AI is a value add to data; in order to leverage AI it needs to be enabled by data. This means that a manufacturer needs to have a good data environment or a route to a good data environment. What we emphasize at DataProphet is that most of the data collection hardware is already installed. Capital equipment that was installed in the last 20 years will have a good set of sensors on them. However, the collection of that data is important, especially from a holistic point of view.
We work with many clients to improve their data environment to reach a state where they can leverage AI. We put a lot of emphasis on AI for Industry 3.0 rather than Industry 4.0, which speaks to working with the current set of sensors on the line and creating value from the data they produce. It is often not necessary to add more data streams before the existing ones are ordered and value is created from them.
One further important point we like emphasizing is that real-time data is often too late. Real-time data is only valuable if you can respond in real-time. In practice, a good AI solution should be able to act in advance of real-time. I think this is well-understood in predictive maintenance but often not in the optimization of control parameters.
In reality, prescriptive actions need to take into account how many changes can be made to the line and how fast they can be made, especially when these recommendations are provided to the control engineers.
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