18 DESIGN WORLD — EE NETWORK 4 • 2020 eeworldonline.com | designworldonline.com
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THE SHIFT FROM REACTIVE TO PRESCRIPTIVE AI
In reactive manufacturing, a quality failure is discovered at the end
of the line and the production team will make a set of reactive
changes to the system to correct for the immediately observed
error. This approach has two core features: the fi rst is that the
defect has already occurred and the second is that the factory
keeps producing poor quality goods until the root cause has been
solved. A prescriptive system is different, as it involves making a
small change now to avoid future quality failures. A small set of
corrective actions are made in anticipation of a quality cost that
is never realized. These prescriptions can help to reduce the cost
of non-quality. In the reactive case, by contrast, one waits for the
quality failure and all the associated costs to occur before correcting
the failures in the system.BEST PRACTICES FOR AUTONOMOUS
MANUFACTURING
The two most important things manufacturers can do to
prepare production systems to achieve the goal of autonomous
manufacturing are:- Identify and save production data. As soon as a plant can
start saving production data, it opens the opportunity to
use that data to optimize the future. That ́s the single most
important thing manufacturers can do now if they haven ́t
yet started. - Improve the quality system. To improve quality, it ́s
important to ensure that the details of the defect are
recorded (type, location, and description), and not just
the fact that a defect has occurred. This will allow AI to
automatically diagnose the root cause of the problem and
to provide continuous directions to the machine or to the
operator—so as to improve quality.
Most plants draw data from
production systems and send
it to engineering or production
teams. Operators are le to
follow their own inquisitiveness
as they look through the data to
devise an optimization or system
improvement. There isn ́t a holistic
view or use of data from the start
to the end of a process to achieve
an overall system improvement. References
DataProphet, https://dataprophet.com/