Mar 12 2019

## More About the Math of the Process Behavior Chart

In statistics on time series with “moving” in their name, each value is correlated with past and future neighbors — that is, the series is autocorrelated. It affects the way you can use these statistics to detect anomalies and issue alarms.

The moving range in the XmR chart is a case in point. Its autocorrelation in the moving range chart is self-inflicted. It is autocorrelated by construction, regardless of whether the raw data themselves are.

*Some* raw data are autocorrelated. For example, when you issue a replenishment order for a part by pulling a Kanban from a bin, you are assuming that the demand for a coming period to match that of the period that just elapsed, with minor fluctuations. Implicitly, you are leveraging the autocorrelation of the part consumption across periods.

On the other hand, if a physical characteristic of a manufactured part is the sum of a constant and noise, then the noises are independent, and therefore uncorrelated. Taking *moving ranges* introduces an autocorrelation between consecutive values that is absent in the raw data.

Jul 16 2019

## Updating the 7 tools of QC

A conversation with Franck Vermet about problem-solving tools for factory operators caused me to revisit the

7 tools of QCfrom 50 years ago and ponder how they should be updated with current data science.Data Science for Operators, as a book, remains to be written. If you google this phrase today, what comes up is training courses offering to “change your career” by attending a “data science bootcamp.” TIBCO Spotfire has “Workflow Operators” but these are programs, not people.So the following are tentative answers to questions that haven’t been asked before.

Continue reading…

## Share this:

## Like this:

By Michel Baudin • Data science • 0 • Tags: 7 tools of QC, data science, Industry 4.0, QC, QC Circles, Quality, SPC