Process Troubleshooting using Patterns

The variability in your process output is caused by variability from your process inputs.

This means that patterns you observe in your process output (as measured by your key performance indicators, or KPIs) are caused by patterns in your process inputs.

Recognizing which pattern you're dealing with can, hopefully, lead you quickly to the source of variability so you can eliminate it.

Stable

Boring processes that do the same thing day in and day out are stable processes. Everyday you show up for work and the process is doing exactly what you expected. Control charts of your KPIs look like this:


Boring is good: it is predictable, you can count on it (like Maytag products) so you can plan around it. Well-defined, well-understood, well-controlled processes often take this form. The only thing you really have to worry about is job security (like the Maytag repairman).

Periodic

Processes where special-cause signals show up at a fixed interval exhibit a "periodic" pattern.


This pattern is extremely common because in reality, many things in life are periodic:

• Every day is a cycle.
• Manufacturing shift structures repeat every 7-days.
• The rotation of equipment being used is cyclical
• Plant run-rates

On one occasion, we had a rash of production bioreactor contaminations. By the end of it all, we had five contaminations over the course of seven weeks and they all happened late Sunday/early Monday. On Fridays going into the weekend, people would bet whether or not we'd see something by Monday of the following week. Here, the frequency is once-per-week and ultimately, the root cause was found to be related to manufacturing shifts, which cycle once-per-week.
All these naturally occurring cycles at varying intervals and the key to solving a the periodic pattern is identifying the periodic process input that cycles at the same frequency.

Step-change

A step-change pattern is when, one day, your process output changes and doesn't go back to the way it was... not exactly "irreversible", but at least "difficult to go back."


Step patterns are also commonly observed in manufacturing because many manufacturing activities, "can't be taken back." For example:

• After a plant shutdown when projects get implemented.
• After equipment maintenance.
• When the current lot of material is depleted and a new lot is used.

One time coming out of shutdown, we had a rash of contamination: every single 500L* bioreactor came down contaminated. It turns out that a project securing the media filter executed during changeover for safety reasons changed the system mechanics and caused the media filter to be shaken loose. Filter stability was restored with another project so that the safety modifications would remain.

Step pattern is harder to troubleshoot than the periodic pattern because the irreversibility makes the system untestable. The key to solving a step pattern is to focus on "irreversible changes" of process inputs that happen prior to the observed step change.

Sporadic

A sporadic pattern is basically a random pattern.


Sporadic patterns are unpredictable and difficult to troubleshoot because at its core, the special-causes in-process outputs are often caused by two or more process inputs coming together. When two or more process inputs combine to cause a different result than if either two inputs alone, this is called an interaction.

A good example is the Ford Explorer/Firestone tires debacle that happened in the early 2000's. At the time, they observed a higher frequency of Ford Explorer SUVs rolling over than other SUVs. After further investigation, the rolled-over Ford Explorers had tires mainly made by Firestone. Ford Explorer owners using other tires weren't rolling over. Other SUV drivers using Firestone tires weren't rolling over. It was only when Firestone tires AND Ford Explorers used in combination that caused the failure.

To be blunt, troubleshooting sporadic patterns basically sucks. The best thing about a sporadic pattern is that it tells you is to look for more complex interactions within your process inputs.

Summary

Because the categories of patterns are not well defined (i.e. "I know it when I see it"), identifying the pattern is subject to debate. But know that the true root cause of the pattern must - itself - have the same pattern.