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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 Digraphs
Posted by:
Gordon Clark on
October 29, 2009 at
5:55PM CST
This posting describes the Interrelationship Digraph which is a tool for use in the seventh step, Study Cause and Effect, of the Hoerl-Snee Process Improvement Strategy. For example, assume that we start with a Cause & Effect diagram displaying potential causes of an effect or quality issue. We want to determine which potential cause or causes are the key causes or drivers.
Form a team of knowledgeable individuals with respect to this quality issue. The team will select a number, e.g., from six to twelve, of the potential causes from the Cause & Effect diagram. Call these potential causes concerns. The process for generating the Interrelationship Digraph will construct causal relationships among the concerns. The word digraph is a combination of the two words diagram and graph. The resulting digraph reflects the collective judgment of the team. Benbow and Kubiak (2005, page 40) specify a procedure for constructing the digraph. List the concerns on a sheet of easel paper or a whiteboard. Pick a pair of concerns. Ask the team to specify whether the first concern influences the second, the second concern influences the first, or whether there is no influential relationship between the concerns. If the team decides there is an influential relationship, draw an arrow from the most influential concern to the other concern. Does the first concern influence the second more than the second concern influences the first? If so, draw an arrow from the first concern to the second. Repeat this assessment for all possible pairs. A good way to proceed is to arrange the concerns in an approximate circular pattern. Start with the concern in the 12 o’clock position and call it the first concern. Compare it with the concern in the next clockwise position. Then, move clockwise and select another concern to compare with the first concern. Repeat this process until all possible combinations of concerns have been compared by the team. The next posting will illustrate the construction of an interrelationship digraph.
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