<![CDATA[Background The textile industry is required to consistently produce high-quality products to maintain competitiveness in an increasingly competitive global market. One of the major quality challenges in spinning industries is the occurrence of product defects that reduce production efficiency and increase manufacturing costs Purpose This study aimed to analyze the quality control performance of Rayon Ne 30 yarn production at PT. X using the Six Sigma DMAIC (Define–Measure–Analyze–Improve–Control) methodology. Methodology A quantitative descriptive approach was employed using historical production and quality control data collected from January to April 2026. Data were analyzed through Critical to Quality (CTQ) identification, Pareto analysis, Defects Per Million Opportunities (DPMO), Sigma capability measurement, Statistical Process Control (SPC) using P-chart, Fishbone analysis, and Five Whys analysis to identify dominant root causes. Findings The results indicated that the production process experienced fluctuating defect levels throughout the observation period, with the highest defect rate occurring in April. Crossing was identified as the dominant defect, followed by Ring and Loose Winding, collectively contributing approximately 85% of total production defects. Implications Process capability analysis showed that the production process had not yet achieved the Six Sigma performance target, while SPC analysis indicated the presence of process variation requiring further investigation. Root cause analysis revealed that machine setting instability, inadequate preventive maintenance, inconsistent operating methods, and operator-related factors were the primary contributors to defect occurrence. Originality Based on these findings, several improvement strategies were proposed, including preventive maintenance scheduling, machine calibration standardization, operator competency improvement, and standardized operating procedures. Since the proposed improvements were not implemented during the study period, their effectiveness could not be statistically validated. Future studies are recommended to conduct industrial implementation and long-term monitoring to evaluate the impact of the proposed improvements on process capability and defect reduction.]]>
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