the internet of things as a network of physical objects that contains embedded technology to sense communicate and interact with the external environment with devices such as smart televisions smart electricity meters and smart refrigerators the industrial Internet of Things is the part of the Internet of Things that focuses on devices and objects used in business settings for example devices can be used to sense and collect data for maritime fleets to help reduce unplanned downtime or for manufacturing systems to provide better control of processes the market associated with the industrial Internet of Things promises tremendous growth industry experts are predicting massive savings and increase productivity in revenue to the tune of up to 15 trillion dollars GDP growth in 20 years connecting tens of billions of devices to the industrial Internet of Things will revolutionize business by increasing process efficiencies overall productivity and reducing costs many industries are already using the industrial Internet of Things extensively the oil and gas industry for example is using the industrial Internet of Things to control drills and monitor the Earth's crust while the utilities industry uses it to remotely control building temperatures the benefits of effectively leveraging the industrial Internet of Things to improve business include increased production and quality better service levels increased network capacity improve troubleshooting and safety and streamlined maintenance Forrester estimates the current market for big data to be valued at about 12 billion dollars a part of this market will no doubt include data from the industrial Internet of Things which is made up of billions of devices collecting and transmitting data to support some of the fastest growing areas in global business when it comes to analyzing this big data there are several big technical hurdles that need to be addressed moving a wide variety of data from a wide variety of sources high volumes of data and high velocity of groupid moving big data through low bandwidth connections and limited access to real-time information updates the impact of not addressing the hurdles is huge bottlenecks increased costs and poor performance the bottom line is that the value of the industrial Internet of Things is diminished managing Big Data requires three steps collecting data sending the data to a central storage location and analyzing the information to achieve real-time insights but with technical hurdles things don't go as planned once this is addressed it's easy to see the positive impact this is where a tunity can help a tunity addresses the technical hurdles head-on enabling companies to move data fast easily and securely over when networks and low bandwidth connections this enables real-time analytics or better decision-making greater efficiency better productivity improved customer satisfaction and significant competitive advantage a tunity helps organizations revolutionize their business by leveraging the power of the industrial Internet of Things to learn more download the Eternity white paper
[Applause] so yes mind pull Roman and I was very glad to hear you again refer to the northern powerhouse because they're coming over from Sheffield this morning I must just say though even though I have come over from Sheffield I am really cold up here I don't know it's light down there so what I wanted to actually talk slight change of tack actually as the CTO my role in IBM it isn't to kind of big up the possibility but actually to worry about the delivery and the sustainability of the solutions that we provide so I want to talk a little bit about what that means for me and what I think the big challenge is so hence I've titled it from exploring to adoption I want to talk about how we move from actually those sort of first tentative experiments through to to scaling 0 so just a little bit of about me as a child there were three things I was really interested in one was nature one was machines especially excavators as all kids are and explorers and and whilst I could talk about biomimicry or robots and collaborative robots to capture those sorts of things which are some of the intersections there's a little area there that I wanted kind of reference out which is the interplay between industrial machinery and explorers and there's a quite a book that's written 90 years ago I'm gonna use it as a bit of a theme and reference and some of the lessons when we look back about what we what we're looking at today and that book was written by Maurice Holland 90 years ago called industrial explorers and hopefully you'll see why I use that as a reference point there's also a small quote there from a Robert Browning poem which the part there goes a man's reach should exceed his grasp or what's a heaven for and the idea behind that particular quote and he's used with a lot of is is actually that you should be reaching slightly further than you're comfortable for in order to be able to achieve what may seem impossible today but will be tomorrow's reality so industry for what it currently exceeds in in this room our grasp what is slightly beyond what we're trying to reach and I've put a few terms up and I could have put many many more but the idea was to kind of just put some things up there and I'm sure some of these are seen as enablers some of them are seen as issues is it some of those that we see we struggle with or is it actually the totality of those the some of them if you will and in the next question is do we believe that's beyond everybody's grasp well some of the stories we're hearing clearly not some people are starting to actually be able to take advantage of some of these things and what I put up here is a very very simple value stream for manufacturing you know you think of an idea you design it fabricate to assemble it and then text and Commissioner and we've heard the term digital twin quite a few times and one of the challenges I want to put to you really is thinking about what digital twin does for us in manufacturing but actually one of the challenges which is double-sided and I've come back round to this at the end and the point I'm making is we get the idea of a digital twin a digital copy of the physical product that we make going end-to-end through the entire project lifecycle but it also allows other people to cherry-pick parts of that value stream and that's the challenge I want you to just have at the back of your mind as we go through this particular presentation so I wasn't going to major on this but you may hear this and you'll hear this in other areas I'm sure these are some of the common lessons and some of the common ideas that people will talk about when they're talking about industry for made smart or whatever in that yes you should start now you should start experimenting don't be afraid to fail you know everything there are things that everybody at all levels can do to start off the journey and you should start with small discrete projects preferably things that give you some form of data point help you with your datasets and improve their forward however the kinds of problems that I deal with and not about experiments and not to do with how do we try little things out it's actually so what once you've kind of convinced you want to do something how do you do something so that you can bet your whole business on it where you can't afford to fail so coming back to that book that I mentioned by Morris Holland the industry explorers you can take great heart from the fact that of the 19 organizations that are listed in that book from 90 years ago 78 percent of them are still in existence now contrast that with only sixty years ago only twelve percent of Fortune 500 companies listed 60 years ago it still exists so clearly you could infer that those companies are doing something right now what were they doing why were they in that book they were researchers they had lots of areas that were trying to innovate and they were trying to bring new practices and processes in the way they made things and what they made into their organizations now was it about the technologies well actually I've read what's in the book are not going to sure you know there's some rubbish ideas in there but it was the fact that actually what they were doing was having a cultural change they were willing to invest in in looking forward it most definitely but it was also something else in there which is where I come from coming from and that is and this will be no surprise to anyone that knows me that they had an underpinning architecture that allowed them to take those ideas from from that just an invention in the research lab and scale into their business now I spent 30 years so it's not many different from Lukas in this space and I have condensed those entire 30 years of clicking architectural tires trying to make things work sorting out problems that scale into just three words and this is my gift to anybody I tell every architect I ever mentor these are the three things and only the three things they need to worry about viability integrity and extensibility if you take nothing else you take those back you make sure that anyone who gives you your solutions understands that those are the three things those a recipe for success for scaling so what I mean by that viability simply will it work will it do what it needs to do okay that may sound simple but actually when you look at that from an industry for made smarter point of view two areas really jump to mind integration integration is not about plugging together technology we can do that we can we that's not an issue integration is about making sure that the data within those things that you put together play nice okay think about it like different paints if you start mixing those up you don't just want a big brown muddy puddle like used to create a kindergarten if you mix everything together it has to be compatible it has to work together so integration is absolutely vital and location and this is often forgotten and and is very very relevant in a manufacturing environment and wonderfully trivial the thing about location and we all like the idea that we couldn't sort of you know potentially know exactly where somebody is in a shop floor we could know where an autonomous vehicle is in relation to them we can worry you know all this kind of stuff – ility mapping wonderful and we think about that and a phone and you could walk outside here up and down the street and you know we're kind of easy to make that assumption however your phone assumes you're at ground level or not somewhere flying in the air it also assumes you're near somewhere you're allowed to go a pathway or a road and stuff like that and so it's approximating and of course it's outside now most manufacturing occurs inside there are large lumps of metal around large lumps of concrete sometimes it's under round it confined spaces etc and the fidelity you need to make sure that a person and a faultless truck don't cross over obviously has to be very very high so location and senses and the ability to better worry about location in your viability both is a very very key point and often often either often overlooked integrity integrity in any solution really means will it do any harm will it do any damage to the stuff that's already in there so when you put something new in can you ensure the integrity of the processes can you ensure the integrity of the data and as well as that within inch before it's also about the security of it and you open stuff up you need to know basically who's accessing it and how one of the key points though on top of that is when you do this you will create a lot of data there's a lot more than you can just normally handle so how do you actually make sure you can make use of all of that and that's part of the integrity as well to make sure that you can exploit it correctly means that you have to have layers on top that can do that kind of analytics and a third part around extensibility and I'm going to use this as a framework as we go through some examples is really around platform to something to be extensible you have to have a common platform that can not that doesn't close off doors doesn't close up avenues it can cope with future change that's true of anybody if you make any complex product you'll understand that concept and the third part and we've heard this a few times already today and it's very become a pervasive term is ecosystems now what I mean by ecosystem isn't just a random collection of people coming together and basically having to transact it's actually a deliberately organized unit that they're able to work going forward and an ecosystem has to be able to swap in and out different partners as it goes along and that's not something just happens by accident so I've got three examples and I've got a challenge I wanted to build on – yeah and kicked-off unlooked-for stories and I have to say I really wanted to put some UK stories in there it would be great if next year the stories all had to be UK only stories I think that would be a big big challenge and I actually think that's something that we should consider very cleanly in this room that my the reason for picking the three stories across a piece is another three-pronged mechanism I use around product enterprise and ecosystem I look at industrial manufacturing and basically I divide that into three things the product that we make the enterprise the place that we make it and each system is the place where the product operates and so I've got one of each of those sort of space stories just to kind of show how I think industry for affects those so first one cone a elevator and lift manufacturer had a huge install base they maintain and look after a large number of lists using the viability integrity and extensibility assessment a couple of things I just draw out viability could they make it work could could they connect up the 1 million plus lifts that they've got yes that was that was a test we had to go out and kind of do all that absolutely that was the integration element within a six month period one of the key things behind this though was around the integrity was around the data and the fact that frankly putting that data into an IBM cloud could in theory mean there iBM has got access and understand people float I'm sends a movement of people that data is connais owned so they entirely have that data and that's really really important to them and I urge you to be aware that when you have that insight that it's highly valuable understanding your information and the insights of that information that you have with it is key to these services going forward and talking to services the last part just as an example around extensibility one of the things that connais were keen to do was actually to build this developer ecosystem so other people could use their platform and there's a cab hailing firm that have built an eco built an API service so that when you come into your apartment in the US this happens to be a new presser for a lift to go down to the ground floor at the same time there's a cab hailed and waiting for you as you get outside on the ground the ground floor or street level or whatever they call it in the US so a second one and they ponies like I couldn't refer to the individuals but this is a an automotive company that was taking 150,000 photographs a day in order to look at paint scratches and blemishes and using the viability integrity and extensibility points here but key thing about the integrity that we wanted to look at was could you repurpose that same data and use it for something else in this case actually checking the doing visual inspection on a task that was performed to check whether or not certain things were performed in the right way certain tasks were completed and but by performing a testing out the viability was could we get to a greater than 95% accuracy of that visual inspection so that was tested in the small amount and then scaled and the point about the scaling is it took three and a half days to train up that capability for that use case now that's really important because that's less it's at least 14 days normally for a new use case about three and a half days per use case means it suddenly becomes viable to being able to add more and more use cases on the same data and extend it across the entire shop floor and more operations and so the last case illiterate reference was was Merce whereby there was a lot of different parties are involved Chang and I think this is quite relevant because it was about having some form of neutral access that everybody could kind of get to go and so this was they wanted to produce a a blockchain proof of concept based on an open and neutral platform that had trusted data in it from and that actually was based on this sort of idea of smart contracts that has now been produced and subject to regulatory review is a joint venture that we've gone into Merce with that will has a whole series of parties signed up and that provides that kind of element of extensibility so just to kind of recap on on the viability integrity and extensibility part you know these are the sort of terms these are the kind of the highlights of what my testing framework has used to kind of say how do you take these from ideas and actually do something with them how do you scale these and do something serious and I won't read them through the whole things all over again but just to kind of pick a couple you know connectivity is key you know that connectivity was key in an aftermarket situations that was in other words something that was a pretty existing install base and then the ability to be able to develop new services reuse existing data these things that absolutely can because obviously when most of these are not starting from a from a greenfield situation so one thing I wanted to I said I'd come back to was that point about the union's about the digital twin so this is a chart that I've taken from CB insights deliberately full at one end this deliberate what it is over the last nine years or so is it a chance of what are called unicorns and apologies if you're not familiar with that will you slightly to move essentially these are startups that have gone to a billion dollars or more in valuation and nine years ago there were you know virtually none and you can see nowadays and it only goes up until last year there's a lot and you'll recognize the air being bees and Spotify is in the uber is early on but there's many many more there and one of the things that these organizations have in common is that they have been able to find something digital that actually has challenged their individual markets so if I take an air B&B or an uber or whatever else you know we can look at the platform they've built and the digital equivalent that they have created that digital twinning concept for me is not a million miles away there is a potential unicorn sitting somewhere who can own just the digital tweening coffee and never touch metal and if you think that's a little bit too far-fetched you only have to look in the construction industry to see how close that is and so just add a wonderful timing I wish I could claim it in addition though just this week iBM has launched released its study or C suite study where we've interviewed 12 now a thousand or so c-suite executives and if anybody wants to know any more about it then you know please see me or someone on the stand and we can get some access to that for you but the key thing about it one of the key messages that type of the report is called incumbents strike back because basically it's it's recognizing the biggest thing is that people are recognizing this potential threat is real in all set and we'll come in manufacturing as well and it's about how they are gearing up and what they are doing in response to that to be out of scale of solutions to ensure they don't get left behind and become one of those 7% that I referred to earlier so thank you very much
the efforts for improving the productivity and efficiency have never stopped under the roof of industrial enterprises this endless race for being better gave chance to three big revolutionary steps initiated by disruptive technologies first the steam engine then electricity finally the computer all revolutionized the way we work and now the Internet of Things ignites a fourth one the Internet of Things is a concept that projects expanding the Internet to anything where a connection can be established in the search of this fourth revolution industrial enterprises embraces Internet of Things and are making their entry into the connected world that we call industrial Internet of Things shortly I IOT it's about connecting machines than factories than enterprises and finally all supply chains collecting analyzing processing the big data and using it securely within the value chain in real time it's data-driven and smart world where decisions are faster and more rational actions are more efficient and real-time factories are smarter with machines talking to the entire world learning and continuously optimizing each other's performance smart factories they require smart machines with our product machine cloud we make your machines connected smart an industrial internet ready machine cloud by I for zero enables machine manufacturers to take their place in the IOT world and be aligned in a big movement for the fourth Industrial Revolution
when I talk to my research colleagues I said if you could start from scratch and have no limitation to communication what then would you do I think that the fighting part it's very good example of a collaboration product with different sectors coming together with the University as the catalyst Ericsson in this case is providing these solutions how to enable new ways of manufacturing to take a leap in the way that you manufacture things we think that project technology will be useful in the four phases the design deployment operation and maintenance phase that are existing in everything that you operate with and they will actually generate different benefits depending on the phase that you're in the operation phases for example the operators will get the information very quickly and re in real-time in the maintenance phase sensors will gather information and tell you exactly when to change the tool or varying or the product and logistics around it will be much easier if you have quick and safe communication systems for a long time automation and regulations have put a lot of fences around machines but actually going more back to a handicraft that you as an operator having the information to actually see what's going on inside the process again Taji is much more safe and reliable we can provide operators with video instructions real-time communications secure transfer remote service support you can sit in the bus on the way to work and start the machine everyone wants to increase the productivity the flexibility there is sustainability by connecting all the tools and all machines and all the robots and all the people I think that we have the right components to make something happen
intelligence and connectivity are moving into the sensors actuators and devices and applications across energy healthcare manufacturing transportation and more combined with internet protocols fog computing and the cloud we now have the industrial Internet of Things unlike the regular Internet the industrial Internet must meet higher standards of security and safety it must also stand the test of time the industrial Internet needs a common architecture to connect sensor to cloud power to factory and cities to medical services the industrial internet consortium is a group of experts from more than 170 companies and different industries their goal is to build and prove a common architecture that spans sensor to cloud inter operates between vendors and works across industries the industrial internet reference architecture I IRA is an open standards-based architecture for the industrial Internet the IEEE IRA helps achieve interoperability provides technology guidance and advances development of standards the key component of the IEEE IRA is connectivity it includes a core data bus and gateways to other standards the central data bus with gateways connects smart machines together into large-scale intelligent systems this data centric connectivity architecture relies on quality of service to support features such as data delivery timeliness ordering durability life span and fault tolerance security is an inherent part of the architecture because of these important features the connected intelligent systems can perform functions that were never possible before data distribution service DDS is the first open international middleware standard directly addressing publish/subscribe communications for real-time and embedded industrial systems DDS delivers unmatched reliability performance and scalability it is the best data centric or connectivity standard for the IEEE IRA is it really bad if your system fails for a few seconds or even a few milliseconds do you measure latency in milliseconds or microseconds does your system track thousands or even millions of real-time data values or do you need industrial scalability does your system have to last for years if you answered yes to at least two of those questions you need DDS over the last 15 years the DDS standard has been successfully deployed in many real-world systems using architectures like the iír a successful DDS based system designs in production today include the Grand Coulee Dam the largest power plant in North America Siemens wind power the world's largest wind turbine manufacturer GE Healthcare the world's largest medical imaging company NASA's launch control at Kennedy Space Center the world's largest SCADA system outtie a part of the world's largest automaker more than 800 of these system designs are powered by RTI we applied what we learned from our customer successes to the IEEE IRA and can apply them to your industrial internet system on behalf of the industrial internet consortium we invite you to learn more about the IEEE IRA and how to apply it in the real world we are happy to share our knowledge with you you
data-driven manufacturing simply means that we can make better decisions about manufacturing processes because computer networks make it possible to give there are lots of data and turn that data into actionable information right now the best example of data-driven manufacturing is machine monitoring connecting machine tools and other manufacturing devices to a computer network and send that data to machine monitoring software with this software managers and supervisors can then take a quick look at what machines are running are not running and take steps to keep them running or get them running again if the machine isn't running the reason will be clear it's in setup the tool is broken it's scheduled maintenance somebody forgot to take an optional stop out of the CNC program that's no longer needed some of the immediate actions that could be taken would include rushing a replacement tool to that machine tool or a longer fix might be starting a set up program to address the issue of excessive set-up time machine monitoring is a good example of data-driven manufacturing because all of the ingredients for data-driven manufacturing come to play the first ingredient is having a network to connect to most shop networks use the Internet because internet enabled technology is easy to set up and inexpensive using the internet makes it possible for all kinds of connectable devices and control systems to be linked together to share and respond to data that's one reason why the term the industrial Internet of Things is popular right now using the Internet also opens the door for cloud-based applications and remote data storage data-driven manufacturing depends on getting the data shops need to make decisions here again machine monitoring systems have taken the lead in promoting the exchange ability and interoperability of machine generated data for example machine modern systems were one of the first applications to take advantage of empty connect the open source standard for the interoperability of machine generated data that translates that data into a common internet-based language since it first appeared at IMTS in 2008 empty connect has been adopted by many machine tool builders and suppliers of all kinds of manufacturing equipment empty connect adapters even make it possible to link older machine tools and simple control systems to deliver usable data to a shop network all kinds of smart sensors are being developed to check on the status and condition of machine tool subsystems and accessories I think it's safe to say that data-driven manufacturing is revolutionary that is it's a real game changer a total reset through manufacturing companies the reason is operators supervisors and managers can get reliable facts and figures to make decisions we no longer have to rely on guesses assumptions or stale incomplete data to determine what steps or actions to take next the real impact of this revolution is apparent in shops that have machine monitoring in place just having uptime readings and OTE ratings on display on big screen TVs encourages shop personnel to take the simple steps that can improve uptime or productivity from 10 to 20 percent off the bat and once the machine monitoring infrastructure and shop culture are in place it's easy for shops to move to other opportunities for connecting machines and manufacturing processes to share and respond to data automatically data-driven manufacturing represents a sweeping advance for manufacturers it's no wonder it's being called the fourth Industrial Revolution or industry 4.0 I'm convinced that machine monitoring is the gateway to this new world machine monitoring sets off a chain reaction that makes manufacturing more profitable more productive and more personally rewarding to all those involved
When we approached the automation manufacturers, the problem was they wanted to know the size of the bottles and the type of cap we have. Thus, we were forced to change bottles and that helped us in finding the machine that is suitable. So we've managed to cut down the number of different bottle sizes and reduced to just 5 to 6 different bottles. When we embarked on the new labouring machines back in 2016, it has solved many of our problems and improved our production capacity by a large amount. We're looking at new technologies such as the new roasting method that will enhance the flavour of the seaseme seed that will be better than what we currently have. The grant has definately helped to achieve what we wanted. The grant application process is quite fast. It is only narrowed down on what you have. It will depend on what you want and what you can achieve to justify the grant from the government. The space that is required, the technical know-how and the financial resources you have. We are always envsioning a robotic arm in automation. What if these machines were to break down? What is the downtime that you allow? Do you depend people in your business to repair them or do you depend on the machine supplier to do so? As for finances, you need to know what is your ROI for these machines.
companies come in all shapes and sizes some are small mom-and-pop shops while others are manufacturing giants and they all have the same goal to make money but in reality some are simply better at it and some get better while others do not the North American manufacturing landscape is changing labor pools are getting smaller wages are increasing and there is the constant pressure to reduce costs unfortunately many companies look to automation as a one-size-fits-all solution but this is not the case the manufacturing automation industry is a large complex multibillion-dollar entity with multiple layers of suppliers developers providers and customers and the range of problems and solutions is endless and often confusing to navigate without extensive automation experience it's almost impossible to make the right choice but there's good news EMTs advanced manufacturing engineers can help your company decide if automation is right for you this process includes gathering and analyzing data accessing existing technologies and detailed risk analysis all under the umbrella of your fiscal objectives the result AMT will let you know the impact of automation before making the investment for over 25 years AMT has saved companies millions of dollars by helping them avoid pitfalls and pulling others out of automation mistakes they have distilled years of experience and have assembled a team of experts that will ensure your investment is safe and profitable if you are considering an automation solution or maybe exploring a company for acquisition AMT will guide you in the right direction and help you understand the impact of automation on your current and future investments you
To me, that isn't true. As low as $20,000 you're able to implement a small system, such as monitoring and inspection systems. These systems are without government grant. With government grant you'll probably pay just a few thousand dollars. The application process takes an average of three months for a small scale project. The first thing you should do is approach systems integrators like ourselves to help them understand their needs. So that we can give them the estimate application cost and from there they can look for a consultant. The consultant is someone who can help SMEs to assist in the proposal to get a government grant. In terms of manufacturing, they can start with either the packaging or quality control line. These are labour intensive areas. Automation wise, you can improve your efficiency and cut unnecessary labour costs. These are the main keypoints of automation.