This posting gives two examples illustrating the Analyze Common-Cause Variation step, step 6, in the Hoerl-Snee process improvement strategy.   Refer to the previous posting for a description of this step.

·         Stratification – Pareto Chart.  The posting on 2/25/2008 describes statistical thinking by a company experiencing a high rejection rate in one of its machine shops.   In order to determine the root cause of these rejections they stratified by classifying the rejections with respect to machine type causing the rejections.   Then they created a Pareto Chart ranking the frequency of rejections by machine type.   They found that 60% of the rejections were due to grinding problems.   This finding did not give them the root cause of the rejections, but it allowed them to focus on grinding operations.  Their next step was to construct a cause and effect diagram and then to design experiments to determine improved grinding procedures.   This next step illustrates the implementation of the Study Cause & Effect step, step 7 in the Hoerl-Snee process improvement strategy.
·         Regression Analysis – Stratification.  The posting on 3/4/2008 describes statistical thinking by Pease Industries to reduce the defect rate of decorative glass inserts for a wooden entry door.   The prevailing opinion was that humidity and temperature variations in the mold department were the root cause.  The team collected data and did a regression analysis using temperature and humidity as independent variables and the number of defects as the dependent variable.   The result was no correlation between the independent variables and the number of defects.  They collected more data and stratified the data by part type, month of occurrence and day of week.   They were surprised by the result showing day of the week strongly affecting the defect rate.   A Chi-Square test showed the day of the week was statistically significant.   The next step was to construct a Cause-and-Effect diagram and do a Is-Is Not analysis.   This step illustrates the Study Cause and Effect step, step 7.

In both of the above examples, the use Cause-and-Effect diagrams, designed experiments and the Is-Is Not analysis required the previous results from the Analyze Cause and Effect steps.   One needs to identify the effects prior to studying the effects.