Design of Experiments with R
Design of experiments (DoE) is one of the most important tools in the Six Sigma methodology. It is the essence of the Improve phase and the basis for the design of robust processes. An adequate use of DoE will lead to the improvement of a process, but a bad design can result in wrong conclusions and engender the opposite of the desired effect: inefficiencies, higher costs, and less competitiveness. In this chapter, we introduce the foundations of DoE and describe the essential functions in R to perform it and analyze its results. We will describe two-level factorial designs using a representative example of how DoE should be used to achieve the improvement of a process in a Six Sigma way. The chapter is not intended as a thorough review of DoE. The idea is to introduce a simple model in an intuitive way. For more technical or advance training a number of references are given at the end of the chapter.
- 2.Allen, T. T. (2010). Introduction to engineering statistics and lean Six Sigma—Statistical quality control and design of experiments and systems. New York: Springer.Google Scholar
- 5.Berger, P., & Maurer, R. (2002). Experimental design: With applications in management, engineering, and the sciences. Duxbury titles of related interest. CA: Duxbury/Thomson Learning.Google Scholar
- 11.Box, G., Hunter, J., & Hunter, W. (2005). Statistics for experimenters: Design, innovation, and discovery. Wiley series in probability and statistics. New York: Wiley.Google Scholar
- 31.Grömping, U. (2011). Cran task view: Design of experiments (doe) & analysis of experimental data. http://cran.r-project.org/web/views/ExperimentalDesign.html. Retrieved 24.01.2012.
- 32.Grömping, U. (2012). Project: (industrial) doe in r. http://prof.beuth-hochschule.de/groemping/software/design-of-experiments/project-industrial-doe-in-r/. Retrieved 24.01.2012.
- 58.Lalanne, C. (2006). R companion to montgomery’s design and analysis of experiments. http://www.aliquote.org/articles/tech/dae/. Retrieved 19.01.2012.
- 63.Lopez-Fidalgo, J. (2009). A critical overview on optimal experimental designs. Boletin de Estadística e Investigación Operativa, 25(1), 14–21. http://www.seio.es/BEIO/files/BEIOv25n1_ES_J.Lopez-Fidalgo.pdf. Retrieved 19.01.2012.
- 70.Montgomery, D. (2008). Design and analysis of experiments. Student solutions manual. New York: Wiley.Google Scholar
- 75.Myers, R., Montgomery, D., & Anderson-Cook, C. (2009). Response surface methodology: Process and product optimization using designed experiments. Wiley series in probability and statistics. New York: Wiley.Google Scholar
- 82.Pyzdek, T., & Keller, P. (2009). The Six Sigma handbook: A complete guide for green belts, black belts, and managers at all levels. New York: McGraw-Hill.Google Scholar
- 85.Rasch, D., Pilz, J., & Simecek, P. (2010). Optimal experimental design with R. London: Taylor & Francis.Google Scholar
- 103.Vikneswaran (2005). An r companion to “experimental design”. http://cran.r-project.org/doc/contrib/Vikneswaran-ED_companion.pdf. Retrieved 19.01.2012.