Frequent engineering changes in manufacturing require worker instructions to be updated quickly and reliably. In many production environments, however, update handling still depends on manual comparison procedures, delayed communication, or repeated traversal of large document collections, limiting responsiveness during ongoing production changes. This paper presents a hierarchical hash-based method for change detection in structured manufacturing documents as the computational core of a worker assistance system for near-real-time instruction updates in the context of in-line qualification. Heterogeneous instruction data are transformed into canonical hierarchical document structures, from which SHA-512 digests are generated at multiple structural levels. During repeated comparison operations, document-state evaluation is reduced to digest comparison, while structural differences can be localized through hierarchical refinement of affected substructures. The method is integrated into a system architecture that combines predecessor-linked version management with role-specific filtering for controlled dissemination of relevant instruction updates. The approach was implemented in an automotive assembly use case involving structured work instructions and evolving production documentation. The evaluation demonstrates that the proposed approach reduces repeated comparison effort relative to conventional field-wise traversal methods while maintaining the ability to localize structural changes through hierarchical refinement. The reported results focus on computational behavior and implementation feasibility in structured manufacturing environments rather than hardware-specific throughput benchmarks. Overall, the results indicate that hierarchical comparison of structured instruction states provides a practical basis for change-aware worker assistance and controlled propagation of instruction updates in evolving manufacturing environments. The evaluation focuses on repeated-comparison scenarios in structured manufacturing settings and does not address semantic interpretation of detected changes or large-scale distributed deployments.