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Adhie Tri Wahyudi
Department of Industrial Engineering, Universitas Setia Budi, Mojosongo, Surakarta 57127, Indonesia
Industrial & Manufacturing Engineering

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Proactive quality control in cotton yarn manufacturing using random forest defect forecasting and CTQ analysis Adhie Tri Wahyudi; Mayang Eka Putri Prastiwi; Erni Suparti; Muhammad Rizki
OPSI Vol 19 No 1 (2026): OPSI - June 2026
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v19i1.16757

Abstract

Quality control in cotton yarn manufacturing is affected by the daily variation in the occurrence of defects and the late detection of abnormal events at the packing stage. This study proposes a decision-support workflow that integrates Random Forest regression for daily defect forecasting with Critical-to-Quality (CTQ) analysis to enable proactive quality control in an industrial case. The model is trained and validated with time ordered evaluation using historical production volume and defect counts, and the performance is reported using MAE, RMSE and sMAPE. Results indicate that the model learns to capture routine up-down defect patterns during the training period, but prediction accuracy is degraded on test days with extreme defect spikes. However, despite this limitation, the forecast outputs provide an early warning signal to prioritize inspection and corrective actions on high risk days. The predicted risk levels are then further linked to CTQ mapping to identify dominant defect types and convert model outputs to actionable defect-specific control recommendations. The proposed integration of machine-learning forecasting with shop-floor quality planning enables more timely and targeted quality interventions in cotton yarn packaging operations.
Co-Authors Erni Suparti Mayang Eka Putri Prastiwi Muhammad Rizki