The Three Questions to ask when shopping for Industry 4.0 Solutions
Predictive vs. Proactive Control Of The Plant Floor
The process of deciding which ai solution is best for your company is becoming
increasingly more difficult as the number of new tech companies offering new solutions
increases. Nevertheless, the sooner you decide, the better. Typically, the biggest hurdle to
overcome is the decision to get started! There are different approaches to applying ai to
the day-to-day production operation, so it is important to understand the options before
deciding. One of the fundamental criteria when evaluating Industry 4.x solutions is the
difference between predictive analytics and proactive analytics.
The primary focus of many ai applications is currently predictive maintenance, or the
ability to predict, based on a complex array of variable, when a machine is likely to fail.
The ability to predict accurately enables maintenance organizations to schedule required
maintenance in advance of machines failing during scheduled production hours. With
accurate predictions, maintenance activity can be scheduled during non-production hours
dramatically improving machine uptime and production throughput. The challenge
however is including enough inputs for the mathematical probability of failure to be
trustworthy. For example, if feed-rate, speed, and time are the inputs available for
calculating probabilities when in reality temperature and pressure are the critical
characteristics, accurately predicting when a failure is likely to occur becomes highly
suspect at best. Replacing a $30,000 motor that hasn’t failed yet based on ai that is
probability-based is a highly stressful decision! With the worst part being no way to
verify whether or not the prediction is accurate and true. Proactive analytics on the other
hand takes an entirely different approach and yields a more certain outcome.
Proactive analytics use a fact-based, data-driven approach to predict machine failures.
Available data gathered from sensors mounted to machines or directly from the PLC
(programmable logic controller) running the machine is used by both approaches. The
difference being one approach uses a highly complex statistical engine to calculate the
probability of failure, while the proactive approach monitors a real-time stream of data
from each of the inputs individually. Each individual machine parameter can be
compared to its design target for optimal performance with ai-driven algorithms that
determines when it’s time to schedule a proactive maintenance activity and notifies
appropriate maintenance personnel. Knowing the difference between these two
approaches will equip buyers and decision-makers to challenge assertions made by sellers
about their predictive maintenance and machine-learning capabilities, and depending on
the answers they receive, be in a much better position to compare solution providers
capabilities leading to a much more intelligent defensible decision once a selection is
made. When implementing any new technology, always be sure to include change-
management as an essential requirement for each deployment.
You can find an example of a proactive maintenance solution at www.trumbleinc.com or
get answers to your questions by emailing [email protected] to explore these
Jeffrey Trumble is the creator of REVEAL, a patented enterprise software solution
developed by Trumble Inc. offering proactive control of product quality, and machine
performance, through a single application.
