An Application of Machine Learning to Marker Prediction in Garment Industry: Marker Length Estimation by Neural Network for the Exponentially Increasing Magnitude of Possible Size Combinations

Abstract

GEMTEX Lab has made some efforts in the application of artificial intelligence to optimize the garment manufacturing process (Zeng, Ge & Bruniaux, 2008; Thomassey & Zeng, 2018), including cutting, sewing, ironing and packing. As for the cutting process, marker making plays an essential role. The exponentially increasing magnitude of possible size combinations due to a considerable larger size number in modern garment mass customization induce a larger workload of marker making. Compared with creating all the markers using commercial marker making software, the application of machine learning technologies for marker length estimation will benefit in both efficiency and accuracy. The results generated from the marker length estimation can be used for providing the input data of cutting order planning (COP) for lean garment production, and as well predicting the objective values of marker length for the guidance and evaluation of marker making. In this study, MLR and RBF NN were used to estimating marker lengths with various sizes (regarding mass production (MP) and mass customization (MC)). The results showed that MLR generally outperforms RBF NN. MLR is more appropriated for general markers containing articles of regular sizes and mixed markers. RBF NN can be potential for markers containing articles of irregular sizes or for group markers.