An Augmented Reality Solution to support Problem Solving
An area in dire need of training innovation is that of maintenance, which represents what should be one of industry’s most acute readiness concerns: a rapidly aging workforce and the struggle to attract the millennial generation to trades is causing a significant skill gap. This is true for both the military and industry.
Take the case of the US Navy. Discovery of engine damage to the Littoral Combat Ship USS Freedom and damage to the combining gear of the USS Fort Worth, both potentially due to human error, led to an order to stand down engineering until all crew could complete a review of procedures for their respective departments. Imagine the industry equivalent – a rollout of a new trucking line that similarly grounds a significant portion of a fleet’s assets, dramatically decreasing utilization, and the financial impact on operators and their companies.
The Gerald R. Ford future aircraft replacement class for Enterprise and Nimitz-class aircraft carriers is designed to operate effectively with nearly 700 fewer crew members. The potential downside of this is that it will provide fewer opportunities for transitional on the job training due to the limited number of maintainers on any given ship and limited time for an individual maintainer to dedicate to training on that ship, requiring a shift in the training qualification process.
Couple this challenge with subject matter experts in maintenance retiring from an environment with existing workforce inexperience, contributing negatively to maintenance productivity, resulting in just 11-28% of ship maintenance jobs being completed on time over the past 5 years. Yet, there are no mechanisms in place to transfer the experts’ long earned knowledge to the next generation of worker.
Think about your industry? How are you dealing with this challenge?
What characterizes the expert? An expert uses their tacit knowledge to support unanticipated deviations from common procedures. They mentally develop and access “lessons learned” and “best practices.” Of course, they are the only ones with access to this knowledge, and normally can only verbalize it.
Although remote collaboration software has become available through mobile devices and augmented reality (AR) heads up devices the information transfer is transient. This software connects personnel that may be remote so they can troubleshoot together. This will benefit the remote employee only for that short transaction, and is a very inefficient method for transferring tacit knowledge as it would require substantial repetition. If presented with the same challenge, the learner may execute the right solution. If presented with a similar yet different case, on the other hand, the learner will still need to use the expert’s advice. Thus, the expert retires and their knowledge has not effectively been transferred. It isn’t just about choosing the right solution for that one problem, but sharing the problem solving process.
Here are a few necessary characteristics for a successful AR problem solving solution:
- Focus on the symptom, not the solution: A novice or new hire should initially be focused on identifying a problem and recognizing the symptoms, not on rote memorization of task procedures. Existing AR software focuses on delivering procedures to the operator and does not build an understanding of the symptomology.
- Localize faults and diagnose root cause: The learner should graduate from symptom recognition to reasoning through alternatives and developing skills to generate and test hypotheses. Hypothesis tests are considered procedures, like everything else with today’s technology.
- Align corrective action with proven hypotheses: Creating an action plan based on a tested hypothesis will lead to more efficient corrective action in future cases. The action plan is the procedure. The operator needs to understand symptomology and understand relationship with probable causes if they are to be an effective contributor in the operation.
- Reinforce correction action mapping: Building mastery will reduce deviations from corrective action procedures. The focus here is on positively reinforcing the learner and supporting transfer of knowledge from one case to another and applying it appropriately. In this way, lessons learned from one problem can be effectively applied to the next.
Currently, AR systems support corrective action. In manufacturing, we are seeing production analytics determine root cause and inform the operator of a corrective action. This is very operational and is not an effective tool for transferring or building knowledge, and certainly will not support the advancement of a new hire efficiently through tacit knowledge adoption. The new hire or learner will not build an understanding of that new truck model or new machinery and will not be as effective a contributor to the operation as they otherwise could be. Even with the AR systems currently available, on the job training is still a required part of the process. The rapidly aging workforce brings significant pressure to transfer this knowledge faster.
If systematically designed, augmented reality may enable trainees to receive highly effective training without the need for intensive instructor interaction or expensive OJT. Never the less, while there are many use cases, there has yet to be a defining framework to direct and optimize knowledge acquisition, transfer, and retention using AR technology. Now, imagine a solution that effectively and efficiently teaches a learner the relationship between symptoms and probable causes and how to test hypotheses to determine the best action plan.
Design Interactive has been optimizing human performance at ludicrous speed since 1998. We develop innovative, engaging augmented and virtual reality training solutions and create biosignatures of human emotion, cognition and physical state that empower consumers. To learn more about how we can leverage technology to improve your business processes, contact us here.