Dr. Donald J. Wheeler has been one of Quality Digest’s most highly read authors for decades.

Outliers are values that don’t “fit in” with the rest of the data. These extreme values are commonly considered a nuisance when we seek to summarize the data with our descriptive statistics.

In an age where data are among the most valuable assets for any business, the ability to retrieve, process, and utilize information efficiently is critical for success.

The Man of La Mancha never got to the unreachable goal—and if you’re being judged by overall equipment effectiveness (OEE), then your manager may also be dreaming an impossible dream. This column will look at problems associated with the use of OEE values.

In last month’s article, “ANOVA and the Process Behavior Chart,” we saw how both techniques use the same basic comparison to answer completely d

Recent labor relations controversies and ongoing arguments about the minimum wage have raised questions as to how a supply chain should share the utility it produces.

When Sir Ronald Fisher created the analysis of variance (ANOVA) in the 1920s, he extended the two-sample t-test to allow the comparison of k sample averages. During the same time period, Dr. Walter Shewhart was creating the process behavior chart.

Managers are commonly fed a diet of report-card data. These data have usually been aggregated into summaries, averages, and totals to characterize the big picture. As useful as such summaries can be, they can also be an obstacle to an effective analysis.

Time: We all have a sense of it, an innate feel for it. We see it and use it every day.

An engineer once told me, “I work on project teams that have an average half-life of two weeks, implementing solutions with an average half-life of two weeks.” Time after time, and in place after place, our improvement efforts often fall short of expectations and fad