Predictive Maintenance with Field Service Management | Blog

“An ounce of prevention is worth a pound of cure.” Benjamin Franklin had no way of knowing it when he rattled off this famous quote, but 230 years after his death, his words provide a cautionary tale for field service companies.

Though Franklin was referring to house fire prevention at the time, the sentiment rings true today. Field service companies are hunting for ways to improve business operations in 2022 and beyond. What some have realized is that field service management software is one of the most effective tools available. It can proactively monitor, prevent, predict, and address equipment failures before they halt progress. Here’s a snapshot look at why a transition to predictive maintenance via field service management software is a necessity.

The platform becomes a treasure trove of data

The most comprehensive versions of field service management software include embedded modules that improve operations at every level. That means modules designed for handling work orders, fleet vehicles and tool inventories, safety monitoring, and third-party contractor relationships. Equally as important, many of these solutions are based in the cloud, which enable ubiquitous access for employees regardless of whether they’re in the field or the office. This shared platform becomes a single source of truth and a historical catalog for critical data.

Related: Productivity Up & Costs Down with Preventive Maintenance

Harnessing the power of the data you’ve collected

There are two important parts of data collection. The first is what we just described — the collection process. The second part is how to utilize those invaluable nuggets of information. Remember the wise words of Ben Franklin? Collecting data empowers field service companies to extend the life of their most vital assets through preventive maintenance. However, this process also helps companies predict when assets may be close to malfunctioning or becoming faulty based on historical trends. Each time an asset is purchased, used or goes bad, field service management solutions help you create historical documentation. Those details can be analyzed through reports illustrating patterns and trends. For example, it’s possible to determine which assets fail at particular times of the year or after a specific number of uses. Pay close attention to those findings, and you can help avoid unnecessary downtime and unscheduled expenses.

Related: Using Big Data to Improve Field Service Operations

Predictive maintenance in practice

Now that we’ve explained how field service management software aids in the collection of data and helps inform future decisions, let’s review an example of this approach in action. Many field service management companies utilize fleet vehicles for their employees to get from one job to the next. And for those companies, fleet vehicles represent a significant investment. Making up a large percentage of the company’s one-time (initial purchase) and ongoing (maintenance and gas) expenses.

Here’s how field service management software targets both areas with predictive maintenance. Suppose the company is using automated work order management to schedule regular tasks like oil changes, tire rotations, and windshield wiper replacements. In that case, management will have hard data that shows metrics like oil life and tire tread depth at recurring service appointments. With that information, a fleet manager can confidently predict the future. Like when the vehicle’s tires will need to be replaced. Those new tires can then be budgeted for, thus avoiding an unplanned expense. This strategy works in theory for any asset, from fleet vehicles to heavy machinery, down to mechanical parts used in job repairs.

Related: Prioritize Vehicle Health with Fleet Maintenance

EnSight+ is a comprehensive field service management software solution. Making it easy for field service companies like yours to practice predictive maintenance. Interested in seeing what the platform can do for you? Book your demo today.


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