PTC, INC Citi Global TMT Conference

Sure. I have been at PTC for a long time, over 30 years. I have spent the first half of my career, let us say, implementing our products. I was the head of our services organization for a decade, and then I have spent the second half of my career in products. I was the general manager of PLM, and now I am EVP of products. These days, I spend most of my time customer-facing, sponsoring our most important clients.

Sure. PTC's customers, we service customers across a bunch of different verticals. Think industrial, FA&D, electronics and high tech, medical technology, automotive. They are our main customers, and they share a certain set of characteristics. That is, our customers make highly complicated electromechanical equipment that is difficult to engineer, it is hard to manufacture, and we say have a long and interesting service life. When we talk about the intelligent product life cycle, let us say the basics of it is that these companies, let us talk about, let us say, a rooftop air conditioner manufacturer, Trane or Carrier or someone like that. Their engineers are in charge of engineering next generation products, and that is their biggest focus. But in the end, engineers produce data for other people. Engineers produce data for manufacturing organization, for the service organization, for the marketing organization, for customer success, et cetera.

Our strategy around the intelligent product life cycle is that for companies that share those characteristics, it works the same way. All of that data originates in engineering, and when changes happen, generally it emanates from engineering. Let me give you just an example that I like to use with customers to explain to you what it is. If you think about a mechanical part, any kind of mechanical part, it starts as a set of requirements in, let's say, Codebeamer or a requirement system. Then an engineer will make a CAD model. They'll put PMI information on it, which is, let's say, manufacturing information. That part gets to be part of an engineering bill of materials.

Then the engineering bill of materials manufacturing engineer will turn it into a manufacturing bill of materials, again in Windchill, and a set of service instructions, or excuse me, manufacturing instructions, and that data will travel. One place it'll go is to ERP. When it gets to ERP, it turns into something different. Either it's called a material master or it's called master data. From there, supply chain people make derivative works, order parts, pick suppliers, et cetera. Likewise, they send the same information to the manufacturing floor, to a manufacturing execution system to build the product. That creates an as-built bill of materials that you could send to a place like ServiceMax or Orbit to be an as-maintained bill of materials.

This is just a simple example of how data, let's say, travels from engineering to all of these different places inside of the industrial enterprise for the purposes of speed and quality.

Yeah. That's one side of the intelligent product life cycle.

Let's say, forward propagation of data. The other thing is, okay, how do we react when there's a change? Let's imagine that the customer did all of that perfectly, then someone decides they want to change a supplier. The first thing they have to do is they have to say, "Okay, well, what's the impact?" The first questions they'll ask themselves is, "Okay, where is everything? How many parts do we have in factories around the world? How many parts do we have in service stocking locations around the world? How many requirements have been built specifically for this supplier?

How many designs in process have already designed this supplier in? This is this idea of originating the product data foundation from engineering through all these other systems that house parts and BOMs across the enterprise, then the ability to react to a change. At the highest level, that is what we are embarking on at PTC.

Yeah. Like everybody, we let our engineers experiment with AI. What that started as, let us say, individual productivity improvements in, let us say, requirements gathering and product managers could automatically do prototypes and things like that. Developers used it to do code generation and test case generation. The QA team started to create test harnesses and automation all with AI and all individually. Then what we discovered is it is just like a factory, you are only as good as your slowest machine.

We have embarked on a more holistic approach to infusing AI end to end in the process. I would say we are in stage 2. We are beyond individual productivity now, let us say coordinated productivity across different functions. Ultimately, we are looking to have agents coordinate across the product life cycle to accelerate things. Have we seen throughput and better quality? Yes, but we are anticipating much more as we get this much more end-to-end process in place.