For those who work with processes, we know that variability is the key factor. The desired state is more consistency and less variability. When processes have wide variability and inconsistent results, we call the process out of control. When processes operate within established limits, the process is considered in control. So how can we understand process variability?
Typically, we attribute process variability to two causes — common cause and special cause. Common cause variation is expected. It is a result of the process design, machinery, and activities.
For example, I walk to the train station every day after work, and it takes six to 10 minutes. The variation is due to factors like how long I have to wait for the elevator, how many times the elevator stops, and how long I have to wait at crosswalk lights. These variations occur every day, and they are expected. They are common cause variations.
To reduce common cause variation, it usually takes experimentation and/or statistical analysis to optimize the process. Experimentation means changing something and measuring the results over time. Statistical analysis means looking at results in different ways — stratifying and categorizing data in diverse manners and employing varying statistical methods like Pareto charts.
For example, I might experiment and collect data and find that if I leave at 4:45 instead of 5:00, the elevators are less busy, and variation in the time to reach the station is reduced.
Then one day it took 12 minutes to walk to the train station. It took longer because someone approached me on the sidewalk and asked for directions. They were lost, so I took a few minutes to explain to them where they are and how to get to where they are going, plus exchange a few pleasantries. But that doesn’t happen very often. In fact, it hardly ever happens. The next day I return to the six to 10 minute window for my walk to the station. It was a special cause of variation.
Special cause variation is typically discovered using root cause analysis. In my example it was easy to identify why it took extra time to reach the station, but frequently the cause of unexpected variation is not so easy to see. It takes an investigation using quality tools like 5 whys or fishbone charts to understand what happened. Then, you can take action to prevent the unexpected cause of variation or simply ignore it because you realize that it happens rarely and the consequences are acceptable (as in my example). I don’t mind missing a train to help someone out.
When addressing process variability, you have to understand if the variation is due to common cause or special cause. The type of variation determines the activities we need to take to reduce variation.