De Mast and Trip (2007) specify that the purpose of Exploratory Data Analysis (EDA) is to identify the dependent (Y) and independent (X) variables that may help understand or solve a quality problem.   However, they point out that EDA can only identify variables that vary in the collected data set.  If the EDA can not identify key variables affecting the system performance, available options include:

1. Collecting additional data and revising the variables recorded
2. Analyzing the available information, designing experiments, and conducting the experimental design

Option 1
The Pease Industries example, described in the posting on 3/4/2008, illustrates the first option.   A team wanted to reduce an 11% defect rate in glass inserts for a wooden entry door.  They thought that humidity and temperature variations were the cause.   They collected data and did a regression analysis where the dependent variable was the number of defects and the independent variables were temperature and humidity.   They found no correlation.   Then the team collected additional data, and they examined defect occurrence as related to part type, monthly occurrence and day of the week.   They found that the defect rate varied with the day of the week.  After investigating why the day of the week was important, they determined that dirty molds caused the elevated defect rate.

Option 2
The posting on 2/28/2008 describes a case study illustrating the second option above.   A company was experiencing excessive variation in its grinding operation.   A team conducted a brainstorming session to identify key factors causing the variation in the grinding operation.   The brainstorming session produced a Cause & Effect diagram.   The posting on 9/15/2008 describes an experimental design conducted to determine which factors were most significant.  The posting on 10/16/2008 describes the analysis of the experimental results.  The company improved the grinding process performance index from .49 to 1.25.

References

1. De Mast, Jeroen and Albert Trip (2007). “Exploratory Data Analysis in Quality-Improvement Projects”, Journal of Quality Technology, 39(4): 301-311.

De Mast and Trip (2007) list the following three steps in performing Exploratory Data Analysis.

1. Display the Data
2. Identify salient features
3. Interpret salient features

The resin output variation example, 3/25/2008 posting illustrates these steps.   The Ricoh team constructed a histogram of the output quantity (Display the data), noticed the bimodal nature of the output quantity (Identify salient features), and this bimodal distribution suggested that the output distributions from lines A and B were different (Interpret salient features).   Histograms of line A and line B output confirmed this conclusion.    Another salient feature of the histograms was the excessive variation in output quantity.   This feature motivated establishment of lower and upper limits and a target value.

References

1. De Mast, Jeroen and Albert Trip (2007). “Exploratory Data Analysis in Quality-Improvement Projects”, Journal of Quality Technology, 39(4): 301-311.