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Project management systems – Six sigma – part 2

Six sigma – part 2

Implementation roles

Six Sigma borrows martial arts ranking terminology to define a hierarchy (and career path).
It cuts across all business functions.

It identifies several key roles for its successful implementation.

  • Executive Leadership

This includes the CEO (Chief Executive Officer) and other key top management team members.
They are responsible for setting up a vision for Six Sigma implementation.
They also empower the other role holders with the freedom and resources to explore new ideas for breakthrough improvements.

Note the similarity to the Project Board for PRINCE2®.

  • Champions

These are responsible for the Six Sigma implementation across the organization in an integrated manner.
The Executive Leadership draws them from the upper management.
Champions also act as mentors to Black Belts.

  • Master Black Belts

These are identified by champions above.
They act as in-house expert coaches for the organization on Six Sigma.
They are fully committed to Six Sigma 100% of their time.
They assist champions and guide Black Belts and Green Belts.
They will focus on the statistics.
They are responsible for ensuring integrated deployment of Six Sigma across various functions and departments.

  • Experts

They are found primarily within Aerospace and Defence Business Sectors.
Experts can work across company boundaries.
The aim is to improve services, processes, and products for their suppliers, their entire organizations, and for their customers.
Experts work not only across multiple sites, but across business divisions, incorporating lessons learned throughout the company.

  • Black Belts

They operate under the Master Black Belts.
Their role is to apply Six Sigma methodology to specific projects.
Similarly to the Master Black Belts they work 100% of their time on Six Sigma.
They primarily focus on Six Sigma project execution, whereas Champions and Master Black Belts focus on identifying projects and functions for Six Sigma.

  • Green Belts

They are not 100% devoted to Six Sigma.
They carry out Six Sigma implementation along with their other job responsibilities.
They operate under the guidance of Black Belts and support them in achieving the overall results.

  • Yellow Belts

These will have been trained in Six Sigma techniques as part of a corporate-wide initiative.
They will not have completed a Six Sigma project.
They will not be expected to actively engage in quality improvement activities.

In many areas Green Belts and Black Belts are empowered to initiate, expand, and lead projects in their area of responsibility.

These roles, as defined above, may not be universally accepted.

Some consider that for Japan's industrial improvement they simply used the technical expertise that they already had, for example, Design, Manufacturing and Quality Engineers, Toolmakers, Maintenance and Production workers to optimize the processes.

The term Six Sigma

Sigma (the lower-case Greek letter σ) is used to represent standard deviation.

Note that from statistics it is a measure of the range of variation from an average of a group of measurements.
It can be very useful as it can be shown that:

68.27% of all measurements fall within one standard deviation of the mean.
95.45% of all measurements fall within two standard deviations of the mean. 99.73% of all measurements fall within three standard deviations of the mean.

The standard deviation is the square root of the Variance.
The variance is a measure of the ‘spread’ of the values about the mean.

If we look at 6 standard deviations between the mean of a process and the nearest specification limit, we will make practically no items that exceed the specifications.
This is where the term Six Sigma derives.
This is the basis of the Process Capability Study, often used by quality professionals.
The term Six Sigma derives from this tool, rather than in simple process standard deviation, which is also measured in sigmas. Criticism of the tool itself, and the way that the term was derived from the tool, often sparks criticism of Six Sigma.

The widely accepted definition of a six sigma process is one that produces 3.4 defective parts per million opportunities (DPMO).
A process that is normally distributed will have 3.4 parts per million beyond a point that is 4.5 standard deviations above or below the mean (one-sided Capability Study).
This implies that 3.4 DPMO corresponds to 4.5 sigmas, not six as the process name would imply.

The 1.5 sigmas added to the name Six Sigma are arbitrary and they are called ‘1.5 sigma shift’.

In a Capability Study, sigma refers to the number of standard deviations between the process mean and the nearest specification limit, rather than the standard deviation of the process, which is also measured in ‘sigmas’.

As process standard deviation goes up, or the mean of the process moves away from the centre of the tolerance, the Process Capability sigma number reduces, as fewer standard deviations will then fit between the mean and the nearest specification limit.

Defect rates can be very greatly influenced by uncertainty in the estimate of standard deviation, and that the defective parts per million estimates produced by Capability Studies often ought not to be taken too literally.

Estimates for the number of defective parts per million produced also depends on knowing something about the shape of the distribution from which the samples are drawn.
Unfortunately, there are no means for proving that data belong to any particular distribution.
You can only assume normality, based on finding no evidence to the contrary.

Estimating defective parts per million down into the 100s or 10s of units based on such an assumption is wishful thinking, since actual defects are often deviations from normality, which have been assumed not to exist.

The ±1.5 sigma drift

The ±1.5 σ drift is the drift of a process mean, which is assumed to occur in all processes.
If a product is manufactured to a target of 100 mm using a process capable of delivering σ = 1 mm performance, over time a ±1.5σ drift may cause the long term process mean to range from 98.5 to 101.5 mm. This could be of significance to customers.

The ±1.5σ shift was introduced by Mikel Harry.

For example, he looked at a process in which 5 samples are taken every half hour and plotted on a control chart, Harry considered the ‘instantaneous’ initial 5 samples as being ‘short term’ (Harry's n=5) and the samples throughout the day as being ‘long term’ (Harry's g=50 points).

Due to the random variation in the first 5 points, the mean of the initial sample is different to the overall mean.
Harry derived a relationship between the short term and long term capability, to produce a capability shift or ‘Z shift’ of 1.5.
Over time, the original meaning of "short term" and "long term" has been changed to result in "long term" drifting means.

Industry is resigned to the belief that it is impossible to keep processes on target and that process means will inevitably drift by ±1.5σ.
In other words, if a process has a target value of 0.0, specification limits at 6σ, and natural tolerance limits of ±3σ, over the long term the mean may drift to +1.5 (or -1.5).

In truth, any process where the mean changes by 1.5σ, or any other statistically significant amount, is not in statistical control.
Such a change can often be detected by a trend on a control chart.
A process that is not in control is not predictable.
It may begin to produce defects, no matter where specification limits have been set.

More information can be found in the online encyclopaedia Wikipedia.

Under PRINCE2 2009 [see ‘The Complete Project Management plus PRINCE2’] planning is covered by the Plans theme.
The purpose of the Plans theme is to facilitate communication and control by defining the means of delivering the products (the where and how, by whom, and estimating the when and how much).
[see Plans - Purpose]

PRINCE2® is a Registered Trade Mark of the Office of Government Commerce in the United Kingdom and other countries.