Jim Heppelmann
President and CEO
Yes, I think at the end of the day, ThingWorx is really interesting independently, but we’re going to use it to rework everything else we have to make that more interesting too. Let me try a quick a pass through PLM-ALM-SLM. Let’s start with SLM, because that’s the most obvious use case. The way you would service a product that you are in daily communication with and know everything about, it’s quite different than the way you’d service a product you haven’t seen in five years and have no clue what it’s status or current configuration, or how it was used or anything else. So how would you change things? Well, in the service world, you would try to move from a reactive break-fix mode to a proactive preventative medicine-type of mode. You’d try to prevent things from breaking as opposed to fix them after the fact. The second thing is when it is time to fix something or even make a preventative intervention, let’s say, you go there knowing exactly what needs to be done. Now, I don’t know, if you’ve ever been in your own home and your oven doesn’t work and you call the repair main, he comes out, he looks at it and he says, okay, now I know what oven you have. I have an idea what’s wrong with it, so I’ll be back tomorrow with all the parts. That means you’ve got to pay the guy to have spent two days, make two trips, everything. That’s just a really expensive service call. If he could have actually called you up and said, hey, I’m going to come out and do something to your oven to prevent a problem, and he shows up with everything. So trying to get to the punch line here, the way we do field service would change. The way we do spare parts planning would change. The way that we do what we call knowledge management – knowledge management is the process of going from a problem to a solution. The way we do that would change, because we would bring in al the data from the product into the process of determining what’s the root cause of the problem and therefore what should we do to solve it. So there are three or four really obvious high-value use cases in SLM. If I move to ALM quickly, you know, the really obvious high-value use case is to be able to maintain the software that’s in that connected product. I mean, if you have an automobile right now and you have a software problem—and by the way, half of the problems with automobiles right now are software problems, literally. If you have a software problem and your car is not connected, you need to bring that car to the dealer and they are going to treat it just like you have a crankshaft problem. They’re going to take the car out of your hands and they’re going to use it for a couple hours. You’re going to get a temporary car or sit in the lobby and have a donut – whatever. But they’re going to fix it in the dumbest way possible. In the new world, they’re just going to push a button or click on an icon and that software is going to be pushed down into your product and implemented, and that’s not just fantasy. I mean, that’s the way Tesla upgrades an entire fleet of automobiles all at once. It’s really incredible. So that’s coming, and that’s really Internet of Things meets ALM, which is you’re doing active management of the software in the entire fleet of products, understanding what problems there are, what security issues there may be. You’re pushing patches down, just like what happens in a data center. You know, you start to think of the car as a computer in the data center, as opposed to some remote, distant chunk of iron. In PLM, the best use cases, I think are in two areas. One is, let’s say, in requirements management to understand how does the customer actually use this product. That’s pretty informative, and today people, they start out with requirements which are sort of like a hypothesis of how the product will be used, but it’s very hard, short of putting some people, let’s say, in some kind of a laboratory, sterile environment where you monitor them. It’s hard to know what they actually do with your products. But with the Internet of Things, you’re gathering data. You know how it was used, how many hours per day, how many duty cycles, at what levels of speed and performance and whatever. A second great PLM example would be quality management. If you think of the way that products are developed today, you have these requirements, you go through an engineering process. You maybe even product a limited production run and you test the heck out of these products because you’re never going to see them again. But what if you actually were going to continue to see them every day and you could do continuous testing? What that means is that when you went beyond the limited production run and the problems start to show up because maybe the customer is actually using it a little different than you thought they would, you’re seeing that and you’re seeing what the problems are. You’re fixing these problems quickly before you replicate them hundreds or thousands of times and then produce hundreds of thousands of warranty issues you’re going to have to deal with later. So quality management – you know, one of our solution areas is windshield quality solutions. I think that’s another great place where we’re going to be able to use this data. So I mean, that’s all really exciting, and then the customers say if I had an Internet of Things platform where I could build all the applications that are unique to my world and then park next to that on the same platform all the applications that PTC has for SLM, ALM, PLM – my God, I would have this incredibly powerful platform and I would really think about doing things differently in my business. That’s where it’s getting really exciting.
Steve Koenig – Wedbush: Great. Thanks a lot for the color, Jim.