How Democratized Artificial Intelligence Can Move Manufacturing to a New Evolution Pace
Figure 1: Move your data to the cloud for large scale multi sources of data and intensive AI and ML computing then
use the outcomes locally to improve your business behaviors in your specific context
specific issues and potentially reach the
target of autonomous system maturity level.
anticipate the alert of a potential failure
of a system within a timeframe – such as
within the next 15 minutes or 10 hours.
In fact, the next data capture disruption
wave can potentially be the blockchain to
get a massive capture of transactions,
contract and ownership behaviors data.
That gives information to the asset
manager or a machine itself to take a
counter-action decision in advance.
What changed from previous reporting
and forecasting method? Now, we are
showing potential futures in real-time.
Leveraging the new computing power
capability, we are now able to process a
large source of data streamed together,
correlate them in real-time, compare
with historical data, then forecast and
show the future, all of that in real-time.
Of course, we can apply this on
predictive maintenance, but also in
production trends, sales trends,
financial trends and many other
individual or consolidated indicators.
Another case could be, you are a plant
manager, and you want to know in real-
time your actuals and your potential
production trends for the next hours.
F OUR M AJOR AI T OPICS ARE K EY FOR
M ANUFACTURING C OMPANIES
1. The predictive analytics (or time
machine) is now starting to be
embedded in business applications
(predictive maintenance, production
monitoring, demand & production &
supply planning and financial planning).
This approach is using a small or
medium set of historical data mixed
with current data, correlated together
in a business context to define a
potential future situation against a
timeline. Let’s take the case of
predictive maintenance: The wish is not
to get an alert when the system fails, but
IIC Journal of Innovation
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