Predictive maintenance is the future of field service — and it just may be the present. Here’s what you need to know about implementing predictive maintenance in your organization.
If there’s one metric that’s top of mind for field service leaders today, it’s asset uptime. In an industry where customer expectations around the speed and quality of service continue to rise, every minute of unplanned downtime is a black mark on your brand.
Fortunately, the Internet of Things (IoT) is helping organizations minimize unplanned downtime by making it easier to monitor equipment health in real time. Instead of basing maintenance schedules on the expected life cycle of a component, companies are using artificial intelligence (AI) and smart sensors to predict failure, based on real-time data and historical trends.
Using technology to drive asset uptime is nothing new. For years, field service organizations have relied on sensors and computerized maintenance management systems to identify potential problems and trigger maintenance requests. But rapid improvements to AI capabilities and a steady decrease in the cost of IoT sensors are helping organizations collect more data and drive automation.
As the volume of work increases, field service leaders are looking for ways to work faster and scale more efficiently. By disrupting the traditional break-fix model of field service and instead only dispatching technicians when a breakdown is imminent, organizations are streamlining their operations and cutting costs. With the average truck roll costing anywhere from $250 to $500, even a slight reduction in the number of site visits is significant.
Keeping Pace With the Rising Volume of Data
Two of the biggest challenges for field service organizations today are the deployment of 5G networks and the corresponding influx of data from IoT-connected devices.
New site workflows can generate thousands of data points. Without the right systems in place, this data tends to sit in static, hard-to-reach silos. Errors are common and insights are difficult to come by — even with the help of large data science teams.
At a time when organizations are being asked to do more with less, the inability to turn critical data into increased operational efficiency is a missed opportunity. For field service leaders, the answer lies in AI-driven automation. Whether it’s using algorithms to ensure that technicians have the right trunk stock or moving from a reactive, break-fix maintenance model to one powered by predictive analytics, organizations are looking for ways to drive efficiency.
Preventive vs. Predictive Maintenance
Today, most field service organizations operate on a preventive maintenance model. Technicians visit sites on a fixed schedule, based on service contracts and manufacturer recommendations. Overhead is high, breakdowns are common, and a significant percentage of site visits result in little more than confirmation that everything is in working order.
Besides being inefficient, the issue with this approach is that it paints assets with the same brush. In an era where devices can identify problems and trigger warnings in real time, there’s no reason to apply the same maintenance schedule to antennas in cold, wintery Chicago and sunny Los Angeles.
Predictive maintenance solves this problem by looking at historical data, environmental factors, and real-time input from connected devices to predict failure months in advance.
Implementing Predictive Maintenance in Your Organization
The benefits of predictive maintenance are relatively clear, but how do you go about actually implementing it across your organization?
First, you need sensors. The IoT is helping companies monitor just about everything, from the usage data of individual components to external factors such as temperature and humidity.
Second, you need data. As much as you can grab. Everything from usage records and service notes to historical trends for that device or region.
Third, you need human input. Although predictive maintenance models can be seeded with dummy data and best practice workflows, the most effective ones will also incorporate human feedback.
Finally, you need an AI engine powerful enough to take all these inputs and predict failure with a high degree of certainty.
For most organizations, the initial implementation will be a blend of automated responses, AI-driven recommendations, and human intervention. A system might dispatch a technician after a component fails. Or it could automatically increase the fan speed in a room to lower the temperature.
Over time, the back office and field force will slowly be phased out as more processes are automated and predictive maintenance takes hold in your organization.
Driving Productivity With Predictive Maintenance
Moving away from preventive maintenance should be the goal for every field service organization. According to the ARC Advisory Group, the impact of unplanned downtime on revenue and profitability has been “vastly underestimated.” In fact, some experts estimate that unplanned downtime costs 10 times as much as planned downtime for maintenance.
For field service organizations, minimizing interruptions in service is paramount. Of course, it’s impossible to avoid unplanned downtime altogether — no amount of planning will keep operations up and running if a winter storm shuts down a region. But companies can use predictive maintenance to maximize asset uptime, deliver better customer experiences, and reduce the burden on their back office and field force.
As you consider whether to build predictive maintenance into your organization, consider the following: if you are not monitoring your equipment on a continuous basis, you need to be willing to repair it on an unplanned basis.