Knowledge transfer training is a growing priority for manufacturers. Retaining the wisdom of an aging workforce is increasingly urgent—but not everybody is skilled in knowledge transfer. An employee may be extraordinarily talented in their role, but lack the capability to communicate how they do it. Knowledge transfer training aims to fill this gap. In this context, augmented reality (AR) is proving increasingly useful as a tool for accelerating the knowledge transfer process, and amplifying the impact of experienced employees.
The U.S. workforce is aging—as it is across the developed world. Manufacturing is disproportionately affected. In the year 2000, the median age for a U.S. worker was 39.4. In manufacturing, it was 40.5. By 2012, the gap had doubled. The median age for a U.S. worker was 42.3. In manufacturing, it was 44.7. When asked by the Manufacturing Institute what concerned them about the aging workforce, 86% of manufacturing leaders said they were ‘somewhat’ or ‘very’ concerned about brain drain, more than any other aspect of the demographic shift.
Over time, employees build up knowledge unique to the organization. Unless that knowledge can be formally transferred, it risks being lost as they retire. New employees may eventually learn the same skills, but the time it takes to do so represents significant productivity loss—and potential scrap, waste, and rework along the way.
Having a skill and being able to teach it are two different things. Until now, many manufacturers have relied on informal knowledge transfer techniques for the looming brain drain problem.
As such, knowledge transfer training has developed as an industry in itself. Third-party organizations are often employed to teach the skill of teaching and help to develop robust internal training programs. This is usually complemented by efforts to standardize the storage and transmission of knowledge.
More experienced employees may create a series of work instructions, for example, or materials that can be used for training; alongside traditional teaching and mentoring. However, the urgency of retaining organizational knowledge is increasing every year. As employees retire, those that are left must pass their experience onto a proportionally greater number of novices.
The major advantage of using AR for knowledge transfer is that it is less dependent on experts’ ability to teach. Knowledge transfer training can help to develop the skills to train, but not everybody will be suited to it, and there may simply be more novices in need of instruction than available, trained, experts can handle.
Building a repository of AR training materials—that can be used as teaching aids, as well as forming an immersive reference library in themselves—can harness the knowledge of more experienced employees, without displacing them from their usual role. Content creation can be handled by content specialists; teaching can be left to those best suited to it. And the resulting material can be used long after the experts responsible have retired.
Knowledge transfer training will continue to be a vital part of transmitting vital expertise to novices. But as the ‘brain drain’ challenge grows, AR can support traditional methods of training, fill any gaps, and ensure that valuable organizational knowledge isn’t eroded by time.