[This post was written and provided by Krista Grochowski and Rich Padula of Havensight Consulting.]
Let’s start with some basic definitions:
Preventative Maintenance (PM): Regularly performed tasks on a working piece of equipment that lessens the likelihood of that equipment failing.
Predictive Maintenance (PdM): Methods designed to determine the condition of working equipment in order to predict when maintenance tasks should be performed.
Predictive Analytics: Statistical and machine learning techniques used to analyze historical data to make predictions about future events.
Most companies use a combination of Preventative and Predictive Maintenance procedures and tasks to keep their equipment running efficiently and their business humming along. However, are there additional efficiencies and cost savings that are being left on the table by only utilizing these two methods for maintenance? We believe there are.
For years, companies have been using mobile applications to capture their maintenance data. The result has been the storage of a greater amount and more accurate data. Now, what to do with all this good data? This is where the use of predictive analytics and machine learning comes in. The links between factors that were not visible by looking at a spreadsheet or a report, begin to show themselves.
What if you had a list of assets that will require corrective maintenance in the next month? What if you knew the appropriate inventory levels to stock and which manufacturers perform the best? These are just a few of the insights that predictive analytics can provide. And the best news is that it’s easy and cost-effective to implement.
If you are considering the use of predictive analytics to increase the efficiencies of your maintenance department, we would welcome the opportunity to become one of your technology partners. We have been working with clients to help them determine how predictive analytics can help their businesses and it only takes 4-6 weeks to get it up and running. If you would like to learn more about this, please call us at 630-339-3030, email email@example.com