Katelyn Burrill | 03.14.18
Summary >

You may remember a not-so-distant past where your techs were held hostage to fixed schedules and slowed down by unpredictable situations. Once they entered the field for the day, they were incommunicado. They were left to their own devices to decipher cryptic (sometimes incomplete) work orders, fend off customers frustrated about long wait times, and scratch their head as they fielded curveballs.

As a field service professional, it’s hard to imagine wanting to journey back in time. The advent of predictive field service management is revolutionizing your team’s ability to work efficiently, collaborate with team members on the go, and delight customers in the process.

It’s also no surprise that predictive parts, predictive maintenance, and predictive analytics all require new technology. But for many field service professionals, this landscape of lofty new terms seems distant, intimidating, and downright confusing.

What are we to make of artificial intelligence, machine learning, data science or the Internet of Things (IoT) when no one can agree on an exact definition and business examples are lacking?

Even for technology enthusiasts, it’s tough to keep up. In this post, we simplify the essential technologies you need to embrace in order to improve your organization’s predictive field service capabilities.

Analytics for Fleet, Parts, Customer Satisfaction

A successful predictive field service operation starts with cold, hard numbers. In order to use any of the subsequent technologies, managers must tap into the field service data around scheduling, dispatch, fleet performance, parts fulfillment (and logistics), technician experience, success rate, and customer satisfaction to name a few. If this information exists offline—or worse, is stuck in someone’s head—it’s time to bring it onto your computer. Once your organization is tracking the appropriate analytics, you’ll be able to make use of big data, the massive amount of information available to your organization. And it really is an impressive amount.

No longer limited to dot-com giants, even traditional brick-and-mortar retailers such as Wal-Mart are collecting more than 2.5 petabytes of data from customer transactions. Every hour. Even more mind-boggling? Only about 0.5 percent of data collected is ever analyzed. The potential is huge, even if your company is not.

Data and analytics are both key to predictive field service. Without them, machines wouldn’t have the basic building blocks necessary for artificial anything, much less putting the guy with the most relevant experience on the job or scheduling for greater flexibility in anticipation of extreme weather.

Internet of Things

And what’s the easiest way to keep getting your data? The IoT, of course.

Instrumental in connecting troves of data, the Internet of Things keeps the devices within your service organization connected. As they collect and exchange data, you can analyze it to streamline issue detection, troubleshooting, and resolution through predictive maintenance.

Predictive maintenance is the next phase in the evolution of maintenance. Prior to the advances in technology that enable predictive maintenance, preventive maintenance programs seemed the only recourse for eliminating costly part failures. But although preventive maintenance can prevent catastrophes, it doesn’t eradicate efficiencies altogether. Because service is based on expected deterioration and lifespan of the equipment, businesses shoulder unnecessary costs associated with performing unnecessary fixes.

With predictive maintenance, you still prevent failures from occurring in the first place, but with added benefit of further increasing efficiencies across the service chain. That’s because the sensors and chips in your equipment account for the current condition of your equipment when scheduling for repair or replacement, only dispatching a technician when performance drops below a certain threshold.

The applications for IoT in field service don’t start or end there. Your equipment can help you uncover parts or models that consistently cause downtime, predict when they’ll need service or how often they’ll fail, and even trigger automatic alerts to speed up response time.

Thanks to smart devices, data is everywhere. How you choose to use it, is up to you… and maybe your AI-powered solutions.

Artificial Intelligence and Machine Learning

So, you’ve embraced data in all its forms?

You’re ready to leverage the true power of AI, and more specifically machine learning.

Machine learning allows you to optimize every aspect of field service delivery by applying AI algorithms across the service chain to find hidden patterns in your data. When it detects service fluctuations and new opportunities, it adjusts your business processes accordingly.

One of the most common applications for machine learning in field service is in scheduling and dispatch optimization. Machine learning allows you to draw conclusions from data to predict job duration, tools and parts needed, the best technician for the job, and the fastest route faster and much more accurately than a human ever could.

Machine learning provides you with more accurate, data-driven predictions so you and your remote workers can make better decisions for increased efficiency on the fly.

There’s No Time Like the Present to Bring Your Field Service Processes Into the Future

If the past is any indicator, the world—and technology as we know it—will continue to change more rapidly than ever. Although you’ll need to adapt continuously to keep up, you don’t need to get bogged down by technology.

To learn more about intelligent and predictive technology, subscribe to the ClickSoftware blog.