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