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June 26, 2008

Study Cause and Effect

This posting discusses the seventh step, Study Cause and Effect, of the Hoerl-Snee Process Improvement Strategy.   Refer to the figure in the April 4 posting for an overview of the process.  Use Britz et al (2000) and Hoerl and Snee (2002) as references.

The previous step analyzed common-cause variation to identify the source (s) of variation.   If the previous step did not identify the source or if knowing the source does not reveal the root cause, we proceed to study cause and effect.  

Some of the tools we might use in this step are:

  • Scatter plot.   A plot of a quality characteristic versus a potential explanatory variable.   See the plot in the 3/28/2008 posting showing the effect of solvent feed ratio on output weight.
  • Cause & Effect Diagram.  A diagram portraying the potential causes of an effect.  See the diagram in the 2/28/2008 posting showing the potential causes of rejections at the grinding operations.  Frequently, the Cause & Effect diagram summarizes the results of a brainstorming session.   However, some improvement efforts will use data to substantiate the cause and effect diagram.
  • Box Plot.   Box Plots depict the relationship between a discrete variable, such as location on a part, and the distribution of continuous variable, such as a dimension.
  • Multi-Vari Charts.   Multi-Vari charts display variations in categories that aid in identifying causes.
  • Interrelationship Digraphs.   Teams construct cause and effect relationships from a list of issues.

The next posting will summarize additional tools for this step.   Subsequent postings will give examples of Box Plots, Multi-Vari Charts and Interrelationship Digraphs.

References
  1. Britz, G. C., D. W. Emerling, et al. (2000). Improving Performance Through Statistical Thinking. Milwaukee, WI, ASQ Quality Press.
  2. Hoerl, R. and R. D. Snee (2002). Statistical Thinking - Improving Business Performance. Pacific Grove, CA, Duxbury.

Posted by Gordon M. Clark at 02:31 PM | | Comments (0)

March 18, 2008

Process Improvement Strategies

A set of fundamental principles define Statistical Thinking, and these principles appear above in the introductory statements to this blog.    A number of different approaches exist for applying Statistical Thinking to improve quality.   Call these approaches Process Improvement Strategies.    The Statistics Division has promoted one originally defined by Hoerl and Snee (1995).    This is the same process improvement strategy described in detail by Britz, Emerling, et al (2000) and Hoerl and Snee (2002).   Call this process improvement strategy the Hoerl-Snee strategy.  

Six Sigma has another process improvement strategy.    Six Sigma uses the DMAIC steps which are Define, Measure, Analyze, Improve and Control.  The DMAIC steps differ from the Hoerl-Snee process improvement strategy.   Our blog posting on January 13, 2008 points out that Statistical Thinking is a crucial concept in Six Sigma.   Clearly Six Sigma regards work as a system of interconnected processes, looks for variation in all processes, and regards understanding and reducing variation as keys to success.

Each element in the Hoerl-Snee strategy maps to an element in the DMAIC strategy.   However, the author thinks that the Hoerl-Snee strategy is more explicit and easier to understand.

The Shainin SystemTM or Statistical Engineering has another approach to quality improvement.   See Shainin (1995) for an overview or Steiner and MacKay (2005) for improvements to Statistical Engineering.   Statistical Engineering does use Statistical Thinking.   Its process improvement strategy places more emphasis on finding and eliminating a dominant cause (The Red X) than the Hoerl-Snee and Six Sigma strategies.  Statistical Engineering does not differentiate between special and common causes.   Also, it places less emphasis on advance planning prior to data gathering.   In addition, Statistical Engineering does not explicitly separate special causes from common causes so that it more effectively identifies the causes and eliminates them.

Approach in Subsequent Postings

First, we will specify the Hoerl-Snee strategy.   This strategy will be illustrated by example applications which we will present next.   After that we will discuss the differences between the three strategies mentioned above.   Case studies will illustrate the differences. 

References

  1. Hoerl, R. W. and R. D. Snee (1995). Redesigning the Introductory Statistics Course. Madison, Wisconsin, University of Wisconsin, Center for Quality and Productivity Improvement.
  2. Britz, G. C., D. W. Emerling, et al. (2000). Improving Performance Through Statistical Thinking. Milwaukee, WI, ASQ Quality Press.
  3. Hoerl, R. and R. D. Snee (2002). Statistical Thinking - Improving Business Performance. Pacific Grove, CA, Duxbury.
  4. Shainin, R. D. (1995). A Common Sense Approach to Quality Management. 49th Annual Quality Congress Proceedings.
  5. Steiner, S. H. and R. J. MacKay (2005). Statistical Engineering: An Algorithm for Reducing Variation in Manufacturing Processes. Milwaukee, Wisconsin, ASQ Quality Press.

Posted by Gordon M. Clark at 10:37 AM | | Comments (0)