Smart manufacturing is stuck because factories can't connect their machines and data. The main problem is fragmented networking protocols and inconsistent data formats that prevent plug-and-play integration. Standardized, open data models with built-in context can fix this, enabling everything from maintenance analytics to quality control.
What we did is we've gone out and identified certain gaps, challenges, and then we moved in gaps and challenges in implementing smart manufacturing. Why have we been a little stuck in this Industry 3.0 era? And even though we've been talking about Industry 4.0 for a while, we seem to be stuck and not make it proliferate at scale. So we have focused— we've built a roadmap around some of the main issues, and we've been tackling it. and still got a few years to go. But interoperability was one of those big challenges that things have not become as plug and play as we would like. I mean, the starting point was very diverse, all the way from diverse networking protocols, never mind to different ways to handle data semantics. And now we're really pushing for more standardized open ways to share that data and become interoperable and pushing for information models. So for a while, there was just a big push, put data however you have it, stick it in the data lake, and you'll be able to do wonderful things with it later. And people have realized maybe we need to put a little more thought into how we structure information. How do we make sure that data has the context we need to do the proper insights and analysis of data and get more value out of the data and do it in a way that you're solving an immediate problem. Let's suppose you went at this with a machine maintenance lens on and you created the data you needed for that problem. But you can— later, later, the next team can come and say, by the way, I need 70% of that data, I need to add Another little bit of data to them to do handle quality, to handle quality in that process.
