Supply Chain
Technology Podcast

SUMMARY KEYWORDS

Location accuracy, supply chain, IoT applications, Xona Space Systems, Roambee, autonomous vehicles, GPS limitations, precision farming, logistics, hyper-precise location, AI and ML, production scheduling, cross-docking, satellite integration, location intelligence.

SPEAKERS

Brian Manning, Speaker 1, Scott Mears, Sanjay Sharma

Scott Mears  00:00

Accurate location data will soon be as crucial as inventory data in managing supply chains.

Sanjay Sharma  00:06

Thumbs up.

Scott Mears  00:08

Traditional location technologies are sufficient for modern supply chain needs.

Brian Manning  00:08

Thumbs up.

Sanjay Sharma  00:13

Thumbs up today.

Scott Mears  00:15

Location accuracy is the most critical factor in improving supply chain efficiency.

Sanjay Sharma  00:17

Thumbs up.

Brian Manning  00:17

Kind of sideways, up.

Sanjay Sharma  00:23

I would know the trick to go from here to here. Yeah.

Scott Mears  00:35

Welcome to the supply chain tech podcast with Roambee. Scott Mears here, Senior Marketing Manager at Roambee and your host, we thank you for joining us today. In this episode, we speak with Brian Manning, co-founder and CEO at Xona space systems, and Sanjay Sharma, Chairman and CEO at Roambee. We explore the crucial role of location accuracy in supply chains, and covering why it’s often overlooked despite its significance. Next, we dive into the challenges and opportunities of integrating precise location data into IoT applications. And finally, we discuss how innovative partnerships like Roambee and Xona space systems are driving the future of location intelligence and enabling the autonomous supply chain of the future. Welcome Sanjay and welcome Brian. It’s great to have you both on the episode.

Sanjay Sharma  01:26

Pleasure, yeah.

Brian Manning  01:28

It’s great to be here.

Scott Mears  01:30

Yeah, it’s wonderful to have you both on and I’m excited to jump into this topic, because it’s a it’s an interesting collaboration that we’re going to be showing on this episode, and I’m excited to really get into the detail of what that collaboration means for the supply chain industry and for innovation and technology as a whole.But before we dive into the ins and outs of this, I always like to ask a bit of an icebreaker question, and I’m going to pose this to both of you, because I think this also is quite a good opening to the conversation as well. Is I’ll maybe go for you. Brian, first is, what would you say is a common misconception people have about location accuracy?

Brian Manning  02:11

Sure, I would say it’s probably that there’s no reason for it to need to be better and so that there’s a kind of a fun story. You know, there’s Moore’s law with like, processing power and how it continuously gets better. Our CTO put one together. We just call it Reid’s law. So His name is Dr Tyler Reed, where, if you look throughout history, position accuracy, has followed a pretty similar trend, that if you go back once upon a time there was celestial and you’re getting, you know, kilometer level positioning for ships. And then that kind of translated into in the, you know, World War Two era, you’re starting to get some of these kind of radio frequency based things, and you’re getting into hundreds of meters, maybe down to 10s of meters. Then GPS came along another 30 years later, and starting to drive it down into single meters, and that’s good enough for cars and whatnot. But what we’re seeing happen now is a lot of technologies needing to go basically to the next step. And it is pretty consistent throughout time that it’s been about every 30 years or so, there’s almost a 10x increase in performance, in in inaccuracy. And that’s something that you know, if you look at, it’s been about 30 years since the last kind of jump, when, when GPS became fully operational. And sure enough, we’re seeing a lot of different technologies out there now that, okay, they’re using GPS. It’s good enough for some things, but have a lot of demand for a lot better accuracy and and even looking forward, we’re already starting to see that, like there’s demands to go way beyond even where we’re at that we’re looking at in the future too. So yeah, I think there’s an interesting misconception there of what we have is good enough and there’s no reason to have better. But there certainly seems to be a pretty consistent trend that says everybody wants it to be better.

Scott Mears  03:59

That’s interesting. And with Sanjay, Would you concur on that one? Or would you say there’s another big misconception with location accuracy?

Sanjay Sharma  04:07

I think it’s, it’s the consumer behavior, right? So if I can see my Uber driving up to me, then why can’t I find the keys of my car in my house, or why can’t I find a carton in 100,000 square feet warehouse with the same level of precision and with the same level of ease and and and location is all about. You know, you’re trying to fix the problem at the root where, you know, brands, company, donor space, is trying to solve, or you’ve fixed the problem. You know, at the tail end, companies like Roambee are doing a lot of corrections to making making location as relevant as possible to our use cases.

Scott Mears  04:10

So that it’s interesting what you both say, because it’s that it seems like the question right now, the location accuracy, but there’s also that demand for wanting at the same time, there seems to be a push to want it, but also a satisfaction. So sort of sticking with you, Sanjay, for a moment, is, why would you say location actually is so important for supply chain right now, to really put more attention on it, to to drive this forward to, like you said, going all the way to a box level. I mean, that would be, you know, location actually on a different level altogether.

Sanjay Sharma  05:37

Yeah, I think there are two use cases, right? So use case of location accuracy is very demanding, not only inside the four walls of the enterprise, but also the outside the four walls. So take an example, right? Companies like Hyundai, Toyota, just name any automotive brand would ship 30,000 40,000 50,000 cars, and they’re all sitting in a parking lot. And the use case is, can you basically tell me this specific red car in a parking lot of 50,000 cars with the level of accuracy that I need? And today, there is none. Okay, it’s it’s guesswork. You would be lucky with a lot of technology at it to get, you know, the location, somewhere in the five to 50 meter kind of a range. Now, translate that car example into containers. If you go, you know, visit the Port of Singapore, of the port of Hamburg or the Port of Oakland. You know, containers are stacked on one on top of the other, the ability to retrieve the container very quickly and put it on the road that will take that container to a store and then put the products on the shelf so it get consumed. I mean, if you start connecting this.it all boils down to location intelligence being more more optimized for for performance, for productivity, right? So we at Rome, we see the location intelligence is the core to everything that we do, and then we talk about the inside the four walls, right? That’s a different kind of location challenge, right? Whether you’re in a hospital and you want to locate medical devices or you want to locate the bed, the patient bed, these are very different challenges. So I feel in any of the upper stacks of applications that needs to be built in. It needs to rely on the core location technology.

Scott Mears  07:53

And with this core technology, you know, getting this out into supply chain and making this work within supply chain to the level that your discussion, discussing Brian for you, what would you say are the really, the current limitations of location accuracy, currently, specifically in supply chains today?

Brian Manning  08:19

Yeah, there’s, I guess, a decent number of different kind of challenges when it comes to what what limits performance. There are, I guess, alternatives to GPS and GPS enhanced. I mean, GPS is kind of the the invisible utility that runs everything. That’s kind of the default today, but it has its limitations and challenges, whether that comes to say it’s accuracy, availability and accessibility, and you can kind of put them in a triangle, and there are enhancements that and alternatives, like things like GPS corrections or, you know, cellular and terrestrial based navigation, but everything has trade offs. And so you know, for example, if you look at that availability, accuracy, and then accessibility triangle, you can get a much higher power, you know, like a cellular signal is a much higher power, for example, than than GPS is. So you have better availability of it, but it’s a lot less accurate in terms of positioning. And now you’re tied to only where there’s cell coverage. And so the you know, accessibility is a lot lower also, and you need a cell modem, a cell plan, and all the other pieces that come along with it. On the other end of it, if you need better accuracy, there’s things like GPS correction services out there, which can get you higher accuracy that you can get down to centimeters today. But you generally for that to work, you need a very high performance receiver, a very high performance antenna. So the accessibility, not only because of the cost of all the devices that you need, goes down, but if you have a small device, like a phone or a watch or an IoT device, you just can’t put a big $500 antenna on it. And so. It’s kind of one of the big challenges that Xona was was founded to address is, how do you how do you hit all three of those marks? How do you provide a service that has very high availability, better availability than GPS, something that can work? We’re maybe not going to work in a tunnel or the basement of a parking garage, but how do you actually start to get through even one or two walls and open up capability where GPS just can’t reach today. How do you get, you know, to solve challenges like Sanjay mentioned with, you know, the car in the parking lot, or what rail something is on, and was really founded around autonomous vehicle world, and so our CTO works at Ford and the autonomous vehicle division, and a lot of the challenges boil down there to, you know, there you have vision systems, you have LIDAR systems that work pretty well in certain environments, but those don’t work in every environment and in the environments where they’re challenged, you need something like GPS to kind of to fill the gaps. The problem, though, is that GPS has enough accuracy to tell you what road you’re on, it doesn’t have enough accuracy to really tell you what lane you’re in, or much less if you’re in the center of that lane. So there’s a big demand for higher accuracy. But the real challenge is accessibility. And how do you make a service that is easy to access, and that means something that can be very easily integrated into low cost hardware? It also means something that’s actually affordable for users to use, and so that’s kind of what we’re aiming to do at Xona, is we’re taking all of that complexity of those high performance receivers and putting it all at the satellite layer so that we can actually provide scalable high performance out to billions of users, instead of just the few that can afford these really high end devices.

Scott Mears  11:41

That’s really incredible to you. What zone are doing, how you’re doing it different to get that level of accuracy. And it was interesting. You showed sort of the example of GPS, or the limitations with this current location accuracy. And sticking with you, Brian, for a moment, you mentioned on integration, because that’s the next stage, right? Let’s, let’s get the location. But then, how do we plug this effectively into our supply chain systems? What do you feel are the challenges you’re finding right now in achieving this moving forward?

Brian Manning  12:14

Just actually getting integrated things in. Yeah, it kind of dovetails with the previous piece there that there’s except there are solutions that are out there, but they mostly require a high performance receiver or some other device that it’s just not tractable or not scalable or not feasible. To add that the only thing in general that actually fits into a lot of these IoT devices, or mobile devices, or small devices, in terms of size, weight, power, etc, are very small GPS or Bluetooth chipsets. And so that’s really what we’ve focused on, is, how do we provide the performance that users need, but how do we provide it into the hardware that they actually have, and that’s that’s been a, really, the core area that the company’s been built around. And that’s what we’ve seen, the big challenges. And it’s a challenge that a lot of people think overlook when they’re trying to build any new service. That the biggest challenge, arguably, with deploying a new service isn’t necessarily putting the infrastructure out there. It’s integration in with the user equipment, and that’s exacerbated worse in GPS than anything else, because there’s 2 billion new GPS devices shipping every year that going in, asking those customers to put in a new chipset or a new antenna or some new capability, really just kind of removes the value, or destroys any value of what your service might have. Because, you know, if you can’t use it, really just doesn’t have a lot of value to it.

Scott Mears  13:52

Wow. 2 billion, that’s, that’s a lot of GPS units. Wow, that’s, that’s quite a fact. And before we dive into the collaboration and the innovations you’ve already teased some of it. I want to first ask you Sanjay as why Xona Space Systems? Why have we chosen them? What I really want to know, you know, what is that innovation that you found? Was, yes, these are the guys that Roambee wants to partners with.

Sanjay Sharma  14:17

I think one is they’re trying to solve the hard problem of autonomous cars, right? So I feel that you know, if you can get to two centimeter accuracy, which is sort of the demand for autonomous car and and if you have sensors that can basically collect a lot of sensing information and process that in real time, you would deliver a better driving experience and also make the car much more safer. So if you take that context and apply it into a supply chain, obviously we don’t need two centimeter accuracy, but imagine this right a very large, multi billion dollar companies production. Income and the capacity, how much they should produce on a daily, weekly, monthly basis, is based on how much of their products are getting consumed by their customers. Now, these products are in rail cars, and the only way they can get a signal that they need to start producing if they knew if the rail car is on a track where the track goes into the facility of the customer, or is it on a track adjusted to it, that is a track that is getting out of the facility to their yard, this simple signal is so important for a multi billion dollar chemical company’s production because if I knew that the empty rail car is coming back to us, I can now plan my production schedule and make sure I produce enough for the next batch and today that visibility is not there. So if you take Xona’s technology and apply it to this context, it becomes very, very relevant to hyper-precise location intelligence that can fuel the production as well as the consumer consumption of these chemicals. Now switch gears to what Brian actually said, Right? Would would we be interested in adding one more chipset, increase the cost of tracking and, you know, make it super proprietary? Absolutely not. So I feel Xona has solved this barrier to entry where it just over the air update. And I’m probably simplifying this, but the concept is, you know, it just over the air update on an existing chip set that’s in your existing device. And by doing so, I can get that hyper precise location that is necessary for the applications, so the ability to scale very fast and and for the customers to derive the returns of these investments become just accelerated. So that’s sort of how we see the marriage of minds here between the two companies.

Scott Mears  17:17

That’s wonderful. I’d also like to hear from you, Brian, from your point of view, why did Sona Space Systems choose remby? Why did you feel remby was the company for you to partner with?

Brian Manning  17:28

Yeah, I mean, we, we’re excited about all the different applications that pulsar can open up. And like I said, we kind of came out of more of an autonomous car world and started on that side. But we also know that, you know, that, you know, automotive does not necessarily have a reputation of being the fastest to adopt things. And so we’ve been looking around a lot of other different applications, and what we foundis the Roambee is IoT devices are pretty much the perfect application for for our pulsar service. And you know what Sanjay mentioned, enabling devices determine their location with with more accuracy and in more places than you can with GPS, really starts to open up an entirely new world of possibilities and insights that we think people aren’t even thinking about yet, and that’s what we saw. Roambee is oneof the companies that is actually thinking that way and is thinking about, what canyou do? Maybe Sanjay, you kind of joked you don’t need two centimeters yet, but just, just wait until you start to get it. What can you do with that? And what other applications can you start to open up? And especially, you know, we always knew that logistics world was an area that location intelligence and location security also, which is something we didn’t even really touch on. Too much that we offer that’s well above what and beyond what GPS can do we know those are important aspects, but we just really know too much about the specifics of it. And you know, how, how would our solution actually fit in? How, how could we benefit, you know, this world, and that’s we’ve been really excited and learned a ton from romby about what are the different challenges. And you know, how can we help them solve them. And, you know, how can we provide them better data to make better insights? Because, you know, especially in the age of AI and everything else, all of these models, all of our capabilities, are only as good and only as accurate as the data you feed them. And so that’s where we’re on, the kind of base layer there of, you know, how do we start to feed just so much better accuracy and so much better capability and all these things that you can really extract insights that just never would have existed in the past.

Scott Mears  19:27

That’s really powerful what you’re sharing here, and you’re totally right. It demands, you know the good quality data to get good quality analysis, and of course, location this, in this instance, I would love to unpack a little bit. I love that you’re sharing examples, both you and I want to unpack. What are the next steps for this collaboration. Then what is the future? You’ve already shared some use cases, which is great, but what do you see as the next steps for this collaboration, and how are you going to bake this into the IoT applications to make this real? And, you know, out. For the companies today, and I’ll stick with you, Brian for now,

Brian Manning  20:03

sure. Yeah, so where we’re at in I guess our journey here, our first production class satellite is going up in June, and so as Sanjay mentioned, with trying to make this just a quick and easy update, we’re working with a lot of different chipset and receiver and module companies to try and make that possible, so that every device out there has the ability, and you have the pulsar firmware in it that you can just basically turn it on. And then you, if you need extremely high accuracy, you need a higher security, just fire it up and go. And so we’re starting to do a lot of that testing on the ground now, integrating and testing with different different customer devices. And then once the satellite goes up, we’ll have kind of beta level service starting later in 25 that we can test with the real satellites and show the real capabilities as we start to, I guess, scale up the constellation from there to start providing real, real services.

Scott Mears  21:00

Wonderful. And the same for you. Sanjay, what do you feel the next steps? What are we going to see from this collaboration in the upcoming in 2025

Sanjay Sharma  21:08

I think Brian and his team are taking the bottoms up approach. We are taking the top down approach. So we are looking at identifying applications that can be commercialized on day one. So I talked about the rail car application, but there are applications where, you know, Unilever products went through a receiving dock door and it needs to come back from a shipping dock door. And today, there is no way to say, did it get into the receiving dock door and come out of another door that is meant to be delivered to a Costco or a Sam’s Club or any of those things. This concept is called cross docking of shipments. So products come in and they they get flavored with different packaging and then comes out. Customers would like to know when it went in and when it came out. Because if they can make this fast enough, they can ship it fast enough to their customers, and they can recognize revenues. So these are the use cases through some examples that I’ve shared on this podcast. Is to identify where the ROI is. So that’s one work we are doing. The second work we are doing is this is very new field. So what kind of pricing models would be applicable to the supply chain use cases, which may not mimic the use cases or the pricing models that Xona has for autonomous cars or other applications. So that’s the second part that we are doing, and the third part is between now and when the satellite goes, goes live in June. Is there some good test work that we can do, build some test harnesses, you know, see if we can connect the dots, getting the data out, bringing it to the cloud, showing it in a user interface. Can we do some of that work that does not require the satellite, and we prepared for pushing the envelope on location accuracy and location intelligence late next year.

Scott Mears  23:11

Wonderful. This is so exciting to hear. And I, and I do hope, Brian, you’ll be doing a live stream of this launch. Will you be doing a live stream?

Speaker 1  23:19

We gotta get that figured out. Yeah, yeah. We’re still ramping up all of our marketing here.

Scott Mears  23:24

So that’d be very exciting to see. I feel, and just looking at the future for a moment, because I feel there’s been a lot of great examples being shared here. Is Brian, how do you see location, intelligence evolving in the future? I mean, you said even going to centimeters, which is just blows my mind to even think about that. Where do you feel this goes? You know, even past 2025, you know, further. Where is this evolving?

Brian Manning  23:53

Yeah, I mean, as I mentioned the beginning, that there’s, there has been an addiction to accuracy that has been there for decades is going to continue to be there for decades going forward. Like, we have customers asking for millimeters already and asking, like, how do we get there and then? So as much as, like, you don’t think, like, why would anybody ever need millimeters? Like, there’s people that want it, there’s people that already need it. And so that reads law of accuracy continuing to get 10x better every 30 or so years is definitely going to continue. And what we get excited about, like I said, is what can ubiquitous, reliable precision really start to unlock in the world? For Sanjay shared a bunch of great examples, but there’s there’s so many more, and even things like, one of the classic uses of high precision GPS is precision farming, and precision farming has just absolutely immense environmental and sustainability impacts. If you look at the amount of water that precision farming has saved, it’s it’s like more than all of the water that people drink in the US in a year. It’s just, it’s almost unbelievable the impact of precision. In farming. The challenges, though, for something like that is that a lot of this technology are still pretty expensive, so they’re still quite limited to high budget farms in developed nations, just largely due to the cost of equipment. And so this is another area we look at like, well, how can pulsar start to play a major part to unlock those capabilities in small farms and in developing countries and in all the places that they can have a really big impact. We’re in the automotive world with autonomy starting to move forward. Autonomy is still kind of a luxury toy, and that I don’t get as excited about that. What I get more excited about is, how do you help guide the ambulance through the snowstorm, or how do you help keep the car safer on the middle of the road when it’s pouring rain or in fog, or in all these environments that humans just can’t operate as safely, and you know, when it’s so I grew up way up north of Michigan, where it is covered in snow. And LIDAR sensors, vision sensors, are going to have some challenges in the winter there, when it’s just clogged up with things and that. But that doesn’t mean we should ignore those areas. It just means that you need to have other sensors on board to help fill the gaps around when those when you’re in those more challenging conditions. So you know, there’s soon going to be more GPS devices on the planet than people, and that’s really our aim, is, how do we help enable all these devices to operate more efficiently, more safely than they can with GPS alone? And I think location, like I said, location, is just kind of the foundational and fundamental building block that nearly all these applications build off of.

Scott Mears  26:35

Wow. It’s clear to me this collaboration transcends supply chain. Really can just be world changing in many ways. And for you, Sanjay, what does that future look like? What excites you about that future past 2025 and onwards, with this location accuracy within supply chains?

Sanjay Sharma  26:58

I think there are two things that excites us, and the future, right? You know, obviously hyper precise location accuracy is extremely important. You know, on the examples that are talked about, but can we derive and infer and predict locations of products, how they will move? So based on where, where the product is today, on the hyper precise location, can I predict how it’s going to move now? It has got a lot more impact when you combine the two together. And you know, with the whole tailwinds of AI and ML and some of these large language models, there is an opportunity to start thinking about, can the can the product that is moving, whose location I’m aware about, can I take all of that data, including other data sets, and predict risk? And risk is very important from a supply chain perspective. And simple risk could be, is the product going to get spoiled, is the product going to get damaged? Is the product going to get compromised on quality? Is the product going to be separated from the transportation carrier? These simple signals are extremely important for keeping the supply chain coming and with the complexities that are now getting created in the existing supply chain, I feel the location accuracy, which is sort of, as Brian said, the foundation of everything, will seed more applications and try to make the entire supply chain world more uncomplex, if I may call it.

Scott Mears  28:32

Yes, please. And it’s just been so interesting to dive into this. And I must say, from the start to the end, I It’s really opened my eyes to how impactful this is going to have, not just on supply chain but the world. And I’m excited to this collaboration flourish. And just for us to sign off this episode, we always like to finish off with a fun thumbs up thumbs down segment. So I’m going to hit you with six questions that might be a big thumbs up or thumbs down. It’d be interesting if you do opposite, and if you could just say thumbs up or thumbs down and just give me the physical for the video watches as well, that would be wonderful, and we’ll see how we do How does that sound

Brian Manning  29:10

Good.

Sanjay Sharma  29:11

Ready.

Scott Mears  29:12

Okay, you ready Brian?

Brian Manning  29:14

Yep.

Scott Mears  29:15

Wonderful. Okay. Number one, accurate location data will soon be as crucial as inventory data in managing supply chains?

Sanjay Sharma  29:25

Thumbs up.

Brian Manning  29:27

Thumbs up.

Scott Mears  29:28

Traditional location technologies are sufficient for modern supply chain needs.

Sanjay Sharma  29:35

Thumbs up today.

Scott Mears  29:40

Interesting companies should invest more in integrating location intelligence into their existing supply chain systems.

Sanjay Sharma  29:48

Yes, thumbs

Scott Mears  29:49

Thumbs up, yes, thumbs up for both on that location accuracy is the most critical factor in improving supply chain efficiency.

Sanjay Sharma  29:57

Thumbs up. Thumbs. That. Wow, that’s

Brian Manning  30:01

Kind of sideways up. I mean, I think location accuracy is critical, but it’s also what you do with it. And so it’s just having the accuracy isn’t that valuable if you don’t actually know how to create the insights with it. So that’s why sideways.

Sanjay Sharma  30:13

I would do the trick to go from here to here. Yeah.

Brian Manning  30:20

We need companies on both ends to make it work so.

Scott Mears  30:23

Yeah, that’s a fair point. And number five, the more precise locate location data we collect, the greater the security risks?

Brian Manning  30:33

No, thumbs down. I don’t think,

Sanjay Sharma  30:35

Oh yes.

Brian Manning  30:36

I think I’m not concerned about that. Okay?

Scott Mears  30:39

And finally, do you think you could survive a week navigate in California with just a paper out, no GPS maps?

Sanjay Sharma  30:46

Yes.

Brian Manning  30:48

Yes, yeah. There’s probably some not radio friendly words during that week, but you can survive so.

Scott Mears  30:55

Yeah, right, yeah, the traditional paper map. Wonderful. Well, I’ve really appreciated you both coming on to the episode today. I think we’ve dived into, you know, really interesting topic, and shared, really what this the advancements of this collaboration could have, not just for both companies, but for supply chain and really the world. Is there anything any of you would like to share, just as a sign off to to the listeners and watchers on this episode.

Sanjay Sharma  31:24

Geospatial intelligence is the next frontier.

Scott Mears  31:28

Yes, it is. Well, wonderful. Thank you both for coming on to the episode. And yes, we’ll just give the the listeners just a little wave, say goodbye and say thank you very much.

Sanjay Sharma  31:37

Well, yeah.

Brian Manning  31:38

Thank you so much.

Scott Mears  31:40

Hi. My name is Scott Mears, and I’m one of the hosts of the supply chain tech podcast. With Roambee. On this podcast, we talk to supply chain heroes from around the world about everything, ranging from the disruptions related to supply chains, their personal experiences with tracking technologies, strategies to build resilience, and much, much more. We already have some recommended videos for you to the side of me, and if any of this sounds interesting to you, do subscribe to our Youtube channel and hit the bell icon so you don’t miss another Roambee video. I’ll see you next time you.