Downtime of industrial machinery is a costly and expensive problem that affects many businesses. Depending on the industry, equipment going down can lead to catastrophic production failure of large-scale machinery, loss of profitability, or even – in the medical equipment industry – loss of life. Companies should do whatever they can to avoid downtime. Thankfully, field service software can be used to predict downtime. Here’s how:
Predictive Maintenance in the Field
There are already some types of predictive data analysis that alert engineers to problems. For example, vibration analysis is used to detect various common mechanical faults such as loose bearings. However, if engineers aren’t notified of the severity of a problem, just an alert doesn’t necessarily do much good.
It’s better if engineers are steered toward the root cause of a problem and are presented with a timeframe for when that problem will develop. For example, on wind and gas turbines, manufacturer are now installing sensors that can record and transmit data to the cloud. To be more specific, what if a grinding machine is experiencing failure? According to independent consulting company Roland Berger, if the analysis can specify that one of the bearings in the spindle motors is worn and has a 95% likelihood of machine failure if the bearing is not replaced within a week, this is very valuable information for a field service technician to have. Today, product maintenance is moving beyond repairs, since companies are beginning to gather data from field service projects and connected products.
Field Service Software Can Be Used to Gather Data and Predict Downtimes
In the oil and gas industry, predictive maintenance results in a 35% to 45% reduction in downtime and a 20% to 25% increase in production, according to a study conducted by the U.S. Department of Energy. These positive benefits show the value of implementing predictive maintenance.
Field service software lets you run remote diagnostic capabilities that can sense maintenance problems before a machine needs extensive downtime. Because this constant influx of data lets technicians know what’s going on with equipment, they can improve the way equipment runs and also reduce downtime. Since mobile workforce software gives technicians access to the customer and equipment history, they will have the ability to look at previous issues a machine has had and troubleshoot quickly, which improves uptime.
Use Information to Schedule Maintenance Appointments Ahead of Time
With predictive maintenance, field service software can be used to collect and analyze data and schedule field service visits ahead of time. Machine manufacturers can use analytics to anticipate problems and repair issues before they seriously hinder a machine’s ability to perform its functions. Predictive maintenance solutions also mean smarter maintenance scheduling, since appointments are made only when they are needed because a machine failure has been predicted. If a tech has determined a problem’s source and has seen where other failures will occur, they can then check the location and inventory of replacement parts and order parts directly from the mobile workforce management software, so that the part will be available when needed. By being able to predict problems and therefore schedule maintenance appointments ahead of time, you can prevent downtime and exceed your customers’ service level agreement (SLA) expectations. Even better, these types of appointments can also be included in your SLAs with customers.