Most discussions about performance focus on averages. While averages give us clues to targets and expectations, understanding variation is extremely important to driving lasting change.
There are two types of variation, common cause and special cause. Common cause variation is innate in all systems, it is sometimes referred to as “noise.” For example, it takes me 28 – 35 minutes to drive to work. Each day is different from the last due to a number of factors associated with the commute. Special cause variation is the result of exceptions to the process environment and often represents a significant change. If there was roadwork for 2 weeks and my commute time increased to 45-54 minutes, I may attempt to find an alternate route or change what time I leave the house for the duration of the roadwork activity.
There are a range of tools that can help differentiate between common cause and special cause variation. Time plots, frequency plots, pareto charts, and scatter plots are visual tools that can help identify special causes. Control charts, hypothesis tests, and regression analysis are data-driven statistical tools used to quantify variation and isolate special causes.
Why does it matter? The type of variation associated with your process dictates what actions you need to take to drive change. When you see special cause variation, you can investigate those data points and take action immediately to remedy or prevent damages. Once the root cause of the variation is understood, you can develop solutions to prevent it from happening in the future (unless the results are good, in which case you can develop solutions to sustain the gains). Because common cause variation is inherent to the process, it cannot be explained by day-to-day differences in data points and requires that you make fundamental changes to the process. The DMAIC (define-measure-analyze-improve-control) process is an effective approach that uses a team to understand entire data patterns and drive lasting process change. Most of the time, common cause variation is more difficult to address.
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