1 00:00:02,000 --> 00:00:05,680 Speaker 1: Last week's episode, we discussed the future of connected transportation 2 00:00:05,960 --> 00:00:08,959 Speaker 1: and the future of connected mass transit. We heard from 3 00:00:09,039 --> 00:00:12,400 Speaker 1: Arthur douna Chief Innovation officer of Avis Budget Group, the 4 00:00:12,440 --> 00:00:15,240 Speaker 1: global car rental company that also earned the popular car 5 00:00:15,320 --> 00:00:19,040 Speaker 1: sharing service zip car, on the Cameron, co founder and 6 00:00:19,120 --> 00:00:22,400 Speaker 1: CEO of the startup Voyage, which is testing and expanding 7 00:00:22,400 --> 00:00:26,840 Speaker 1: the use of autonomous vehicles in retirement communities, and Marcus Velts, 8 00:00:27,360 --> 00:00:31,080 Speaker 1: he leads the Siemens Intelligent Traffic Systems business in North America, 9 00:00:31,600 --> 00:00:34,360 Speaker 1: which is actively innovating to change the way we use 10 00:00:34,400 --> 00:00:45,360 Speaker 1: public transportation. Everything is getting smarter, from our watches, to 11 00:00:45,440 --> 00:00:48,720 Speaker 1: our homes to our cars. In a more connective future, 12 00:00:49,240 --> 00:00:52,200 Speaker 1: even the way our favorite things get processed and made 13 00:00:52,440 --> 00:00:55,880 Speaker 1: will be getting smarter. Factories where machines can tell their 14 00:00:55,920 --> 00:00:59,080 Speaker 1: teams in real time their status and eventual need for 15 00:00:59,120 --> 00:01:03,560 Speaker 1: repairs or a future and manufacturing worker safety can be 16 00:01:03,600 --> 00:01:08,000 Speaker 1: bolstered by real time data collected from new perspectives. The 17 00:01:08,080 --> 00:01:11,200 Speaker 1: next generation of wireless innovation with future five G networks 18 00:01:11,560 --> 00:01:15,240 Speaker 1: will create new opportunities for connected factories and the ever 19 00:01:15,319 --> 00:01:19,520 Speaker 1: changing landscape of manufacturing. Thanks to support from team Able 20 00:01:19,640 --> 00:01:23,360 Speaker 1: for Business Today will explore our advancements in five G 21 00:01:23,520 --> 00:01:27,960 Speaker 1: connectivity will enable innovations in the manufacturing industry that could 22 00:01:28,000 --> 00:01:44,160 Speaker 1: shape the future of production. In general, consumers want the 23 00:01:44,240 --> 00:01:48,480 Speaker 1: products exactly customized to their needs, to their body type, 24 00:01:48,520 --> 00:01:52,280 Speaker 1: to their foot size, etcetera. And so Henry Ford's you 25 00:01:52,280 --> 00:01:53,920 Speaker 1: can have any car if you want, as long as 26 00:01:53,960 --> 00:01:57,800 Speaker 1: it's black, just doesn't fit anymore. So the future is 27 00:01:57,880 --> 00:02:02,360 Speaker 1: a lot more digital, it's a lot more short term 28 00:02:02,440 --> 00:02:05,000 Speaker 1: in terms of having to respond to orders more quickly. 29 00:02:06,400 --> 00:02:10,440 Speaker 1: That's Irene Patrick. She is the senior director of Industrial 30 00:02:10,480 --> 00:02:15,800 Speaker 1: Innovation in the Internet of Things Group at Intel. Intel 31 00:02:15,960 --> 00:02:19,000 Speaker 1: is one of the world the largest manufacturers of computer chips. 32 00:02:19,440 --> 00:02:22,560 Speaker 1: All kinds of companies use those chips to power their businesses, 33 00:02:22,760 --> 00:02:27,040 Speaker 1: including other manufacturers. It's Irene's job to understand the challenges 34 00:02:27,080 --> 00:02:30,720 Speaker 1: those companies face so Intel can build solutions to help 35 00:02:30,720 --> 00:02:34,160 Speaker 1: the industry adapt to the future. The economies of scale 36 00:02:34,160 --> 00:02:37,320 Speaker 1: production that we've been living off of for the last 37 00:02:37,320 --> 00:02:41,440 Speaker 1: several decades really don't meet the needs of customization, highly 38 00:02:41,480 --> 00:02:44,800 Speaker 1: personalized kinds of products, and so we've got to find 39 00:02:44,800 --> 00:02:49,000 Speaker 1: ways to help factories become a lot more agile. Factories 40 00:02:49,040 --> 00:02:52,680 Speaker 1: are not often thought of as being agile, but several 41 00:02:52,680 --> 00:02:56,880 Speaker 1: technologies are coming together to make production lines far more responsive. 42 00:02:57,639 --> 00:03:01,320 Speaker 1: This is often referred to as industry full point oh. 43 00:03:01,520 --> 00:03:06,040 Speaker 1: Industry four dot O is really giving workers from the 44 00:03:06,080 --> 00:03:09,840 Speaker 1: factory floor clear to the C suite a very real 45 00:03:10,200 --> 00:03:15,120 Speaker 1: presentation of the operating environment in real time that's derived 46 00:03:15,240 --> 00:03:20,560 Speaker 1: from data that's collected from sensors throughout the factory. Eventually 47 00:03:20,560 --> 00:03:23,320 Speaker 1: this will reach clear into the supply chain, into the 48 00:03:23,480 --> 00:03:26,840 Speaker 1: enterprise resource systems, and so industry four dot O is 49 00:03:26,840 --> 00:03:29,360 Speaker 1: really saying, do we have a digital model of the 50 00:03:29,360 --> 00:03:32,960 Speaker 1: physical world. Can I use that digital model to simulate 51 00:03:33,160 --> 00:03:36,000 Speaker 1: what might happen if I change something in the physical world? 52 00:03:36,400 --> 00:03:39,120 Speaker 1: And then how can I improve things in the physical 53 00:03:39,160 --> 00:03:42,720 Speaker 1: world based upon those digital simulations. So it's really all 54 00:03:42,760 --> 00:03:46,000 Speaker 1: based on data, and that data is connected through an 55 00:03:46,000 --> 00:03:50,320 Speaker 1: intricate system of senses and connected machines. As complicated as 56 00:03:50,360 --> 00:03:55,040 Speaker 1: it sounds, is all driven by something rather simple. Manufacturing 57 00:03:55,200 --> 00:03:58,880 Speaker 1: only happens because somebody wants to buy something, and as 58 00:03:58,960 --> 00:04:02,280 Speaker 1: people's ideas about what they want to buy change, our 59 00:04:02,320 --> 00:04:05,520 Speaker 1: production has to change also, and we've been very slow 60 00:04:05,520 --> 00:04:07,240 Speaker 1: to do that. By the way, in the industrial space, 61 00:04:07,760 --> 00:04:11,680 Speaker 1: Intel is in the business of enabling the manufacturing ecosystem, 62 00:04:11,720 --> 00:04:15,720 Speaker 1: so irone is focused on understanding challenges facing the entire industry, 63 00:04:16,080 --> 00:04:19,560 Speaker 1: so Intel can create solutions at scale. We don't sell 64 00:04:19,600 --> 00:04:23,680 Speaker 1: the manufacturers, We sell to their suppliers, and so much 65 00:04:23,720 --> 00:04:26,920 Speaker 1: of the power behind things, much of the compute power 66 00:04:27,520 --> 00:04:30,680 Speaker 1: is provided by Intel, but it takes an ecosystem to 67 00:04:30,720 --> 00:04:35,560 Speaker 1: deliver that. The ecosystem as it sits today, where individual 68 00:04:35,600 --> 00:04:38,960 Speaker 1: companies provide an ingredient, is not going to be effective 69 00:04:39,000 --> 00:04:44,600 Speaker 1: into the future. We can't project Intel's thirty year journey 70 00:04:44,600 --> 00:04:48,680 Speaker 1: toward digitization into an existing manufacturer who hasn't been on 71 00:04:48,680 --> 00:04:51,240 Speaker 1: that same journey, and so we really have to understand 72 00:04:51,279 --> 00:04:54,120 Speaker 1: what the pressing problems are at a whole bunch of 73 00:04:54,200 --> 00:04:59,920 Speaker 1: levels of digital maturity, today's manufacturing landscape, increasingly driven by automation, 74 00:05:00,200 --> 00:05:04,280 Speaker 1: robotics and three D printing, might well look unrecognizable to 75 00:05:04,360 --> 00:05:09,159 Speaker 1: Henry Ford. According to Irene, the factories of the future 76 00:05:09,240 --> 00:05:14,279 Speaker 1: will be more efficient, faster, and increasingly data driven. Of course, 77 00:05:14,640 --> 00:05:18,000 Speaker 1: to make data actionable, you have to collect it, process 78 00:05:18,040 --> 00:05:21,840 Speaker 1: it and deliver results, and that's where future five G 79 00:05:22,000 --> 00:05:26,520 Speaker 1: networks could play a crucial role. More bandwidth, faster, networks 80 00:05:26,520 --> 00:05:30,080 Speaker 1: and lower latency could allow more manufacturers to make better 81 00:05:30,080 --> 00:05:36,160 Speaker 1: decisions in real time, increasing efficiency and improving products. In 82 00:05:36,160 --> 00:05:39,719 Speaker 1: this episode, we'll look at the latest innovations in manufacturing, 83 00:05:40,120 --> 00:05:42,479 Speaker 1: explore what it means for a factory to be smart, 84 00:05:42,960 --> 00:05:46,960 Speaker 1: and meet an entrepreneurs using technology to improve workers safety. 85 00:05:47,800 --> 00:05:56,000 Speaker 1: I'm as Velosian. Welcome to this time tomorrow. Kara I 86 00:05:56,040 --> 00:05:59,080 Speaker 1: really pointed out that companies make things because people want 87 00:05:59,279 --> 00:06:02,160 Speaker 1: or need to buy something. But when you're buying something, 88 00:06:02,480 --> 00:06:04,680 Speaker 1: how often do you think about how it's made. I 89 00:06:04,720 --> 00:06:07,279 Speaker 1: think it's something we're all becoming more conscious of, you know, 90 00:06:07,360 --> 00:06:10,360 Speaker 1: making sure that products we use aren't causing too much 91 00:06:10,400 --> 00:06:13,360 Speaker 1: environmental damage and that the workers who make them have 92 00:06:13,640 --> 00:06:16,800 Speaker 1: decent working conditions. That's right. I joke with Irene that 93 00:06:16,880 --> 00:06:21,240 Speaker 1: manufacturing is not exactly a clickbait topic, but it is 94 00:06:21,279 --> 00:06:24,240 Speaker 1: so fundamental to how we live. From cars to clothes 95 00:06:24,279 --> 00:06:27,600 Speaker 1: to robots, they're all manufactured in some capacity, and as 96 00:06:27,600 --> 00:06:31,279 Speaker 1: technology gets more advanced, new manufacturing techniques are created and 97 00:06:31,320 --> 00:06:34,280 Speaker 1: deployed at massive scale. Yeah, and you mentioned that Irene 98 00:06:34,360 --> 00:06:37,880 Speaker 1: works in the Internet of Things group at Intel, which 99 00:06:37,920 --> 00:06:41,320 Speaker 1: is a wild title. It is, you know, we think 100 00:06:41,360 --> 00:06:45,560 Speaker 1: of IoT in terms of meaning a smart speaker or 101 00:06:45,680 --> 00:06:49,479 Speaker 1: a connected fridge, but industrial machinery can also be a 102 00:06:49,520 --> 00:06:52,480 Speaker 1: part of that same connected future for sure, And as 103 00:06:52,480 --> 00:06:55,479 Speaker 1: we've spoken about before, there's a lot of enthusiasm that 104 00:06:55,560 --> 00:06:58,320 Speaker 1: the internd of things could really be enabled by future 105 00:06:58,360 --> 00:07:01,799 Speaker 1: five G networks because what about extracting and processing data 106 00:07:02,000 --> 00:07:05,440 Speaker 1: from those connected things? And that's something I discussed with 107 00:07:05,480 --> 00:07:09,160 Speaker 1: Pat mccuska, the CEO of fast Radius, which is a 108 00:07:09,160 --> 00:07:12,600 Speaker 1: company that's using three D printing on an industrial scale 109 00:07:12,880 --> 00:07:15,280 Speaker 1: to reimagine the supply chain. And I spoke with a 110 00:07:15,280 --> 00:07:18,360 Speaker 1: really cool guy named Sean Peterson who started a company 111 00:07:18,400 --> 00:07:21,840 Speaker 1: called strong Arm Technologies, which is a platform that's using 112 00:07:21,920 --> 00:07:26,400 Speaker 1: data to help improve workers safety and reduced workplace injuries 113 00:07:26,400 --> 00:07:29,160 Speaker 1: for factory and warehouse workers. Before we get to that, 114 00:07:29,480 --> 00:07:31,680 Speaker 1: let's go back to Irene, who introduced me to the 115 00:07:31,720 --> 00:07:38,120 Speaker 1: intriguing concept of dark data. I was always curious about 116 00:07:38,160 --> 00:07:40,800 Speaker 1: how things work and how things are put together. A 117 00:07:40,920 --> 00:07:43,880 Speaker 1: logical extension to that if you're a systems engineering which 118 00:07:43,920 --> 00:07:46,400 Speaker 1: is some of my background training, is how did these 119 00:07:46,440 --> 00:07:50,679 Speaker 1: systems fit together? And how can we improve to produce 120 00:07:50,800 --> 00:07:53,760 Speaker 1: better outputs. The kinds of things I work on now 121 00:07:53,920 --> 00:07:58,280 Speaker 1: are really looking at the problem spaces in the industrial 122 00:07:58,440 --> 00:08:02,320 Speaker 1: production environments in three to five to eight years out, 123 00:08:02,880 --> 00:08:06,240 Speaker 1: and then I bring that information back to our developers 124 00:08:06,240 --> 00:08:10,000 Speaker 1: and designers in the Industrial Solutions division, and then we 125 00:08:10,160 --> 00:08:13,440 Speaker 1: use that information to project out the kinds of solutions 126 00:08:13,440 --> 00:08:15,760 Speaker 1: that will be required and the way in which will 127 00:08:15,760 --> 00:08:18,040 Speaker 1: have to go to market with those solutions. Over time, 128 00:08:19,400 --> 00:08:22,400 Speaker 1: we've gotten pretty accustomed to the notion of a smart product. 129 00:08:22,920 --> 00:08:24,840 Speaker 1: You can have a smart fridge that tells you when 130 00:08:24,840 --> 00:08:28,320 Speaker 1: you're out of milk, or a smart coffee maker that, well, 131 00:08:28,600 --> 00:08:30,920 Speaker 1: I'm not untidy sure what they do, but they're out there. 132 00:08:31,600 --> 00:08:35,560 Speaker 1: And in the manufacturing world, experts of building and preparing 133 00:08:35,960 --> 00:08:41,439 Speaker 1: for smart factories, we talk about making factories smarter. It's 134 00:08:41,480 --> 00:08:44,200 Speaker 1: not that we have dumb factories now, it's that we 135 00:08:44,280 --> 00:08:47,359 Speaker 1: have factors that were set up to be very, very predictable. 136 00:08:47,840 --> 00:08:50,640 Speaker 1: Those factories are going to have to become much better 137 00:08:50,720 --> 00:08:54,640 Speaker 1: at dealing with ambiguity and dealing with variability. I'm not 138 00:08:54,720 --> 00:08:58,240 Speaker 1: just cranking out the same thing millions and millions of times. 139 00:08:58,280 --> 00:09:02,040 Speaker 1: I'm now having to think about how do develop inventory 140 00:09:02,080 --> 00:09:04,400 Speaker 1: control systems that bring me exactly what I need when 141 00:09:04,440 --> 00:09:06,880 Speaker 1: I need it. I've got to have material handling systems 142 00:09:06,880 --> 00:09:10,920 Speaker 1: that are completely agile and that really aren't like conveyor 143 00:09:10,960 --> 00:09:14,520 Speaker 1: belts of old. As we look towards the future, I 144 00:09:14,600 --> 00:09:17,120 Speaker 1: was curious as to how the next generation of wireless 145 00:09:17,160 --> 00:09:20,680 Speaker 1: connectivity and all the promise of future five G networks 146 00:09:20,880 --> 00:09:25,080 Speaker 1: could change manufacturing. If I had unlimited bandwidth, then I 147 00:09:25,080 --> 00:09:28,960 Speaker 1: could track a lot more things. So, for example, I 148 00:09:29,000 --> 00:09:31,920 Speaker 1: could instrument all of my workers in a way that 149 00:09:32,040 --> 00:09:35,560 Speaker 1: I could maximize their safety. I would understand their current 150 00:09:35,640 --> 00:09:39,760 Speaker 1: health status in terms of temperature, respiration, in terms of 151 00:09:39,800 --> 00:09:43,400 Speaker 1: the breathing environment around them. I could also merge data 152 00:09:43,440 --> 00:09:47,600 Speaker 1: in a way that would be more useful, particularly in 153 00:09:47,720 --> 00:09:52,400 Speaker 1: complex environments where the safety issue is very important. If 154 00:09:52,440 --> 00:09:55,240 Speaker 1: I had unlimited bandwidth, really understanding a lot of those 155 00:09:55,280 --> 00:09:59,560 Speaker 1: internal operations would be very useful. So five G offers 156 00:09:59,600 --> 00:10:02,880 Speaker 1: a lot of opportunities, particularly if I'm trying to work 157 00:10:02,880 --> 00:10:06,440 Speaker 1: in difficult environments where I have something moving, for example, 158 00:10:06,840 --> 00:10:10,120 Speaker 1: a mobile robot then flight. She has a lot of opportunities, 159 00:10:10,320 --> 00:10:13,880 Speaker 1: but it's going to require careful thought, and it's going 160 00:10:13,960 --> 00:10:17,520 Speaker 1: to require other things. Other technology used to be brought 161 00:10:17,559 --> 00:10:20,880 Speaker 1: along with it. It turns out that the future of 162 00:10:20,880 --> 00:10:24,640 Speaker 1: manufacturing is all about variability, which puts a premium on 163 00:10:24,679 --> 00:10:29,040 Speaker 1: collecting datamonitor machine health. I've got to have machines where 164 00:10:29,080 --> 00:10:31,360 Speaker 1: I know their health, I know their status, and I 165 00:10:31,360 --> 00:10:34,680 Speaker 1: can predict when they're likely to fail. If I understand 166 00:10:34,760 --> 00:10:38,640 Speaker 1: machine health before that tool breaks, I can actually schedule 167 00:10:38,640 --> 00:10:41,880 Speaker 1: a machine for maintenance so that I'm maximizing the uptime. 168 00:10:42,400 --> 00:10:44,959 Speaker 1: There's a lot of stuff going on around Internet of 169 00:10:45,080 --> 00:10:49,199 Speaker 1: Things related to what we call releasing dark data. Machines 170 00:10:49,240 --> 00:10:51,760 Speaker 1: are doing a lot of things. We just haven't always 171 00:10:51,800 --> 00:10:54,240 Speaker 1: had access to the data that describes what that is, 172 00:10:54,840 --> 00:10:57,080 Speaker 1: and so when we think about the Internet of things, 173 00:10:57,120 --> 00:11:00,439 Speaker 1: it's really about putting sensors in a lot of places 174 00:11:00,480 --> 00:11:03,080 Speaker 1: where we really don't have a lot of insights into 175 00:11:03,120 --> 00:11:06,520 Speaker 1: what the machine is doing or what the machines health is. 176 00:11:06,920 --> 00:11:08,800 Speaker 1: We're still going to have workers, they're going to be 177 00:11:08,840 --> 00:11:12,360 Speaker 1: doing different things, though. In the I T industry, for example, 178 00:11:12,440 --> 00:11:15,760 Speaker 1: the fastest growing jobs right now are jobs that didn't 179 00:11:15,800 --> 00:11:18,240 Speaker 1: have titles five or eight years ago. We're going to 180 00:11:18,320 --> 00:11:21,560 Speaker 1: see the same thing in manufacturing systems. Engineering is going 181 00:11:21,600 --> 00:11:25,079 Speaker 1: to become much more important. Simulation and digital design is 182 00:11:25,080 --> 00:11:27,800 Speaker 1: going to become much more important. Those are two that 183 00:11:27,880 --> 00:11:31,079 Speaker 1: come to mind that don't have as bigger role right 184 00:11:31,120 --> 00:11:33,559 Speaker 1: now that will five years from now or ten years 185 00:11:33,600 --> 00:11:38,440 Speaker 1: from now. Karl, I like what Irene was saying in 186 00:11:38,559 --> 00:11:42,120 Speaker 1: terms of augmented intelligence, reminding me a little bit of 187 00:11:42,120 --> 00:11:45,839 Speaker 1: what Gary Kasparoff, the chess grandmaster, once said after he 188 00:11:45,920 --> 00:11:49,760 Speaker 1: got beaten by IBM's Computed Deep Blue. Kasparov said that 189 00:11:49,800 --> 00:11:53,959 Speaker 1: the future will belong to centaurs human machine hybrids. I mean, 190 00:11:54,040 --> 00:11:56,599 Speaker 1: I use machines every day to get me or I 191 00:11:56,679 --> 00:11:59,840 Speaker 1: need to go. That's right, but you know, the future 192 00:12:00,080 --> 00:12:01,800 Speaker 1: is closer than we think in that regard. In our 193 00:12:01,800 --> 00:12:03,959 Speaker 1: episode on sports and gaming, you remember we talked to 194 00:12:04,040 --> 00:12:07,520 Speaker 1: Charlie Hahn, who helped build Hollow Lens at Microsoft, and 195 00:12:07,520 --> 00:12:10,920 Speaker 1: he explained how they're are headset can be used by 196 00:12:10,960 --> 00:12:13,560 Speaker 1: someone who's repairing the part of an airplane, and in 197 00:12:13,600 --> 00:12:17,319 Speaker 1: real time they can receive information and instructions directly in 198 00:12:17,360 --> 00:12:20,199 Speaker 1: their field of vision. So I can imagine a world 199 00:12:20,480 --> 00:12:23,640 Speaker 1: where every factory worker is getting information about what they're 200 00:12:23,679 --> 00:12:26,720 Speaker 1: making or their environment. Yeah. I think Charlie Hahn was 201 00:12:26,760 --> 00:12:30,720 Speaker 1: talking about a partnership between Microsoft and quantas doing exactly 202 00:12:30,800 --> 00:12:34,120 Speaker 1: what you described and Charlie talked about how hollow lens 203 00:12:34,200 --> 00:12:37,680 Speaker 1: and augmented reality in general could be powered by future 204 00:12:37,679 --> 00:12:41,240 Speaker 1: fight G networks with more bandwidth and lower latency, allowing 205 00:12:41,240 --> 00:12:45,800 Speaker 1: for steamless toggling between the real and virtual worlds mixed reality. 206 00:12:45,840 --> 00:12:48,920 Speaker 1: He calls it in terms of industry that a our 207 00:12:49,040 --> 00:12:53,040 Speaker 1: future couldn't make workers more informed, more efficient, and even safer, 208 00:12:53,440 --> 00:12:55,120 Speaker 1: And we're actually going to talk about that with Sean 209 00:12:55,200 --> 00:12:57,400 Speaker 1: Peterson of strong Arm a little bit later on the show. 210 00:12:57,800 --> 00:13:00,000 Speaker 1: Right before that, though, I do want to spend a 211 00:13:00,000 --> 00:13:03,080 Speaker 1: at more time exploring this notion of the smart factory 212 00:13:03,559 --> 00:13:06,400 Speaker 1: to better understand it. Ice, but with the CEO of 213 00:13:06,480 --> 00:13:10,760 Speaker 1: fast Radius, Pat mccuske, about advanced manufacturing techniques that are 214 00:13:10,760 --> 00:13:17,520 Speaker 1: being pioneered right now. Fast Radius is a manufacturing technology firm. 215 00:13:17,600 --> 00:13:22,160 Speaker 1: We help companies launch and develop new products and business 216 00:13:22,160 --> 00:13:26,720 Speaker 1: models uniquely enabled by advanced manufacturing. Companies who want to 217 00:13:26,760 --> 00:13:30,760 Speaker 1: go embrace a new innovative product, be at a consumer 218 00:13:30,800 --> 00:13:34,080 Speaker 1: device or a medical device, or an aerospace or industrial device, 219 00:13:34,600 --> 00:13:38,240 Speaker 1: and they want a partner to go figure out the 220 00:13:38,280 --> 00:13:41,360 Speaker 1: best way to design and manufacture that product. That's where 221 00:13:41,360 --> 00:13:44,559 Speaker 1: fast Radius comes in, and then we actually produce products 222 00:13:44,600 --> 00:13:48,280 Speaker 1: at our factor here in Chicago and in Louisville. We 223 00:13:48,400 --> 00:13:50,839 Speaker 1: have a software platform that we've built from the ground 224 00:13:50,920 --> 00:13:53,600 Speaker 1: up that helps customers through this entire journey from the 225 00:13:53,640 --> 00:13:57,240 Speaker 1: design stage all the way through to production. The fast 226 00:13:57,320 --> 00:14:00,320 Speaker 1: Radius Chicago plant was named one of the nine best 227 00:14:00,320 --> 00:14:03,040 Speaker 1: factories in the world by the World Economic Forum in 228 00:14:04,240 --> 00:14:08,679 Speaker 1: because of its use of new manufacturing technologies, specifically what's 229 00:14:08,720 --> 00:14:12,120 Speaker 1: called additive manufacturing. Though you may know it by a 230 00:14:12,120 --> 00:14:15,480 Speaker 1: different name, three D printing and additive manufacturing are kind 231 00:14:15,480 --> 00:14:18,840 Speaker 1: of synonymous terms, and three D printings has been around 232 00:14:18,880 --> 00:14:21,840 Speaker 1: for thirty forty years now. You know, you have maker 233 00:14:21,880 --> 00:14:24,360 Speaker 1: bot and the kind of consumer thing that kids have 234 00:14:24,400 --> 00:14:26,200 Speaker 1: at their school or you may have as a hobbyist, 235 00:14:26,720 --> 00:14:28,640 Speaker 1: and so we typically refer to that as three D 236 00:14:28,720 --> 00:14:33,400 Speaker 1: printing additive manufacturing, while conceptually the same thing kind of 237 00:14:33,440 --> 00:14:37,120 Speaker 1: connotes more industrial grade, so you're really making real end 238 00:14:37,200 --> 00:14:39,120 Speaker 1: us parts. This is parts that are going on to 239 00:14:39,200 --> 00:14:42,160 Speaker 1: the engine of an airplane or a car. So there's 240 00:14:42,320 --> 00:14:44,840 Speaker 1: subtractive manufacturing, which is a traditional you know, start with 241 00:14:44,840 --> 00:14:47,720 Speaker 1: a block of steel and and carve something out of 242 00:14:47,760 --> 00:14:49,800 Speaker 1: that block of steel to create the shape and geometry 243 00:14:49,800 --> 00:14:54,040 Speaker 1: you're trying to produce additive is adding layers of material 244 00:14:54,480 --> 00:14:57,720 Speaker 1: one at a time to sort of build up additively 245 00:14:58,000 --> 00:15:01,640 Speaker 1: the product that you're making. These new advanced manufacturing techniques 246 00:15:01,680 --> 00:15:05,440 Speaker 1: like additive, they're really hard. It's not like your two 247 00:15:05,480 --> 00:15:07,800 Speaker 1: D printer where you just plug it in and hit 248 00:15:07,800 --> 00:15:09,920 Speaker 1: a button and press print and outcomes a beautiful part. 249 00:15:09,960 --> 00:15:12,840 Speaker 1: It's actually a really difficult process that you have to 250 00:15:12,840 --> 00:15:14,920 Speaker 1: control all kinds of variables, from the humidity in the room, 251 00:15:14,920 --> 00:15:17,440 Speaker 1: to the vibration to the temperature of the resident, etcetera. 252 00:15:18,160 --> 00:15:20,400 Speaker 1: To be honest, I didn't know that three D printing 253 00:15:20,400 --> 00:15:24,400 Speaker 1: had been around for decades or how transformative additive manufacturing 254 00:15:24,440 --> 00:15:27,360 Speaker 1: could be an industrial context. But it turns out the 255 00:15:27,400 --> 00:15:31,600 Speaker 1: supply chains of the future could be entirely digital. Today, 256 00:15:31,680 --> 00:15:34,360 Speaker 1: if you want to have a set of spare parts 257 00:15:34,440 --> 00:15:37,280 Speaker 1: for your product, you make, you know, a hundred thousand 258 00:15:37,320 --> 00:15:38,960 Speaker 1: of them, a million of them, whatever you think you need, 259 00:15:39,440 --> 00:15:42,120 Speaker 1: and you store those in a warehouse for years, in 260 00:15:42,160 --> 00:15:45,240 Speaker 1: some cases decades. Now what you can do is move 261 00:15:45,320 --> 00:15:49,200 Speaker 1: those parts into a digital warehouse that's managed by fast radius, 262 00:15:49,280 --> 00:15:52,200 Speaker 1: and we produced the parts on demand when you need them, 263 00:15:52,240 --> 00:15:54,520 Speaker 1: no more, no less than you need We like to 264 00:15:54,520 --> 00:15:57,640 Speaker 1: say we're creating the world's biggest warehouse, a billion square feet, 265 00:15:58,200 --> 00:16:01,160 Speaker 1: but it's all digital, right, There's no a coal warehouse 266 00:16:01,880 --> 00:16:06,240 Speaker 1: that paints a picture of an entirely different design process. Today, 267 00:16:06,400 --> 00:16:09,160 Speaker 1: the normal process is to design a product and then 268 00:16:09,200 --> 00:16:12,480 Speaker 1: put in a giant order for parts. Tomorrow, more and 269 00:16:12,520 --> 00:16:15,160 Speaker 1: more businesses will be able to develop products in real 270 00:16:15,200 --> 00:16:18,880 Speaker 1: time and manufacture the necessary parts as the product evolveds. 271 00:16:19,880 --> 00:16:23,520 Speaker 1: So what makes all of this possible. We're gathering all 272 00:16:23,600 --> 00:16:27,760 Speaker 1: this data which allows us to build what many would 273 00:16:27,760 --> 00:16:30,520 Speaker 1: consider a quote unquote smart factory. We use a term 274 00:16:30,560 --> 00:16:33,560 Speaker 1: called the digital thread. So having that data all the 275 00:16:33,560 --> 00:16:38,040 Speaker 1: way from the materials formulation to the production and every 276 00:16:38,040 --> 00:16:40,520 Speaker 1: piece of data right the temperature in the room, the vibration, 277 00:16:40,760 --> 00:16:43,360 Speaker 1: who was a technician who worked on it, What is 278 00:16:43,360 --> 00:16:46,320 Speaker 1: the diversion of the design, What is the machine number 279 00:16:46,360 --> 00:16:48,200 Speaker 1: that was run on? What time? How long do it think? 280 00:16:48,640 --> 00:16:50,600 Speaker 1: What are the data points that you need to collect 281 00:16:50,840 --> 00:16:53,600 Speaker 1: in order to drive real value in a factory environment. 282 00:16:54,920 --> 00:16:58,440 Speaker 1: Just as Irene outlined, the flexible manufacturing of the future 283 00:16:58,720 --> 00:17:03,320 Speaker 1: will be underpinned by stint class data gathering and processing. Today, 284 00:17:03,480 --> 00:17:07,439 Speaker 1: WiFi and hard wired networks player b ruling factories. The 285 00:17:07,480 --> 00:17:10,240 Speaker 1: future five G networks promised to be faster and more 286 00:17:10,240 --> 00:17:14,400 Speaker 1: reliable and support more connected devices. Right now, we're using 287 00:17:14,440 --> 00:17:17,640 Speaker 1: just traditional WiFi, but we can absolutely envision an environment 288 00:17:17,640 --> 00:17:20,359 Speaker 1: where we would have a five G network that would 289 00:17:20,359 --> 00:17:24,000 Speaker 1: allow us to gather even more data more quickly, more efficiently, 290 00:17:24,480 --> 00:17:26,920 Speaker 1: and so we're keeping an eye on that and exploring, 291 00:17:27,119 --> 00:17:28,600 Speaker 1: you know, what are the limits of WiFi and how 292 00:17:28,600 --> 00:17:30,960 Speaker 1: do we think about maybe transitioning to five G. So 293 00:17:31,240 --> 00:17:34,040 Speaker 1: not going to happen tomorrow, but we're excited about just 294 00:17:34,080 --> 00:17:36,520 Speaker 1: this greater bandwidth and what we may be able to 295 00:17:36,560 --> 00:17:41,400 Speaker 1: do with regard to gathering even more data intensive points 296 00:17:41,400 --> 00:17:44,199 Speaker 1: of capture that would allow us to drive even richer insight. 297 00:17:49,080 --> 00:17:52,959 Speaker 1: The future with five G is coming today. T Mobile 298 00:17:53,040 --> 00:17:56,160 Speaker 1: is leading the five G charge with thirty billion dollars 299 00:17:56,160 --> 00:18:00,320 Speaker 1: invested in their network to deliver new capabilities in proved 300 00:18:00,320 --> 00:18:04,080 Speaker 1: connectivity and true mobility provided by an advanced network from 301 00:18:04,080 --> 00:18:06,720 Speaker 1: T Mobile for Business could change the way we all 302 00:18:06,760 --> 00:18:09,800 Speaker 1: live and work. The five G era will take the 303 00:18:09,840 --> 00:18:13,199 Speaker 1: best technologies available today in the wireless space so that 304 00:18:13,240 --> 00:18:17,040 Speaker 1: you can offer new capabilities to your business customers. T 305 00:18:17,200 --> 00:18:19,720 Speaker 1: Mobile for Business knows that the future of business will 306 00:18:19,760 --> 00:18:23,400 Speaker 1: be powered by advancements in wireless networks, with these new 307 00:18:23,440 --> 00:18:26,119 Speaker 1: technologies opening the doors for better ways to get the 308 00:18:26,200 --> 00:18:30,280 Speaker 1: job done. Business is changing. Learn more at t Mobile 309 00:18:30,320 --> 00:18:37,639 Speaker 1: for business dot com. So Carrot, this idea of the 310 00:18:37,720 --> 00:18:42,359 Speaker 1: digital warehouse is interesting. If more companies moved to that model, 311 00:18:42,760 --> 00:18:44,560 Speaker 1: we might be able to replace a lot of these 312 00:18:44,560 --> 00:18:49,280 Speaker 1: traditional warehouses that store products with trees and forests, or 313 00:18:49,320 --> 00:18:52,359 Speaker 1: at the very least, to reduce the environmental cost of 314 00:18:52,359 --> 00:18:54,840 Speaker 1: shipping pots around the world. I actually think that the 315 00:18:54,880 --> 00:18:59,080 Speaker 1: definition of factory might change in the near future, especially 316 00:18:59,080 --> 00:19:01,280 Speaker 1: when so much is store it in the cloud. We'll 317 00:19:01,320 --> 00:19:04,320 Speaker 1: see three D printing on an industrial scale, and I 318 00:19:04,320 --> 00:19:07,439 Speaker 1: had no idea that three D printing technology had been 319 00:19:07,480 --> 00:19:09,879 Speaker 1: around for so long or what it could even enable. 320 00:19:10,440 --> 00:19:13,240 Speaker 1: Actually learned that some of the new breed of antennas 321 00:19:13,280 --> 00:19:17,240 Speaker 1: that will underpin future five G are actually being three 322 00:19:17,320 --> 00:19:20,160 Speaker 1: D printed, so we sort of see the wheel coming 323 00:19:20,240 --> 00:19:22,760 Speaker 1: full circle. Of course, all of this talk of three 324 00:19:22,840 --> 00:19:27,000 Speaker 1: D printing and automated manufacturing does raise questions about what 325 00:19:27,080 --> 00:19:30,800 Speaker 1: happens to factory workers who are being displaced. Irene talked 326 00:19:30,800 --> 00:19:33,720 Speaker 1: about developing new skills, but that might not be available 327 00:19:33,760 --> 00:19:36,119 Speaker 1: to everyone. That's true, and it's part of a bigger 328 00:19:36,119 --> 00:19:39,280 Speaker 1: conversation about what happens to labor in an age of automation. 329 00:19:40,000 --> 00:19:42,639 Speaker 1: But in the meantime, there are some bright spots for 330 00:19:42,680 --> 00:19:45,680 Speaker 1: the workers of today when it comes to technological innovation. 331 00:19:46,240 --> 00:19:49,840 Speaker 1: I spoke with Sean Peterson, who is using technology, but 332 00:19:50,240 --> 00:19:53,320 Speaker 1: his goal is to improve worker safety for what he 333 00:19:53,440 --> 00:20:00,399 Speaker 1: calls industrial athletes, and industrial athlete is so what we 334 00:20:00,440 --> 00:20:02,000 Speaker 1: like to call em manual labor. The way that we 335 00:20:02,040 --> 00:20:04,879 Speaker 1: look at blue collar work is the same that we 336 00:20:04,960 --> 00:20:09,000 Speaker 1: look at professional athletes. You go out, you put your 337 00:20:09,000 --> 00:20:10,520 Speaker 1: body on the line to put bread on the table. 338 00:20:11,040 --> 00:20:13,159 Speaker 1: Professional athletes do that for a few hours a day 339 00:20:13,240 --> 00:20:15,639 Speaker 1: with the best equipment in the world. But there's guys 340 00:20:15,680 --> 00:20:17,040 Speaker 1: that go out and do it for eight hours a 341 00:20:17,119 --> 00:20:19,160 Speaker 1: day and they do it for the course of forty years, 342 00:20:19,200 --> 00:20:21,440 Speaker 1: six days a week. So putting your body on that 343 00:20:21,800 --> 00:20:25,040 Speaker 1: level of rigor just to provide for the family if 344 00:20:25,080 --> 00:20:28,919 Speaker 1: your own life, for us, earns the moniker of industrial athlete. 345 00:20:29,800 --> 00:20:32,200 Speaker 1: We're an industrial safety science company and we drive the 346 00:20:32,200 --> 00:20:35,160 Speaker 1: world's leading risk metrics for these industrial athletes to help 347 00:20:35,160 --> 00:20:37,880 Speaker 1: reduce injuries on average of thirty year of a year. 348 00:20:38,520 --> 00:20:41,840 Speaker 1: That's Sean Peterson. He didn't always think he would become 349 00:20:41,840 --> 00:20:45,359 Speaker 1: a tech entrepreneur. Originally he thought he would join the 350 00:20:45,400 --> 00:20:49,919 Speaker 1: family construction business. I grew up thinking I was going 351 00:20:50,000 --> 00:20:53,520 Speaker 1: to run or participate in the family construction company. And 352 00:20:53,560 --> 00:20:56,000 Speaker 1: my father passed away in the job. Sorry, I went 353 00:20:56,000 --> 00:20:59,240 Speaker 1: off and I became an entrepreneur, and along that entrepreneurial 354 00:20:59,320 --> 00:21:02,200 Speaker 1: journey fell in love with this problem. That are these injuries, 355 00:21:02,200 --> 00:21:05,280 Speaker 1: that are these these opportunities to provide a level of 356 00:21:05,320 --> 00:21:07,960 Speaker 1: design intent to make a better life for these people. 357 00:21:09,440 --> 00:21:13,600 Speaker 1: Not only are manufacturing and warehouse workers exposed to potential injury, 358 00:21:14,160 --> 00:21:17,359 Speaker 1: the physical nature of their job exposes them to long 359 00:21:17,480 --> 00:21:21,440 Speaker 1: term bodily risk. Just think about lifting a box over 360 00:21:21,480 --> 00:21:25,560 Speaker 1: your head thirty or forty times. How many opportunities there 361 00:21:25,600 --> 00:21:29,280 Speaker 1: are for something to go wrong. That's where strong arm 362 00:21:29,320 --> 00:21:33,480 Speaker 1: comes into play. We created a wearable sensor that measures 363 00:21:33,520 --> 00:21:36,520 Speaker 1: all of the risk that an industrial athlete goes through 364 00:21:36,560 --> 00:21:39,800 Speaker 1: throughout his day. It's a small center, It's like twice 365 00:21:39,840 --> 00:21:41,800 Speaker 1: the size of a TIC tech box, and you wear 366 00:21:41,840 --> 00:21:43,159 Speaker 1: it on your chests. Are you wear it in a 367 00:21:43,160 --> 00:21:45,040 Speaker 1: shirt pocket or you clip it onto your false safety 368 00:21:45,080 --> 00:21:48,480 Speaker 1: harness and we provide alerts to help avoid those injuries 369 00:21:48,520 --> 00:21:51,360 Speaker 1: to that individual real time, and then we drive metrics 370 00:21:51,359 --> 00:21:53,920 Speaker 1: bacted management to help them make better decisions on how 371 00:21:53,920 --> 00:21:57,320 Speaker 1: to invest in further infrastructure, in training and equipment to 372 00:21:57,400 --> 00:22:00,600 Speaker 1: help eliminate those injuries in the future. And the platform 373 00:22:00,640 --> 00:22:04,000 Speaker 1: we have is called the FUSE platform. The FUSE platform 374 00:22:04,119 --> 00:22:06,560 Speaker 1: can do things like tell you that you're twisting in 375 00:22:06,600 --> 00:22:09,280 Speaker 1: a way that is unhealthy for your back, or that 376 00:22:09,320 --> 00:22:12,840 Speaker 1: you're approaching an area that is especially risky. We're looking 377 00:22:12,880 --> 00:22:14,919 Speaker 1: at all of the motions that he's going through that 378 00:22:14,920 --> 00:22:17,719 Speaker 1: they're risky and how they relate to the degradation of 379 00:22:17,760 --> 00:22:20,080 Speaker 1: his lumbar spine or his lower back, or just his 380 00:22:20,160 --> 00:22:22,800 Speaker 1: chance of getting a muscule skeletal injury, and we provide 381 00:22:22,800 --> 00:22:25,480 Speaker 1: an alert before he does something risky. We then drive 382 00:22:25,680 --> 00:22:28,840 Speaker 1: further insight through other types of environmental awareness, so we 383 00:22:28,920 --> 00:22:33,879 Speaker 1: collect heat information, humidity, air quality, as well as looking 384 00:22:33,880 --> 00:22:37,000 Speaker 1: out for ordorless gases, things that people would be worried about, 385 00:22:37,119 --> 00:22:40,159 Speaker 1: and we provide an early alert around those exposures. That 386 00:22:40,200 --> 00:22:43,280 Speaker 1: alert would either say, hey, you should change whatever respirator 387 00:22:43,280 --> 00:22:45,960 Speaker 1: you're wearing because we've noticed you're going through higher levels 388 00:22:45,960 --> 00:22:48,800 Speaker 1: of exposure and then you previously thought you would is 389 00:22:48,840 --> 00:22:51,399 Speaker 1: an area where you should be wearing a hardhead? Is 390 00:22:51,440 --> 00:22:53,040 Speaker 1: there an area where you need a certain amount of 391 00:22:53,080 --> 00:22:56,600 Speaker 1: training on? Of course, today there are ways to communicate 392 00:22:56,640 --> 00:22:59,480 Speaker 1: this type of information. Think about all of those hard 393 00:22:59,520 --> 00:23:02,320 Speaker 1: hats on minds you see on construction sites. But in 394 00:23:02,359 --> 00:23:05,440 Speaker 1: the future, it may be possible to serve this information 395 00:23:05,520 --> 00:23:09,399 Speaker 1: in real time with contextual relevance. You're working in a 396 00:23:09,440 --> 00:23:13,159 Speaker 1: really hot environment, so we're gonna measure that heat and 397 00:23:13,200 --> 00:23:15,399 Speaker 1: once you hit over that exposure rate, we're gonna give 398 00:23:15,400 --> 00:23:17,400 Speaker 1: you haptic feedback to say get out of the truck 399 00:23:17,400 --> 00:23:19,760 Speaker 1: for fifteen minutes and go have some water. It's just 400 00:23:19,800 --> 00:23:22,280 Speaker 1: like someone tap you on the shoulder to say, hey, 401 00:23:22,320 --> 00:23:24,960 Speaker 1: watch out for this. We provide all of that information 402 00:23:24,960 --> 00:23:27,520 Speaker 1: that insight directly to the individual, and then at the 403 00:23:27,600 --> 00:23:29,639 Speaker 1: end of the day, all that information is shot up 404 00:23:29,640 --> 00:23:33,040 Speaker 1: to the cloud, where managers and the greater ecosystem in 405 00:23:33,080 --> 00:23:36,120 Speaker 1: the value chain can now get insight into that risk 406 00:23:36,880 --> 00:23:39,119 Speaker 1: and all of that data can then be used to 407 00:23:39,160 --> 00:23:44,760 Speaker 1: make bigger decisions about future optimizations, addressing problems before they arise. 408 00:23:45,760 --> 00:23:47,919 Speaker 1: A quick skin of our data showed us that on 409 00:23:48,040 --> 00:23:52,439 Speaker 1: this conveyor line, essentially that people were twisting so fast 410 00:23:52,960 --> 00:23:54,520 Speaker 1: that that's why they were getting hurt. It was a 411 00:23:54,560 --> 00:23:57,040 Speaker 1: sagital twisting velocity, which is one of the factors of 412 00:23:57,040 --> 00:23:59,800 Speaker 1: our GROTHM that we collect. We said, they're twisting so fast, 413 00:24:00,080 --> 00:24:03,240 Speaker 1: no training, there's no extoskeleton, there's no way that we 414 00:24:03,320 --> 00:24:05,119 Speaker 1: can change this. We're going to have to go for 415 00:24:05,160 --> 00:24:08,159 Speaker 1: an infrastructural change. So we recommended a conveyor belt that 416 00:24:08,200 --> 00:24:10,000 Speaker 1: had a forty or five degree bend in it to 417 00:24:10,080 --> 00:24:12,880 Speaker 1: just eliminate that twisting. What we saw was not only 418 00:24:13,080 --> 00:24:17,600 Speaker 1: a complete elimination of that injury entirely, but the uptime 419 00:24:17,600 --> 00:24:19,920 Speaker 1: and the factory was such that we're able to now 420 00:24:19,960 --> 00:24:22,480 Speaker 1: afford such a greater r y that we equipped the 421 00:24:22,480 --> 00:24:25,159 Speaker 1: rest of the warehouse with safer equipment. So we're just 422 00:24:25,280 --> 00:24:27,760 Speaker 1: helping them get better at getting the people who are 423 00:24:27,760 --> 00:24:30,400 Speaker 1: in the door that are doing this job, which by 424 00:24:30,440 --> 00:24:32,760 Speaker 1: injury rate, is the riskiest job in the world. And 425 00:24:32,760 --> 00:24:34,960 Speaker 1: now we're just using technology to help them retain those 426 00:24:34,960 --> 00:24:37,360 Speaker 1: individuals and then help those people be safer so they're 427 00:24:37,440 --> 00:24:42,000 Speaker 1: less problems going forward unless turnover it. The FUSE platform 428 00:24:42,119 --> 00:24:45,479 Speaker 1: collects data that can help drive decision making. But this 429 00:24:45,560 --> 00:24:49,119 Speaker 1: carries with it obvious privacy concerns, So Sean says that 430 00:24:49,200 --> 00:24:52,040 Speaker 1: strong Arm is thoughtful about the kind of data they collect. 431 00:24:52,480 --> 00:24:55,640 Speaker 1: Our devices are built with that industrial athlete in mind, 432 00:24:55,960 --> 00:24:57,760 Speaker 1: all the way down to the way the information is 433 00:24:57,800 --> 00:25:00,480 Speaker 1: not only delivered, but what's also collected. We don't touch 434 00:25:00,560 --> 00:25:04,080 Speaker 1: anything over Hippo. We're not driving any biometrics individually on 435 00:25:04,119 --> 00:25:09,159 Speaker 1: this person. It's all externally monitable information, and the information 436 00:25:09,240 --> 00:25:12,520 Speaker 1: is built on a compliant database that can allow us 437 00:25:12,520 --> 00:25:15,359 Speaker 1: to anonymize that individual's risk profile at the drop of 438 00:25:15,359 --> 00:25:19,000 Speaker 1: the hat. Sean believes that that risk profile can help 439 00:25:19,080 --> 00:25:24,119 Speaker 1: keep workers safe, ultimately creating more efficiency and productivity. In 440 00:25:24,160 --> 00:25:26,360 Speaker 1: the same way that the industry of the future may 441 00:25:26,400 --> 00:25:30,240 Speaker 1: allow us to create individualized products, it could also allow 442 00:25:30,280 --> 00:25:35,080 Speaker 1: manufacturers to tailor work to individual workers. What we're really 443 00:25:35,080 --> 00:25:37,160 Speaker 1: trying to get clients to understand is that you can't 444 00:25:37,200 --> 00:25:39,560 Speaker 1: just drive people through through print metric. You have to 445 00:25:39,560 --> 00:25:43,000 Speaker 1: focus on the capabilities of that individual and what is 446 00:25:43,040 --> 00:25:45,480 Speaker 1: safe for them. Otherwise it's like filling a bucket with 447 00:25:45,480 --> 00:25:47,600 Speaker 1: a hole in the bottom. You can't just work these 448 00:25:47,640 --> 00:25:50,359 Speaker 1: people to the bone. You need to be incredibly sensitive 449 00:25:50,440 --> 00:25:55,520 Speaker 1: to what the individual capabilities are in higher additionally or 450 00:25:55,720 --> 00:25:58,760 Speaker 1: trained additionally in and around that. The greater good here 451 00:25:58,840 --> 00:26:01,239 Speaker 1: is just we want to continue to honor workforce in 452 00:26:01,359 --> 00:26:04,119 Speaker 1: order to create a better network of innovation inside of 453 00:26:04,119 --> 00:26:07,600 Speaker 1: a safety angle, and that's what we're driving for. So 454 00:26:07,800 --> 00:26:10,840 Speaker 1: if anyone hates it, it's never gonna work, and that's 455 00:26:10,920 --> 00:26:14,080 Speaker 1: the truth of it. Many of the initiatives Sewn describes 456 00:26:14,119 --> 00:26:18,000 Speaker 1: are already possible today, so I was curious what might 457 00:26:18,040 --> 00:26:21,359 Speaker 1: be possible when future five G networks have been fully deployed. 458 00:26:22,480 --> 00:26:25,600 Speaker 1: In the future, we are really excited about the potential 459 00:26:25,600 --> 00:26:27,440 Speaker 1: for things like five G to enable us to deliver 460 00:26:27,480 --> 00:26:30,120 Speaker 1: that insight in real times athlete and also go back 461 00:26:30,200 --> 00:26:32,520 Speaker 1: up to management. So if that person is a remote 462 00:26:32,560 --> 00:26:34,600 Speaker 1: worker and they're down, we can now send someone to 463 00:26:34,640 --> 00:26:36,800 Speaker 1: where they are to help assist or see what the 464 00:26:36,800 --> 00:26:40,399 Speaker 1: problem is. As well as allowing for real time response. 465 00:26:40,920 --> 00:26:43,760 Speaker 1: Five G that is widely used could allow companies like 466 00:26:43,840 --> 00:26:46,920 Speaker 1: strong Arm to partner with new industries in areas where 467 00:26:46,960 --> 00:26:50,960 Speaker 1: connectivity is challenging. Better networks enable us to reach further 468 00:26:51,000 --> 00:26:56,440 Speaker 1: outside of industries, both in higher risk and more constrained areas. 469 00:26:56,480 --> 00:27:00,199 Speaker 1: So we primarily operate through geologistics and light manufacturing. I 470 00:27:00,240 --> 00:27:03,760 Speaker 1: started this company because of my personal connection and construction. 471 00:27:03,840 --> 00:27:07,240 Speaker 1: So our next vertical is construction, then remote work, then 472 00:27:07,400 --> 00:27:17,440 Speaker 1: oil and gas um and then heavy manufacturing. Available now 473 00:27:17,480 --> 00:27:20,600 Speaker 1: from my Heart a new series presented by Tembile for business, 474 00:27:21,040 --> 00:27:24,800 Speaker 1: The Restless Ones join host Johnson Strickland as he explores 475 00:27:24,840 --> 00:27:27,800 Speaker 1: the upcoming five Year Revolution and the business leaders who 476 00:27:27,840 --> 00:27:31,119 Speaker 1: stand right on the cutting edge. There are certain decision 477 00:27:31,160 --> 00:27:33,640 Speaker 1: makers who are restless. They know there is a better 478 00:27:33,680 --> 00:27:36,560 Speaker 1: way to get things done, and they're ready, curious and 479 00:27:36,640 --> 00:27:40,600 Speaker 1: excited for the next technological innovation to unlock their vision 480 00:27:40,600 --> 00:27:46,600 Speaker 1: of the future. These restless Ones are in pursuit of bigger, better, smarter, stronger. 481 00:27:47,200 --> 00:27:51,640 Speaker 1: They seek new partners, new strategies, new processes. They pursue 482 00:27:51,680 --> 00:27:55,760 Speaker 1: innovative platforms and solutions to propel their teams, businesses, and 483 00:27:55,880 --> 00:27:59,639 Speaker 1: industries forward. In each episode, we'll learn more from the 484 00:27:59,640 --> 00:28:02,680 Speaker 1: restler Ones themselves and dive deep into how they think 485 00:28:02,720 --> 00:28:06,240 Speaker 1: the five Year Evolution could propel that business forward. The 486 00:28:06,280 --> 00:28:08,720 Speaker 1: Restless Ones is now available on the i Heart radio 487 00:28:08,760 --> 00:28:16,679 Speaker 1: app wherever you listen to podcasts. Soka from knowing beforehand 488 00:28:16,720 --> 00:28:19,280 Speaker 1: that a machine needs to be repaired to on demand 489 00:28:19,320 --> 00:28:22,360 Speaker 1: product printing and analyzing data to drive work is safety. 490 00:28:22,840 --> 00:28:25,000 Speaker 1: That seems to be a surprising amount to get excited 491 00:28:25,040 --> 00:28:27,439 Speaker 1: about in the future of manufacturing. I didn't even know 492 00:28:27,600 --> 00:28:30,480 Speaker 1: I could get excited about the future of manufacturing now, 493 00:28:30,560 --> 00:28:32,800 Speaker 1: but it's for It is really cool, and so much 494 00:28:32,800 --> 00:28:35,359 Speaker 1: of what we talked about comes down to data and 495 00:28:35,440 --> 00:28:38,160 Speaker 1: making better decisions in real time. It reminds me of 496 00:28:38,160 --> 00:28:40,480 Speaker 1: one of the early episodes of This Time Tomorrow, when 497 00:28:40,480 --> 00:28:43,720 Speaker 1: we talked to the Menlo Park fire chief. He explained 498 00:28:43,760 --> 00:28:46,880 Speaker 1: that the lower latency and higher bandwidth promised by future 499 00:28:46,880 --> 00:28:50,080 Speaker 1: five G networks could enable him to keep track of 500 00:28:50,160 --> 00:28:53,800 Speaker 1: his firefighters as they entered burning buildings, and that idea 501 00:28:53,800 --> 00:28:57,920 Speaker 1: of monitoring and optimization is exactly what Sean was talking about. 502 00:28:58,160 --> 00:29:01,400 Speaker 1: Personalization is such a key theme of the new economy, 503 00:29:01,560 --> 00:29:05,560 Speaker 1: and the personalization of products is something everyone can understand 504 00:29:05,720 --> 00:29:08,960 Speaker 1: fairly easily. But I love what Sean was talking about 505 00:29:09,000 --> 00:29:13,600 Speaker 1: in terms of personalizing worker responsibilities in a factory. To 506 00:29:13,640 --> 00:29:16,920 Speaker 1: make this vision real, though, factories and businesses will have 507 00:29:17,000 --> 00:29:21,240 Speaker 1: to adapt to become more agile. In Irian's words, well, 508 00:29:22,000 --> 00:29:26,080 Speaker 1: how will I get customized sucks otherwise. I think one 509 00:29:26,080 --> 00:29:28,480 Speaker 1: of the biggest takeaways that I learned while researching this 510 00:29:28,560 --> 00:29:31,520 Speaker 1: episode is that, well, we don't know exactly how things 511 00:29:31,560 --> 00:29:35,000 Speaker 1: will change. That there are too many technical and cultural 512 00:29:35,080 --> 00:29:38,440 Speaker 1: drivers converging for manufacturing to kind of stay as it 513 00:29:38,520 --> 00:29:41,200 Speaker 1: is today. Yeah, and whether it's the expansion of the 514 00:29:41,240 --> 00:29:45,320 Speaker 1: Internet of Things, or robotics or three D printing, industry 515 00:29:45,520 --> 00:29:49,160 Speaker 1: is becoming more digital and the new infrastructure of future 516 00:29:49,160 --> 00:29:52,520 Speaker 1: five gen networks could be the platform that digital future 517 00:29:52,760 --> 00:29:59,320 Speaker 1: is built upon. On the next episode of This Time Tomorrow, 518 00:29:59,640 --> 00:30:02,520 Speaker 1: we'll look how technology is changing one of the world's 519 00:30:02,560 --> 00:30:08,280 Speaker 1: oldest businesses, agriculture. From robots suerial imaging, we explore how 520 00:30:08,320 --> 00:30:11,360 Speaker 1: technology is changing the way we farm and the role 521 00:30:11,480 --> 00:30:15,320 Speaker 1: that five G could play. I'm as vlachen see you 522 00:30:15,400 --> 00:30:24,840 Speaker 1: next time. No matter what you're after, T Mobile for 523 00:30:24,920 --> 00:30:28,280 Speaker 1: Business is here with a network born mobile and built 524 00:30:28,320 --> 00:30:30,600 Speaker 1: from the ground up for the next wave of innovation, 525 00:30:31,400 --> 00:30:35,360 Speaker 1: from mobile broadband to IoT to workforce mobility, and everything 526 00:30:35,440 --> 00:30:38,480 Speaker 1: in between. T Mobile for Business is committed to helping 527 00:30:38,520 --> 00:30:41,840 Speaker 1: you move your business forward with the products and services 528 00:30:41,920 --> 00:30:45,120 Speaker 1: you need, as well as the dedicated, award winning customer 529 00:30:45,200 --> 00:30:49,400 Speaker 1: service you'd expect from America's most loved wireless company. Business 530 00:30:49,480 --> 00:30:53,200 Speaker 1: is changing. Learn more at T Mobile for Business dot com.