This paper aims to provide a rapid and accurate method to predict the amount of sewing thread required to make up a garment.
Three modeling methodologies are analyzed in this paper: theoretical model, linear regression model and artificial neural network model. The predictive power of each model is evaluated by comparing the estimated thread consumption with the actual values measured after the unstitching of the garment with regression coefficient R2 and the root mean square error.
Both the regression analysis and neural network can predict the quantity of yarn required to sew a garment. The obtained results reveal that the neural network gives the best accurate prediction.
This study is interesting for industrial application, where samples are taken for different fabrics and garments, thus a large body of data is available.
The paper has practical implications in the clothing and other textile‐making‐up industry. Unused stocks can be reduced and stock rupture avoided.
The results can be used by industry to predict the amount of yarn required to sew a garment, and hence enable a reliable estimation of the garment cost and raw material required.
Jaouadi, M., Msahli, S., Babay, A. and Zitouni, B. (2006), "Analysis of the modeling methodologies for predicting the sewing thread consumption", International Journal of Clothing Science and Technology, Vol. 18 No. 1, pp. 7-18. https://doi.org/10.1108/09556220610637477Download as .RIS
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