You are about to leave on a road trip vacation. Before leaving you checked the fluid levels, made an oil change, and checked the tires. You are diligent about performing the manufacturer recommended maintenance. This is called Preventive Maintenance. You’re, in a preventive way, trying to assure that your machine will be running with no problems. And this is the exact same process big asset-intensive companies do with their preventive maintenance programs.

But, is that preventive maintenance enough to assure there will not be problems with your car?. The answer is no. Even when the preventive maintenance is ideal to prevent failures, we need to complement it with something else to avoid or reduce unplanned breakdowns. In our example, imagine that the car itself anticipates that with the current coolant level, the RPMs for the past few hours, the temperature and humidity of the environment, it will likely fail in the next 45 min. The car can provide you a warning to stop the car and add more coolant because it will begin to overheat in the next 30min and possibly fail in 45min. That approach is called Predictive Analytics which results in you performing Predictive Maintenance.

Predictive Analytics uses Data Scientists to analyze the data generated by maintenance history and sensors. The result are predictive models (mathematical and statistical models) to predict almost in real time when a machine will fail with high rates of probability and accuracy.

Predictive Maintenance can be improved significantly by using the power of Predictive Analytics. The goal is not to replace traditional preventative maintenance programs, but to work together in order to minimize maintenance costs and prioritize what pieces of equipment need to be prioritized.