For Industrial Artificial Intelligence to have a real impact in solving production problems, the algorithms must have a deep understanding of your production process. Watch our latest Vlog below to learn how it’s done – and why it’s so important.

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Transcript:

[Eden] Hi everyone, it’s Eden from Seebo. Welcome again to our V blog. Our subject for today is how to embed process expertise into artificial intelligence and why it’s so important. Here with me again is the co-founder and COO of Seebo, Liran Akavia.

[Liran] Hello, Eden.

– Hi, Liran. Liran is an expert in the field of industrial artificial intelligence and reducing manufacturing losses, and has extensive experience working with the world’s leading manufacturers. Liran, we all know that process expertise is important. This is the key to meaningful process analysis. Many manufacturing executives that speak with us are asking, “Can algorithms solve my process inefficiencies when there is no process expertise in the algorithm?”

– So it’s a true concern. Honestly, algorithms without process understanding will have no value to the manufacturing team. Just algorithms to analyze a production line will provide insignificant results.

– Okay, Liran, I get that. So can process expertise be embedded into the algorithm?

– In short? Yes. And I’d like to show you something. In my opinion, it’s very interesting. I’d like to speak a little bit about how the algorithm is thinking about the data that is coming from the process and what is the big difference that is made when the algorithm actually understands the process. Let me take you through a quick journey.

Okay.

– So a traditional algorithm, let’s say just for this example, we look at three temperature data sets or tags. This is temperature data number 532, and this is temperature data number 667, and temperature data number 780.

A normal algorithm will simply look at this data and will try to understand if it’s within the value or a little bit about its behavior, and in most cases, will come back with no meaningful results. Now, let’s take a look on how Process-Based Artificial Intelligence is looking at the data, or how algorithms that actually understand the process look at the same piece of data.

So just for this example, let’s say that there are three assets, asset A, B and C, and now when we understand the assets, we also know the physical order of those temperature sets. So we know that 780 is the first one and 532 is the second one physically, like the material is going first through asset A and then through asset B. With that knowledge, we can suddenly make a lot of beautiful calculations. For example, we can calculate the difference in time and material flow between those two temperature sensors, and by using this data, we can calculate the delta T, in this example, how much the material got hotter or colder during the time that it went between 780 to 532.

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With that, we can calculate the first derivative that tells us the beautiful story of how the temperature is increasing or decreasing. Now with that value, we can maybe realize that the problem is not the absolute temperature parameter. The problem is the speed of increase and speed of decrease. So this is just one example of why for the algorithm to understand the plant topology and the flow of material is so dramatically important.

– Okay, Liran, that sounds really interesting, but very difficult in theory. Every production line is unique, so do you need to reinvent the wheel every single time?

– So you’re right again. This is extremely difficult to do that. And when we built Seebo, we were concerned exactly because of that.

On the one hand, we put for ourselves a clear goal to provide value to manufacturers reducing the process inefficiencies. On the other hand, we understood that algorithms with no process expertise within them will be meaningless. And on the other hand, we understood that every production line is different.

So you actually need a tool or technology that is able to take a flow diagram, a P&ID, a production description, and somehow inject it into the algorithm, and I’d like to quickly show you how it works and what makes the Seebo technology so unique in understanding the actual manufacturing process.

So the journey starts with the manufacturer’s data. Usually, we look at process data, raw material data, quality data and sometimes other data sources, for example, the ambient temperature. Then we build a unified multi-source schema, so we actually take all the data sources and put them inside one database while they’re synced and connected to each other, and then we do something that no one else does: we build a digital model of the process, technically taking the P&IDs and converting them into a digital appearance, and then using our proprietary tools, we automatically convert it to code and inject it into the core process algorithms.

Now, the core process algorithms has in them the data and the plant topology, the flow of material, and the algorithms can do incredible calculations, for example, calculate dynamic traceability, understand buffers or production loops or even parallel processes. We can suddenly clean the data in a meaningful way. We understand the batch and changeovers and many, many other things, and the result is being digested through a straightforward, ready-to-use interface for the production team.

So if I’m going back to your core question today, algorithms with no process expertise will probably provide poor results. Embedding process expertise is a difficult task and we took upon ourselves this task to solve it and make it scalable.

– Thank you, Liran, for those meaningful insights, and thank you everyone for watching us and for those incredible questions you are sending. See you again, next time.

To learn more about Process-Based Artificial Intelligence and how it can be used to reduce production losses, check out the Seebo website.