Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
The experts do not know the recipe used for each individual pizza.
References
Allen, T. T. (2010). Introduction to engineering statistics and lean Six Sigma—Statistical quality control and design of experiments and systems. New York: Springer.
Berger, P., & Maurer, R. (2002). Experimental design: With applications in management, engineering, and the sciences. Duxbury titles of related interest. CA: Duxbury/Thomson Learning.
Box, G., & Jones, S. (1992). Designing products that are robust to the environment. Total Quality Management, 3(3), 265–285.
Box, G., Hunter, J., & Hunter, W. (2005). Statistics for experimenters: Design, innovation, and discovery. Wiley series in probability and statistics. New York: Wiley.
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.
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.
Lalanne, C. (2006). R companion to montgomery’s design and analysis of experiments. http://www.aliquote.org/articles/tech/dae/. Retrieved 19.01.2012.
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.
Mee, R. (2009). A comprehensive guide to factorial two-level experimentation. New York: Springer.
Montgomery, D. (2008). Design and analysis of experiments. Student solutions manual. New York: Wiley.
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.
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.
Rasch, D., Pilz, J., & Simecek, P. (2010). Optimal experimental design with R. London: Taylor & Francis.
Taguchi, G., Chowdhury, S., & Wu, Y. (2005). Taguchi’s quality engineering handbook. USA: Wiley.
Vikneswaran (2005). An r companion to “experimental design”. http://cran.r-project.org/doc/contrib/Vikneswaran-ED_companion.pdf. Retrieved 19.01.2012.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media New York
About this chapter
Cite this chapter
Cano, E.L., Moguerza, J.M., Redchuk, A. (2012). Design of Experiments with R. In: Six Sigma with R. Use R!, vol 36. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3652-2_11
Download citation
DOI: https://doi.org/10.1007/978-1-4614-3652-2_11
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-3651-5
Online ISBN: 978-1-4614-3652-2
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)