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Statistical Thinking to Improve Quality
This blog examines the use of data analyses and statistical tools in a framework of statistical thinking to improve quality. The following principles form the basis for statistical thinking:

* All work occurs in a system of interconnected processes,
* Variation exists in all processes, and
* Understanding and reducing variation are keys to success.

Statistical thinking significantly improves the effectiveness of data analyses and statistical tools.
Pareto Chart
Tuesday November 3, 2009
Pareto Chart
Posted by: Gordon Clark at 7:13PM CST on November 3, 2009

We use Pareto Charts to rank problems or causes with respect to their frequency of occurrence.  The charts highlight those causes which result in the most quality problems.  

Pareto charts get their name from Vilfredo Pareto (1848 – 1923) who was an economist.   He analyzed and studied the unequal distribution of wealth.   Dr. Juran in the 1940s stated a principle of the “vital few” and the “trivial many” (see Juran and Godfrey (1999)).   That is, in many situations a few problem categories (about 20%) will produce the most problems (about 80%).  Juran called this principle “Pareto’s principle of unequal distribution.”

We illustrate the application of Pareto Charts using a case study taken from Gijo (2005).  A company was experiencing a high rejection rate in one of its machining shops.   They did not know the root causes of these rejections nor how to reduce their occurrence.   They started by examining existing records, and they classified the defects by the individual operations causing the defect.   The analysis of data by this classification is called stratification.   Using the results, they constructed a Pareto chart. The following figure presents the chart.  

 

The chart shows that 60% of the rejections were due to grinding problems.   Based on the Pareto Chart they started a study improve grinding operations.   This study resulted in designed experiments to determine improved grinding operating procedures.   The resulting analyses lead to operating procedures that significantly reducing rejections and rework due to grinding operations.  

References

  1. Juran, J. M. and A. B. Godfrey (1999). Juran’s Quality Handbook, 5th Edition, New York, McGraw-Hill.
  2. Gijo, E. V. (2005). "Improving Process Capability of Manufacturing Process by Application of Statistical Techniques." Quality Engineering 17(2): 309-315.