Abstract
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.
Highlights
The global market size of the apparel industry is similar to that of the global automotive market [1], but it is a representative labor-intensive industry in which most apparel manufacturers still rely on a human measurer to count production quantities and measure work time
We propose a new method to remotely measure, quantify, and visualize the level of skill (LS) and level of difficulty (LD) of garment sewing work using a power monitoring system to overcome the problem of error-prone human measurement and replace the experience of experts with a numerically based standard
The reliability of expert decisions regarding the LS, the relationship of time and quality to LS and LD, and the weight factors that can be applied to inform operator relocation when optimizing production lines are numerically confirmed based on the results of the experiments
Summary
The global market size of the apparel industry is similar to that of the global automotive market [1], but it is a representative labor-intensive industry in which most apparel manufacturers still rely on a human measurer to count production quantities and measure work time. Human-conducted manual measurements are prone to error and may not accurately reflect the actual work status [2]. For this reason, most apparel manufacturing facilities rely on the line manager’s empirical judgment without knowing the exact level of skill (LS ) of hundreds or thousands of sewing machine operators. The outcome of this consideration depends entirely on the judgment of managers with extensive experience. This expert-based decision system creates two important issues [3]. It is impossible to make adjustments in the production line without
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