<P> When the process triggers any of the control chart "detection rules", (or alternatively, the process capability is low), other activities may be performed to identify the source of the excessive variation . The tools used in these extra activities include: Ishikawa diagram, designed experiments, and Pareto charts . Designed experiments are a means of objectively quantifying the relative importance (strength) of sources of variation . Once the sources of (special cause) variation are identified, they can be minimized or eliminated . Steps to eliminating a source of variation might include: development of standards, staff training, error - proofing, and changes to the process itself or its inputs . </P> <P> When monitoring many processes with control charts, it is sometimes useful to calculate quantitative measures of the stability of the processes . These metrics can then be used to identify / prioritize the processes that are most in need of corrective actions . These metrics can also be viewed as supplementing the traditional process capability metrics . Several metrics have been proposed, as described in Ramirez and Runger . They are (1) a Stability Ratio which compares the long - term variability to the short - term variability, (2) an ANOVA Test which compares the within - subgroup variation to the between - subgroup variation, and (3) an Instability Ratio which compares the number of subgroups that have one or more violations of the Western Electric rules to the total number of subgroups . </P> <P> Digital control charts use logic - based rules that determine "derived values" which signal the need for correction . For example, </P> <Dl> <Dd> derived value = last value + average absolute difference between the last N numbers . </Dd> </Dl>

Four basic characteristics of an optimal process are