Statistical Thinking to Improve Quality

• Experimental Design.  A systematic planned variation of input factors for an actual process.   The experimenter observes the effect of these variations on important quality characteristics.   The 1/30/2008 posting mentions the use of designed experiments by an OEM manufacturer to determine an improved raw material composition.  The 2/28/2008 posting discusses the effort by a company to reduce the rejection rate at one of its machine shops.   Based on a Cause & Effect diagram, project members selected four factors for further analysis based on designed experiments.   These factors were Feed Rate, Wheel Speed, Work Speed, and Wheel Grade.   Analysis of the experimental results identified “optimum” levels for the four factors.
• Model Building.  One could construct a model of a process that predicts quality performance based on input variables.   The 2/18/2008 posting describes the actions of a Midwest manufacturing firm to reduce time delays experienced by customers contacting their order processing center.  They constructed a simulation model of the order-taking process.  Using the simulation model they determined the staffing level of customer service representatives by the hour of a work day to meet time-delay objectives.  Why don’t software companies use simulation models to specify technical support personnel requirements?

Subsequent postings will illustrate the use of experimental design and model building to Study Cause and Effect.

Posted by Gordon M. Clark at 03:57 PM | Permalink | Comments (0)

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.

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 | Permalink | Comments (0)