Hi, folks, my name is Dr. Michael Grant, and I’m the CTO of DataProphet. To get an expert execution system running in your business, we need to be able to engage with your engineering teams, your production systems, teams, and your quality teams. This is because the data that describes your production system lives between those three teams:
Working with those three teams, we get the data from your system, and we’re able to start installing PRESCRIBE as an EES.
We find that the operationalization of AI systems in manufacturing and specifically those around expert execution systems requires a lot of faith from humans. This means spending time with folk building trust in the prescriptions that we’re generating – both that they are sensible, and that they are self-consistent.
We’ve had many experiences where we make a suggestion to go and increase prescription in order to increase the value on parameter A, and correspondingly make a change on parameter B. What’s super important is to demonstrate that those two changes remain consistent.
For example, if we’re going to increase the flow rate, we should also increase pump pressure. As both of these speak to the movement of a liquid through a tube. And so when we’ve got our system going, we’ve generated these prescriptions, we spent time with your production teams and your engineering teams socializing results, how they’re generated. This builds trust in the process, trust in the system and then helps with the adoption of the prescriptions into daily operating procedures.
We create a sense of comfort for people who are experiencing the change or the installation of an EES into the production system. We do this through the socialization of our results and our models within. We spend time talking through our prescriptions, how we arrive at them, and be able to demonstrate that our prescriptions remain self-consistent that the increase in parameter A corresponds with a change in parameter B that makes sense in terms of the physics of your problem.
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