Predictive analytics uses historical data, machine learning, and artificial intelligence to predict what will happen in the future. It helps application users across a variety of industries by answering the question, “What is most likely to happen based on my current data, and what can I do to change that outcome?”
For manufacturers, answering this question can help keep machines—and ultimately, the business itself—running. Let’s look at how predictive analytics can be applied to a common problem in the manufacturing industry.
Problem and Solution
For manufacturers, equipment failure can mean business failure. Machine downtime can cost millions of dollars a year in lost profits, repair costs, and lost production time for employees.
By embedding predictive analytics in their applications, manufacturing managers can monitor the condition and performance of equipment and predict failures before they happen. They can plan ahead and reallocate the load to other machines to reduce any impact on production.
Data and Techniques
In a manufacturing model, data to be analyzed may include maintenance logs maintained by the technicians, especially for older machines. For newer machines, data coming in from the different sensors of the machine—including temperature, running time, power level durations, and error messages—will be very useful.
You can use a few different predictive techniques to teach a predictive model how to flag machines that may need attention soon:
- Map sensor readings against the actual state of machines. In this approach, you run a simple clustering algorithm to see if sensor values from different machines can logically put those machines into three different groups, then compare the groupings created by the algorithm with the actual states of the machines. Ideally, the outcome for the groups generated by the algorithm will match the reality, and you can apply the model to predict future states with relative accuracy.
- Identify correlations between sensors. Predictive analytics models may be able to identify correlations between sensor readings. For example, if the temperature reading on a machine correlates to the length of time it runs on high power, those two combined readings may put the machine at risk of downtime.
- Predict future state using sensor values. Since the machine status is a known value, you can run a classification algorithm to create a predictive model that predicts the state of the machine based on sensor values. This model can be subsequently used to predict and flag the state of machines based on the combination of new sensor values.
It’s Time to Act
Once you have trained a predictive model in the manufacturing space, you can use it to determine the likelihood of breakdowns. Plan ahead to shut machines down for preventive maintenance as needed.
You can also use predictive analytics to limit or prevent any impact on your production pipeline. By knowing which machines will go down and when, you can identify another machine or manufacturing shop that can pick up the missed load so your business doesn’t slow down.