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The Seven Quality Control Tools in a Nutshell: R and ISO Approaches

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Quality Control with R

Part of the book series: Use R! ((USE R))

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Abstract

The aim of this chapter is to smoothly introduce the reader to Quality Control techniques from the so-called Seven Basic Quality Control tools: Cause-and-effect-diagram, check sheet, control chart, histogram, Pareto chart, scatter diagram, and stratification. These are basic but powerful tools when used wisely.

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Notes

  1. 1.

    Also known as Ishikawa diagram or “fishbone” diagram.

  2. 2.

    Some authors replace “stratification” by “flow chart” or “run chart” in the original list.

  3. 3.

    The ggplot function requires a data frame object as input data, so the pdensity vector is converted to a data frame in the first argument.

  4. 4.

    As expected from the simulation!

  5. 5.

    Define, Measure, Analyze, Improve, and Control.

  6. 6.

    Descriptions are from the standards summaries at the ISO website http://www.iso.org.

  7. 7.

    At the time of writing, the development stage is Final Draft International Standard (FDIS) , see Chapter 4 to find out more about standards development stages.

References

  1. Anhoej, J.: Qicharts: quality improvement charts, url http://CRAN.R-project.org/package=qicharts. R package version 0.2.0 (2015)

  2. Cano, E.L., Moguerza, J.M., Redchuk, A.: Six sigma in a nutshell. In: Six Sigma with R, Use R!, vol. 36, pp. 3–13. Springer, New York (2012). doi:10.1007/978-1-4614-3652-2_1. url http://dx.doi.org/10.1007/978-1-4614-3652-2_1

  3. Cano, E.L., Moguerza, J.M., Redchuk, A.: Six sigma with R. Statistical Engineering for Process Improvement, Use R!, vol. 36. Springer, New York (2012). url http://www.springer.com/statistics/book/978-1-4614-3651-5

  4. Dahl, D.B.: Xtable: export tables to LaTeX or HTML, url http://CRAN.R-project.org/package=xtable. R package version 1.7-4 (2014)

  5. Ishikawa, K.: What is total quality control? The Japanese way. Prentice Hall Business Classics. Prentice-Hall, Englewood Cliffs (1985)

    Google Scholar 

  6. Ishikawa, K.: Guide to Quality Control. Asian Productivity Organisation, Tokyo (1991)

    Google Scholar 

  7. ISO: ISO/IEC 31010:2009, Risk management – Risk assessment techniques. International standard (2010)

    Google Scholar 

  8. ISO: ISO/IEC 19795-1:2006, Information technology – Biometric performance testing and reporting – Part 1: Principles and framework. International standard (2016)

    Google Scholar 

  9. ISO TC69/SC1–Terminology and Symbols: ISO 3534-1:2006 - Statistics – Vocabulary and symbols – Part 1: General statistical terms and terms used in probability. Published standard (2010). url http://www.iso.org/iso/catalogue_detail.htm?csnumber=40145

  10. ISO TC69/SC1–Terminology and Symbols: ISO 3534-4:2014 - Statistics – Vocabulary and symbols – Part 4: Survey sampling. Published standard (2014). url http://www.iso.org/iso/catalogue_detail.htm?csnumber=56154

  11. ISO TC69/SC4–Applications of statistical methods in process management: ISO 11462-1:2010 - Guidelines for implementation of statistical process control (SPC) – Part 2: Catalogue of tools and techniques. Published standard (2010). url http://www.iso.org/iso/home/store/catalogue_tc/catalogue_detail.htm?csnumber=42719

  12. ISO TC69/SC4–Applications of statistical methods in process management: ISO 7870-4:2011 - Control charts – Part 4: Cumulative sum charts. Published standard (2011). url http://www.iso.org/iso/catalogue_detail.htm?csnumber=40176

  13. ISO TC69/SC4–Applications of statistical methods in process management: ISO 7870-3:2012 - Control charts – Part 3: Acceptance control charts. Published standard (2012). url http://www.iso.org/iso/catalogue_detail.htm?csnumber=40175

  14. ISO TC69/SC4–Applications of statistical methods in process management: ISO 7870-2:2013 - Control charts – Part 2: Shewhart control charts. Published standard (2013). url http://www.iso.org/iso/catalogue_detail.htm?csnumber=40174

  15. ISO TC69/SC4–Applications of statistical methods in process management: ISO 7870-1:2014 - Control charts – Part 1: General guidelines. Published standard (2014). url http://www.iso.org/iso/catalogue_detail.htm?csnumber=62649

  16. ISO TC69/SC4–Applications of statistical methods in process management: ISO 7870-5:2014 - Control charts – Part 5: Specialized control charts. Published standard (2014). url http://www.iso.org/iso/catalogue_detail.htm?csnumber=40177

  17. ISO TC69/SC7–Applications of statistical and related techniques for the implementation of Six Sigma: ISO 13053-1:2011 - Quantitative methods in process improvement – Six Sigma – Part 1: DMAIC methodology. Published standard (2011). url http://www.iso.org/iso/catalogue_detail.htm?csnumber=52901

  18. ISO TC69/SC7–Applications of statistical and related techniques for the implementation of Six Sigma: ISO 13053-2:2011 - Quantitative methods in process improvement – Six Sigma – Part 2: Tools and techniques. Published standard (2011). url http://www.iso.org/iso/catalogue_detail.htm?csnumber=52902

  19. Kume, H.: Statistical Methods for Quality Improvement. The Association for Overseas Technical Scholarships, Tokyo (1985)

    Google Scholar 

  20. PMI: A guide to the projet management body of knowledge. Project Management Institute (PMI), Newton Square (2013)

    Google Scholar 

  21. Roth, T.: Qualitytools: Statistics in Quality Science, url http://www.r-qualitytools.org. R package version 1.54 (2012)

  22. Sarkar, D.: Lattice: Multivariate Data Visualization with R. Springer, New York (2008). url http://lmdvr.r-forge.r-project.org. ISBN 978-0-387-75968-5

  23. Scrucca, L.: Qcc: an R package for quality control charting and statistical process control. R News 4/1, 11–17 (2004). url http://CRAN.R-project.org/doc/Rnews/

  24. Wickham, H.: Ggplot2: Elegant Graphics for Data Analysis. Use R!. Springer, New York (2009)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Statistics, Rey Juan Carlos University, Madrid, Spain

    Emilio L. Cano & Javier M. Moguerza

  2. Statistics Area, DHEP, The University of Castilla-La Mancha, Ciudad Real, Spain

    Emilio L. Cano

  3. ENUSA Industrias Avanzadas, Madrid, Spain

    Mariano Prieto Corcoba

Authors
  1. Emilio L. Cano
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  2. Javier M. Moguerza
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  3. Mariano Prieto Corcoba
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Cano, E.L., Moguerza, J.M., Corcoba, M.P. (2015). The Seven Quality Control Tools in a Nutshell: R and ISO Approaches. In: Quality Control with R. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-319-24046-6_3

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