Doe and Six Sigma

Quality Progress

Volumn 37, Issue 3, 2004, Pages 66-69

  Conklin, Joseph D ^a  

^a U S DEPARTMENT OF ENERGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DESIGN OF EXPERIMENTS (DOE); DISCRETE DATA;

APPROXIMATION THEORY; COMPUTATIONAL METHODS; DATA REDUCTION; DEGREES OF FREEDOM (MECHANICS); ERROR ANALYSIS; PRESSURE GAGES; PRODUCT DEVELOPMENT; RAW MATERIALS; RESOURCE ALLOCATION; THERMOMETERS;

QUALITY CONTROL;

EID: 1642305415     PISSN: 0033524X     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Review

References (11)

1. To keep this example simple, assume vendor, material and formula are the important variables affecting TFP and the 36 batches reflect a statistically adequate sample size. A random order for processing and testing is important for minimizing the impact of unknown variables. Also assume the DOE is conducted along sound planning principles. See Statistics for Experimenters: An Introduction to Design, Data Analysis and Model Building by George Box, William Hunter and J. Stuart Hunter John Wiley & Sons, 1978) for more information about sound planning.
2. In real applications, each batch wouldn't contain the same number of units. Keeping the same number in each batch creates a balanced dataset, simplifies the analysis and makes the main points of the example easier to present. Computer programs such as PROC GLM in SAS's statistical package can be used to analyze datasets with different numbers of units in each batch. See SAS/STAT User's Guide, version 6, Vol. 2 (SAS Institute, 1989), for more information on PROC GLM.
3. B.J. Winer, Statistical Principles in Experimental Design, second edition, McGraw-Hill, 1971, p. 400.
4. I b i d.
5. Excluding a variable should not be done lightly, but it is often necessary because of time and resource constraints.
6. Part of sound planning for DOE projects is collecting suggestions for variables from the people and groups most responsible for or most affected by the process.
7. This is true in our example because it uses an orthogonal design, which is easier to analyze than a nonorthogonal design. See "Efficient Experimental Design With Marketing Research Applications" by Warren F. Kuhfield (Journal of Marketing Research, November 1994, pp. 545-557) for a discussion of orthogonal and nonorthogonal designs.
8. The "F" in F ratio and Pr > F refers to the F distribution, an important statistical distribution, particularly in DOE analysis. See An Introduction to Linear Statistical Models by F.A. Graybill (McGraw-Hill, 1961) for more information.
9. The exact cutoff value should be determined in the planning stage after discussions take place between the people or groups who know the process and the people or groups who are helping to design the experiment.
10. In practice, process improvement usually takes place after several DOEs, each one building on the things learned in the last one.
11. B.J. Winer, Statistical Principles in Experimental Design, see reference 3.