Authors: Leonard Perry, Nicholas Barker
Addresses: Industrial and Systems Engineering, University of San Diego, San Diego, CA 92110, USA. ' Space Exploration, Networks & Space Systems, The Boeing Company, Kennedy Space Center, FL 32815, USA
Abstract: Six Sigma quality initiatives remain relatively rare in the service sector. While a robust assortment of statistical tools is available, the prevalence of attribute data in the service sector often obfuscates the applicability and appropriate selection of such tools. In the service sector, the normal distribution is not as commonplace as in the manufacturing industry. Accordingly, required approximations and assumptions in common statistical analysis are frequently invalid. In the service industries, where attribute data is most often encountered, one must use certain statistical quality improvement tools with particular caution. By nature, rarely does empirical data characterising service processes fit the normal distribution. Fortunately, there is an array of tools and methods for handling non-normal data that broaden the applicability and usefulness of Six Sigma. This article presents a statistical toolkit particularly useful for non-normal or more specifically, attribute or discrete data.
Keywords: Six Sigma; non-normal data; service sector; tools; techniques; quality improvement; service quality; attribute data; discrete data.
International Journal of Six Sigma and Competitive Advantage, 2006 Vol.2 No.3, pp.313 - 333
Published online: 16 Oct 2006 *Full-text access for editors Access for subscribers Purchase this article Comment on this article