CONTROLE ESTATÍSTICO DE PROCESSOS APLICADO À PRODUÇÃO DE SUTIÃS PÓS-CIRÚRGICOS: ANÁLISE DA ESTABILIDADE POR MEIO DA CARTA DE CONTROLE P DE LANEY

Abstract

Brazil occupies a prominent position in the world scenario of plastic surgeries and aesthetic procedures and, in parallel, the textile and apparel industry moved around R$ 203.9 billion in 2024. The apparel sector is characterized by the high participation of manual operations and, especially in the manufacture of post-surgical clothing, by the need for strict quality control. In this context, this study aimed to analyze the stability of the production process of a clothing company specialized in the manufacture of post-surgical clothing, located in Campo Grande – MS, through Statistical Process Control (SPC). The research focused on the production line of bras models 2844 and 2842, the main products sold by the company on the Shopee marketplace. The methodology is characterized as exploratory and applied, with a qualitative and quantitative approach. The data were prospectively collected by the researcher during 14 days of production, through the daily record of the quantities produced and the non-conformities identified in the final inspection of the products. In all, 1723 pieces were inspected, of which 53 had some type of defect, resulting in an average rate of non-conformities of 3.08%. The initial analysis using the conventional control chart p indicated signs of process instability. However, considering the variability of the sample size and the presence of overdispersion, Laney's p-chart was adopted, which presented all 14 samples within the control limits, indicating that the process was statistically stable. The most recurrent defects were hook stitching, changed labels, and twisted handles, evidencing opportunities for improvement in the stages of sewing, assembly, and product identification. It is concluded that the conventional p-chart could lead to an incorrect interpretation of the stability of the process, while Laney's p-chart proved to be more adequate to the characteristics of the data analyzed. Although the process is under statistical control, the average defect rate highlights the need for actions aimed at reducing common causes of variation and continuous quality improvement.