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Synthesis maps for multivariate ordinal variables in manufacturing

机译:制造中多元序数变量的综合图

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摘要

Many quality characteristics of products or services are commonly evaluated on ordinal scales with a finite number of categories. A systematic analysis of categorical variables collected over time may be very useful for a profitable management strategy. In order to measure customer satisfaction or quality improvement in a process, two or more quality characteristics are often conjointly measured and summarized by suitable indexes. A common practice suggests evaluating a synthetic index by mapping each outcome of a multivariate ordinal variable into numbers. This procedure is not always legitimate from the measurement theory point of view. In this paper an alternative approach based on the algebraic theory of the ordered sets is proposed. This method avoids mapping multivariate components into numbers. Multivariate ordinal variable components are synthesized by ordering the multivariate sample space. The ordering criterion is defined on the basis of the specific characteristics of the process at hand. Practical effects in the use of this method are shown on a series of application examples.
机译:产品或服务的许多质量特征通常是在具有一定数量类别的有序规模上进行评估的。对随着时间推移收集的分类变量的系统分析可能对盈利的管理策略非常有用。为了测量过程中的客户满意度或质量改进,通常会同时测量两个或更多个质量特征并通过合适的指标进行总结。一种常见的做法是建议通过将多元序数变量的每个结果映射成数字来评估综合指数。从测量理论的角度来看,此过程并不总是合法的。本文提出了一种基于有序集的代数理论的替代方法。此方法避免将多元分量映射到数字。多元有序变量分量是通过对多元样本空间进行排序而合成的。排序标准是根据当前过程的特定特征定义的。在一系列应用示例中显示了使用此方法的实际效果。

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