Manual order picking remains a central intralogistics activity, but performance is constrained by non-value-added travel and by search and verification effort at the shelf. Augmented-Reality pick-by-vision systems promise context-sensitive guidance directly in the field of view of the workers, yet practical deployment must cope with deviations such as empty compartments without breaking task flow. This paper presents the design and prototype implementation of a feedback-driven adaptive assistance concept for picking. Following a Design Science Research process, requirements were derived from a scenario analysis and an expert interview, then realized in a Unity-based simulation prototype that combines egocentric route guidance with bin-level highlighting and a deterministic correction mode. When a stock shortage is reported at the target shelf, the system switches to a predefined reserve location and recalculates guidance accordingly. The prototype was assessed in an exploratory qualitative think-aloud study (N = 5). Participants reported high confidence in task completion (mean 8.8/10) and highlighted the route line and shelf framing as helpful cues. They also noted usability issues with the visibility and affordance of the stockout reporting control. This highlights an AR trade-off, as prominent overlays can increase guidance visibility but may obstruct the environment.      «

Manual order picking remains a central intralogistics activity, but performance is constrained by non-value-added travel and by search and verification effort at the shelf. Augmented-Reality pick-by-vision systems promise context-sensitive guidance directly in the field of view of the workers, yet practical deployment must cope with deviations such as empty compartments without breaking task flow. This paper presents the design and prototype implementation of a feedback-driven adaptive assistanc...     »