Before moving into IT, I served in various capacities in manufacturing. One of these was Quality Assurance.

I was fortunate to work for a manager who was an early adopter of SQC (Statistical Quality Control) and SPC (Statistical Process Control). I was assigned to a lab on the factory floor chartered with examing first cut die samples from the vendor. Think of a die as a "mold" which would be installed in a large press and then used to stamp the material (sheet metal, copper, aluminum, etc.) into the shape that the design engineer specified. Visualize a huge "cookie cutter".

It was our job to ensure that the first samples from the die vendor matched the specific measurements, angles, slopes, etc. of the design. We used SQC principles in evaluating the quality of the dies. We measured and compared the variations between the samples provided, plotted the mean, range, standard deviation, and other measures of the distribution of data of the samples.

We would also apply SPC in obtaining random samples after a die was put into production to ensure the actual manufacturing process was functioning properly. That is, we determined if particular characteristics fell into predetermined ranges (the predetermined ranges determined if the process was functioning correctly.) Occasionally we'd reject a entire batch of units if sampling indicated the process had gotten out of range.

We'd also perform random Acceptance Testing on the assembly line as all the components were brought together into the final product. 

This real-life knowledge and experience helped me later as I was completing my Managerial Science degree at the University of Cincinnati. Managerial Science is a combination of Management, Operations Research, Statistics and IT studies.