Analyze Common-Cause Variation
This posting discusses the sixth step, Analyze Common Cause Variation, of the Hoerl-Snee Process Improvement Strategy. Refer to the figure in the April 4 posting for an overview of the process. Use Britz et al (2000) and Hoerl and Snee (2002) as references.
Common-cause variation affects all of the data which distinguishes this step from the Address-Special-Causes step. The purpose of the Analyze-Common-Cause-Variation step is to identify sources of variation. Locating the sources of variation might also reveal its root cause without significant additional analysis. On other occasions, knowing a source of common-cause variation might require further analysis to determine its root cause. This additional analysis is performed in the next step, Study Cause and Effect.
Some of the tools we might use in this step are:
- Stratification. Define a stratification factor such as the day of the week or machine. Partition the factor into logical categories. Compare the data for each category to highlight differences.
- Disaggregation. Define quality measures for sub-processes or individual process steps. Study the variation in the individual sub-processes. How does it contribute to the overall process variation?
- Pareto Chart. Classify defects into categories. Highlight the categories having the most frequent occurrences.
- Histogram. Plot the distribution of quality measures. One or more peaks might indicate the presence of categories that could be examined by stratification.
- Regression Analysis. Existing opinion might suggest one or more input variables that influence the output quality measure. A regression analysis might verify this opinion or indicate that these variables have negligible effect.
References
- Britz, G. C., D. W. Emerling, et al. (2000). Improving Performance Through Statistical Thinking. Milwaukee, WI, ASQ Quality Press.
- Hoerl, R. and R. D. Snee (2002). Statistical Thinking - Improving Business Performance. Pacific Grove, CA, Duxbury.
