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New York State School of Industrial and Labor Relations (ILR) ​first of all fantastic be here for everything IOT type event I mean most of us have been around this sector for a while you know I've spent most of our time at barbecues explaining what we do all day so it's good to have a room of people at least you know a good way along that journey so I'm gonna take some time today just to talk about what I do some of the learnings that we've seen you with GE and particularly what we're seeing in this space and to give you just a flavor I think of what are some of the challenges we see in the realities of the industrial side of IOT so I'm going to start with with this gentleman I'm just going to deviate a little bit away from the sort of energy sector but this guy's is probably not the poster child for digital and IOT you'd imagine but this gentleman is an Indonesian farmer just before I go any further there's anybody anybody farmer in the audience Indonesian farmer perhaps just before I get myself into too much trouble okay so this guy's an Indonesian farmer and oh he's a recipient of a solution that one of our partners has put in place using a piece of technology that we've been building and Indonesian farms and they've been much about them are typically around about three-quarters of an acre pretty small parcels of land and the Indonesian government will tell you that you need an acre of land to be above the poverty line so the vast majority of these farmers are really struggling to make ends meet year on year and so we've been working with a company in Indonesia to help these guys sense their farms very very cheaply and what we essentially do is once a week somebody will walk around the perimeter of the farm and then selected points inside the crops and measure just with a normal phone some some basic data recorded in a smart phone upload into the cloud collect that data and feed it back to the farmer and the farmer uses that information to change the fertilizer the feed and the balance of the watering in and around his his plot what we're seeing is using that real simple technology sensing clouds and basic analytics that the farmers can improve their yield by about 70% so all of a sudden these guys are above the poverty line they are living a completely different existence they're able to expand them their farms and they're able to do so much more so a real simple use of IOT but a real use of IOT and and I use it because it's great to see something actually being used in reality I see so many pilots and so many CIOs particularly who just don't know where to start and I was a CIO in a prior life and I think we all burnt with big projects you know taking on these monster projects and just you know ending up in trouble and fundamentally I think with the IOT space nobody really has that clarity yet as to where we're going most CIOs will tell you that big data and IOT or a big part of what they need to do but the roadmaps aren't clear so I come across a lot of pilots a lot of prototypes but not a lot of mainstream projects and I think it's incumbent on us the people in this room to help the industry really understand what are those key projects one of those money backs projects now that we can do to get the winds on the board to drive the bigger change going forward so my first contention today is that there is a massive opportunity out there and we waste most of the opportunity we've got customers I talk to in my space so power the utility space aviation healthcare overwhelmingly the first thing we talk about is they say well we know how do we start collecting data how do I sense data and my contention is that we actually waste most of the data that's out there one of my large mining customers told me that they use half of 1% of the data they collect and as the data they collect and fundamentally that's because most of the data is locked up in individual systems now we make equipment and one of things we did very early on we said our systems are our solutions our products need to be open so if you take equipment off on our jet engine all of the data is commonly available and we'll make it available to to the operator manufacturers need to let go of the data they need to let go of the proprietary nature of their data and make it more widespread no we were talking a few of us the other day about you know Barangaroo and just thinking about the amount of data that a Barangaroo would generate and a really are we able to bring those to that data together in the example that somebody threw it was if you could harness the data coming down in the lift you know how many people are actually coming down at a lift one time the weather outside know it was raining for example and then know that there wasn't a train or a bus coming aggregate that data you'd have an amazing demand based forecasting system to give to an uber people coming it's raining there's no train coming send cars but we can't do that because that all that data is hidden away in the islands so fundamentally I think the biggest hurdle we see with our customers is islands of data its proprietary data and this need to bring it all together and I think when we can do that that's when we see the big wins it's not necessarily adding extra cost it's not necessarily adding extra sensors so the second thing really I want to talk about is in in my world we sell equipment and most of our customers to big things that they search for reliability and efficiency and I put up the the 1989 Jaguar XJS that's probably the pinnacle of both of those two things right I'd


cover to this car for many years v12 engine spends more time in the mechanics yaadon on the road right but you know fundamentally we sell equipment and those are the two things people strive to get so for example a jet engine that we put on the side of a plane fuel efficiency is becoming more and more important a Qantas wants to fly from Perth to London they do that because they have flight efficiency services we help them monitor the engine and drive that engine further than this ever been planned to be driven from a reliability perspective if there's a problem in flight that engine is generating about a terabyte of data an hour and by the time it lands we can have somebody there to fix it on arrival so the sense of reliability and efficiency are absolutely the two key paramount things in the industrial space one of my all and gas customers for example has a solution we've put in place in their Koski coal seam gas wells we have a very simple iPad based solution that their field engineers can carry around that monitors about 20 different data points in and around the wells and it can give them indications of when the wellhead is going to fail to the point where we can say in three days time this particular well is going to get clogged don't get worried about fixing it today don't drive the 500k is out to where it is you don't need to do it today but you can go tomorrow because it's going to fail the day after now that ability to understand how you change your maintenance in your servicing has an enormous amount of value you know the parallel is just like us in a car now everyone changes their oil that you know or gets their car serviced at 12,000 KS because that's what it says in the in the corner but in reality everybody's car is fundamentally different you know they might come off the line the same but the way you drive your car might be very different the way I Drive my car and if you drive your car hard it might break down at 10,000 KS and give you a problem or if you drive it really well you might be able to stretch that service out so getting that balance of reliability and efficiency and how you balance out your servicing is absolutely paramount so those are the things that we look for in this space and where we're seeing the real winners are the customers that are striving not for the massive increases but for the 1% and the 1% right now are those money bucks projects I've got a project with a company that's hauling iron ore they buy our locomotives our locomotives are about a three million dollar piece of equipment and right in the middle it has two racks there are 900 sensors in and around the locomotive it's collecting data from from the wheels the bearings the fuel system the engine management system collecting all that data in real time it's doing a lot of edge processing on the locomotive itself and then it's sending data back up to the cloud depending on what sort of coverage it's got it's on satellite attendance rudimentary data but once it comes back into the yard he'll send the rest the ability for that locomotive to continually monitor itself means that we can drive performance fuel efficiency around the locomotive but where you can also stop things like a derailment one of the biggest issues for any kind of freight operator is derailment if a train falls over you've got a big problem typically where you've got single rail tracks it stops all the other trains behind it and if you're pulling iron ore for example you're losing production because you're not getting that iron ore out to the port right so the ability for us to understand that this particular bearing is overheating and this train is going to fall over within the next 20 minutes allows the the operators to do something completely different do they slow it down they pull into a siding or whatever so again small simple winds small simple projects in the beginning but these are the projects which are the money box projects that are then buying the future for us and the 1% savings they were really always striving for today the slides got a picture of a pigeon any ending pigeon fanciers here this is always interesting one I think it's if I love that phrase pigeon fancier my grandfather was a pigeon fancier as was the queen manuel noriega and picasso just three notable pigeon fences but as a child I remember sitting with my grandfather and he had a new pigeon I was probably four or five new pigeon and he was going out to race it for the first time and this bird was four hundred pounds in the UK so this is sizable investment and he was going to launch this bird for the first time and I said to me how do you know it's gonna go we were fifty case away from home I said how do you know this bird is gonna make it home and he said well we don't well firstly we don't know if it's gonna make it home and then I was this inquisitive child and so I said well you know how does it do it you know has the and we went through all the different theories the magnetic things in its head and you know flying around his circles to orient itself he said but fundamentally we don't know we don't know how that bird makes it home and it's one of many many factors and each bird may work in a different way and so the reason I quote the story was is we look at the big data space oftentimes the solutions we put in place feel like that so we make a system called predicts it's our the way I describe it's like iOS the industrial Internet if you want to build an IOT application in the industrial space predicts is like an underlying platform it manages your data ingestion in an industrial space does all the analytics and it does the visualization on the backend right and in the middle there's this big kind of watch of analytics and what we typically find is there isn't usually one solution for most problems there's a lot of different analytics that the that we use because the transfer prong function is you get into more and more complex datasets starts to get really really confused and so what we typically find is the customers will put the data through three or four of these different analytics routines maybe in your network some traditional regression analysis traditional condition monitoring and blend the


output to get a really good view of what the what the solution should be and it kind of feels to me like the pigeon we don't really always have clarity of what that transfer function is when we get into this big data space but again more and more experimentation I have a large team of data scientists that I pull upon and I think you know I have a 13 year old son I've said to him be a data scientist you know there's so much money in that and so much in short supply we can't get enough data scientists and fundamentally the ability for data scientists to take that analytic output but blend it with almost a physics reality it's really important you know we've been making jet engines for 50 60 years we've got people that understand the hardware if I can get a great data scientist and somebody who understands the hardware and put the two together we've got a real winning combination and it's that balance of physics and analytics which is really really driving the change so my overall message here is you know again start something let's try these projects it's not necessarily the end game that we have to reach for everyone's looking for that that you know 30 40 50 million dollar project when in reality it's baby steps right now they the hype the media the consumer side of IOT is racing along a breakneck speed but the industrial world is somewhat of a lie guard in this space so and then just to kind of just wrap this up you know I I think you know your comments earlier on are absolutely true Australia's in an amazing place if you think about our marketplace for IOT I typically look at you know two quite distinct markets I look after an Asia region I've got customers in developing nations where we're putting in new infrastructure you put in a new oil environment you put in a new grid new power station we censor eyes those from the very beginning and then we've got the developed nations and I've put Australia in their category where we're trying to squeeze you know the most out of what we've got you know whether it's optimizing a mine site whether it's optimizing the grid whether it's looking to get a gas turbine to perform better fundamentally our ability to sensor eyes to analyze and optimize is absolutely critical and Australia has always had an amazing history of innovate we have a real need as we want to remain competitive in this space so I think all the the conditions are right for us to be incredibly successful not only for our domestic use but to also export to the broader world so excited to be here thanks for the time gonna be a good panel discussion this afternoon so thank you very much [Applause] Mannes College The New School for Music.

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SOCIAL NETWORK ANALYSIS UNIVERSITY OF MICHIGAN  
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