Loss Function Analysis with R

  • Emilio L. Cano
  • Javier M. Moguerza
  • Andrés Redchuk
Chapter
Part of the Use R! book series (USE R, volume 36)

Abstract

Most features defining a product are not usually important to the customer. Only a few of them are critical to quality, in particular, those defining what the customer expects. To meet these expectations, the processes involved in the development of the final product should be correct. This is the Six Sigma way: high-quality processes lead automatically to high-quality products. This is related to the concept of cost of quality, which is the cost of having a low-quality product (from the customer’s perspective). Some managers still think that this concept is equivalent to total quality cost, which corresponds to the amount of money expended in implementing quality methodologies and improving processes. To avoid misunderstandings, we will refer to the cost of quality as the cost of poor quality. The cost of poor quality will result in a quantifiable loss for the organization and for society in general. This loss can be modeled by a function. In Six Sigma, this function is based on the variability of the process. In this chapter, we will analyze the quality loss function introduced by Taguchi and explain how to use it to calculate the average loss of a process.

References

  1. 53.
    Knight, E., Russell, M., Sawalka, D., & Yendell, S. (2007). Taguchi quality loss function and specification tolerance design. https://controls.engin.umich.edu/wiki/index.php/Taguchi_quality_loss_function_and_specification_tolerance_design in Michigan chemical process dynamics and controls open textbook. Accessed 20.09.2011.
  2. 89.
    Roy, R. (2010). A primer on the Taguchi Method (2nd ed.). Michigan: Society of Manufacturing Engineers.Google Scholar
  3. 99.
    Taguchi, G., Chowdhury, S., & Wu, Y. (2005). Taguchi’s quality engineering handbook. USA: Wiley.Google Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Emilio L. Cano
    • 1
  • Javier M. Moguerza
    • 1
  • Andrés Redchuk
    • 1
  1. 1.Department of Statistics and Operations ResearchRey Juan Carlos UniversityMadridSpain

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