Cross-silo human training in operational assembly: Integrating machine feedback for enhanced efficiency

In low-volume, multi-product manufacturing, workers must respond quickly and flexibly to changes in various work operations. However, there is currently a shortage of skilled workers, necessitating an effective method for training a wide range of workers with diverse characteristics. In this study, we first evaluated the feasibility of machine learning (ML) models for recognising complex assembly works. We next constructed the Feedback Integrated Expert Level Description System (FIELDS), which incorporates an ML model and functions for data collection, management, and user feedback. FIELDS can collect real-time work data from an action camera attached to trainees, analyse their assembly work from the data using the ML model, and provide feedback based on the analysis result. We evaluated the effect of the feedback using three metrics, the number of reduced missing processes, the distance from the regular processes, and the total work time. The feedback from the ML model was shown to enhance the trainees’ awareness of their proficiency and foster improvement. This result reveals a potential power of machine feedback for improving the efficiency of worker training. Consequently, this study contributes to offer a visible solution to enhance productivity and adaptability through cross-silo worker training in manufacturing environments. • Feasibility of AS (Action Segmentation) models for complex assembly work evaluated. • FIELDS system developed; collects real-time video for AS analysis. • Based on AS, FIELDS assesses proficiency; gives feedback on errors, order, time. • Experimental results show machine feedback improves trainee performance.