Augmented reality (AR) is increasingly adopted in Industry 4.0 logistics to support tasks such as picking, sorting, and inventory handling. A core requirement for these systems is the reliable, real-time identification of objects and locations, commonly achieved using QR codes. In industrial environments, however, mobile operators and wearable devices introduce challenges, including motion blur, changing distances and angles, limited on-device computation, and intermittent network connectivity. This paper proposes an architecture for QR code detection and recognition tailored to AR devices used in warehouses. The contribution is architectural rather than algorithmic: we design an end-to-end identification pipeline and a hybrid processing strategy that can run fully on-device when connectivity is limited or offload processing to a server when device resources are constrained. We also incorporate tracking and buffering mechanisms to improve user experience and reduce duplicate reads. An experimental study evaluates the feasibility of QR decoding across different printed code sizes, camera types, and viewing angles, providing concrete guidelines for QR code sizing and scanning distances in practice. The proposed architecture supports near-real-time operation and provides a practical reference for deploying QR-based identification in AR-assisted industrial workflows.