Here's a second reply from me that you might find helpful. Occasionally when I was in industry we conducted what we called "Buy Back Studies". Essentially what we did was go to the point of final sale for our products and purchase them just like a normal person who would buy the product. Then we would bring the product back into our analytical labs and evaluate certain characteristics. One example was photographic film. We'd purchase a box of sheet film, then analyze the physical properties of the film. Things like amount of silver/sq. ft., grain size, and binary types of characteristics like physical defects (yes/no, by type, like a line, spot, or some other issue). Then our focus was on just displaying summary data visualizations such as silver/sq. ft variation, grain size distribution, etc. Our goal was to see the variation over time that our customers were experiencing and compare that variation to our specs and what we thought our manufacturing processes were actually doing.
At one point, I discovered something that I thought was troubling...over time and multiple subsequent batches of film, we were picking up a decided downward trend in silver coverage/sq. ft....like we had lost the handle on how much silver to coat for the product. When we sat down with the product and process engineers and shared our findings...they were very pleased. Indeed over time they had INTENTIONALLY been reducing the silver coverage content of this particular product as part of a material cost saving initiative. We just happened to detect this activity in our study. As for the oft asked, "How many samples do I need?"...Well that was an easy one for us. We had a budget to purchase the materials and that was the deciding factor...no statistics involved there.
So the moral of our story was use data visualizations to summarize findings. As Yogi Berra said, "You can see alot by just looking."