Causes of Variation

Let’s understand the causes of variation. Moreover, variation is part of our everyday lives. However, both at work and in our private lives we make allowances for its effects from the process of getting to work in the morning to the output of a complex manufacturing system. Above all, we need to get a quantitative feel for the variation in our processes.

There are two basic elements to this variation:

• The central tendency
• The spread.

Most importantly, we need to have a handle on both these since they are vital to a successful process. Moreover, it’s no good being the right temperature on average if, to achieve this, you’ve got one foot in the fire and one in the fridge!

The potential cause of variation

At this stage, it is important to note the two potential causes of variation that can affect a process. Moreover, the illustration of these will be by using means of a simple example of driving to work in the morning. And, even when we set off at exactly the same time, following the same route, in the same car it is apparent that arrival time will vary.

Common Cause (Unassignable) Variation:

This is a variation that is inherent in the process; it is always there. Moreover, in the process of getting to work this will mean things like waiting time at fixed traffic lights, or the driver’s mood and condition, or weather conditions. And, only fundamental action on the process can change common causes. For example, changing the route to avoid the traffic lights will remove that cause of variation.

Special Cause (Assignable) variation:

This is variation due to transient causes outside the process norms and can usually be traced back to the specific cause. In the journey to work example, this would include road works, breakdowns, etc. In many cases, action can be taken to achieve a reduction in the future effect of these ‘transient problems’. For example, better maintenance to avoid breakdowns, which does not fundamentally change the process.

Statistical Control

Accordingly, a process that is subject only to common cause variation is described as being “In Statistical Control”. This is sometimes reduced to “In Control” or described as “Stable”. This essentially means it is predictable, and we know what is coming (within limits). When a process is under the influence of special causes it is described as being “Out of Statistical Control” , “Out of Control” or “Unstable”.

Managing Process

• To effectively manage a process we need to be able to distinguish between In Control and Out of Control conditions.
• For doing this, we need to establish what the natural limits of the common cause variation are.
• And to begin this process we need to put the data into context.

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