<![CDATA[High product rejection rates in textile manufacturing increase production costs and material waste, highlighting the need for systematic quality control to support operational sustainability. This study aimed to identify the predominant defects, evaluate process stability, investigate the root causes of product rejection, and develop statistical process control (SPC) procedures for baby sock production at PT XYZ. A mixed-methods action research design was employed, focusing on the diagnostic and action-planning stages. Production and rejection records collected from April 1 to June 25, 2026, were analyzed using Pareto analysis and proportion control charts (p-charts). Interviews, direct observations, fishbone diagrams, and the Five Whys technique were used to identify the underlying causes of product defects. Of the 718,266 units produced, 67,898 were rejected, resulting in a rejection rate of 9.45%, which exceeded the company's target of 3%. Yarn and elastic breakage accounted for 84.84% of all rejected products. Observations from April and June fell outside the calculated control limits, indicating that the production process was not statistically stable. The identified root causes included inadequate machine maintenance, inconsistent raw material inspections, undocumented operating procedures, variations in operator experience, and insufficient measures to mitigate excessive heat. Based on these findings, standard operating procedures (SOPs) and process flowcharts were developed to standardize material inspections, production monitoring, defect documentation, and corrective actions. The proposed SPC framework provided a structured approach to improving quality control consistency and supporting operational sustainability by minimizing potential material waste and production inefficiencies. However, its effectiveness in reducing product rejection rates remained unverified, as the proposed procedures had not yet been implemented and evaluated. Further implementation and performance assessment were therefore necessary to determine whether the framework could achieve measurable improvements in product quality, process stability, and resource efficiency.]]>
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