Controlling and reducing process variability is an essential aspect for maintaining the product or service quality. Even though most practitioners believe that an increasing process variability is often a more severe concern than a shift in location, barely a few research paid attention to the cost-efficient monitoring of process variability. Some of the existing studies addressed the dispersion aspect, assuming that the quality characteristic is Gaussian. Non-normal and complex distributions are not uncommon in modern production processes, time to event processes, or processes involving service quality. Unfortunately, we find no literature on economically designed nonparametric (distribution-free) schemes for monitoring process variability. This article introduces two Shewhart-type cost-optimized nonparametric schemes for monitoring the variability of any unknown but continuous processes to fill the research gap. The proposed monitoring schemes are based on two popular two-sample rank statistics for differences in scale parameters, known as the Ansari–Bradley statistic and the Mood statistic. We assess their actual performance for a set of process scenarios and illustrate the design along with the implementation steps. We discuss a practical problem related to product quality management. It is expected that the proposed schemes will be beneficial in various industrial operations.

中文翻译：

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用于监控过程变异性的两种经济优化的非参数方案

控制和减少过程可变性是保持产品或服务质量的一个重要方面。尽管大多数从业者认为增加的过程可变性通常比位置转移更严重，但很少有研究关注过程可变性的成本效益监测。一些现有的研究涉及色散方面，假设质量特性是高斯的。非正态分布和复杂分布在现代生产流程、事件发生时间流程或涉及服务质量的流程中并不少见。不幸的是，我们没有找到关于经济设计的非参数（无分布）方案来监测过程变异性的文献。本文介绍了两种休哈特型成本优化非参数方案，用于监测任何未知但连续过程的可变性，以填补研究空白​​。提议的监测方案基于两个流行的双样本等级统计量，用于衡量尺度参数的差异，即 Ansari-Bradley 统计量和 Mood 统计量。我们针对一组流程场景评估它们的实际性能，并说明设计和实施步骤。我们讨论了一个与产品质量管理相关的实际问题。预期建议的计划将有利于各种工业运作。被称为 Ansari-Bradley 统计量和情绪统计量。我们针对一组流程场景评估它们的实际性能，并说明设计和实施步骤。我们讨论了一个与产品质量管理相关的实际问题。预期建议的计划将有利于各种工业运作。被称为 Ansari-Bradley 统计量和情绪统计量。我们针对一组流程场景评估它们的实际性能，并说明设计和实施步骤。我们讨论了一个与产品质量管理相关的实际问题。预期建议的计划将有利于各种工业运作。