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Reducing Variability With DOE⑴Apply powerful design of experiments (DOE) tools to make your system more robust to variations in component levels and processing factors."Six Sigma" is the new rallying cry for quality improvement in the process industry. For example,Dow aims to generate an extra $1.5 billion per year in profits after training 50,000 of their employees on the methods of Six Sigma.3 Statistical tools play a key role in achieving savings of this magnitude. In fact,"sigma" is a Greek letter that statisticians use as a symbol for standard deviation - a measure of variability. If a manufacturer achieves a Six Sigma buffer from its nearest specification,they will experience only 3.4 off-grades per million lots. This translates to better than 99.99966% of product being in specification. To illustrate what this level of performance entails,imagine playing 100 rounds of golf a year with two putts per hole being the norm (par): At Six Sigma you'd make a three-putt (bogey) only every 163 years!4 Even Tiger Woods would be envious of this level of quality.
Of all the statistical tools employed within Six Sigma,design of experiments (DOE) offers the most power for making breakthroughs. Via an inspirational case study,this article demonstrates how DOE can be applied to development of a formulation and its manufacture to achieve optimal performance with minimum variability,thus meeting the objectives of Six Sigma programs. Armed with knowledge gained from this article and the example as a template,chemists and engineers from any of the process industries (pharmaceutical,food,chemical,etc.) can apply these same methods to their systems and accomplish similar breakthrough improvements.
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