Editor’s note: Originally published in Field Service, a quarterly print magazine by Field Service Digital and Predix ServiceMax. Check out the full magazine in print or online.
These days, service leaders are adept at using data to run smart, efficient operations. For many, the bulk of that data reflects the work that their technicians do: The number of jobs that were completed correctly the first time, say, or how consistently the company meets its SLAs. But there’s another pile of data—equipment performance—that promises to redefine the roles of technicians and managers alike.
Field Service Digital asked three industry pros to weigh in on what data service leaders can use to gauge equipment performance—and how best to use that data to make decisions.
Jeffrey Moore, Service Operations Manager, Kodak Alaris
We collect and use a lot of data, but most of it relates to our service delivery commitments, attach and renewal rates, and logistics delivery. We are limited in terms of product performance data that we collect. We have the ability to collect several different meter counts, power on time, number of scans and lamp time. We also collect data on the number and frequency of cases, and part uses per product type and serial number.
Lubor Ptacek, VP of Product Marketing, ServiceMax
The challenge today is no longer the availability, volume, type or quality of equipment data. The real challenge is making sense of that data and translating it into actions that achieve the desired outcomes. Generic analytics software too often proves unhelpful. The software is difficult to set up to deliver a recurring benefit. The solution is an industrial application like asset performance management (APM), which is built to interpret equipment data into specific actions that improve performance, such as lower operating cost and reduced downtime.
Michael Blumberg, President, Blumberg Advisory Group
At the most basic level, field service leaders should capture data about machine downtime. This includes data related to machine utilization (hours/days of operation), environmental conditions (temperature, weather, vibration), operating issues (voltage, current, resistance, capacitance) and interoperability issues (circuits, components, parts). More-advanced data collection should include data related to the quality, frequency and quantity of machine output and onboard diagnostics, assuming this is part of the equipment configuration.
1 Comment
William K.
on January 13, 2019 at 10:32 am
Data by itself has only marginal value. Real value is created when the data is condensed into knowledge, which can then be evaluated to provide insight and understanding. But the utilization of data is neither simple nor trivial, it certainly requires a skill set and effort to condense the huge volumes of data into knowledge. Thus it is important to understand the whole process and commit to providing those resources to benefit from the collection of the data. The size of the task needs to be understood before making the choice to start collecting the data.
We collect and use a lot of data, but most of it relates to our service delivery commitments, attach and renewal rates, and logistics delivery. We are limited in terms of product performance data that we collect. We have the ability to collect several different meter counts, power on time, number of scans and lamp time. We also collect data on the number and frequency of cases, and part uses per product type and serial number.
The challenge today is no longer the availability, volume, type or quality of equipment data. The real challenge is making sense of that data and translating it into actions that achieve the desired outcomes. Generic analytics software too often proves unhelpful. The software is difficult to set up to deliver a recurring benefit. The solution is an industrial application like asset performance management (APM), which is built to interpret equipment data into specific actions that improve performance, such as lower operating cost and reduced downtime.
At the most basic level, field service leaders should capture data about machine downtime. This includes data related to machine utilization (hours/days of operation), environmental conditions (temperature, weather, vibration), operating issues (voltage, current, resistance, capacitance) and interoperability issues (circuits, components, parts). More-advanced data collection should include data related to the quality, frequency and quantity of machine output and onboard diagnostics, assuming this is part of the equipment configuration.
Data by itself has only marginal value. Real value is created when the data is condensed into knowledge, which can then be evaluated to provide insight and understanding. But the utilization of data is neither simple nor trivial, it certainly requires a skill set and effort to condense the huge volumes of data into knowledge. Thus it is important to understand the whole process and commit to providing those resources to benefit from the collection of the data. The size of the task needs to be understood before making the choice to start collecting the data.