Well now we're going to take a look at the FedEx critical tracking system, the demo that Adam and I were talking about earlier. So this is basically a nationwide view of all of our shipments and vehicles and as we drill down into that you'll notice that those bubbles expand into more clusters and what we can see here is in the Atlanta market we have 15 extra vehicles, that's what the blue means is we've got too many vehicles and 13 too few vehicles in Chattanooga so our planners may look at this and basically reposition vehicles accordingly. If we want to look at the activity that's currently going on on this, this is powered by Lifecycle Data Services in the background and we've got a continuous feed of what's going on with shipments and vehicles and as they move we get updates on this screen appropriately. Now when we drill into the Dallas market we can see that we've got a few extra vehicles here. We're going to drill down in one specific vehicle here and this is actually going to Atlanta and this is a pharmaceutical shipment where we need to maintain the temperature. We're going to shift over here to the tracking view and this is going to look at this specific shipment for its course and we're going to fast forward it for the purpose of this demo here. So this is actually replaying something that happened in the past. You'll notice that the trucks up here at the top we've got this funny looking red shape that basically represents a fence as we call it at FedEx. This is a fence that tells you that we're exactly 15 minutes away from this pickup location and we can associate many different events with fences. In this case the customer wanted to be notified when we're 15 minutes away from their dock door so they can get prepared for us. And the reason why the shape is funny is because if you traveled every possible road into this location this is exactly where you're 15 or 5 minutes away. In this case this is a one minute fence and we're going to associate an automatic arrival at the pickup location with that. So our drivers don't need to tell us that, you know, I basically arrived at the location. We can infer that by the position of the vehicle in our reasoning system that we have in the background.
2:00Now for this particular shipment we're maintaining a temperature of 2 to 8 degrees Celsius for the entire shipment and these are the remote probes that we have deployed in the trailer. So we're getting real time readings again through live cycle data services and this is the historical breadcrumb of the sensor data as it's happened over time. Now we've got something bad that happened. The temperature control unit, the TCU, has a status of off and it's red for some reason. So that's bad. We're going to continue monitoring this and make sure nothing else wrong happens. We've got a reasoning system in the background here that's basically tracking the temperature and the sensor reads over time and we're detecting that there's an upward trend. So this is another indication that somebody in our control center would basically view these abnormal events and monitor those and take proactive measures. So we'll notice that it's going up and it's getting close to the 8 degree range that we're supposed to maintain. So we're going to start to take corrective action here. We're going to contact the driver and figure out what's going on with this. Now our reasoning engine just told us that we're a degree and a half increase within just the last 30 minutes which is very abnormal and our temperature control unit is off. So at this point the driver is sending us a message. He says that I've blown a fuse in the unit. I need a spare because my spare failed and I need immediate repair. So our control center agent would find the nearest repair facilities that might have this fuse. So they'll bring up a reroute. So this is a dynamic thing that happens whenever something bad happens. Thermo King. And basically we'll find all of the repair facilities. In this case it's a Thermo King location in Birmingham. And we'll notice that it's going to add 22 minutes to our route. So let's select that route option. Now you'll notice that the gray line was the normal route that we originally were planned on. And the blue is the new route that we're going to go on. So now our reasoning engine is picking this up and saying, "Hey, you just detoured off the route that you're supposed to be on. You're 22 minutes late." So that's okay. We're going to continue to track that because we need to fix this temperature problem that's going on.
4:03Now we've got a fence at the facility there. And you'll notice that we can blend in satellite imagery as well. So if that's important. And we'll notice that we're getting another piece of data from our reasoning engine. It's telling us that our vehicle speed is decreasing. Which is what we'd expect since they're stopped at the repair facility. But in this case we're not on the planned route and the vehicle's decreasing speed. So somebody from a security perspective might be interested in that. Because we may have some very high value goods on the back of the truck that we need to make sure aren't being in danger. So now our temperature control unit is showing normal again. You'll notice that our sensor reads are going back into normal operating condition. And now we're going to progress on to the delivery location. And we've got fences conveniently located at the delivery location as well. So we can notify a delivery recipient that we're getting near their location. So as we continue to progress here, we'll look at the delivery location. You'll notice the scheduled arrival times there is 2:36 and scheduled departures 3:36. So we're going to spend 60 minutes at the delivery. When we go into the ETA timeline here, we'll notice that the shipment was on schedule for the majority of the shipment. And then we had this 22-minute spike where we added the reroute to fix the repair. And basically, this is okay because the promise time to the customer actually was, we delivered 22 minutes ahead of schedule for the promise time. And the actual delivery time was met. So the customer's happy. We weren't at risk for a claim, which could be a very high dollar claim for us if we're shipping high value perishable goods on the back of the truck. And we still met the customer requirements. So everybody's happy in the end. Having technology and capabilities like lifecycle data services helps us to visualize this in a very low latency ways that we can proactively take measures to fix situations. Can I just ask one question? Sure. About like thresholds as opposed to, like that sort of made it look as if you were, the operative was all, the agent was already looking at what was happening.
6:04Correct. So are you capable of setting a threshold so that if the temperature changes, that's when this kicks in? Absolutely. Okay. Yeah, so we're tracking moving averages. So we're tracking that the temperature increased by a spike of X amount. And if that happens over the course of some window of interest, so we're looking at the last 30 minutes in this case, a degree and a half in the last 30 minutes, that would have been the case where if somebody wasn't looking at this, they would have been notified that something bad's happening with this. And there is a more aggregate view that shows multi-shipment, multi-vehicle for this as well. And they get notified of those events, and then they can drill down into this perspective to do some diagnostics. Yeah, so this reasoning engine is your secret souls. Yes. And it's based on, I mean, all the rules are in our secret sauce, but it's based on the open source rules project from JBoss. And the mapping technology is ESRI's Flex API. Right. So another important feature, I think you asked before about why we selected Adobe, we can plop in third-party component libraries. So we can plop in the ESRI library. FedEx didn't write this mapping engine behind us. We don't want to do that, right? There are parties that specialize in that. So we can integrate and extend the Adobe platform by putting these together and mesh them together. Right. You can plug a lot of different things in there as time goes on. Yes. That's right. Well, great. Well, thanks for showing that demo. That was fun. Thank you, Michael.