Efficient work measurement system of manufacturing cells using speech recognition and digital image processing technology

Sim, Eoksu; Lee, Hyoung‐gon; Lee, Jung-Chul; Park, Jin-Woo

doi:10.1007/s00170-005-2557-5

Cited by 6 publications

(1 citation statement)

References 11 publications

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“…Meanwhile, an attempt to analyze the load of the sewing operation by measuring and analyzing the operating time was also made [13,14]. In addition, digital image analysis or work measurement based on vision technology has been attempted [15][16][17]. Studies have also been conducted on a method of simultaneously analyzing time and motion [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Remote Sensing of Sewing Work Levels Using a Power Monitoring System

Jung

Park²,

Lee³

et al. 2020

Applied Sciences

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The measurement of sewing work in the labor-intensive garment industry depends considerably on the person performing the measurements, making it difficult to quantitatively define the level of skill (LS) of the sewing machine operator and the level of difficulty (LD) of the unit process. In this study, a power monitoring system attached to the sewing machine was used to remotely collect power consumption data, which were then analyzed to extract the working times for a series of sewing tasks. LS of each operator was then classified and LD of each process was analyzed in terms of working time and quality. Finally, the resulting LS and LD weight factors considered to optimize the subject garment production line were compared against those proposed by experts. The LS weight factor proposed by the experts was ~15% less than that indicated by the experimental results, whereas the LD weight factor proposed by the experts was ~15%–40% greater than that indicated by the experimental results. The results of this study suggest that the proposed method could be applied in real time to inform the arrangement of line workers to increase the productivity of a garment production line.

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