AR-COMPASS: A Framework for Tacit Knowledge Capturing in Industrial Maintenance

Abstract

Tacit knowledge is crucial in industrial maintenance, yet it remains challenging to document and transfer through conventional training methods. This article addresses this by introducing the augmented reality (AR)-COMPASS framework, which structures tacit expertise into six knowledge types using AR: collaborative routines, operational troubleshooting, motor skills and tool handling, practical shortcuts, adaptation to context, and safety awareness and risk assessment. Together with an overarching operator skill dimension that integrates the various knowledge types, this framework captures the embodied expertise that guides effective maintenance performance. To evaluate this framework, a case study was conducted in which an AR prototype was developed. The system integrates motion tracking, video documentation, and decision prompts to capture individual-level tacit knowledge. An expert mechanic's maintenance procedure was recorded, and the captured knowledge was embedded into the AR workflow. Subsequently, an A/B test was conducted with 10 participants to compare AR-guided instructions with a traditional paper-based manual. In the paper-based manual group, completion time improved by 9.4% and errors decreased by 60% between runs. The AR-guided group achieved substantially larger gains, with an 18.1% reduction in completion time and a complete elimination of critical errors in the second run. Participants also reported higher usability and confidence when using AR. These findings highlight the potential of the AR-COMPASS framework to transform tacit knowledge into explicit, step-anchored guidance delivered at the point of need, providing a foundation for more reliable and digitally supported industrial maintenance.