Building Anti-Fragile Organisations by Tony Bendell

Author:Tony Bendell
Language: eng
Format: epub
Publisher: Gower Publishing Company
Published: 2014-03-09T16:00:00+00:00

Figure 4.5 The technical meaning of Six Sigma

There are a number of interrelated current usages of the term Six Sigma. These include:

• Its use to describe a statistical or technical target corresponding to six standard deviations to the specification limits either side of the target, or equivalently three non-conformances out of one million opportunities, or Best in Class. See Figure 4.5.

• A variation reduction and process improvement programme that uses the project-based DMAIC(T) approach.

• A measurement-based strategy for improvement.

• An improvement ‘philosophy’.

The Six Sigma toolkit is extensive, with some commentators counting 141 statistical tools and concepts in the GE approach, and 140 in Caterpillar’s. Perhaps the most useful tools include, in particular, the Seven Tools of Quality Control, various Statistical Process Control (SPC) charts, Experimental Design and specifically Taguchi methodology, and Root Cause Analysis. Useful non-statistical tools include Failure Modes Effects and Criticality Analysis (FMECA or FMEA), Quality Function Deployment (QFD), Poka Yoke or Mistake Proofing, and some right brain and creativity tools.

Having introduced the Six Sigma concept, we can now return to the nine potential fragility-related issues with Six Sigma that we introduced at the start of this section:

1. The use of the DMAIC, and particularly DMAIC(T), project management approach is essentially anti-fragile, since it is a planned, evidence-based, based on application in the real world, approach. The fact that it is often presented as linear, when redefinition of the project, remeasurement and/or reanalysis may in reality be needed, is potentially a fragile feature if implemented in this way, as it will imply that this prescriptive formulation will prevent or delay real discovery of causes or solutions.

2. In practice, many Six Sigma Black and Green Belt training programmes are based around training by rote, rather than education, which by definition is fragile as it is non-adaptive. This delivery method is partly because the toolkit is so extensive; although this itself is based on how Six Sigma is taught. With an education approach to understand the basis and interconnections of the tools, providing some insight to the underlying concepts rather than learning specific tool use by heart, Belt candidates can work on a reduced core toolset but have the basis for more broad identification and selection of appropriate tools. This is how the Six Sigma Green and Black Belt courses taught by the author and his colleagues, through Services Limited, are delivered. The other reason is that, unfortunately, much current Six Sigma training provision is provided by trainers who themselves do not fully understand the statistical basis of the tools they train on. Clearly, whilst practitioner experience is essential for robust Six Sigma training, deep statistical understanding is also crucial for anti-fragile Six Sigma training.

3. The question of a possibly inappropriate statistical toolkit arises not primarily because of the number of tools in the toolkit, but what they are. Largely because of the background of trainers as non-statistical experts, the content of many Six Sigma training programmes are somewhat historical, reflecting statistical methods that the author taught to undergraduates more than 40 years ago.

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