In my previous article, I discussed the merits and cautions of the “acceptance number” equal zero (c=0) sam

Aerospace standard AS9138—“Quality management systems statistical product acceptance requirements” was issued this year (2018), a few years after its accompanying guidance materials in sectio

In this all-manufacturing episode, we look at the STEM pipeline into manufacturing, supplier development, how to make sense of manufacturing data and, no, manufacturing is not dead.

Parts One and Two of this series illustrated four problems with using a model-building ap

‘In God we trust; all others bring data.” “Follow the data.” “Let the data talk.” Nice clichés, but there’s one problem... data can’t talk. In fact, data don’t say a darn thing. Data are bits of raw information.

Flow quality management (Flow QM) is a logistical alternative to handling product in lots for the purpose of assessing and mitigating defects.

It’s an open secret that many automotive and aerospace manufacturers have unacceptably high defects and costs. And where defects are on the rise, quality costs aren’t far behind.

I recently got hold of the set of data shown in figure 1. What can be done to analyze and make sense of these 65 data values is the theme of this article. Read on to see what is exceptional about these data, not only statistically speaking.

Quality is related to processes.

Confidence intervals show the range of values we can be fairly, well, confident, that our true value lies in, and they are very important to any quality practitioner. I could be 95-percent confident the volume of a can of soup will be 390–410 ml.