Department of Statistics, Govt Graduate College (B) Gulberg, Lahore, Pakistan.
Department of Statistics and Computer Science, University of Veterinary and Animal Sciences, Lahore, 54000, Pakistan.
Sci Rep. 2023 Apr 5;13(1):5562. doi: 10.1038/s41598-023-32781-4.
The control chart is the most valuable tool in the manufacturing process to track the output process in the industries. Quality specialists always want a visual framework that recognizes sustainable improvements in the monitoring processes. The efficiency of a control chart is increased by utilizing a memory-based estimator or by using any extra information relevant to the key variable. In this study, we present Extended EWMA (EEWMA) and EWMA based monitoring charts for observing the process location using moving average (MA) statistic under two different situations, i.e., when some extra information is known and unknown. We also propose an EEWMA control chart using Auxiliary Information. The output of these charts is evaluated and contrasted to the various existing charts on the basis of average run length (ARL). The comparison indicates that the proposed charts outperform rivals in identifying all types of shifts in the process location parameter. The implementation of these plans is also rendered to incorporate them in a practical situation.
控制图是制造业中最有价值的工具，可用于跟踪工业生产过程中的输出过程。质量专家一直希望有一种可视化的框架，能够识别监测过程中的可持续改进。通过利用基于记忆的估计器或利用与关键变量相关的任何额外信息，可以提高控制图的效率。在本研究中，我们提出了扩展 EWMA（EEWMA）和 EWMA 监测图，用于在两种不同情况下使用移动平均（MA）统计量观察过程位置，即当某些额外信息已知和未知时。我们还提出了一种使用辅助信息的 EEWMA 控制图。这些图表的输出基于平均运行长度（ARL）进行评估和对比，以与各种现有图表进行对比。比较表明，所提出的图表在识别过程位置参数的所有类型偏移方面均优于竞争对手。还实施了这些计划，以便将其纳入实际情况。
