This posting discusses the third step in the Hoerl-Snee Process Improvement Strategy. Refer to the figure in the posting on April 4 for an overview of the process. Use Britz et al (2000) and Hoerl and Snee (2002) as references.
After collecting data on key measures, the next step is to analyze process stability based on that data. First we define a stable process or one that is in-control. Shewhart (1931, p. 6) states: “..a phenomenon will be said to be controlled when, through the use of past experience, we can predict, at least within limits, how the phenomenon may be expected to vary in the future.” More recently, Montgomery (2005, p. 148) states: “In any production process, …., a certain amount of inherent or natural variability will always exist. …. this natural variability is often called a ‘a stable system of chance causes.’ A process that is operating with only chance causes of variation present is said to be in statistical control. … We refer to those sources of variability that are not part of the chance cause pattern as ‘assignable causes.’ A process that is operating in the presence of assignable causes is said to be out of control.” Montgomery references Shewhart for the terminology chance and assignable causes. He states that many now use the terminology common cause rather than chance cause and special cause rather than assignable cause.
An important characteristic of a stable or in-control process is that it is predictable. This comes from Shewhart’s definition. That is, one can predict future behavior from past behavior. Breyfogle (2003, p. 1109) and Wheeler (1993, p. 124 and 128) state that an in-control process is predicable whereas a process that is not in-control is unpredictable. This means that statistical methods such as t tests and ANOVA are inappropriate for unstable processes.
The definitions stated above immediately suggest methods for identifying whether a process is in-control. They include run charts and SPC control charts. A run chart is a time plot of quality characteristic and a control chart is a run chart with control limits. Using the points on these charts that signal lack of control, we can conduct investigations to determine what caused these points to be different. Two previous postings that do that are:
- Rosin yield run chart on March 21. The team found that their cause was a drop in air pressure.
- Customer complaint process posted on January 30. The investigation identified the high defect rate in October, 91 was due to a supplier using the wrong material.
Two major reasons for assessing stability and removing assignable causes prior to addressing common-cause variation are:
- Detecting and removing assignable causes is easier than reducing the variation due to common causes. Remove the low hanging fruit first.
- The analysis approach to removing assignable causes is different than reducing common-cause variation. Common causes affect variation in all data points for a stable process. When searching for the root cause of variation in specific observations due to an assignable cause, one can focus on the differences between these observations and the others.
Consider the possibility of wasting effort when a process is in-control (stable) but some results do not meet targets. Managers could pressure staff to find the cause of specific results not meeting targets. That is, managers could direct staff to find causes for specific undesirable outcomes when the variation is present in all outcomes.
1. Breyfogle, Forrest W. (2003). Implementing Six Sigma: Smarter Solutions Using Statistical Methods, Second Edition, John Wiley & Sons, Inc.
2. Britz, G. C., D. W. Emerling, et al. (2000). Improving Performance Through Statistical Thinking. Milwaukee, WI, ASQ Quality Press.
3. Hoerl, R. and R. D. Snee (2002). Statistical Thinking - Improving Business Performance. Pacific Grove, CA, Duxbury.
4. Montgomery, Douglas C. (2005). Introduction to Statistical Quality Control, John Wiley & Sons, Inc.
5. Shewhart, Walter A. (1931). Economic Control of Quality of Manufactured Product, D. Van Nostrand Company, Inc.
6. Wheeler, Donald J. (1993). Understanding Variation: The Key to Managing Chaos, SPC Press, Inc.