Analysis of the modeling methodologies for predicting the sewing thread consumption

TLDR

The study aims to provide a rapid and accurate method to predict the amount of sewing thread required to make a garment. Three modeling approaches—a theoretical model, a linear regression model, and an artificial neural network—were compared by evaluating their predictive accuracy using R² and root‑mean‑square error against measured thread consumption. Both regression and neural network models can predict thread consumption, with the neural network achieving the highest accuracy. The study’s results are applicable to the textile industry, helping reduce unused stock and improve cost estimation.

Abstract

Purpose This paper aims to provide a rapid and accurate method to predict the amount of sewing thread required to make up a garment. Design/methodology/approach 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 R 2 and the root mean square error. Findings 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. Research limitations/implications This study is interesting for industrial application, where samples are taken for different fabrics and garments, thus a large body of data is available. Practical implications The paper has practical implications in the clothing and other textile‐making‐up industry. Unused stocks can be reduced and stock rupture avoided. Originality/value 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.