» The 1.5 Sigma Shift

The 1.5 Sigma Shift

1990
  • Mikel Harry

The 1.5 sigma shift is an empirical correction used in 六西格玛 calculations to account for the long-term dynamic variation of a process. It posits that over time, a process mean will tend to drift by about 1.5 standard deviations from its short-term centered position. This shift is the reason a 6 sigma process corresponds to 3.4 DPMO, not the theoretical 2 defects per billion.

Disclaimer: the 1.5 sigma shift is one of the most debated aspects of Six Sigma. It originates from the observation that short-term data, collected over a brief period, typically shows less variation than long-term data from the same process. This is because over longer periods, factors like tool wear, material variations, operator differences, and environmental changes cause the process mean to drift. The 1.5 sigma value was determined empirically by Motorola engineers as a reasonable general estimate for this drift. By incorporating this shift, Six Sigma provides a more realistic, long-term view of 工艺性能. The calculation for DPMO is thus based on the probability of an outcome falling outside a specification limit that is [latex]4.5\sigma[/latex] from the drifted mean ([latex]6\sigma – 1.5\sigma[/latex]). Critics argue that the 1.5 value is arbitrary and that a well-controlled process should not drift this much. Proponents argue it’s a pragmatic adjustment that makes the Six Sigma standard more applicable to real-world industrial processes.

UNESCO Nomenclature: 3308
– Industrial engineering and technology

类型

抽象系统

中断

递增

使用方法

广泛使用

前体

  • Walter A. Shewhart’s work on common and special cause variation
  • long-term vs. short-term 过程能力 studies
  • empirical observations of industrial processes
  • control chart theory

应用

  • calculating long-term process capability (Cpk, Ppk)
  • setting realistic quality targets in 制造业
  • risk assessment in financial modeling
  • reliability engineering for long-life products

专利:

NA

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Related to: 1.5 sigma shift, process drift, six sigma, dpmo, 统计过程控制, process capability, long-term variation, motorola, quality control, standard deviation.

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