Automated work cycle classification and performance measurement for manual work stations

Abstract The increasing demand for highly customized products requires flexible, reactive and adaptive manufacturing systems. Accurate and up-to-date information about the processes is a strict requirement to meet these needs. Real-time data capturing technologies, such as RFID, have already been used for some years in manufacturing environments, mainly for inventory management, planning and quality control. However, these systems fail to generate information on the performance of the operator in the system. This paper presents a video-based system that automates the analysis of manual assembly line work stations and generates near real-time information to support workers in their pursuit of continuous improvement. A work cycle classification method was developed to detect anomalous and problematic situations in the work flow. Besides the classification of work cycles, the method also generates performance indicators to analyze the performance of the operator in the system. These performance indicators are visualized in an operational dashboard, which reveals the improvement potential of the work station.