1660343833181 Indresh

How tech & data are changing field work

Sept. 26, 2017

Data has transformed field-service workers into oracles on the go. 

The data revolution has transformed industries from trucking and manufacturing to retail and

fashion. But one industry that has uniquely benefited from data analytics and its resulting advances? Service.

Today, connected products, mobile technology and the data that streams in from devices in the field means service workers and manufacturers can collect useful information to service machines; better anticipate and address customer needs; train new hires and grow teams faster; and power individuals to set and meet numbers-based goals.

This technological metamorphosis has changed the relationship between these field workers, their manufacturers and the customers that employ them to keep their machines up and running. As the technology continues advancing, the relationship between these groups will undergo seismic shifts as well.

Relationships have evolved alongside data

With the advent of mobile technology and data-collection developed specifically for field workers, service in the field has become leagues more advanced. Field techs are now armed with the knowledge of how to approach any situation with detail, information and research on their side—the history of the machines, how they typically function and whatever outages might have occurred in the past. Essentially, data has transformed field-service workers into oracles on the go, ones who can solve problems at the touch of a button and satisfy customers on a regular basis. Mobile technology and data has taken the idea of collecting tribal knowledge and systemized it, bringing a real-time, omni-channel approach to the service-HQ relationship.  

Ultimately, this has forged a first for the industry: a digital relationship between manufacturers and service workers. With real-time data flowing in, metrics like location information, schedules and availability are within arm’s reach for dispatchers at HQ, helping them be more effective in allocating resources and service in an outcomes-based model, where companies sell long-term service contracts that deliver consistent outcomes and end-results.

Imagine, Rolls Royce sells flight hours instead of engines to deliver yearly proactive service to its machines, or SolarCity operates on a power-purchase agreement to sell electricity instead of physical panels. By bringing manufacturers and service workers closer together through real-time data, this type of future is in grasp.

Smarter machines are helping manufacturers accomplish more

As all of this data from connected machines accumulates and stockpiles, manufacturers can access greater insight into a machine’s lifecycle: to know in advance when it will need service. Today, machines are no longer islands—they’re fully connected. When analyzing the data that comes in, such as weather patterns causing wear-and-tear on a wind turbine, these patterns and identifying anomalies can help manufacturers better understand when an outage may occur and plan their service proactively for their customers.

This kind of analysis is known as asset-performance management—because field service is more than just attending to problems after they’ve happened, but making sure that an asset is performing at its best, constantly. But paying this close attention to a machine’s collected data lends itself to a new way of service: predictive service.

Now, whether it’s an impending issue or just a general efficiency fix, manufacturers can dispatch techs before an outage even occurs—providing a broader “health” service to customers that guarantees them a future-functioning machine for a solid amount of time. With service workers as the conduits to this new relationship between manufacturers and their customers, providing an outcome (rather than a simple one-off service) is very likely to become the new norm.

Using this data to solve real-world problems

Data is nothing without a why—analyzing mountains of numbers doesn’t mean anything if you’re not looking to solve a certain question. In field service, the why is moving toward this next generation operating model to solve larger problems.

I’ll give you an example. At a recent conference put on by my company, a field tech from medical device company Medtronics gave a presentation. He discussed how mobile technology and pre-delivered information had helped him get a radiation-therapy machine up and running in the middle of the night, ensuring that any patients lined up the next day were not impacted. At the end of the speech, he got emotional—his mother had passed away from cancer, and he wanted to make sure that everyone with the disease had a chance at remission.

Ensuring that a radiation-therapy machine doesn’t go down is truly a matter of life and death here. But imagine using technology to anticipate the (natural) outages that might occur and dispatching a tech to fix the problem between therapy sessions, so no patients are left in the lurch. Or what if Medtronics could use this technology to optimize the machines to stretch for three shifts instead of just two, before needing to recuperate? Making care and treatment more accessible is one part of this business that actually matters—and that comes from data.

Digital transformation has gotten us to this point—we’ve watched as techs have become more independent, more informed and more valued by manufacturers. We’ve also seen customers become more grateful and loyal to manufacturers and their field workers for providing real-time productivity and revenue benefits. Now, in the evolution of service as an industry, it’s time to use this digital transformation to make a real-world impact—on customers, manufacturers, and the techs that do it all.

Indresh Satyanarayana is chief architect at ServiceMax, From GE Digital.