The postings on 3/21/2008, 3/25/2008, 3/28/2008 and 4/1/2008 present the Resin Output Variation Example to illustrate Statistical Thinking and the Hoerl-Snee Process Improvement Strategy.   This example also makes extensive use of Exploratory Data Analysis.

Ricoh’s Numazu plant made raw material used as ingredients for copy machine toner.   The company had a team which monitored the process in order to achieve continual quality improvement.  

• The team examined a Resin Yield Run Chart and noted that the yield ratio sometimes exceeded 1.0 which was theoretically impossible.   The 3/21/2008 posting includes this run chart.   The run chart indicated the presence of an assignable cause which they eliminated by preventing a drop in air pressure.  However, yields still exceeded 1.0.  They decided that this result was due to variation and suspected that this variation would degrade finished product quality.   The team started an effort to discover the source of variation and to make changes to eliminate it.
• The team collected output quantity data and constructed histograms.   The posting on 3/25/2008 shows the histograms.   The overall output quantity histogram clearly shows two peaks indicating a combination of two component distributions.   After the second batch processing step, the process splits a batch into two parts which are processed on two separate lines, i.e., line A and line B.   The posting on 3/25/2008 shows histograms for the output quantities of the two lines.    Comparing the line A and line B histograms shows that the two lines have different distributions for their output quantities.
• Next the team constructed a Cause & Effect Diagram to show potential causes of the output quantity variation and the differences between the two lines.   The 3/25/2008 posting presents this Cause & Effect Diagram.    The posting on 3/28/2008 describes the identification and elimination of two variation causes.  They investigated the procedure for dividing the resin after the second processing step.   They discovered that some material remained in the reaction tank after sending material to the two lines.  This mean that line B had less input and thus less output than line A.   They changed the dividing procedure and found no significant difference between the output quantities of the two lines. 
• The second potential cause described on the 3/28/2008 posting involved the solvent feed ratio.   They constructed a scatter plot showing that increasing solvent feed ratio was correlated with increasing output.   This correlation was inconsistent with the team’s understanding of the physical process.    They found that the ratio measurement was affected by the time the solvent was in the tank.   They changed the procedure to insure the solvent had stabilized prior to measurement.   Examination of a control chart showed that the variation in output quantity was still excessive.
• The posting on 4/1/2008 describes the elimination of a third cause, and the posting shows a control chart.   This control chart clearly shows a significant reduction in output variation.  

The exploratory data analysis included examination of four different graphical displays.  They are a run chart, histograms, a scatter plot, and several control charts.  De Mast and Trip (2007) points out that Good (1983); Hoaglin, Mosteller et al (2000); and Bisgaard (1996) note that graphical presentations are preferred in Exploratory Data Analysis.   They are more effective is showing an individual what he did not expect to see.

References

Bisgaard (2006) gives us an example where Exploratory Data Analysis leads us to narrow the scope of the quality improvement investigation.   The example involves the production of small outboard motors by an assembly line.    Monthly quality reports showed an unacceptable number of defective motors that caused costly rework.   The Vice President of Manufacturing formed a team for the purpose of reducing the number of defects.  

The first task performed by the team was to flowchart the assembly line.   This is consistent with the first step in the Hoerl-Snee Process Improvement Strategy (See the posting on 4/8/2008).   After the base motors were painted and dried, the motors traveled on a ten station line for the purpose of installing accessory components.   These accessory components included the carburetor, brackets, the propeller, and electrical systems.     Next, the team examined tables specifying defects and their type.    The team found the tables difficult to analyze.   To assist the analysis the team constructed Pareto charts specifying the defects by type of defect.   For example, missing fasteners, loose fasteners, and missing operations.  These Pareto Charts did not suggest principal causes.  The team decided to categorize the defects by the station on the line where the defect originated.   For example, a loose fastener on the carburetor, the defect originated at station 3.   An incorrectly mounted spark plug wire would have occurred at station 9.  The Pareto Chart categorizing defects by station appears below.

The team focused on station 9.  The workers on the line revealed that design of the motors had changed leaving station 9 with more work than the other stations.   The team redesigned the assembly line reducing the work load at station 9.   A number of other changes were made such as improved lighting.   The result was a dramatic reduction in the occurrence of defects.

De Mast and Trip (2007) claim that this example illustrates the use of exploratory data analysis change the focus of the problem from “too many defects” to “too many defects from station 9”.  They state that the example illustrates the use of exploratory data analysis to identify a KPOV.  

My viewpoint is that this example illustrates the identification of a KPIV.   That is, the assembly line station.    Admittedly, the next phase of the improvement effort was clearly more focused on station 9.    We must remember that quality improvement is often an iterative process.   That is, successive Plan-Do-Check-Act (PDCA) cycles.   Identifying a KPIV on a cycle may result in that KPIV being a KPOV on the next cycle.

1. Bisgaard, S. (1996). "Qualilty Quandaries: The Importance of Graphics in Problem Solving and Detective Work." Quality Engineering 9(1): 157-162.
2. De Mast, Jeroen and Albert Trip (2007). “Exploratory Data Analysis in Quality-Improvement Projects”, Journal of Quality Technology, 39(4): 301-311.