Higher Education Department, Lahore, Pakistan.
University of the Punjab, Lahore, Pakistan.
PLoS One. 2023 Sep 15;18(9):e0290727. doi: 10.1371/journal.pone.0290727. eCollection 2023.
Quality control (QC) is a systematic approach to ensuring that products and services meet customer requirements. It is an essential part of manufacturing and industry, as it helps to improve product quality, customer satisfaction, and profitability. Quality practitioners generally apply control charts to monitor the industrial process, among many other statistical process control tools, and to detect changes. New developments in control charting schemes for high-quality monitoring are the need of the hour. In this paper, we have enhanced the performance of the mixed homogeneously weighted moving average (HWMA)-cumulative sum (CUSUM) control chart by using the auxiliary information-based (AIB) regression estimator and named it MHCAIB. The proposed MHCAIB chart provided an unbiased and more efficient estimator of the process location. The various measures of the run length are used to judge the performance of the proposed MHCAIB and to compare it with existing AIB charts like CUSUMAIB, EWMAAIB, MECAIB (mixed AIB EWMA-CUSUM), and HWMAAIB. The Run length (RL) based performance comparisons indicate that the MHCAIB chart performs relatively better in monitoring small to moderate shifts over its competitor's charts. It is shown that the chart's performance improves with the increase in correlation between the study variable and the auxiliary variable. An illustrative application of the proposed MHCAIB chart is also provided to show its implementation in practical situations.
质量控制（QC）是一种确保产品和服务符合客户要求的系统方法。它是制造业和工业的重要组成部分，因为它有助于提高产品质量、客户满意度和盈利能力。质量从业者通常会应用控制图来监控工业过程，以及许多其他统计过程控制工具，以检测变化。控制图方案的新发展是高质量监测的必要条件。在本文中，我们使用基于辅助信息的（AIB）回归估计器增强了混合同质加权移动平均（HWMA）-累积和（CUSUM）控制图的性能，并将其命名为 MHCAIB。所提出的 MHCAIB 图提供了过程位置的无偏且更有效的估计器。各种运行长度的度量标准用于判断所提出的 MHCAIB 的性能，并将其与现有的 AIB 图表（如 CUSUMAIB、EWMAAIB、MECAIB（混合 AIB EWMA-CUSUM）和 HWMAAIB）进行比较。基于运行长度（RL）的性能比较表明，MHCAIB 图在监测小到中等偏移方面的性能相对优于其竞争对手的图表。结果表明，随着研究变量和辅助变量之间相关性的增加，图表的性能会提高。还提供了一个说明性的应用示例，以展示其在实际情况中的实施。
