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68 SMT Magazine • April 2016 In the industry, there have been a lot of control, a lot of measurement, but there were limited level of connectivity in between these systems. So now, everybody wants to extract maximum output. The more data you get, the better de- cisions you can make with your processes and systems in the future. But in order to do so, you really have to ar- chitect everything in a way that, first, you do a certain level of processing. And then that level of processing, analytics is found at the enter- prise. And then there is that connectivity be- tween machines or devices. It's important for everyone to invest into technologies that contain local and distributed computation, local intelligence and control, and connectivity, and we see this as a big oppor- tunity because we are already are in this space; our customers are designing, testing, monitor- ing, controlling and measuring the things and the systems of the IoT. Las Marias: With industrial IoT, definitely huge amount of data will be generated in one produc- tion line in just a day. I think one of the challenges here will be the analysis of that data, and getting the correct, accurate data. Kranjc: I've been talking to a couple of large companies who manage huge machineries. We are talking about hundreds of millions of dol- lars of investment. For them, it is critical that every single machine runs continuously. If they are shut down for an hour, it cost millions. Now, they have all the intelligence, they have sensors, measurement units, and they all feed this into a corporate enterprise level system. But the problem that they have now, since it is so large, is struggling to extract those bits and pieces of information that are really essential for them because they see so much, and they don't know really what the problem may be. How can they come up with a next generation solution that would provide a lot of local intel- ligence and really publish what is essential? Now, we have a capability that helps acquire and generate data at extreme speeds. But the key is how much we can process. Because if we don't process and analyze that huge amount of data, then what is the point? We have tools that help engineers get in- credible insights and make predictive decisions out of that huge amount of data; in applications such as machine condition monitoring for en- ergy and infrastructure. Las Marias: In the smart factory, how can NI help usher the evolution, the convergence of the factory plant and the enterprise? Kranjc: One of our key advantages is the ability to scale to multiple deployment targets and a wide variety of applications, from small embed- ded wearable devices to autonomous robotics or monitoring and controlling the entire opera- tion, with the same platform and development environment. Like in the example of Airbus, where they used our board-level products so that they can process images locally and com- municate with their corporate systems and feed- back information to the engineer about what to do, what tool to be used. We provide that high- level access point, so that they can really start working and focus on a problem, and not think about how to architect from a component point of view. That's a huge time savings. Las Marias: What role can we expect NI to play in the future of the T&M market, and what does the future hold for the company? Kranjc: I believe the future indicates growth for us because we are fortunate to have such a good staff and really smart people who are motivat- ed. We have very capable management, and I believe that we are in the frontline of innova- tions in this industry. SMT " The more data you get, the better decisions you can make with your processes and systems in the future. " EnablinG ProcEss innovaTion ThrouGh TEsT and mEasurEmEnT soluTions