In a recent blog post on M2M Now, Marne Martin, CEO of ServicePower, posits that IoT solutions enable faster, better field service, for which customers are willing to pay a higher premium.
That may be true, however IoT solutions come in many forms, and they don’t all correlate directly to an increase in service quality and revenue. Some IoT solutions are designed to provide timely alerts, but they could do more. For example, Martin writes, “Sensors connected to the internet can anticipate and communicate asset failure, for example a drop in tire pressure on a huge truck delivering ore for refining. This data can be fed back real-time to the leaser on an analytics dashboard. To build additional value into this notification the data can be automatically fed into a service management platform which can use the information to locate the nearest available engineer with the right skills and parts to fix the problem.”
Alerts and warnings from sensors are helpful, but receiving a notification is still several steps away from solving the issue. Deploying the “right” technician for every service order depends on the nature of the underlying problem. (Is it just a slow leak requiring an air top-up? Or does it require a tire change? Or worse yet, is the sensor defective?) Frequently-occurring faults, which are more common and familiar to a wide range of technicians, are easier to diagnose and repair. But since individual components are becoming more and more reliable, the majority of equipment problems and service orders relate to different types of system-level problems that are relatively infrequent. (i.e., lots of different problems that just “seem to happen” from time to time.) Not surprisingly, those unusual or long-tail situations are more expensive and time-consuming to resolve because they require a specialist with the ability to recognize important clues from the machine and within a virtual library of technical information. The key for technicians is to avoid getting lost in the forest of fault codes and instead to identify which symptoms mark the path to a first-time-fix (FTF).
In this new IOT world, equipment sensors provide so much information that even when a service call is warranted, it doesn’t necessarily improve FTF or mean time to repair (MTTR). (A medical equipment manufacturer reported that each of its products generates one diagnostic code per second–86,400 per day—but admitted it can analyze less than 1% of that data.) To fully harness and leverage the value of IOT data, manufacturers need technology that empowers all technicians to be able to fix any problem: i.e., technology that makes even inexperienced technicians perform like seasoned pros.
A key first-step is to move troubleshooting away from step-by-step procedures and toward symptom-cause databases. A dynamic fault isolation database captures all of the symptoms, causes and solutions for every known failure mode for each type of equipment; this database of known equipment issues can simultaneously evaluate all known failure modes based on key indicators, and re-direct the troubleshooting process based on each new symptom, observation and fault code. This approach guides the technician to the root cause of an equipment problem, by asking relevant and appropriate questions that lead to a qualified, validated answer. In this way, service and support personnel receive the real-time guidance they need to isolate just about any problem—whether it was anticipated by engineers or experienced by field technicians. With the correct diagnosis, customer support can quickly locate and dispatch a technician with the “right” skills and parts.
Most customers are willing to pay premium prices for services that achieve higher uptime. Machine sensors are useful, and can help workforce scheduling systems assign field technicians to jobs that fit their skills. However, the last mile of service and support is the most critical; that’s where field technicians spend less time diagnosing and confirming symptoms and focus instead on effectively repairing problems. That’s the type of “premium” field service that customers will pay for.