The use of AI for process optimization in manufacturing is gaining rapid traction. I want to believe that at this point if a company is not leveraging the capabilities tied with AI, it’s only because they are at least in the process of exploring it.
Here’s the thing, Industrial AI, which is defined as the use of advanced analytics applied to data from the production floor, to enhance manufacturing performance, is suitable for many different manufacturing industries.
With that in mind, the idea that not all factories are using AI for process optimization at this point is mind-boggling. But I think we are on the right track. One reason for this is that I am seeing more and more “industrial influencers” making the shift.
I’ll give you an example.
I recently stumbled across an article discussing Tesla’s move to automated factories.
The Tesla Model Y, which is scheduled for production in 2020, will be part of the industrial revolution they’re undergoing: “Tesla CEO Elon Musk recently expressed his intention to make the Model Y production system a “manufacturing revolution”, Elon has been going back and forth on how much to simply copy the existing manufacturing process for the Model 3 versus trying something new, untested, and ambitious. For now at least, it sounds like Elon wants to do the latter.”. says Trent Eady
The effect that Tesla has on the automotive community is one of the largest, as they are held accountable for the catalyzation of the whole industry transitioning from gasoline to electric propulsion. To think their shift to smarter factories will have a similar effect is not only optimistic, but I want to think it’s realistic.
This revolution that Tesla intends to undergo, is a testament to the understanding of the short and long term business benefits that come with adopting Industry 4.0 initiatives.
Not only will Tesla be optimizing processes with the use of AI, but they will be reducing costs at a great scale.
And they’re not alone.
According to a recent report by Capgemini, by the end of 2022, automotive manufacturers expect that 24% of their plants will be smart factories and 49% of automakers have already invested more than 250 million dollars in smart manufacturing.
But still, although many companies understand the need to connect machines and gather data, it’s important to understand that using AI for process optimization is not only about gathering data. It is about aggregating and analyzing it.
This can be challenging as not all companies and manufacturers understand the need to have systems that also leverage the real-time data. But the ones who do, are definitely making faster and better data-driven decisions, which translate to optimizing processes.
At Seebo, we are leveraging industrial AI technologies to ensure the gathered data is being put to use, primarily, for smarter decision making to optimize production. By employing digital twin analytics, automated root cause analysis, and predictive analytics – process manufacturers have visibility into production processes and their losses. What this means, is that they have the ability to not only understand why they suffer from quality and yield losses, or what these losses are but also when they will happen next. This allows them to prevent them from happening in the future.
By being able to predict and prevent losses in production quality and yield, companies using Seebo are improving profit margins and production quality at the same time.