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. 2019 Feb 15;29(1):010903.
doi: 10.11613/BM.2019.010903. Epub 2018 Dec 15.

Mistaken assumptions drive new Six Sigma model off the road

Sten Westgard  1 , Hassan Bayat  2 , James O Westgard  1   3
Affiliations

Mistaken assumptions drive new Six Sigma model off the road

Sten Westgard et al. Biochem Med (Zagreb). .

Abstract

Oosterhuis and Coskun recently proposed a new model for applying the Six Sigma concept to laboratory measurement processes. In criticizing the conventional Six Sigma model, the authors misinterpret the industrial basis for Six Sigma and mixup the Six Sigma "counting methodology" with the "variation methodology", thus many later attributions, conclusions, and recommendations are also mistaken. Although the authors attempt to justify the new model based on industrial principles, they ignore the fundamental relationship between Six Sigma and the process capability indices. The proposed model, the Sigma Metric is calculated as the ratio CVI/CVA, where CVI is individual biological variation and CVA is the observed analytical imprecision. This new metric does not take bias into account, which is a major limitation for application to laboratory testing processes. Thus, the new model does not provide a valid assessment of method performance, nor a practical methodology for selecting or designing statistical quality control procedures.

Keywords: Sigma metrics; Six Sigma; allowable total error.

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Conflict of interest statement

Potential conflict of interest: None declared.

Figures

Figure 1
Figure 1
Relation of Sigma Metric (SM) to industrial process capability indices (Cp, Cpk) and process control metric (ΔSEcrit) for SQC selection and design. ΔSEcrit - critical systematic error. TL - tolerance limit. TV - target value. Μ - observed mean. pTE - permissible total error. Bias - observed trueness. SD - observed imprecision.
Figure 2
Figure 2
Quality planning tool for selection/design of SQC procedures having 2 levels of controls. The probability for rejection is plotted on y-axis versus the size of systematic error on bottom x-axis and the sigma-metric on the top x-axis. In the key at the right, the different power curves correspond, top to bottom, to the list of control rules, the probability for false rejection (Pfr), total number of control rules (N), and number of runs (R) over which the rules are applied. This chart was produced by the EZ Rules3 computer program. Vertical line represents examination procedure with observed sigma-metric of 4.0.
See this image and copyright information in PMC

References

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