Determination of efficiency indicators of the stand for intelligent control of manual operations in industrial production

Manual operations remain essential in industrial production because of their flexibility and low implementation cost. However, ensuring their quality and monitoring execution in real time remains a challenge, especially under conditions of high variability and human-induced errors. In this paper, we present an AI-based control system for tracking manual assembly and propose a novel methodology to evaluate its overall efficiency. The developed system includes a multicamera setup and a YOLOv8-based detection module integrated into an experimental stand designed to replicate real production scenarios. The evaluation methodology relies on timestamp-level comparisons between predicted and actual execution stages, using three key metrics: Intersection over Union (IoU), Mean Absolute Scaled Error (MASE), Residual Distribution histograms. These metrics are aggregated into a unified efficiency index E_total for reproducible system assessment. The proposed approach was validated on a dataset of 120 assemblies performed at different speeds, demonstrating high segmentation accuracy and identifying stage-specific timing deviations. The results confirm the robustness of the control system and the applicability of the evaluation framework to benchmark similar solutions in industrial settings.