I’ve had this idea for a while now – create a blog post and video tutorial discussing what Statistical Process Control is and how to use different Control Chart “tests” in Tableau.
I’ve spent a significant portion of my professional career in business process improvement and always like it when I can integrate techniques learned from a discipline derived from industrial engineering and apply it in a broader sense.
It also gives me a great chance to brush up on my knowledge and learn how to order my thoughts for presenting to a wide audience. And let’s not forget: an opportunity to showcase data visualization and Tableau as the delivery mechanism of these insights to my end users.
So why Statistical Process Control? Well it’s a great way to use the data you have and apply different tests to start early detection. Several of the rules out there are aimed at finding “out-of-control,” non-normal, or repetitive parts within a stream of data. Different rules have been developed based on how we might be able to detect them.
The video tutorial above goes through the first 3 Western Electric rules. Full details on Western Electric via Wikipedia: here.
Rule 1: Very basic, uses the principle of a bell curve to put a spotlight on points that are above or below the Upper Control Limit (UCL) or Lower Control Limit (LCL) also known as +/- 3 standard deviations from the mean. These are essentially outlier data points that don’t fall within our typical span of 99.7%.
Rule 2: Takes into consideration surrounding observations. Looking at 3 consecutive observations are 2 out of 3 above or below the 2 SD mark from the average. In this rule the observations must be on the same side of the average line when beyond 2 SD. Since we’re at 95% at 2 SD, having 2 out of 3 in a set in that range could signal an issue.
Rule 3: Starts to consider even more data points within a collection of observations. In this scenario we’re now looking for 4 out of 5 observations +/- 1 SD from the average. Again, we’re retaining the positioning above/below the average line throughout the 5 points. This one really shows the emergence of a trend.
I applied the first 3 rules to my own calorie data to see detect any potential issues. It’s very interesting to see the results. For my own particular data set, Rule 3 was of significant value. Having it in line as the new daily data funnels in could prevent me from going on a “streak” of either over or under consuming.
Interact with the full version on my Tableau Public profile here.