Variation is an important phenomenon of the output of every manufacturing and production process. To deal with the natural and special cause variations in the process, quality practitioners mostly apply control charts. There have been regular advancements over time in the design structures of these charts such as runs rules, fast initial response, sampling mechanisms among many others. In this article, auxiliary‐information‐based progressive mean (AIB‐PM) control chart has been proposed, in which study variable is found correlated with another auxiliary variable. The development of the proposed AIB‐PM structure utilises both the study and auxiliary variables. It is based on the regression estimator to introduce an unbiased and efficient estimate of the location parameter of the study variable. The performance assessment is carried out using average run length as a metric under zero‐state and steady‐state modes. The proposed AIB‐PM chart is compared with some existing competitors and found that it performs uniformly superior than the existing competitors at small and persistent shifts in the process mean. An illustrative example using a real data set is presented to show the implementation of the proposed method.

中文翻译：

---

在设计渐进均值图以有效监视过程位置时

变化是每个制造和生产过程的输出的重要现象。为了应对过程中自然和特殊原因的变化，质量从业人员通常会使用控制图。这些图表的设计结构在时间上有定期的改进，例如运行规则，快速的初始响应，采样机制等。在本文中，提出了基于辅助信息的渐进均值（AIB-PM）控制图，其中发现研究变量与另一个辅助变量相关。拟议的AIB-PM结构的开发利用了研究变量和辅助变量。它基于回归估计器来引入研究变量的位置参数的无偏且有效的估计。在零状态和稳态模式下，使用平均运行长度作为度量标准进行性能评估。将拟议的AIB-PM图表与一些现有竞争对手进行比较，发现在流程平均值小而持续的变化时，它的性能始终优于现有竞争对手。给出了使用真实数据集的说明性示例，以展示所提出方法的实现。