Understanding Current Process Performance Using “Control Chart” – An Essential Tool in PI and PI CME

June 8, 2010 at 12:38 am Leave a comment

This is the second part of my discussion about understanding current performance in an improvement initiative.

Understanding how a current process actually functions is absolutely essential to improving that process. If you don’t have this information you cannot measure the magnitude of change after an improvement effort. To effect change in a system or process that process has to be stable. A system or process in “chaos” is not amenable to systematic improvement. One way to understand if a process is stable is through the use of “Control Charts”. I am not going to give you a “Control Chart” lesson here but strongly encourage you to gain a working understanding of the important function control charts have in improvement initiatives and how to interpret basic control charts. They can be an invaluable tool.

First, a cautionary note. Not all control charts are created equal. Different charts are used for attributes data than variable data. Attributes data tell you the extent of the presence or absence of something. It is either there or it is not there. For example the lack of signatures on disclosure forms. Variable data are used to distinguish amounts of something that can increase or decrease over time or – for example weight. If you think control charts would be useful tools then learn how to construct them pr get with a quality professional who has the expertise to help you out.

Control charts present graphic displays of process stability or instability over time. A process that is seldom done the same way twice or is done differently by different people may be said to be “out of control”. If there is too much variation in a work process the processes is said to be “out of control” and cannot be effectively improved. A process has to be carried out in a fairly consistent (stable) manner in a setting for it to be amenable to process improvement. Control charts are a statistical tool that can be used to gain an understanding of variability in a process. Well constructed control charts allow you to distinguish between common causes of variation in a process and variation resulting from special causes.

Every process has variation. Some variation is simply the result of numerous, ever-present differences in the process. This is common cause variation. Common cause variation will always be found in a process. Some variation may be the result of causes which are not normally present in the process. This could be special cause variation. Special cause variation has to be explained and eliminated before effective process improvement can occur. One important function of Control Charts is that it can differentiate between common causes and special causes of variation.

One goal of using a Control Chart is to achieve and maintain process stability. Stable processes have displayed a certain degree of consistency in the past and are expected to continue to do so in the future. This consistency is characterized by data falling within control limits based on plus or minus 3 standard deviations (3 sigma) of the center line.

If analysis of the control chart indicates that the process is currently under control (i.e. is stable, with variation only coming from common sources in the process) then data from the process can be used to predict the future performance of the process. If the chart indicates that the process being monitored is not in control, analysis of the chart can help determine the sources of special cause variation, which can then be eliminated to bring the process back into control. A control chart allows significant change to be differentiated from the natural variability of the process.

A control chart usually consists of:

  • Data points representing a statistic (e.g., a mean, range, proportion) of a quality characteristic in samples taken from the process at different times.
  • The mean of this statistic using all the samples (e.g., the mean of the means, mean of the ranges, mean of the proportions)
  • A center line drawn at the value of the mean of the statistic
  • A  standard deviation of the statistic calculated using all the samples
  • Upper and lower control limits indicating the threshold at which the process output is considered statistically ‘unlikely’ – typically at 3 standard errors from the center line

The chart may have other optional features, including:

  • Upper and lower warning limits, drawn as separate lines, typically two standard errors above and below the center line
  • Division into zones, with the addition of rules governing frequencies of observations in each zone
  • Annotation with events of interest, as determined by the person in charge of the process’s quality

Entry filed under: CME, Improvement, PI CME. Tags: , , , , .

ACRE Response to CEJA CME in the News and on the Blogs June 9, 2010

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