Lean production is built on the explicit assumption that each step is operated predictably and capably. Predictable operation can only be achieved and maintained by using process behavior charts.

It has been a while since I have written about statistics, and I get asked a lot about a way to calculate sample sizes based on reliability and confidence levels.

In the past two months we have looked at how three-sigma limits work with skewed data.

One of the key ideas in lean manufacturing is that defects should be detected as early as possible.

In 1500, China’s economy was the strongest in the world. But by the 19th century, the United States, Western Europe, and Japan had leapfrogged over China by churning out goods and services in vast quantities while the former superpower stalled.

You’ve set aside Sunday afternoon to bake some cookies, but you discover you have run out of eggs. Your partner in marital bliss has gone out and taken the car.

According to a recent LNS Research survey, 37 percent of quality leaders cite an inability to measure quality metrics as their No.

Last month I looked at how the fixed-width limits of a process behavior chart filter out virtually all of the routine variation regardless of the shape of the histogram.

If you think it’s hard to tell how you’re doing at your job, imagine being a hockey goalie. Let’s say you block every shot in a game. Was that performance due to your superior skills? Or maybe just to a lack of skill in your opponents?

The oldest myth about process behavior charts is the myth that they require “normally distributed data.” If you have ever heard this idea, or if you have ever taught this to others, then you need to read this article.