Sustainable Supply Chain

Optimizing Supply Chain Efficiency with Lightweight, Cloud-Delivered Location Intelligence - A Chat With Cognosos' Adrian Jennings

February 06, 2023 Tom Raftery Season 1 Episode 290
Sustainable Supply Chain
Optimizing Supply Chain Efficiency with Lightweight, Cloud-Delivered Location Intelligence - A Chat With Cognosos' Adrian Jennings
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Show Notes Transcript

I recently had the pleasure of interviewing Adrian Jennings, Chief Product Officer at Cognosos on the Digital Supply Chain Podcast.

We discussed how location intelligence technology is now becoming common in enterprise-level logistics and how it can benefit different industries. Adrian has over 23 years of experience in the real-time location industry and gave us a great insight into how Cognosos is changing the game in the industry. He explains how their solution focuses on addressing manual, spatially distributed processes that are often invisible.

We discussed how traditional tracking systems were very cumbersome and required a lot of expert installation, positioning, and calibration, and how Cognosos' lightweight infrastructure devices and cloud-delivered AI system is different.

We also discussed two use cases for the solution and how it can be used to improve efficiency and reduce overstocking in both the logistics and healthcare industries.

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Adrian Jennings:

If you ever see me speak at a conference, my first slide is always a puppy with a slipper, and I always ask people, if you had a puppy, and the puppy kept stealing your slippers. Would you hire someone to find your slippers for you or would you train the puppy not to? And people laugh cuz it sounds absurd to hire a slipper finder. But guess what? That's what people do.

Tom Raftery:

Good morning, good afternoon, or good evening, wherever you are in the world. This is the Digital Supply Chain podcast, the number one podcast focusing on the digitization of supply chain, and I'm your host, Tom Raftery. Hi, everyone. Welcome to the digital supply chain podcast. My name is Tom Raftery. And just before we start, I wanted to remind you, that if you'd like to become a supporter of this podcast, to help me continue creating informative and engaging episodes. Simply click on the support link in the show notes, or go-to tiny url.com/d S C pod. You can make a small donation starting at just three euros a month. That's less than the cost of a cup of coffee. But it would really help me with this podcast. Okay. Would that out of the way. On with today's show. And today I have a special guest, Adrian, Adrian, welcome to the podcast. Would you like to introduce yourself?

Adrian Jennings:

Sure. Hi. Thanks Tom. Um, Yeah, I'm Adrian Jennings and I'm the, Chief Product Officer at Cognosos and we supply real-time location intelligence solutions for, for logistics and healthcare industries.

Tom Raftery:

Okay. And tell me a little bit about that, Adrian, because we've had real time logistics tracking before through things like for example those little devices you stick in your ODB ports in, trucks and things like that. Is that what you're doing or what is it you're doing?

Adrian Jennings:

It's I mean in, in a sense, yes, that's what we're doing. That's that's all part of the same industry for sure. And what we're doing is is we're not doing anything new. We're doing things in a new way, but what we're doing has been done before. And it's exactly as you say, some kind of device. And that might be something that flows into your ODB port. It might be the GPS in your cellphone. You know, there are all different ways to figure out the location of things and you know, it turns out that knowing where all your things are is very helpful if you're managing complex processes. And I think it's something that's Google has helped us understand in our personal lives, right? I mean, the last 10 to 15 years, location intelligence has become core to our kind of smartphone experience. And I think it's often surprising to people that, that that technology is not pervasive across industry, that people aren't using similar technologies to manage their complex processes. So that's the big picture context of what we do. It's kind of bringing location intelligence that we know in our personal lives to these enterprise logistics, situations.

Tom Raftery:

I remember speaking to a Caterpillar exec a few years ago, and he talking about how they had real time location services on the devices that they built, these big caterpillar, back haulers or whatever they are. And they had them in the yard where they were manufacturing them because they would have a lot with, you know, maybe a hundred or 200 or 300 of these devices out there. And if they had to find an individual one, This was how, this was how they did it. Is it, is it that kind of location tracking services or is it for, you know, is that, are there other use cases or?

Adrian Jennings:

Yeah, I mean, there are tons of use cases. Yeah, that's a good example. And, and, and that, that's a good example where vehicles in particular these days are starting to have telematics in there where they have kind of built in tracking but not all. And that's not always usable, right? Because not of those vehicles are finished or functional and you might not be able to turn that off. So, so we are definitely in the realm of kind of an overlay solution that is independent from the vehicle you know, a tracking tag that you attach for the time that it's in the yard, but yes, you that that's exactly it. Now why does, why does Caterpillar put those tracking systems in there? Well, they put them in there because during their operational life, it's helpful to know where things are and there are all kinds of reasons on a construction site or in the agricultural setting to track things, right? And probably we in everyday life are most used to buses being tracked. And, a while ago they put GPS on buses, right? And now we can all stand at a bus stop. And the bus stop has digital signage and it says the bus, next bus will be here and you know, in four minutes and you can pull out your app and you can see where it is and and that kind of thing. So that's, that's like you're kind of over the road tracking. And when you think about supply chain and supply chain visibility and digitization and all these things. A lot of effort has been put into that kind of out in the world tracking piece. Where's my stuff out in the world? Where's where's my load of parts on its truck on its way to my plant? Whereas where's the ship at sea with the container on? But very little in fact investment has been put into where's my stuff when it gets to your facility. And, you know, caterpillar, there's an example where they're leveraging their onboard to do that. But a lot of people lose track of things the fence line. You know, inbound parts in a manufacturing facility come in and they knew exactly where it was until it checked in at the gate, and now they don't really know where it is in the yard until it randomly shows up at a dock door somewhere for unloading. Same thing on the outbound side, and it's not just these open areas as well. The same is true one of our verticals is in hospitals where we help manage fleets of critical equipment. And of course, it's rather paramount to lay your hands on the equipment that you need in a hospital exactly when you need it.

Tom Raftery:

Sure. I mean, we're not talking an MRI machine obviously, cuz they're quite static,

Adrian Jennings:

well, yeah, those

Tom Raftery:

like defibrillators or something.

Adrian Jennings:

Yeah, yeah. And um, you know, syringe pumps and recently of course, everybody learned what ventilators were and how critical they are, and why it's important to be able to get one when you need one. But there's really, I mean, you can think of any, any situation, any industry where I call it, this is the, this is my technical jargon term for the, for where the need is. Any place where there's manual, spatially distributed processes. So what do I mean by that? Like, manual processes, things that people do and spatially distributed meaning stuff gets moved all over.

Tom Raftery:

Building site.

Adrian Jennings:

um, A building site, a, a manufacturing plant a hospital there's all kinds of places where the process requires the thing being processed to move from kind of one specialist location to another, to have things

Tom Raftery:

Airports, I imagine would be another one.

Adrian Jennings:

Airports, great example. And not just on the managing the airport side. There's also, there's a whole that, you know, that moment when you're on the plane and the, and the, and the guy with the fluorescent jacket comes on and starts peering in the cockpit and you think, no, damn, now we're gonna be on a maintenance hold. There's that whole maintenance side of aviation that you don't see, which is also massively complex. So, yeah. Hundreds of examples that you can think of of places where manual processes occur and, and those processes distributed around. And the overwhelming majority of them, there's no visibility into where things are and therefore where they are in the process and therefore how the process is even running. And no real opportunity to err, approve, optimize, improve because it's kind of a bit of a black hole. A lot of people will call that the black hole.

Tom Raftery:

And you mentioned that your solution is a little bit different. How is it

Adrian Jennings:

different?. Yeah. so I've been doing this stuff for a long time now. It'll be, I'm, I'm just coming up to my 23rd anniversary in the real time location industry. I kind of joke with people that have been in this industry since it, before it was an industry really, back in those days, we were, we were inventing technologies with the tools we had at the time. We were trying out different things. Gosh, there isn't, I don't think there's anything I haven't tracked at some point cars and people and and airplanes. Even monkeys and cats and camels at one point as well, so that was back in those days, right? And so a lot of the solutions that are available on the market were invented in that time in the, let's say mid to late two thousands. On the healthcare side, you had people like, CenTrak and Sono who are doing interesting things with ultrasound and infrared. You had people like, Ubisense and Time Domain back in those days. The companies that I've both worked for doing interesting things with ultra wideband, a very precise tracking technology. You had people doing things with wifi, Cisco, were doing things like that. A company called Ercahow there's a whole, cadre of companies using the tools of the time and that's what's become incumbent in the industry. Those things have been kind of industrialized and perfected to the extent that they can be. So what do we do different? Well, so those solutions tend to be rather cumbersome. Back in those days, people were building for performance. It was obvious early in this industry that the more granular the location information like you know, which, which site am I at? Okay, interesting. Which floor of the hospital am I on? Okay, interesting. Which, which wing of this floor am I on? More Interesting. Which room am I? Now you're talking. Right? So that was obvious. And it was also obvious that you had to do that with a high degree of confidence. It's no good. It's no good. Which room am I in? Being a coin toss, right? You're gonna make decisions. It has to be the high degree of certainty. So back in those days, those systems were kind of designed for performance uncertainty, which, which is fine,

Tom Raftery:

Mm.

Adrian Jennings:

but the tools of the. The only way to achieve that were really to create these incredibly cumbersome systems. Any tracking system requires some kind of reference devices and a mobile device, which you would call the tag. And the tag figures out where it is in relation to those reference devices. Right? So gps reference devices are satellites, okay, fine. Well, for these indoor and these kind of localized systems there were devices you had to install. You'd build 'em to the ceiling, you'd build 'em to the walls, whatever it was, these infrastructure devices. And overwhelmingly, these solutions required a huge number of them that required expert positioning and expert deployment and calibration, and often required wiring to power them or communicate. And these were giant expensive construction projects. So it kind of killed, kind of knocked the r o ROI out of it. So what is, this is a long answer to your question, sorry, but I'm circling back to what does Cognosos do differently? Well, one, one thing Cognosos did differently was to be founded only a few years ago in the era of cloud and ai. So Cognosos had right from the start at its disposal tools that these incumbents just never had back in the day. And we'll get into this I'm sure, but the, what we do differently is we've taken a ground up approach to saying, yes, granularity and confidence is critical. You can't not have that. There's a whole industry of people that don't have that. We, we'll get into that too. but how do you get that and make the infrastructure lightweight and easy to deploy and easy to manage and maintain? How do you, how do you maintain the performance and knock a whole chunk of cost out of these systems? And that's what Cognosos has been able to do by leveraging these tools that just weren't available before when this industry was invented.

Tom Raftery:

Okay, so you have some kind of infrastructure devices that are lightweight that communicate with a cloud delivered AI system, and that helps you to track where things are...

Adrian Jennings:

exactly, yes. In a nutshell, of course, the devil's in the details and AI is one of those terms bandied around for almost anything including many things that aren't particularly well, let's say I , they're certainly A, but not particularly I and so, you know, AI in these industries has tended to mean, the application of giant machine learning, big data thinking to huge data sets. What could I do if I monitored everything about my manufacturing part or plant or hospital or distribution center. And could I notice trends? Could I predict issues? We're using AI right at the other end of the scale, so right down in the sensors themselves that figure out where things are. So let, let's talk about that a little bit. So, infrastructure, traditionally you've got systems, which first of all had a lot of on-premise processing potentially a lot of on device processing. And the key with these things is that they're easy to maintain and, and live with, right? As well as straightforward and low cost to deploy. So, What, what people started to move towards was, you know, battery powered infrastructure. Okay, that's great. But what they did is just move the burden from running wires to power them to giant expensive battery management programs. So our infrastructure, we just use Bluetooth, low energy beacons. Little BLE beacon and you can think Apple Air Tag or Tile or, or any of these things, which is just a just an little thing that pings out a, little ble signal on a regular beat, very low power consumption. Stick a battery and it lasts for 10 years, you know, put it in the ceiling, forget about it until you're repaint it. Right. So that's easy. And so we put those up in the ceiling and the tags listen to those devices and all the tag does is it says, okay, I've heard five beacons. And here's the signal strength. And it just passes that raw data right along through a gateway. We have a, we have our own low power consumption, low frequency data link, which has a very long range and outdoors, two mile range indoors, you know, think of a hospital floor or a, a wing of a, of a factory. So, very few of those devices that passes this raw data straight to the cloud. And that gives us the ability to apply a giant brain to the problem of solving for location,

Tom Raftery:

right,

Adrian Jennings:

cloud sized brain. And and the way we solve for location is with machine learning. This was kind of one of those fun things for me when Cognosos was recruiting for my role and I started chatting to them. This was certainly a couple of years ago. I was like, wow, you guys, didn't know how it was done at all. You went a completely different way. You've, you've kind of bucked the trend. And the trend is how do I figure out the X, Y and possibly Z and or Zed coordinates of of this object. And that's really hard to do in any, with any degree of accuracy. And Cognos came along and said, look, if you wanna know which room in a hospital, right, you're in as an example, which dock door you're at, at a warehouse, which parking space you're in, at a, at a trailer yard. That's a classification problem. Which, which room is which? You know what's really good at classification AI, like really good you know, many hundreds of billions of other people's money has been spent developing algorithms to do things like recognize cats from dogs. You know, you feed a bunch of pictures of a cat and say, these are all cats, and here's a bunch of pictures of dogs, and say, these are all dogs. Now you've got an image recognition algorithm can tell cats from dogs. Okay? That technology is very mature and It makes very high quality inferences now classification inferences based on pretty sparse input data. So what is sparse input data in the world of RTLS? That's not many beacons in the ceiling. That's what that is. So that sounds great. Right? So, because you know, number one, how do you, how do you get this infrastructure simple? Well, first of all, you use these cheapo beacons that last forever on batteries. Secondly, you don't put very many of them up there. And the third thing that machine learning loves is variation. It has to have features to learn from. So this room had better be pretty different from the one next door. So the enemy of traditional location systems has been walls, and furniture, and equipment, that bounce and block and otherwise mess up and modify your beautiful, pristine tracking signals that worked so well in the lab. GPS is great until you put a thunderstorm between you and the satellite. And most RTLS is great until you put a wall between you and the infrastructure device. But we love that machine learning feeds on that variation. Every bounce, every blockage, every wall is grist for the mill. So, you end up with this way of figuring out, we'll stick with the hospital. And the example of, which room am I in, in this room? I heard these four beacons. They had this signal strength. Pass that to the AI and it spits out, oh, I know which room you're in. You are here. I, I'm 98% confidence in that inference. And and that's all because of the nastiness of the environment, not despite so, you know, it just makes for a much better way to locate things. High quality, high granularity location. It doesn't require this overwhelming burden of infrastructure to feed it. Just lightweight network of beacons. And oh, by the way you can put them where's convenient. Not where we tell you to, you know, the traditional RTLS deployment was okay, we're gonna send a team of PhDs in, we're gonna spend two or three days surveying. We're gonna tell you exactly where to hang infrastructure devices. And we're gonna come on and twiddle knobs and sliders and calibrate and tweak until it's balanced and perfectly working. And now everybody back slowly away and, you know,

Tom Raftery:

Make no changes to the environment. Yeah,

Adrian Jennings:

Exactly. And everything's gonna work fine. Now we just need you to put some beacons kind of down the hallway every 30 or 40 feet or so in the hospital. We don't ask you to put anything inside patient rooms. Try doing that in the hospital. That's not gonna work out. We don't ask you to put poles up every 30 feet in your trailer yard. Who's gonna do that? Sparse man infrastructure. Teach the AI and and off you go.

Tom Raftery:

And

Adrian Jennings:

what's game changing.

Tom Raftery:

and what about the tags?

Adrian Jennings:

So the tag now is a device that just sits there listening And here we have to differentiate, right, because we do indoor things and we do outdoor things.

Tom Raftery:

Okay.

Adrian Jennings:

So indoors we rely, we've been talking about Bluetooth. It's a good example cause people understand how that works. But that, that we use for indoor tracking. Absolutely. That might be a plant, it might be, as I said, manufacturing facility. It might be inside a warehouse wherever you need to find things. Outdoors we use a combination actually of, of GPS and Bluetooth. And so, so the tag tags are always kind of specific to the task at hand. The, the curse of RTLS and R F I D companies is you go to, RFID shows, right? R F I D Journal and you're a RFID tag company and your display is a whole tabletop of a hundred different variations of the same tag with different shapes and sizes for all the different things you have to attach it to. So that means, you know, we have a Bluetooth tag that attaches to hospital equipment. It's small, it goes in a cradle, it has anti tamper. So you know, if someone's trying to steal the device, theft is, is real. We have staff tags that more like badges that track people with. On the outdoor side, we have car tags that hang off a rear view mirror of a, of a car for auto manufacturers that has a GPS and Bluetooth in it. We have a tag that goes on trailers, which is a different thing again for for warehousing and logistics. So, you know, the tag itself is variable. What each tag has in common is it collects raw data, passes it back over a common Cognosos, wireless data link said, passes it up to our big giant AI in the cloud and, and that does the number crunching even with gps. We're using the AI to improve what GPS can natively do. GPS can be very, very precise if you wanna throw enough money and complexity at it, but a but a simple GPS tag that lasts for four or five years on a single, on a battery, that's hard to do, but yeah, if you throw machine learning at it, you can make amazing improvements in accuracy.

Tom Raftery:

Fascinating. Fascinating. And you've mentioned some of the industries. Do you have any use cases, any wins from customers that you can speak to?

Adrian Jennings:

Yeah. Let's pick an outdoor one and indoor one, shall we? So let's start with outdoors and a common use case for us is in a logistics yard. And here's something a lot of people don't know when they buy a new car, is just how many people have driven it before you climb in?

Tom Raftery:

Hmm.

Adrian Jennings:

You know, you get your new car and it says it's got it's got six or seven miles on the, on the clock.

Tom Raftery:

Yep.

Adrian Jennings:

How the heck did it do that who's been driving it? Well, you know what? They drive all over the place. They come off the assembly line, they go out into get transferred to the logistics organization. They may have a yard of, gosh, 15, 20,000 vehicles sitting out there. And they go through some more process steps. They, they usually have some accessorization done. You can't do everything on the assembly line. And if you buy a car and you or a truck and you have the towing hitch added. Well, the logistics organization does that. It comes off the line, it goes into the yard, they park it, and then it goes off for its tow hitch and it comes back again and it might go off underbody coating and it might get its floor mats and its user manuals and all kinds of stuff happens. And those steps require someone to get in the car and drive it from one place to another. It's a manual, spatially distributed process. So the cars hop around, they move around. They, they, they come outta the plant. They go to first point arrest. Then they go out and they get parked. They come to the vehicle processing center. They may go back again. They may bounce back and forth. Somebody might bump into it. Now it has to come back and get repaired, go back out again. Don't tell anybody their car got bumped into before it was brand new and delivered to them all pristine. Then it has to get pre-staged at the railhead or the truck out before it gets loaded and finally shipped, Exhausting and, these cars will move 6, 7, 8, 10 times when the perfect process is actually three or four moves only. So what you get is this massive inefficiency of cars being shunted and shuffled, and nobody has any visibility of that. Well, you certainly do. If you track the car, we know exactly where it started and stopped and where it moved. And we even actually have a driver tag now, so you knew who's making the moves which is really helpful information for the operators. And it gives them the opportunity to see the process and understand where the inefficiencies are and fix the process and error- proof it, and monitor it and get cars from the plant to the train much faster than they do today. It's a, it's one of the last remaining big bottlenecks in that outbound supply chain. And I always kind of, I only half joke about this, that our solution comes with a health warning, which is, when you see what's actually going on in your process, you are going to be horrified, but and that's nearly always true, nearly always true. So I always tell people we're a part supplier of technology and we're part counselor to, to soothe cuz everybody has their problem. So that's one example, outdoors, give the visibility in the yard, help to optimize the process, reduce all these spurious moves, literally every time a car is moved, there's an opportunity that someone might scrape it. You can make a massive RoI impact by just avoiding that kind of yard rash that uh, So that's outdoors, indoors, kind of similar thing, little, slightly different angle. Let's think about hospitals and this will get us into you know, back to the old school of doing things. One of the old school mantras of rtls, you know, real-time location systems is if you lose assets, well we'll send you an asset tracking solution. So, if you can't find it, you bring up your phone, you run a search on what you're looking for on, it pops up in a Mac and here it's and that traditionally in hospitals has been how this has been pitched, right? Nurses are frustrated. They can't find what they need. The knee jerk reaction is buy more stuff. Because if you have more, then there's a higher probability that you can find one when you need one.

Tom Raftery:

Yep.

Adrian Jennings:

Meaning hospitals have. Probably twice as much equipment as they need. The average utilization for a piece of mobile equipment in a hospital is around 40%, which is horrifically low. But what are you gonna do? There's, you know, to provide the quality of care you need to lay your hands on the equipment. So traditionally people would say, okay, fine, give the nurse a cell phone. You can't find the syringe pump. Pull up a search. Where are the syringe pumps? Here's one. Go and grab that and bring it to where I need it. Well, that's not what we do. We think that's useful, but we think that's the minimum level of value you can derive outta these things. Hospitals have clean rooms where they pre-stage clean equipment. And there's a whole department, a biomed department whose job it is to maintain and clean equipment and make sure it's staged. Now, why can't a nurse find a particular piece of equipment? Well, the clean room got depleted. It ran out of IVs or wound vacs or ventilators, or whatever it is. Now I have to go on a safari to find one. Well, we know where everything is. We can count how many things are in the clean room. We can automatically tell biomed when there's a surge in demand or the room is falling below thresholds. So the, the use case in a hospital is kind of around automating that replenishment process. And now, how do you find a wound vac when you need one? Just go to the clean room. It's always gonna be one. We're gonna take care of it. We're gonna tell biomed every time they need to get up there with with some clean stock. There's a couple of examples of process optimization that you can do when you get the visibility of the disposition of all your assets. Where's my stuff translates into how's my process translates into process improvement and optimization.

Tom Raftery:

Cool, cool, cool. And where to next for a Cognosos? I mean, what, what do you see coming down the line?

Adrian Jennings:

Gosh, well, more of the same. You know, we're a, we're a rapidly growing company, so we're at that point in our growth where we have to maintain pretty solid focus. So we are currently heavily engaged in vehicle manufacturing logistics and in asset management in healthcare, mostly in hospitals, but that's starting to extend now into smaller facilities too. The next frontiers for us to kind of expansions of those things. So moving into workflow management in hospital. That means tracking caregivers and patients. Why do they do that? Well, you and I, and probably everybody listening, has had an experience where they've sat in an examination room for an hour with nobody coming to see them. It's a terribly inefficient process. They pulled you out of the waiting room into the examination room. Now this valuable resource, it's room that they've built and equipped. It's now sat there not providing any value, cuz the value in examination room happens when you're being examined.

Tom Raftery:

Mm.

Adrian Jennings:

So how do you fix that? Well, if we could better manage and understand, okay, why do you sit there? The doctor got delayed. Well we knew that cuz we know where the doctor is. We didn't need to pull you out of the comfy chair in the in the waiting room. So that's kind of next up for us in healthcare, I think is moving into workflows and that gets us into helping people manage things like urgent care centers and specialist imaging centers. Mentioned MRIs helping people optimize the throughput with MRIs to make sure they're maximizing the value of that expensive asset. And of course, a lot of that imaging is done in specialist imaging centers. Then on the logistics side we're definitely moving beyond automotive manufacturing logistics these days, getting pulled much more into all those other kinds of logistics that involve moving trailers and trucks from one place to another. So that can be be you know, food and beverage and, and garment and pharmaceuticals. All of them have similar supply issues so we're starting to get pulled into that a lot more now as well. So, as I said, kind of more of the same, expanding our footprint in the industries that we, that we're very familiar with.

Tom Raftery:

Cool. Cool. We're coming towards the end of the podcast now, Adrian. Is there any question that I haven't asked that you wish I had or any aspect of this we haven't touched on that you think it's important for people to be aware of?

Adrian Jennings:

Yeah, I think we touched on this a little bit. I think it's worth reiterating just to kind of get the point across. I mentioned that you know, traditionally the incumbents in this industry have, have shot for you know, the granularity and the value. Let's create the value, but have ended up with very expensive, difficult to deploy solutions. There's a newer part of the industry really again driven by Bluetooth that have gone the other way, which is, look it, it's pretty, just to do a pretty standard Bluetooth tracking system is pretty easy. Now forget about all the fancy stuff that we do. If you just had normally you'd have like a, a Bluetooth scanner installed in the ceiling, you'd have a tag. Think of as I said Tile and, and Air Tags and if each of those little infrastructure devices says, okay, here's the tags I can hear you must be near me. Super easy, very cheap. Simple phone app, web-based desktop app. Great. Got yourself, an RTLS solution. Go sell it. But the value's not really there. The value's not that great. The accuracy's not there. It's gonna tell you, okay, where's this I need to, I need a wound vac. Well, it's somewhere in the third floor at the west end of the third floor. Okay. Well, I mean, that's better than not knowing anywhere. And in the hospital where it is, I can go, I can go find that. I'll open and close a few doors and eventually I'll, I'll find it. And it's like the Bluetooth sorry, not the Bluetooth, Google, you know, they put the blue dot on the map, but they put the big blue circle around it. And what Google is saying is, eh, somewhere inside that circle. In fact, what Google is saying is we have 50% confidence you're inside that circle. But you could be outside anyway. Somewhere in there you can find it. So there's this traditional and now even more so notion that what's the value of RTLS? The value of RTLS is finding lost things. If you have a bad process, if you have a process where stuff's going missing, the most proximal symptom is I never can find stuff. And so, oh, here you go. This is cheap. This is a thing finding solution. Well, that's just, it's not a good solution. It's not creating value. What I always say to people is, you know, the value of RTLS is not finding lost things. It's making sure your things are never lost in the first place. I shouldn't have to drive around a trailer yard reading the numbers off the front of the trailers, trying to find the one I'm supposed to pick up and take over to the dock. I should go straight to where I know the trailer is, cuz it's supposed to be pre-staged there. And I know it was pre-staged properly because someone had control of the process. I should be able to just go to the clean room. So, the advice I think is, don't buy thing, finding solutions if your problem is losing things, buy solution optimization, process optimization solutions, so your things never go missing.

Tom Raftery:

Yeah,

Adrian Jennings:

Sounds obvious, but it's

Tom Raftery:

Prevention is better than cure, as they say.

Adrian Jennings:

Yeah. Yeah. My thing, I always, so if you ever see me speak at a conference, my first slide is always a puppy with a slipper, and I always ask people, if you had a puppy, and the puppy kept stealing your slippers. Would you hire someone to find your slippers for you or would you train the puppy not to? And people laugh cuz it sounds absurd to hire a slipper finder. But guess what? That's what people do. I can't find my trailers. I can't find my cars, they can't find my wound vacs. Let's, let's hire someone to go find them for us or actually buy a technology to go find it for us. That's silly. Train the puppy, fix the process. So there's a whole world of these low cost, low value, tracking systems that are thing, finding solutions. Don't go there. Don't go there. They just really don't provide the roi. I mean, I think that's the advice I would give

Tom Raftery:

people. Cool. Super. Great. Adrian, if people would like to know more about yourself or about Cognosos or any of the things we mentioned in the podcast today, where would you have me direct them?

Adrian Jennings:

I think probably straight to the, to the website is a good place to go. And if you'd like, I can send you a link that that you could share perhaps when you distribute the podcast. But there's some great information there. And Yeah, I'm easy enough to track down on LinkedIn and I love chatting about this stuff, as you can tell.

Tom Raftery:

Cool. All right, great. Adrian, that's been really interesting. Thanks a million for coming on the podcast today.

Adrian Jennings:

Yeah. Thanks for having us, Tom. It's been it's been fun.

Tom Raftery:

Okay, we've come to the end of the show. Thanks everyone for listening. If you'd like to know more about digital supply chains, simply drop me an email to TomRaftery@outlook.com If you like the show, please don't forget to click Follow on it in your podcast application of choice to be sure to get new episodes as soon as they're published Also, please don't forget to rate and review the podcast. It really does help new people to find a show. Thanks, catch you all next time.

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