Nafisah Ibrahim A, Almazah Mohammed M A, Al-Rezami A Y, Hussain Saddam
Department of Statistics and Operations Research, College of Sciences, King Saud University, P. O. Box 2454, 11451, Riyadh, Saudi Arabia.
Department of Mathematics, College of Sciences and Arts (Muhyil), King Khalid University, 61421, Muhyil, Saudi Arabia.
Sci Rep. 2025 Aug 22;15(1):30964. doi: 10.1038/s41598-025-16531-2.
Statistical Process Control is essential for ensuring process stability and detecting variations in a production environment. This study introduces a control chart based on the Exponentially Weighted Moving Average (EWMA) that uses an adaptive sample size. The proposed approach enhances shift detection by dynamically adjusting the sample size in response to changes in process variation. Extensive Monte Carlo simulations were performed to assess the performance of the proposed control chart, focusing on metrics such as the Average Run Length (ARL) and the Standard Deviation of Run Length (SDRL). The findings show that the new chart surpasses both the Fixed Sample Size EWMA (FEWMA) and the Variable Sample Size EWMA charts, particularly in detecting small to moderate shifts in the process. This approach strikes a balance between detection sensitivity and computational efficiency, enabling prompt identification of process changes while maintaining robustness during in-control conditions. To illustrate its practical applicability, a real-world dataset was analyzed, demonstrating the effectiveness of the proposed method in actual process monitoring scenarios.
统计过程控制对于确保生产环境中的过程稳定性和检测变化至关重要。本研究介绍了一种基于指数加权移动平均（EWMA）的控制图，该控制图使用自适应样本量。所提出的方法通过响应过程变化动态调整样本量来增强偏移检测。进行了广泛的蒙特卡罗模拟以评估所提出控制图的性能，重点关注平均运行长度（ARL）和运行长度标准差（SDRL）等指标。研究结果表明，新的控制图优于固定样本量EWMA（FEWMA）和可变样本量EWMA控制图，特别是在检测过程中的小到中等偏移方面。这种方法在检测灵敏度和计算效率之间取得了平衡，能够在过程处于受控状态时迅速识别过程变化，同时保持稳健性。为了说明其实际适用性，分析了一个实际数据集，证明了所提出方法在实际过程监测场景中的有效性。
