Department of Medical Biochemistry, Acıbadem Mehmet Ali Aydınlar University, School of Medicine, Istanbul, Turkey.
Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy.
Biochem Med (Zagreb). 2020 Feb 15;30(1):010901. doi: 10.11613/BM.2020.010901.
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.
自 20 世纪 80 年代中期以来,六西格玛方法已在工业、医疗保健和实验室医学中得到广泛应用。过程的性能通过西格玛度量(SM)进行评估,6 西格玛代表世界级的性能,这意味着只有 3.4 或更少的缺陷(或错误)百万机会(DPMO)预计会发生。然而,从统计学上讲,6 西格玛对应于 0.002 DPMO,而不是 3.4 DPMO。造成这种差异的原因是引入了 1.5 个标准差(SD)偏移,以考虑过程在其目标周围的随机变化。相比之下,对于正态分布数据(如总测试过程的分析阶段),应考虑 1.5 SD 偏移;在实践中,该偏移已包含在与 SM 相关的所有类型的计算中,包括非正态分布数据。这导致 SM 与实际水平有很大偏差。为了确保 SM 值准确反映过程性能,我们得出结论,在必要且正式适当的情况下,应使用 1.5 SD 偏移。此外,1.5 SD 偏移不应被视为与 SM 相关的所有计算中自动包含的常数参数。