Compared to previous WBR Field Service events, many attendees seemed to have completed the initial implementation of a service management solution and were now looking for the next step in customer support efficiency. Many attendees were also looking for tools that could guide inexperienced service technicians through the process of accurately diagnosing equipment problems to increase first-time-fix and reduce no-fault-found rates.
Identifying a Need For Improved Performance and Efficiency
Our exhibit space drew representatives from across the spectrum, from heavy equipment to high-tech and medical manufacturers to process plant, life sciences and network operators. And companies we’d only met once started referring others to our booth. For example, following one demo several attendees were so excited they sent at least three other companies to meet us. Compared to last year, when we were the “new guys”, our booth space was one of the busiest at the conference.
All the attendees we met expressed a common need to improve product performance, increase equipment uptime, reduce warranty and service costs, and boost customer satisfaction. In their search for greater efficiency and cost reduction, companies often focus on enabling mobile workers and optimizing support queues but that covers only a fraction of the problem because 40-50% of support time is spent on diagnosing the problem. Manufacturers recognize the importance of improving fault isolation, which is why so many have become enamoured by initiatives tied to the internet of things (IoT) and advanced analytics. Unfortunately, those approaches are proving to be much less than a cure-all.
Challenging The Internet-of-Things
My keynote presentation titled “Closed-Loop Support: Making IoT Useful in the Real World” was well-received because it directly challenged much of the hype (and even the conventional wisdom) around IoT. Most manufacturers we meet will admit they’re overwhelmed by a flood of diagnostic data coming from equipment sensors. They find that much of the data collected is irrelevant to understanding the root cause of equipment problems, and often all those error codes make it harder to correctly identify the actual defect. Without additional information, which doesn’t come from sensors, quality and reliability can never be improved. Until the specific failure modes and frequencies are understood, trying to accurately correlate diagnostic patterns to part replacements is unrealistic.
Filtering Data To Improve Technician Efficiency
In the final analysis, what really got my attention at Field Service USA was how many manufacturers had lost confidence in the PLM/SLM-fueled promise of better reliability through IoT (i.e., equipment quality will improve if you just add more sensors, collect more data and run more analysis). These companies are already generating oceans of information and yet 65% of the time they still have to send technicians on-site to diagnose and repair equipment. They’ve realized that real improvements can be made only by enabling product design and customer support to capture critical details from customers and field technicians about the causes of performance degradation and equipment downtime. Many companies now recognize that IoT is not a panacea, and are looking for ways to intelligently reuse/ repurpose the data they collect. Only by identifying and tracing specific failure modes and root causes will technicians be able to stop swapping parts and engineers be able to start corrective actions.