Remote service models have become increasingly attractive for capital equipment and infrastructure manufacturers over the last several years, for some very compelling reasons: Big picture, skilled maintenance technicians and engineers are in increasingly short supply. Businesses face an HR cliff, with a huge swatch of their workforce slated to retire over the next decade, taking with them decades of knowledge and experience that can’t be readily replaced. Losing access to these fonts of institutional and technical knowledge creates significant operational challenges, and companies are actively seeking solutions to fill that void.
Part of the solution has always been to do more with less – focus less experienced technicians on easier problems and apply the highest-skill workers to the most demanding challenges. But it’s not always practical or practicable, as an on-site technician is typically required to diagnose equipment issues in a break-fix service scenario. So how do you know when a more advanced skill set is needed, and how do you get those resources to your customer site in a timely manner?
IOT and AR solutions have begun to help companies address these challenges. Remote monitoring and diagnostic tools are increasingly being incorporated to new equipment and retrofitted on manufacturer’s installed base across healthcare, manufacturing, oil and gas, power GT&D infrastructure, building systems, vehicles, and other industries in which downtime can be costly. These solutions help manufacturers predict and prevent failures, minimizing disruption for end users. But failures still happen, and maintaining appropriate field service operations with a dwindling and less experienced workforce is still a challenge.
Enter “remote expert” tools and systems. What if your best service tech could be in San Jose, Austin, Raleigh, and Cleveland, all in the same day? They increasingly are – virtually at least – brought to the scene by a young technician using hands-free systems to communicate with their home office. In many cases these remote experts can see what the tech sees via a rather simple headset with mounted cameras. They can direct less experienced technicians, walk them through processes, and train them along the way. Acting as a force multiplier, these tools allow for hands free communication and collaboration in the industrial work. They’ve helped companies reduce truck roles and field service costs, reduce maintenance timelines, reduce customer downtime, and increase customer satisfaction.
Remote AR assistance tools present an alternative to the “remote expert” model, but are arguably less effective and harder to implement. Rather than connecting field technicians to more experienced colleagues, these tools provide field techs with hands-free, heads-up access to equipment manuals and troubleshooting guides. Implementation requires development of custom systems and applications (not to mention extensive workforce training), and carrying higher upfront costs. These cost and implementation challenges remain near-term barriers to widespread adoption of AR tools for industrial O&M applications.
As the pandemic unfolds, easy to implement and low cost “remote expert” solutions should benefit. As I’ve already noted, the most skilled workers in many fields are by nature older and more vulnerable to Covid-19. In many fields, such as oil and gas or construction, older workers have already opted out of higher risk, physically demanding jobs due to health and safety concerns. Traveling state to state during a pandemic is likely to be a concern for many older individuals, not to mention a tangible business risk. Simultaneously, perspectives on remote work are shifting quickly across the economy as workers adapt to office-oriented remote collaboration technologies. The combined impact of these changes is a reduction in the barriers to adoption and an increase in perceived value of remote expert field service solutions. While the pandemic will fade, it will likely have a lasting impact on the use of remote collaboration tools and technologies.