Six Sigma is a structured, data-oriented method to reduce defects, waste and quality issues of any kind in production, services, management and further business activities. Six Sigma is a trademark of Motorola, that shaped the term in the eighties. The method is based on a combination of established techniques of quality assurance, basic and advanced methods of data analysis and systematic training of the staff involved with the Six Sigma related processes.
Six Sigma has shown that it not only increases the quality, but also that it can lead to tremendous cost reductions. Some spectacular success stories from huge companies have been published. Jack Welch (the former CEO of General Electric, one of the world’s largest producing companies) has said: “Six Sigma is the most important initiative GE has ever undertaken - it is part of the genetic code of our future leadership.” Welch attributes Six Sigma for billions of dollars in cost reductions.
Many other companies claim huge cost reductions after introducing Six Sigma at their production sites, as well. Motorola (one of the leading members of the group of companies that developed the Six-Sigma-approach) for example claims to have saved 11 billion dollars after introducing Six Sigma twelve years ago. Allied Signals mentions cost reductions of about a billion dollars over a couple of years.
The term Six Sigma represents the statistical goal to reduce the number of defects to a negligible amount, corresponding to the Six Sigma value of a (corrected; see below) normal distribution curve: Six Sigma tries to push failures and quality issues to the outer bounds of the distribution to reduce the problems to rare outliers of an otherwise faultless process.
To reach this “Six-Sigma-Goal” a process cannot have more than 3.4 defects per 1 million possibilities, where defect is defined as any form of undesired outcome for the process under inspection. Please be aware that 3.4 failures per million in fact represents a z-value of 4.5 of the normal distribution instead of 6 as the method allows for a dynamic shift of 1.5 sigma (defined by Motorola as long term dynamic mean variation).
Therefor one of the basic Six Sigma related tools is the Six-Sigma-Calculator to calculate the number of failures for one, two, ..., Six Sigma Processes. Naturally, there are much more advanced techniques to be applied based on the processes over the different levels of a Six-Sigma-project.
The strength of Six Sigma lies in the empirical, data-driven approach (and the use of quantitative measures of performance) to reach the goal of process improvements and reductions in variation. In “Six-Sigma-Quality-Improvement-Projects” the work is organized following the Six-Sigma-DMAIC model:
Define: In the definition phase the goal and scope of a project are defined, issues are collected that need to be tackled to reach a higher (better) sigma level.
Measure: In this phase data is gathered about the current situation to form a baseline of the process and to identify problems.
Analyse: Identification of the causes of quality issues and confirmation of these using data analysis.
Improve: Implementation of solutions that have been developed based on the knowledge gained in the Analysis-phase.
Control: This phase ensures that the improvements implemented in the previous phase stay valid and active
Each of these steps makes use of specific analytical (quantitative) methods that are part of the overall spectrum of methods suggested for Six Sigma. Further information regarding Six Sigma can be found in two books that discuss the Six-Sigma-methodology and its application: Six Sigma: The Breakthrough Management Strategy (2000) by M. J. Harry and P. Schroeder and The Six Sigma Handbook (2001) by T. Pyzdek.
Statistica™ supports the data collection and analysis on every level of a Six-Sigma-project and can hence serve as the analytical basis of Six-Sigma-initiatives and implementations for companies of any size. The software has the following functionalities:
Statistica™ supports advanced methods like Machine Learning / Data Mining algorithms to be applied as alternative approaches in Six-Sigma-projects. These can be the foundation of an innovation advantage over the competition.
Additionally, the workspace provides a visual interface to design and automate analyses. Using workspaces, it is possible to share the work between all involved parties easily. For example, a Black-Belt might design analyses as a workspace to be later used by Green-Belts for application in the field.
Further advantages are:
With Statistica Server it is possible to design company-wide applications for quality assurance and improvements using Six Sigma. The customization capabilities allow to shape Statistica into a tool that feels and acts like it was specifically designed for your needs. The platform provides:
StatSoft is your reliable partner when it comes to the software TIBCO® Data Science / Statistica™.
We assist you with the selection, configuration and installation and will also provide you with the needed software- and methodological-know-how. In analytical projects we can aid with consulting and execution. Together we can generate more insights from your data and deliver a sustainable value.