{"id":6787,"date":"2019-04-08T00:00:00","date_gmt":"2019-04-08T00:00:00","guid":{"rendered":"https:\/\/slcontrols.com\/how-you-can-increase-profits-by-moving-from-planned-to-predictive-maintenance\/"},"modified":"2021-10-15T09:50:30","modified_gmt":"2021-10-15T09:50:30","slug":"how-you-can-increase-profits-by-moving-from-planned-to-predictive-maintenance","status":"publish","type":"post","link":"https:\/\/www.slcontrols.k8s-stg1-global.sq1.io\/en\/how-you-can-increase-profits-by-moving-from-planned-to-predictive-maintenance\/","title":{"rendered":"How You Can Increase Profits by Moving from Planned to Predictive Maintenance"},"content":{"rendered":"

Unplanned downtime caused by equipment or machine breakdown costs money and eats into your profits. Modern technology provides a solution that offers significant improvements compared to other methods that are used to maintain machines and prevent unplanned downtime. That solution is predictive maintenance.<\/p>\n

In other words, using real-time data to predict in advance when a machine is likely to breakdown as well as identifying the cause of that failure. Maintenance can then be scheduled before this event occurs.<\/p>\n

This is different from the planned maintenance model, a model which is widely used but which has several flaws.<\/p>\n

The Problem with Planned Maintenance<\/h2>\n

Most manufacturing facilities use planned equipment and machine maintenance models. In other words, they work from a pre-determined schedule to organise maintenance work on machines, often based on the number of hours the machine has been in operation.<\/p>\n

This model is outdated, however. Of course, planning maintenance is better than leaving maintenance until something breaks. Planned maintenance is far from perfect, however.<\/p>\n

Typically, planned maintenance schedules often come from OEMs who base maintenance schedules on best practices, generalisations, and, when they have it, aggregated data.<\/p>\n

Planned maintenance schedules cannot take into account the specific circumstances of your manufacturing facility, however. After all, a machine on your production line will operate under a completely unique set of parameters compared to any other machine of the same make and model in any other manufacturing facility. This creates the potential for unplanned downtime.<\/p>\n

This is the problem that predictive maintenance can solve.<\/p>\n

You Should No Longer Accept Unplanned Downtime<\/h2>\n

Unplanned downtime is something that manufacturing facilities have always sought to combat. After all, unplanned downtime, however minor, means lost productivity which, ultimately, hits the bottom line.<\/p>\n

In previous decades, however, when planned maintenance schedules were the best option for preventing breakdowns, unplanned downtime was unavoidable \u2013 a necessary evil, so-to-speak.<\/p>\n

This is not the case today, not least because technologies exist that make it possible to eliminate unplanned downtime caused by equipment failure.<\/p>\n

Unplanned downtime is also unacceptable in modern manufacturing environments because of the highly competitive nature of many industries and the pressures that exist on profit margins.<\/p>\n

Eliminating Unplanned Downtime with Predictive Maintenance<\/h2>\n

Predictive maintenance is the next step forward from planned maintenance schedules. At its most basic, predictive maintenance involves using sensor technologies to capture real-time data from machines<\/a>. You then use software algorithms and reports to identify anything that indicates the potential for a breakdown.<\/p>\n

Crucially, you will have this information while the machine is still operational. You can then arrange for maintenance work during a period of planned downtime.<\/p>\n

It’s also possible to take predictive maintenance even further with digital twin technologies<\/a>, for example, or machine learning.<\/p>\n

With a digital twin of your machine or production line, you can test different scenarios in the virtual world using real-time data to predict performance and maintenance issues under different conditions.<\/p>\n

With machine learning, you can build self-improvement into the predictive maintenance algorithm to further automate and enhance the process<\/a> while driving even greater efficiencies in the machine maintenance process.<\/p>\n

Benefits of Predictive Maintenance<\/h2>\n