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• Exploratory Data Analysis
• Designed Experiments • Interrelationship Digraphs • Study Cause and Effect • Analyze Common Cause Variation • Process Improvement • Process Capability Indices • Rosen Yield Example • Hoerl-Snee Strategy • Is–Is Not Analysis • Cause and Effect Diagram • Pareto Chart • Flowchart • Special Cause • Basic Concepts • History • Six Sigma
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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.
Interrelationship Digraph Example
Posted by:
Gordon Clark on
October 29, 2009 at
5:54PM CST
This posting gives an example of an Interrelationship Digraph which is a tool for use in the seventh step, Study Cause and Effect, of the Hoerl-Snee Process Improvement Strategy. The quality issue is the potential causes or factors contributing to late deliveries. We take our example from Benbow and Kubiak (2005). The interrelationship digraph appears below.
A concern with a large number of input arrows is affected by a large number of other concerns. Thus, it could be a source of a quality or performance metric. ‘Poor scheduling of the trucker’ has 4 input arrows. A measure of poor scheduling performance of the trucker could indicate the magnitude of system problems causing late delivery. References:
Send This | Categories: Interrelationship Digraphs
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