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Six Sigma revisited: We need evidence to include a 1.5 SD shift in the extraanalytical phase of the total testing process

机译:再访六西格玛:我们需要证据表明在整个测试过程的分析外阶段应包括1.5 SD偏移

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摘要

The Six Sigma methodology has been widely implemented in industry, healthcare, and laboratory medicine since the mid-1980s. The performance of a process is evaluated by the sigma metric (SM), and 6 sigma represents world class performance, which implies that only 3.4 or less defects (or errors) million opportunities (DPMO) are expected to occur. However, statistically, 6 sigma corresponds to 0.002 DPMO rather than 3.4 DPMO. The reason for this difference is the introduction of a 1.5 standard deviation (SD) shift to account for the random variation of the process around its target. In contrast, a 1.5 SD shift should be taken into account for normally distributed data, such as the analytical phase of the total testing process; in practice, this shift has been included in all type of calculations related to SM including non-normally distributed data. This causes great deviation of the SM from the actual level. To ensure that the SM value accurately reflects process performance, we concluded that a 1.5 SD shift should be used where it is necessary and formally appropriate. Additionally, 1.5 SD shift should not be considered as a constant parameter automatically included in all calculations related to SM.
机译:自1980年代中期以来,六西格码方法已在工业,医疗保健和实验室医学中广泛采用。流程的性能由sigma度量标准(sigma metric,SM)评估,6 sigma代表世界一流的性能,这意味着预期只会发生3.4或更少的缺陷(或错误)百万机会(DPMO)。但是,从统计学上讲,6 sigma对应于0.002 DPMO,而不是3.4 DPMO。产生这种差异的原因是引入了1.5个标准偏差(SD)偏移,以说明过程围绕其目标的随机变化。相反,对于正态分布的数据,例如整个测试过程的分析阶段,应考虑1.5 SD偏移;实际上,这种变化已包括在与SM有关的所有类型的计算中,包括非正态分布的数据。这会导致SM与实际水平的较大偏差。为了确保SM值准确反映过程性能,我们得出结论,在必要且形式适当的地方应使用1.5 SD偏移。此外,不应将1.5 SD偏移视为与SM相关的所有计算中自动包含的常数参数。

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