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SIX SIGMA

David Plaut, December, 2009

Six sigma (6-sigma), as is true for any process improvementsystem, seeks to improve the quality of outputs by identifying andremoving the causes of defects (errors) and minimizing variability in the processes.

In a nutshell, six sigma (6-sigma) can be discussed at three levels:as a management system, as a methodology, and as a metric (astatistic).

As management system, consider 6-sigma as another in a long

series of approaches to improving almost any process. Similar to6-sigma are zero defects, continuous quality improvement (CQI),total quality improvement (TQI) and to an extent, LEAN. Thesenames all suggest a common goal: improvement in a process orprocesses. Each 6- Sigma project is supported by a team withdefined responsibilities and follows a defined sequence of stepsand has quantified targets.

As a methodology we can consider the following as vitalingredients in achieving the statistical goal of 6-sigma:

Understanding and managing customer requirements Aligning key business processes to achieve those requirements Utilizing rigorous data analysis to minimize variation in thoseprocesses Driving rapid and sustainable improvement to business processes

The third level, 6-sigma as a metric, is the source of the name 6- sigma. 6-sigma refers to 3.4 defects per one million opportunities (DPMO). 6-sigma started as a defect reduction effort (as in zero defects) inmanufacturing and was then applied to other processes for thesame purpose quality improvement. A 6- sigma project will usemanagement, methods and metrics at the same time.
http://en.wikipedia.org/wiki/Statistical_dispersionhttp://en.wikipedia.org/wiki/Statistical_dispersion


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The metric as something with which you are all familiar for 6-sigma refers to the Gaussian (bell, normal) curve.

As you see in the figure, very little of the area under the curve is to

the right or left of 3 sigmas (the values in the table below the curveare the areas (in percentage) between the + and number. Forexample about 68% of the area is between -1 and + 1 SD (sigmas).

The figure is somewhat incorrect, as the values below the curveshow it is still possible for a value to lie more than 3 sigmas (SDs)from the mean, it is so rare as to make it difficult to show the truecurve which is really asymptotic to the x-axis.

Over time there has been, necessarily, less emphasis onachieving the literal definition of 6- sigma with only 3.4 defectsper million opportunities (DPMO). Today the emphasis is more onthe idea of process improvement or continuing processimprovement beginning with an understanding of therequirements of the end user. Some examples of 6-sigmaprojects are: turnaround time for cardiac marker results for theED, the number of contaminated blood cultures, and improperpatient identification.

1 2 3 4 5 6

68.3% 95.4 99.73 99.99 99.9999 99.9999998

Lower s ecification level
http://en.wikipedia.org/wiki/Defects_per_million_opportunitieshttp://en.wikipedia.org/wiki/Defects_per_million_opportunitieshttp://en.wikipedia.org/wiki/File:6_Sigma_Normal_distribution.jpghttp://en.wikipedia.org/wiki/File:6_Sigma_Normal_distribution.jpghttp://en.wikipedia.org/wiki/File:6_Sigma_Normal_distribution.jpghttp://en.wikipedia.org/wiki/Defects_per_million_opportunitieshttp://en.wikipedia.org/wiki/Defects_per_million_opportunities


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Once the requirement is defined to the satisfaction of the 6-sigmateam (e.g. doctors, laboratory staff, nursing staff, andphlebotomists), an agreement on what data need be collectedand analyzed is established. Then a protocol is developed to

measure the number of defects. In the case of the turnaroundtime in the ED, the requirement might be a time from samplecollection to a report to the ED physician of less than 60 minutes.

Thus any sample that took more than 60 minutes to report is adefect. If this happens once for every 100 samples the defectrate is 1% or 2.4 sigma. In order to achieve a 4 sigma successrate only 1 in 10 000 samples would take longer than 60 minutesto turn around. From this it is obvious why the emphasis cannot

be on 6 sigma for every process in the clinical laboratory. While6-sigma may not be possible (for any number of reasons), doesnot mean that a process improvement using a sigma metriccannot be undertaken. In any process improvement, it isnecessary to know from where you start and where you want togo -- a goal must be set. In other words, where is the processnow in terms of DPMO and where do we want to be at the end of the process improvement?

While 3.4 defects per million opportunities might work well for certainproducts or processes, it might not operate optimally or cost-effectively for others. A patient identification process might need higher standards, for example, than a 6-sigma goal on blood glucose levels (where the existingsigma is about 3 if mostly due to instrumentation which cannot do better).The basis and justification for choosing 6 (as opposed to 5 or 7, for example) as the number of standard deviations is not clearly explained.This is related to the idea that 6-sigma as a goal is cast in stone. It could

be argued that the process improvement goal take into consideration theneeds of the user rather than an arbitrary 6-sigma.

6-sigma has not met without some criticism. A Fortune article stated that"of 58 large companies that have announced 6- Sigma programs, 91percent have trailed the S&P 500 since". The reason for this is perhaps that
http://en.wikipedia.org/wiki/Fortune_(magazine)http://en.wikipedia.org/wiki/S%26P_500http://en.wikipedia.org/wiki/Fortune_(magazine)http://en.wikipedia.org/wiki/S%26P_500


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6- Sigma is effective at what it is intended to do, but that it is "narrowlydesigned to fix an existing process" and does not help in "coming up withnew products or disruptive technologies." Many of these claims have beenargued as being in error or ill-informed.

A BusinessWeek article stated that the introduction of 6- sigma at 3M mayhave had the effect of stifling creativity. It cites two Wharton School professors who say that 6- sigma leads to incremental innovation at theexpense of blue-sky work. This phenomenon is further explored in thebook, Going Lean, which provides data to show that Ford 's "6 Sigma"program did little to change its fortunes.

In addition, the 6- sigma model assumes that the process data alwaysconform to the normal distribution . The calculation of defect rates for situations where the normal distribution model does not apply is notproperly addressed in the current 6- sigma literature.

Because 6-sigma centers on change, fear of change is animportant topic to discuss in the meetings on how to perform thestudy and how to implement it. It has been my experience intalking about changing QC rules that fear of change or related to

it (if not just another way of showing fear of change) is the remarkWe have always done it this way. It must work. Somethingmay appear to work (We dont fail proficiency surveys.) but notin every sense. There may be other criteria against which workmight be defined. In the case of QC the number of false rejectsapproaches 10%. These are things that need to be carefullyconsidered as 6-sigma (or whatever name you want to use forprocess improvement) is brought up.

There are few if any processes that cannot be improved. 6-sigmais one approach which has been shown to work, but which is notwithout its critics. Perhaps an eclectic approach to processimprovement might be a way to incorporate parts of many of these ideas based on your needs and resources.
http://en.wikipedia.org/wiki/BusinessWeekhttp://en.wikipedia.org/wiki/3Mhttp://en.wikipedia.org/wiki/Wharton_School_of_the_University_of_Pennsylvaniahttp://en.wikipedia.org/wiki/Fordhttp://en.wikipedia.org/wiki/Normal_distributionhttp://en.wikipedia.org/wiki/BusinessWeekhttp://en.wikipedia.org/wiki/3Mhttp://en.wikipedia.org/wiki/Wharton_School_of_the_University_of_Pennsylvaniahttp://en.wikipedia.org/wiki/Fordhttp://en.wikipedia.org/wiki/Normal_distribution


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(Authors note: I have prepared a bibliography with references to 6-sigma as wellas other systems for improving processes. I will send a copy to you by e mail if yousend me a note at [email protected] . Again I am pleased to acknowledge thehelpful comments of William McLellan.)
mailto:[email protected]:[email protected]

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