Multi-Output Regression in the Textile Industry: Time, Waste and Price Prediction
TEKSTIL VE KONFEKSIYON, vol.36, no.1, pp.60-70, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 36 Issue: 1
- Publication Date: 2026
- Doi Number: 10.32710/tekstilvekonfeksiyon.1489112
- Journal Name: TEKSTIL VE KONFEKSIYON
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Page Numbers: pp.60-70
- Keywords: Machine learning, ensemble learning, multi-output regression, textile, apparel
- Dokuz Eylül University Affiliated: Yes
Abstract
Machine learning and data mining techniques provide businesses with cutting-edge data-driven decision-making capabilities. Their popularity is growing because they enable more accurate and consistent evaluation and prediction of current and future situations based on previous data. This study used machine learning methodologies to address three of the textile industry's most pressing concerns: lead time, cloth waste, and price. A multi-output regressor model in which three subjects are predicted simultaneously is also investigated, in addition to training individual models for each subject. XGBoost is the model with the best lead time prediction results, with an R2 of 0.86 and an MAE of 8.35. When all three subjects are predicted at the same time, XGBoost achieves R2 of 0.88 and MAE of 3.79. These findings indicate that, in addition to single models, multi-output models are also promising.