what is the process likely to do in the future?
But how can a process be predictable when it displays the high levels of random variation that we so often see in the real world? Control charts solve this problem by identifying the upper and lower bounds of process performance. Using a simple set of analytical rules, interpretation of the chart relative to these ‘control limits’ provides information on whether the process is stable or not. For a process that is stable, the limits show the range of random performance variation that we can expect the process to produce, given that nothing unexpected affects the process. This random variation is created by chance, or common, causes – the myriad factors that are always present within processes. The Process Behaviour Chart below shows an example of process performance data – the percentage of patients who are seen in an A&E department – for which the Government target is 98%. The chart shows that performance has improved in two stages. During period 1 the level of common cause variation w
