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Basic Features of Statistical Engineering Techniques, SET 

290 words, 2 minutes reading time

The term Statistical Engineering Techniques, SET for short, was coined by Dorian Shainin, a pioneer in Quality Control during World War II.

I read his article in 1968 Fall, my 1st Quarter in Graduate School in Chemical Engineering at The Ohio State University.

Here is a short simple summary.

Several variables may affect the performance of a given process/product. Some of these variables have a great effect on performance, some only a moderate effect and some have no effect at all. For solving any developmental, production or research problem all these variables have to be investigated.

In practice, usually there are only a few variables that have a great effect on the process/product performance and hold key to the problem. But these key variables are not known to us nor are we sure that the list of variables that we have made for investigation includes these key variables.

By varying all the listed variables simultaneously and in a systematic manner in a few well designed experiments the variables that contribute little to the process/product can be readily identified and eliminated from ‘investigation. This procedure simplifies the problem considerably. By repeating this procedure it is possible to isolate and identify the source of the production trouble.

For the developmental problem this procedure leads to the correct and optimum combination of the variables of interest. For the production problem this procedure invariably identifies the cause of the trouble – the key variable, even if this was not included in the original list of variables under investigation.

SET uses the judgment and expertise of the trained professional staff of the company to the maximum advantage but it does not rely on it. If their hunches are correct the problem is solved very quickly and a lot of time and effort is saved. If it is incorrect as it happens so often, nothing is lost, the use is made of the information made available by experimental work and the problem area narrowed down.

About Author

Mukul Mehta

Mukul Mehta has over 40 years of proven industrial experience in chemical , polymer, and plastics industry. Worked as a Sr. Manager, Statistics and Computer Aided Research for BF Goodrich Chemical, a Fortune 500 company, and then as a software entrepreneur, promoted “quantitative, predictive modeling in one minute or less as a mantra for R&D and New Product Development.” Many multi-million dollar successes for dozens of Corporate R&D clients in chemical and pharma industry. Trained over 750 R&D chemists, engineers and managers to Speedup New Product Development through statistical design of experiments.

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