminum? Basically how 118 00:06:56,000 --> 00:06:59,599 Speaker 1: much is how much does it contain what you're looking for? 119 00:07:00,080 --> 00:07:03,880 Speaker 1: And then you as an operator can then make the decision, oh, 120 00:07:03,920 --> 00:07:06,400 Speaker 1: there's too much waste in here. It's not worth it 121 00:07:06,440 --> 00:07:08,880 Speaker 1: for me to spend so much fuel to transport it, 122 00:07:08,960 --> 00:07:11,720 Speaker 1: so much water, so many chemicals, so much power and 123 00:07:11,800 --> 00:07:15,120 Speaker 1: electricity to process it. Or if it has above a 124 00:07:15,160 --> 00:07:17,840 Speaker 1: certain threshold, then that makes you helps you make a 125 00:07:17,880 --> 00:07:21,400 Speaker 1: smarter decision about where to send that truck of material, 126 00:07:21,600 --> 00:07:25,440 Speaker 1: so scan it, then choose to dump it or ship 127 00:07:25,520 --> 00:07:28,880 Speaker 1: and then it just enables smart routing. Basically, it helps 128 00:07:28,920 --> 00:07:34,640 Speaker 1: you avoid so much more downstream power, chemicals, water, energy 129 00:07:34,760 --> 00:07:39,960 Speaker 1: that you maybe would have otherwise wasted. Another cool technology 130 00:07:40,280 --> 00:07:44,840 Speaker 1: is drones and robots. So in mining, oftentimes you'll dig 131 00:07:44,920 --> 00:07:47,280 Speaker 1: up a whole bunch of material and then you'll just 132 00:07:47,720 --> 00:07:50,280 Speaker 1: basically leave it in a stockpile on your site and 133 00:07:50,280 --> 00:07:53,760 Speaker 1: then get to it later for processing. So humans have 134 00:07:53,840 --> 00:07:57,360 Speaker 1: to walk around and take manual measurements or use their 135 00:07:57,400 --> 00:08:00,200 Speaker 1: eyes to gauge how much material is in a aisle 136 00:08:00,280 --> 00:08:02,640 Speaker 1: of rock. That takes a lot of time, and that 137 00:08:02,680 --> 00:08:05,520 Speaker 1: takes a lot of people just walking about, sometimes an 138 00:08:05,560 --> 00:08:09,240 Speaker 1: inclement weather, sometimes maybe in dark, dangerous conditions. You can 139 00:08:09,280 --> 00:08:11,920 Speaker 1: do that much faster, much easier with a drone that 140 00:08:12,000 --> 00:08:15,360 Speaker 1: just flies overhead. It uses a smart camera on board 141 00:08:15,920 --> 00:08:19,840 Speaker 1: to make an estimate of the volume of material. That 142 00:08:20,040 --> 00:08:23,280 Speaker 1: is in a stockpile, and then that gets you information faster. 143 00:08:23,400 --> 00:08:25,920 Speaker 1: That gets you information much more frequently, which is great. 144 00:08:26,240 --> 00:08:30,080 Speaker 1: Same thing with robots. So precision mining is something that 145 00:08:30,120 --> 00:08:32,480 Speaker 1: a lot of companies are really really interested in. It 146 00:08:32,520 --> 00:08:35,960 Speaker 1: means they're not just blindly digging material out of the ground. 147 00:08:36,120 --> 00:08:37,920 Speaker 1: I'm not saying that they are now, but they can 148 00:08:37,920 --> 00:08:40,360 Speaker 1: get better, right, they can become more accurate, and then 149 00:08:40,400 --> 00:08:44,760 Speaker 1: if you have really agile robots that can dig in 150 00:08:45,120 --> 00:08:48,840 Speaker 1: very very precise ways, that helps you reach your ultimate 151 00:08:49,240 --> 00:08:55,800 Speaker 1: mineral better. Probably the hottest technology in the mining industry 152 00:08:56,160 --> 00:09:00,120 Speaker 1: is autonomous haul trucks. So these are the trucks that 153 00:09:00,200 --> 00:09:03,959 Speaker 1: transport the mineral that you just dug up from your mind. 154 00:09:04,320 --> 00:09:08,160 Speaker 1: Then they transport it to your processing site. Now, these 155 00:09:08,200 --> 00:09:10,520 Speaker 1: are the big, giant trucks with the wheels that are 156 00:09:10,520 --> 00:09:15,120 Speaker 1: bigger than people. Okay exactly. So I think a lot 157 00:09:15,160 --> 00:09:17,280 Speaker 1: of the public and a lot of I didn't know 158 00:09:17,320 --> 00:09:20,000 Speaker 1: this before, but the mining industry has been using fully 159 00:09:20,040 --> 00:09:24,600 Speaker 1: automated hall trucks for a decade or more. Autonomous. Fully 160 00:09:24,640 --> 00:09:30,480 Speaker 1: autonomous so no driver there. Either they travel on a 161 00:09:30,559 --> 00:09:34,800 Speaker 1: preset GPS route or they can be remote controlled by 162 00:09:35,160 --> 00:09:37,720 Speaker 1: you know, like a joystick from a control tower, which 163 00:09:37,760 --> 00:09:40,800 Speaker 1: is really cool and I think that UM sounds fun. 164 00:09:41,520 --> 00:09:45,120 Speaker 1: So that's been a really great application of technology UM 165 00:09:45,200 --> 00:09:49,520 Speaker 1: and it's spreading through that to those to be autonomous. 166 00:09:49,640 --> 00:09:52,920 Speaker 1: It's been popular. For instance, for one company that we 167 00:09:53,000 --> 00:09:55,960 Speaker 1: profiled called Frio Tinto, a lot of their operations are 168 00:09:55,960 --> 00:10:00,599 Speaker 1: in Australia, so compared to other major mining companies, Australia 169 00:10:00,640 --> 00:10:06,120 Speaker 1: has a really high cost of labor. So for Rio Tinto, 170 00:10:06,960 --> 00:10:10,320 Speaker 1: if you can automate as many processes as you can 171 00:10:10,600 --> 00:10:14,520 Speaker 1: and avoid high labor costs, that directly translates into their 172 00:10:14,520 --> 00:10:17,520 Speaker 1: bottom line. And it's not just cost right, it's also 173 00:10:17,600 --> 00:10:22,400 Speaker 1: removing humans from potentially risky or dangerous situations. On the 174 00:10:22,440 --> 00:10:26,280 Speaker 1: equipment side, if you can put data and autonomy into 175 00:10:26,320 --> 00:10:29,560 Speaker 1: that truck, you can route it. Again, it's with smart routing, 176 00:10:29,640 --> 00:10:32,480 Speaker 1: so you can route it better, less wear and tear 177 00:10:32,720 --> 00:10:35,679 Speaker 1: on the internal gears and tires, and sometimes you can 178 00:10:35,720 --> 00:10:39,400 Speaker 1: see up to forty or increase in the lifespan of 179 00:10:39,440 --> 00:10:41,920 Speaker 1: your tires, which is really cool. So it sounds like 180 00:10:42,040 --> 00:10:44,240 Speaker 1: each one of these things is a percent here, percent 181 00:10:44,320 --> 00:10:47,040 Speaker 1: there that really adds up to make a real impact 182 00:10:47,040 --> 00:10:49,439 Speaker 1: on the company's bottom line, I think. So, yeah, I 183 00:10:49,760 --> 00:10:53,160 Speaker 1: think there's no magic bullet now that gets you with 184 00:10:53,200 --> 00:10:57,640 Speaker 1: one technology yield. But yeah, it's it's a conglomeration of 185 00:10:57,679 --> 00:10:59,800 Speaker 1: a lot of different things. And that's actually a good 186 00:10:59,800 --> 00:11:04,680 Speaker 1: point is that many companies are experimenting with bringing a 187 00:11:04,880 --> 00:11:08,839 Speaker 1: suite of these technologies altogether. So a lot of companies 188 00:11:08,920 --> 00:11:15,720 Speaker 1: are experimenting with fully, fully automated and fully electric underground minds, 189 00:11:15,800 --> 00:11:19,240 Speaker 1: which is really really cool. So underground minds are tend 190 00:11:19,280 --> 00:11:23,280 Speaker 1: to be more risky, right, um, and they tend to 191 00:11:23,280 --> 00:11:27,800 Speaker 1: involve more investment. They start as open pit or above ground, 192 00:11:27,960 --> 00:11:31,080 Speaker 1: and then once they run out of easy to dig 193 00:11:31,320 --> 00:11:34,760 Speaker 1: material or mineral, then they move underground. So it's almost 194 00:11:34,760 --> 00:11:37,880 Speaker 1: an evolution of the mine. Um. So a lot of 195 00:11:37,960 --> 00:11:41,959 Speaker 1: companies are experimenting with going fully autonomous, fully electric for 196 00:11:42,080 --> 00:11:44,400 Speaker 1: underground minds, which I think is really cool. And again 197 00:11:45,000 --> 00:11:49,600 Speaker 1: there's that human element of an improving workers safety and 198 00:11:49,840 --> 00:11:54,160 Speaker 1: improving impact on the environment as well. So last Sunday, 199 00:11:54,240 --> 00:11:56,640 Speaker 1: I was eating lunch and I was I was watching 200 00:11:56,760 --> 00:12:02,200 Speaker 1: YouTube confession and I found myself watching a Bloomberg short 201 00:12:02,280 --> 00:12:06,840 Speaker 1: documentary on space mining. I saw that you did. It 202 00:12:06,880 --> 00:12:09,640 Speaker 1: was fantastic. You know, shout out to the Bloomberg media 203 00:12:09,679 --> 00:12:12,480 Speaker 1: team for making it. It was really fantastic. And one 204 00:12:12,520 --> 00:12:14,240 Speaker 1: of the points they made was that a lot of 205 00:12:14,280 --> 00:12:18,079 Speaker 1: the technologies that they are working on for space mining 206 00:12:18,120 --> 00:12:21,600 Speaker 1: may not ever mine an asteroid, but they are being 207 00:12:21,600 --> 00:12:25,640 Speaker 1: able to be used on minds here on Earth. Where 208 00:12:25,640 --> 00:12:28,000 Speaker 1: are most of the advances coming from. So are these 209 00:12:28,040 --> 00:12:31,160 Speaker 1: being you know, developed specifically for mining or from space mining, 210 00:12:31,240 --> 00:12:34,400 Speaker 1: or they borrowing from other industries? No, I mean, believe 211 00:12:34,480 --> 00:12:37,280 Speaker 1: it or not. Even though mining has been using autonomous 212 00:12:37,320 --> 00:12:39,960 Speaker 1: hall trucks, for instance, for a decade, a lot of 213 00:12:39,960 --> 00:12:43,760 Speaker 1: other industries are very very far along. And that's one 214 00:12:43,800 --> 00:12:46,800 Speaker 1: thing that I think the mining industry has been really 215 00:12:46,840 --> 00:12:51,000 Speaker 1: good at is looking at other industries, other pure industries 216 00:12:51,040 --> 00:12:54,320 Speaker 1: sometimes and then learning from them, learning what they did well, 217 00:12:54,440 --> 00:12:57,280 Speaker 1: and learning what technologies had the most impact. A lot 218 00:12:57,360 --> 00:13:01,280 Speaker 1: of airlines, for instance, use predictive maintenance on their aircraft 219 00:13:01,520 --> 00:13:05,040 Speaker 1: to Yeah. So it's a piece of software that uses 220 00:13:05,080 --> 00:13:10,199 Speaker 1: AI to read the sensor data coming from each individual part, 221 00:13:10,240 --> 00:13:14,319 Speaker 1: each individual um or something. Yeah, and then if it 222 00:13:14,480 --> 00:13:17,559 Speaker 1: senses that, oh, the vibration is off by a tiny 223 00:13:17,640 --> 00:13:21,080 Speaker 1: little bit, it can predict I think that means that 224 00:13:21,160 --> 00:13:23,240 Speaker 1: the engine will break down, or I think it means 225 00:13:23,280 --> 00:13:27,560 Speaker 1: that this gear will start to become more ineffective in 226 00:13:27,679 --> 00:13:29,960 Speaker 1: two weeks time or something like that, and it'll raise 227 00:13:30,000 --> 00:13:32,080 Speaker 1: its hand and it'll send you an alert. And that 228 00:13:32,160 --> 00:13:35,360 Speaker 1: means you can schedule your operations and maintenance at much 229 00:13:35,440 --> 00:13:39,360 Speaker 1: much better times. You can avoid catastrophic failures, and you 230 00:13:39,400 --> 00:13:42,560 Speaker 1: can schedule your maintenance to happen maybe on downtimes when 231 00:13:42,600 --> 00:13:47,040 Speaker 1: you're not losing a lot of dollars by bringing that 232 00:13:47,520 --> 00:13:50,560 Speaker 1: machine down. So Mining has been learning a lot from 233 00:13:50,760 --> 00:13:54,040 Speaker 1: the airline industry on predictive maintenance and bringing a lot 234 00:13:54,080 --> 00:13:58,040 Speaker 1: of that technology to for instance, it's hall trucks or 235 00:13:58,840 --> 00:14:05,280 Speaker 1: the mills and processors in mining refineries. Um, same thing 236 00:14:05,480 --> 00:14:08,120 Speaker 1: for the automotive industry. I think Mining has done a 237 00:14:08,160 --> 00:14:12,559 Speaker 1: really good job at looking at automation in the automotive 238 00:14:12,600 --> 00:14:16,960 Speaker 1: manufacturing space and trying to figure out what it can learn, 239 00:14:17,040 --> 00:14:20,280 Speaker 1: what it can take from that. So I don't think 240 00:14:20,280 --> 00:14:23,280 Speaker 1: it's necessarily a bad thing that mining has been a 241 00:14:23,320 --> 00:14:26,920 Speaker 1: little bit late to the game to adopt digital technologies, 242 00:14:26,920 --> 00:14:29,120 Speaker 1: because it means they have all these great lessons to 243 00:14:29,200 --> 00:14:31,840 Speaker 1: learn from and maybe it gets them to their result faster. 244 00:14:32,000 --> 00:14:37,520 Speaker 1: Even you mentioned that Rio Tinto has has autonomous hauling vehicles. 245 00:14:38,040 --> 00:14:39,760 Speaker 1: Can you talk about some of the advantage that the 246 00:14:39,800 --> 00:14:43,120 Speaker 1: other competitors have made, So who is leading in digital 247 00:14:43,160 --> 00:14:47,640 Speaker 1: in mining across all technologies? From our research at least, 248 00:14:47,640 --> 00:14:50,760 Speaker 1: it seems that Rio Tinto is a pioneer and on 249 00:14:50,800 --> 00:14:53,640 Speaker 1: the leading edge. Is there a reason for that? Um? 250 00:14:53,680 --> 00:14:56,040 Speaker 1: I think it has to do with the fact that 251 00:14:56,120 --> 00:15:01,920 Speaker 1: they really did their homework and understood their own markets 252 00:15:01,960 --> 00:15:06,960 Speaker 1: really well and then identified automation as the technology to 253 00:15:07,080 --> 00:15:10,640 Speaker 1: make the biggest impact on their operations, and that a 254 00:15:10,680 --> 00:15:12,120 Speaker 1: lot of that has to do with kind of the 255 00:15:12,200 --> 00:15:15,360 Speaker 1: labor costs that we talked about earlier, UM, But they've 256 00:15:15,400 --> 00:15:21,720 Speaker 1: just been really methodical about applying automation to high value 257 00:15:22,520 --> 00:15:26,840 Speaker 1: segments and high value pieces of equipment over the past decade. 258 00:15:26,880 --> 00:15:28,920 Speaker 1: Like I said, they've had this program for ten years, 259 00:15:29,160 --> 00:15:31,800 Speaker 1: so they've been able to reap a lot of rewards. 260 00:15:32,080 --> 00:15:35,800 Speaker 1: So I think Rio as a leader there. We talked 261 00:15:35,840 --> 00:15:39,920 Speaker 1: a lot about sustainability earlier, and you know, there are 262 00:15:39,920 --> 00:15:42,000 Speaker 1: a lot of companies out there that are making carbon 263 00:15:42,040 --> 00:15:46,320 Speaker 1: neutral pledges and even carbon negative pledges, which is really exciting. 264 00:15:46,840 --> 00:15:49,080 Speaker 1: But the same thing is happening in mining, So Anglo 265 00:15:49,120 --> 00:15:52,120 Speaker 1: American is a good example of a company that has 266 00:15:52,160 --> 00:15:56,840 Speaker 1: a goal of making mining carbon neutral. In a recent 267 00:15:56,880 --> 00:15:59,360 Speaker 1: episode on the battery supply Chain, I believe it was 268 00:15:59,400 --> 00:16:01,880 Speaker 1: with James Rith, he talked about Dimeler wanting to come 269 00:16:01,960 --> 00:16:05,960 Speaker 1: up with a carbon neutral battery that would also involve 270 00:16:06,080 --> 00:16:10,720 Speaker 1: carbon neutral mining cobalt, I would assume, or lithium. So 271 00:16:10,760 --> 00:16:13,720 Speaker 1: how does how would carbon neutral mining work? Well, I 272 00:16:13,760 --> 00:16:16,160 Speaker 1: think in the near term it will probably involve a 273 00:16:16,240 --> 00:16:23,760 Speaker 1: lot of carbon offsets rather than technologies that actually avoid emissions, 274 00:16:23,800 --> 00:16:27,280 Speaker 1: but a lot of software. For instance, energy management software 275 00:16:27,520 --> 00:16:30,720 Speaker 1: in a mining processing plant can cut down on your 276 00:16:30,800 --> 00:16:34,120 Speaker 1: energy usage by so much and that can be really helpful. 277 00:16:34,880 --> 00:16:37,280 Speaker 1: They're also thinking about the same thing in terms of water, 278 00:16:37,400 --> 00:16:40,280 Speaker 1: so water use in mining is a huge issue, especially 279 00:16:40,320 --> 00:16:43,400 Speaker 1: because a lot of mining operations happen in water stressed 280 00:16:43,720 --> 00:16:47,560 Speaker 1: regions or countries. So there's a lot of innovation going 281 00:16:47,600 --> 00:16:51,280 Speaker 1: on with trying to help the industry be able to 282 00:16:52,280 --> 00:16:57,120 Speaker 1: get that one percent of material out of the one 283 00:16:57,720 --> 00:17:01,280 Speaker 1: percent that you dug up using as little water, as 284 00:17:01,400 --> 00:17:05,280 Speaker 1: little energy, as little chemicals as you can. So more 285 00:17:05,320 --> 00:17:08,720 Speaker 1: stuff is going to mean more mining, more advances, so 286 00:17:08,760 --> 00:17:11,640 Speaker 1: the mining companies can survive all this, uh this more 287 00:17:11,720 --> 00:17:14,280 Speaker 1: mining that's going to come. What's next? Is there a 288 00:17:14,280 --> 00:17:17,280 Speaker 1: big overarching theme you're seeing coming up? I think we're 289 00:17:17,280 --> 00:17:19,800 Speaker 1: still in the early days in mining and in a 290 00:17:19,800 --> 00:17:22,760 Speaker 1: lot of heavy industry overall. Actually a lot of industries 291 00:17:22,800 --> 00:17:27,080 Speaker 1: are just figuring out how to apply these technologies make 292 00:17:27,200 --> 00:17:29,920 Speaker 1: them work because a lot of these technologies, let's be honest, 293 00:17:29,920 --> 00:17:32,080 Speaker 1: they're very expensive because a lot of them are new. 294 00:17:32,640 --> 00:17:34,520 Speaker 1: So how to make them work and how to make 295 00:17:34,760 --> 00:17:39,160 Speaker 1: them cost effective? I think strategy as well. There hasn't 296 00:17:39,359 --> 00:17:45,040 Speaker 1: seemed to be one silver bullet digital strategy that has 297 00:17:45,320 --> 00:17:49,360 Speaker 1: emerged as the go to model in mining. I think 298 00:17:49,359 --> 00:17:51,560 Speaker 1: oil and gas is actually a little bit further ahead 299 00:17:51,560 --> 00:17:54,200 Speaker 1: in that, but it hasn't happened yet in mining, which 300 00:17:54,200 --> 00:17:57,199 Speaker 1: I think is actually really exciting. So a number of 301 00:17:57,200 --> 00:18:00,840 Speaker 1: the companies that we profile in our upcoming peace they're 302 00:18:00,880 --> 00:18:05,840 Speaker 1: experimenting with different strategies. They're experimenting with who takes care 303 00:18:06,080 --> 00:18:09,159 Speaker 1: or who takes responsibility for technology. Is that the central 304 00:18:09,200 --> 00:18:12,439 Speaker 1: office or is it each mining site? Or you know, 305 00:18:12,640 --> 00:18:15,439 Speaker 1: do I develop more technology in house so that I 306 00:18:15,480 --> 00:18:17,920 Speaker 1: can own it, or do I buy it from partners 307 00:18:17,960 --> 00:18:21,320 Speaker 1: who maybe further along, but obviously that's an additional cost. 308 00:18:21,960 --> 00:18:24,600 Speaker 1: So I think it's an exciting UM time because there's 309 00:18:24,640 --> 00:18:27,359 Speaker 1: a lot of figuring out left to do UM and 310 00:18:27,359 --> 00:18:29,920 Speaker 1: then just going back to what we said at the beginning, right, 311 00:18:30,200 --> 00:18:33,359 Speaker 1: mining is directly linked to economic output. Mining is one 312 00:18:33,359 --> 00:18:36,000 Speaker 1: of the oldest industries probably on Earth. It's probably the 313 00:18:36,000 --> 00:18:39,120 Speaker 1: oldest industry to find. You have to find the rock 314 00:18:39,160 --> 00:18:42,639 Speaker 1: to make the tool right right exactly. So in order 315 00:18:42,880 --> 00:18:47,040 Speaker 1: to create all of these promising new things, and in 316 00:18:47,160 --> 00:18:49,720 Speaker 1: order to create the world of tomorrow filled with all 317 00:18:49,760 --> 00:18:52,560 Speaker 1: these great technologies that we all want and look forward to, 318 00:18:53,760 --> 00:18:58,159 Speaker 1: it's a lot of that is going to depend on mining. Daniel, 319 00:18:58,359 --> 00:19:02,600 Speaker 1: Thanks for joining us. Thanks mor it's been fun. Bloomberg 320 00:19:02,640 --> 00:19:04,720 Speaker 1: an e F is a service provided by Bloomberg Finance 321 00:19:04,840 --> 00:19:07,679 Speaker 1: LP and its affiliates. This recording does not constitute, nor 322 00:19:07,720 --> 00:19:11,159 Speaker 1: it should it be construed as investment advice, investment recommendations, 323 00:19:11,240 --> 00:19:14,040 Speaker 1: or a recommendation as to an investment or other strategy. 324 00:19:14,080 --> 00:19:16,520 Speaker 1: Bloomberg an e F should not be considered as information 325 00:19:16,560 --> 00:19:19,840 Speaker 1: sufficient upon which to base an investment decision. Neither Bloomberg 326 00:19:19,880 --> 00:19:23,400 Speaker 1: Finance LP nor any of its affiliates, makes any representation 327 00:19:23,520 --> 00:19:25,919 Speaker 1: or warranty as to the accuracy or completeness of the 328 00:19:25,960 --> 00:19:28,960 Speaker 1: information contained in this recording, and any liability as a 329 00:19:29,000 --> 00:19:31,000 Speaker 1: result of this recording is expressly disclained