<![CDATA[This study aims to analyze the quality control of car tire production at Company X using the Statistical Process Control (SPC) method. The data used in this research are secondary data obtained from GitHub, consisting of weekly production and defect data observed over one year with a total of 52 observations. The analysis was carried out using descriptive statistics, histograms, P-charts, and cause-and-effect (Ishikawa) diagrams. The histogram results indicate that the distribution of defects is right-skewed, showing that most defects occur at a moderate level, although several extreme values are present. The initial P-chart analysis revealed several points below the lower control limit, indicating the presence of special cause variation and suggesting that the production process was not fully stable. After removing out-of-control data and recalculating the control limits, the revised P-chart showed that all data points were within the control limits, indicating that the production process was statistically under control. Furthermore, the Ishikawa diagram identified potential causes of defects related to human factors, machines, methods, materials, and the working environment. Overall, the results demonstrate that SPC is an effective tool for monitoring production stability and providing a systematic basis for improving quality consistency in car tire manufacturing processes.]]>
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