Ever since Motorola implemented the Six Sigma standards of quality, it has become fashionable in business circles to talk about it. Many consultancy firms successfully hyped it and also encashed on it. What is so magical about the figure 3.4 defects per million? Is it such a sacrosanct figure or just an arithmetical gimmick? Is Six Sigma a hype or is there any true benefit to be derived from it? This article examines and tries to demystify these issues.

Some time back when the famous Dabbawalas of Mumbai were presented with an award for achieving Six Sigma, the poorly educated dabbawalas were heard asking each other ye sigma kya cheeze hai? (What is this `Sigma'?) Even though the answer is elementary for statisticians, there are glaring gaps in its understanding among the practitioners and even the self-appointed `quality consultants'. "Many of them claim expertise in Six Sigma when they barely understand the tools and techniques and the Six Sigma roadmap." Hence, I start by a quick overview of the fundamentals. When any product is made in large quantities, even the most sophisticated machine will not be producing `exactly' identical products. There are bound to be slight variations, however small it might be. For example, if the product, say a cellphone, is claimed to weigh just 80 gms, individual cellphones produced may actually have weights like 80.03 gms, 79.98 gms, 80.05 gms, etc. Of course, the average weight of many such products made over a period of time will be extremely close to the target value of 80 gms, if not 80 gms itself. The variation of actual values from the target value is measured by sigma, a Greek symbol, which stands for the standard deviation. Smaller the value of standard deviation or sigma, the closer the product values are to the target and larger the value of sigma, the wider the values are scattered around the target value. Hence, the attempt should be to reduce the value of sigma to the minimum possible. Slight variations in individual values, which are unavoidable as explained earlier, are called random variations. But if there exists any specific cause, like malfunctioning of the machine, material defects, operator fatigue or mistake or following the wrong method, etc., it will give glaring deviations to the average of the values got each day or significant fluctuations of individual values. Hence, to keep a track of the quality, each day a sample of products is taken and their average is noted. The average so got should ideally be the target value itself, or, if not, `acceptably close' enough to it. Similarly, the variations in values of individual products produced in a day should also be within `acceptable limits'. Now the big question is, what is this so called `acceptable limit'?

Global CEO Magazine, Six Sigma, Defects Per Million Opportunities, DPMO, American Society for Quality, ASQ, Statistical Process Control, SPC, Motivational Benefits, Organizational Benefit, Target Specification, Zero Quality Count, ZQC, Brand Marketing.