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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.
Analyze Common-Cause Variation A
Posted by:
Gordon Clark on
October 29, 2009 at
5:59PM CST
An additional example appears below illustrating the Analyze Common-Cause Variation step, step 6, in the Hoerl-Snee process improvement strategy. Refer to the posting on 5/18/2008 for a description of this step. Following the example, the posting summarizes some suggestions by Breyfogle (2003) to assist in stratification and disaggregation.
Histogram – Stratification. The posting on 3/25/2008 describes statistical thinking by a team at Ricoh’s Numazu plant. The plant makes raw material used as ingredients for copy machine toner. The team wanted to reduce variation in output quantity which indicated a lack of control of the underlying process. After removing a special cause, the team constructed a histogram of the output quantity. The histogram clearly displayed excessive variation and two peaks. The process flow chart showed a split after phase 2 into 2 separate lines, i.e., line A and line B. Separate histograms for the two lines showed the output from line B was consistently lower that line A. Constructing separate histograms for the two lines illustrates stratification by line. Next, the team conducted a brainstorming session to formulate their collective thinking about the causes of excessive variation and the differences between the two lines. They documented the results with a cause and effect diagram. The brainstorming session and the construction of a cause and effect diagram illustrate step 7, Study Cause & Effect. Stratification requires identifying a stratification factor, such as time of the day, and the partitioning of this factor into logical categories. What tools may we use to aid in the selection of a stratification factor? The team in the example above noticed two peaks in a histogram. Breyfogle (2003) provides some guidance for this question.
Disaggregation may be aided by constructing a process map such as the one used in the posting on 2/21/08. The process map (Breyfogle, 2003, p. 103) is a flowchart with key process input variables listed for each step in the process. References1. Breyfogle, F. W. (2003). Implementing Six Sigma. Hoboken, New Jersey, John Wiley & Sons, Inc.
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