On this blog, I’ve already spoken about Process Behavior Charts (PBC), mostly to share that I wrote a little Christmas story to explain what they are and how they are used, but I never explained how to build one. That was the original plan for this article, but as I explained what PBCs are and why you might want to use them, the article turned into a very long piece, and I think it’s better to split it into two. The article about building the PBC is here.

What are Process Behavior Charts and Why Are They Useful?

Have you ever looked at some data and seen it going up or down?
What do you do when it goes up? What do you do when it goes down?
Do you react whenever a data point doesn’t look like what you hoped it would be?

Here’s the good news: you should stop reacting to every data point.

In every data set you look at, you’ll see some variation, but not all variations are equal. In every process, some variation is expected; this is routine variation. Reacting to routine variation is just a waste of time because you can’t do anything about it. If you are unhappy with what you see, you must change the system producing that data. On the other hand, from time to time, something unexpected occurs—something outside the usual way of working pops up. Here, you should react and figure out why something unfamiliar just happened. You want to learn about it. Did it produce something better than usual? Why is that? Can we change our system to have more of that? Was it bad? How can we change the system to have less of that?

This leads to a question: How can you sort routine variation from special-cause variation? How can you know when to react?

Process Behavior Charts are tools designed to do just that. They help distinguish signals (special cause variation) from noise (common cause variation).

These charts were pioneered by Walter A. Shewhart in the 1920s and later popularized by W. Edwards Deming and Donald J. Wheeler, who further refined their use in quality management.

How Do They Work?

The idea is to draw a graph (actually two, but I’ll only show you how to build the first one) displaying your data alongside two limits: the Upper Natural Process Limit (UNPL) and the Lower Natural Process Limit (LNPL). You don’t get to choose what these limits are; they are calculated from the data available. They are an indication of how your system is working, not how you would like it to be.

With these charts come a few rules for detecting signals. Something interesting is happening if you see:

Rule 1: Any single point outside of the natural process limits.
Rule 2: Eight consecutive points on the same side of the central line.
Rule 3: Three out of four consecutive data points that are closer to the same limit than they are to the central line.

These are the rules shared by Mark Graban in his book Measures of Success. Note that other rules exist and that you might encounter them if you continue digging into this subject.

How to Compute the Data

0. Collect the data. You want to make sure that you have a defined process for data collection to ensure that it’s always done the same way. You should probably describe that process alongside your graph or at the place you are collecting the data.
1. Make sure the data is time-ordered.
2. Choose a baseline period of at least 6 points and less than 20 that you think properly characterizes your process. Avoid using data if you know something uncommon occurred at the moment of measurement.
3. Plot the data as a running record.
4. Plot the central line. This is the arithmetic mean of your baseline point values (BaselineAverage).
5. Calculate the moving range. This is the absolute value between two successive points (BaselineMovingRangeAverage).
6. Calculate the Natural Process Limits:
   • UNPL = BaselineAverage + (2.66 * BaselineMovingRangeAverage)
   • LNPL = BaselineAverage - (2.66 * BaselineMovingRangeAverage)
7. Plot the NPLs on the Running Record.

And here is the kind of chart you should see:

I’ve started working on automating signal detection, and you can see a Rule 1 signal with one point above the UNPL.

In the next article, I’ll share how to build one in Google Sheets based on the data collected via Google Forms and Apple Shortcuts.

Want to Know More About PBCs?

This is a super, super short introduction to PBCs. You can find some really good resources out there that will dig deeper than I will, with better explanations and details. I was primarily interested in showing my entire workflow from data collection on my phone, PBC calculation, and PBC displayed on the phone, and raising a little awareness about this tool.

Donald Wheeler’s Understanding Variation is still sitting on my bookshelf, but I’ve heard it’s an interesting read and the source of learning for the authors of the next resources I’m about to share.
I learned about PBCs from Mark Graban’s book Measures of Success. This is an easy read that clearly demonstrates why most of the time we use numbers quite poorly, leading to overreaction, pressure, and no real improvement.

If you’re working in an Agile setting or using Kanban, Dan Vacanti’s Actionable Agile Metrics Volume II shows how to use PBCs for flow metrics.

If you are looking for a super good free resource, the team at CommonCog created a tool to create PBCs and offers a free one-week course over email that is super interesting along with articles.

Hey, if you are also interested in this kind of subjects, and are looking for ways to improve your organization let’s have a chat!.