A process is a set of sequential activities that convert the input to the desired output.
Process stability is the ability of the process to perform within a predictable limit. We can also state stability of a process refers to its predictability.
Why processes become unstable?
Processes tend to become unstable over time due to variations. The variations occur either because of the inherent nature of the process or some external or forced changes, called disturbances.
When the process is operating at a stable state, we can expect the process to produce comparable results over a period. Thus, even though the process has variations, it would repeat similar variations in the future under a stable state.
And that is how, the process behaves predictably.
Types of Variations
- Common Cause Variations: We call the inherent process variations as common cause variations.
- Assignable Cause Variations: When the process suffers from an external disturbance or a significant drift, we call these disturbances Assignable Cause Variations or Special Cause Variations.
When a process exhibits an assignable cause, meaning some disturbance, it becomes unpredictable. Because the disturbances are unpredictable or non-repeatable to some extent.
How to differentiate?
Though the concept sounds simple, it is not always possible to differentiate the special cause variations and common cause variations under general observations.
Dr Shewhart of Bell Laboratories introduced a simple statistical tool to help differentiate the special cause variations from the common cause variations.
Based on statistics, all the data (99.73%) would fall between the boundaries of
Mean ± 3 Standard Deviation
He drew a chart with
- Mean of the data as the centre line,
- 3 Standard Deviation above the mean – as Upper Boundary also called as Upper Control Limit or UCL, and
- Mean – 3 Standard Deviations as Lower Boundary or as Lower Control Limit, LCL.
We can consider any data point falling beyond this boundary as significantly different from the rest of the data points.
We call these significantly different data points as Special Causes.
Stability – A Stable Process
We categorise A process without any Special Causes as a Stable Process and processes with one or more special cause as an unstable process.
Usually, continuous processes consist of zones of common and special causes. The zones without any special cause are Stable Zones. Unstable zones are the period of the process exhibiting a special cause.
Why is Process Stability Important?
- We cannot predict the process performance when it has a special cause variation.
- The presence of a special cause indicates significant drift or disturbance in the process. Hence, it is important to address and monitor the stability.
- Its presence also distorts the sample data properties and in turn, leads to inaccurate estimation of population behaviour.
- We cannot consider an Unstable process for improvement under Six Sigma DMAIC, or SPC or under any other initiatives.
Managing Special Causes
If you are the process owner, you can start plotting the observations on a control chart. If the control limits are comfortably within the specification limits, then the special cause indicates the need for adjustment in the process.
What to do, If you are considering the process for any improvement and sample data shows a special cause? It may indicate that the sample data is not the true representative of process behaviour.
- You may try to increase the sample size.
- Consider extending sample collection duration.
- If sample number is a constraint, then plan for extended sample duration with reduced sampling frequency.
- Only when you have one or two special cause variations, and you are certain that you know the root cause of the special cause variation, and you have taken necessary steps to prevent its recurrence, you may omit the data point from the dataset. You must be cautious as well as confident from the process side while considering this option. Justification and approval from Black Belt and Champions will ensure you are not misrepresenting the process by omitting that point.