Debbie Moosa Thistle QA

Quality Management involves philosophy, principles, methodology, techniques, tools and metrics. Six Sigma can be considered as an umbrella that covers all of these, but it also refers to new features and metrics. This is in fact, a new marketing approach for TQM. Champions, Master Black belts, Black belts, and Green belts describe the training and implementation structure. However the emphasis on Six Sigma is also truly a more quantitative science.

Measure of quality that strives for near perfection Disciplined, data-driven approach for eliminating defects Statistical representation describes quantitatively how a process is performing To achieve a Six Sigma, a process must not produce more than 3.4 DPM opportunities

The roots of Six Sigma can be traced back to Carl Frederick Gauss ( ) who introduced the concept of the normal curve Six Sigma as a measurement standard can be traced back to the 1920’s when Walter Shewhart showed that 3 Sigma from the mean is the point where a process requires correction In 1984 Bill Smith a Motorola engineer coined the term “Six Sigma” (Six Sigma is a federally registered trademark of Motorola)

In the early and mid 1980’s, Motorola engineers decided that measuring defects in thousands of opportunities was not good enough, instead they wanted to measure the defects per million opportunities Motorola developed this new standard and created the methodology As a result of Six Sigma efforts they documented more than $16 Billion in savings over the last 11 years Since then hundreds of companies around the world have adopted Six Sigma as a way of doing business

Requires that “tolerance limits” be defined in order to describe good quality and identify poor quality or defects Provides a standard scale – the Sigma scale - for measuring the quality of any process Identifies the goal for how good quality should be – Six Sigma for “world class quality” With Six Sigma, quality becomes measurable and manageable in a quantitative and objective way

Point A: Where You Are Now Point B: Where You Want To Be

Plan what you want to do Do it right Check the data Act on the results

Point A: Where You Are Now Point B: Where You Want To Be

Define or Design - Understand the process to be improved and set a goal Measure – Measure the current state Analyse – Develop cause and effect theories; search for the real cause and scientifically prove it Improve – Take action Control – Measure to verify improvement has taken place and take actions to sustain the gains

Point A: Where You Are Now Point B: Where You Want To Be Finish

 Six Sigma’s DMAIC methodology is nothing but a search for the real causes of problems.

There are two methods and both are useful in the lab Measure Outcome Measure Variation Inspect outcomes and count defects Measure variation of process Calculate DPM Convert DPM to sigma metric Control process outcome Calculate s, bias and sigma metric

Measure outcome is useful for pre-analytical processes e.g. specimen ID and labelling or post analytical processes e.g. TAT and error reports You monitor the output of the process, keep count of the defects, calculate DPM, then convert to a Sigma metric using a standard table

Measure variation is useful for the analytical process where you evaluate the imprecision (SD, CV) and inaccuracy (bias) of the measurement procedure, calculate a Sigma metric, then establish proper QC to monitor the process

The performance goal for any production process should be Six Sigma which means that Six Sigma’s or SDs of process variation should fit within the tolerance limits or quality requirement for the process For analytical testing process, the precision is expressed as a SD (s) of the measurement procedure and that is the “sigma” of interest The ideal is to have a small enough SD such that 6 times s will fit within the target plus or minus the tolerance limit

Assuming a Gaussian distribution for the variation of a process, the area in the tails of the distribution can be used to estimate the expected defects.

For example, if the product specifications enclose 2SDs, the area in the tails would correspond to a 4.5% defect rate or defects per million (DPM). The 4.5% figure doesn’t sound too bad, but the DPM doesn’t sound very good. For 3SDs the defect rate would be less than 0.27% or 2700 DPM; for 4SDs the defect rate would be % or 63 DPM; for 5SDs the defect rate would only be 0.57DPM and with a 6 SD the defect rate would only be DPM.

There would seem to be little to be gained from improving process performance beyond 5 Sigma The advantage is that small shifts in the process mean can actually be tolerated without increasing the defect rate significantly

A shift or bias of 1.5 sigma would hardly cause any defects in a Six Sigma process

Since shifts or biases equivalent to 1.5s are difficult to detect by statistical QC, a Six Sigma process provides a better guarantee that products will be produced within the desired specifications and with a low defect rate Another way to look at this is that a Six Sigma process can be monitored with any QC procedure e.g. with 3SD limits and low N any important errors or problems will be detected and can be corrected

As process capability decreases to 5 Sigma to 4 Sigma to 3 Sigma, the choice of QC procedure becomes more and more important in order to detect important problems. Processes with lower capability may not even be controllable to a defined level of quality

CLIA has defined the acceptability criteria for performance in proficiency testing surveys for approximately 80 regulated analytes. These criteria are often thought to be “loose” and not very demanding for analytical performance, but that conclusion is based on a goal of 2-3 sigma processes. If the goal were to establish 5-6 sigma processes, improvements in precision would be needed for many tests today.

Example: Cholesterol has a 10% tolerance specification as set out by the CLIA criterion for the acceptable performance in PT events. A 5 sigma process should have a CV of 2.0% and a 6 sigma should have a CV of 1.7%. The following table shows the CLIA criteria and corresponding precision that would be needed to establish 5 and 6 sigma processes for chloride and calcium.

AnalyteCLIA Acceptable Performance 5 Sigma- Precision 6 Sigma- Precision Chloride5%1%0.83% Calcium1.0 mg/dl0.2 mg/dl0.17 mg/dl

These figures should be useful for evaluating the current performance of lab methods Six Sigma Quality Management sets demanding standards of performance for laboratory testing processes

To determine the Sigma metrics for processes in your laboratory, the following information is needed: The quality required for the test e.g. CLIA allowable total error for acceptability in PT The imprecision of the method e.g. the SD or CV The inaccuracy of the method e.g. bias

Six Sigma = TV-bias Precision TV = target performance, acceptable values? Bias = how far (%) the value is from the mean Precision = your lab’s precision on EQA

For Cholesterol, the CLIA allowable total error is 10% A method with a CV of 2% and a bias of 2% would give a Sigma metric of 4 [(10-2)/2] A method with a 2% CV and no bias would be a 5 sigma process [(10/2)] It would take a CV of 1.7% and no bias to achieve 6 sigma performance [10/6]

In spite of all the quality management, QA, quality assessments and quality improvement programmes in place today, no one knows the quality of healthcare because we haven’t defined standards for acceptable quality and assessed the level of defective results and outcomes Six Sigma offers the methodology and metrics to make quality measurable and understandable

I hope you’ll agree that the answer is “YES” Six Sigma is worth learning about – it will improve laboratories and healthcare if implemented properly It’s easy to do for labs and testing processes because it’s a logical data-driven technique Not only is it worth learning about, it’s worth doing well.

Six Sigma Quality Design & Control, 2 nd edition, James O. Westgard Six Sigma Basics: DMAIC Like Normal Problem Solving, Chew Jian Chieh A review on Six Sigma in laboratories, Gras and Phillippe Six Sigma Quality Management and Desirable Laboratory Precision, James O. Westgard