Predictive vs. Proactive Control of The Plant Floor

cbeecherShop Floor Control

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 4.0 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 variables, 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 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  etermines 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 or get answers to your questions by emailing to explore these topics further.

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.