A STATISTICAL PROCESS CONTROL APPLICATION FOR SEWING FAULTS IN MEN'S SUIT PRODUCTION


ÖZEREN F. , İLHAN İ.

TEKSTIL VE KONFEKSIYON, cilt.21, ss.397-404, 2011 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 21 Konu: 4
  • Basım Tarihi: 2011
  • Dergi Adı: TEKSTIL VE KONFEKSIYON
  • Sayfa Sayısı: ss.397-404

Özet

Sewing process is one of the most important stages in labour intensive ready-made clothing enterprises. Quality faults occurring during this process adversely affect the product quality and product efficiency, and also increase the production cost. The aim of this study is to investigate whether the jacket production process is under control in a men's suit production enterprise and to detect the processes with highest rates of sewing faults in sewing department and finally to make suggestions for improving the quality control. Among the Statistical Process Control methods; control list, p control chart and Pareto analysis were used in the study. "p control chart" was used to test whether the production process is controlled in the enterprise. Furthermore, the statistical methods were employed to determine the issues that need to be done in the improvement efforts and to detect the relations between the process groups supposedly effective on faults occurring in men's jacket production and the amount of faults. One Way Anova, Duncan test and Qui-Square analysis were used for the statistical analysis. Also, the processes with highest amounts of sewing faults and the effects of these processes on fault rates were investigated with Pareto analysis. As a result, it was concluded that the production process was statistically not under control in the ready-made clothing enterprise chosen for this study, the fault rates of process groups differed between the weeks and there was a statistically significant relation between the quality faults and the weeks. In addition, this study demonstrated that the investigation of each process group by drawing their p control charts would make significant contributions to foresee the results and prepare more effective the improvement plans.