Cheryl Hild

SIX SIGMA* ON CONTINUOUS PROCESSES: HOW AND WHY IT DIFFERS

Six Sigma initiatives are focused on investigating the causes of variability and developing processes and products with less variation in output performance. This philosophy indicates the applicability of Six Sigma methodologies to all processes: batch,..

COMMON MYTHS ABOUT SIX SIGMA

Although the popularity of Six Sigma initiatives has grown due to success, certain myths are encountered with increasing frequency. The methodologies do not cure all organizational ills. This article explores some misconceptions encountered in communica..

CHARTING AUTOCORRELATED DATA: GLTIDELINES FOR PRACTITIONERS

Data from many industrial processes are autocorrelated. Standard control-charting techniques are based on a model of independence and tend to produce out-of-control signals in the presence of positive autocorrelation. Thus, common-cause variation is ope..

ILLUSTRATION OF SIX SIGMA* ASSISTANCE ON A DESIGN PROJECT

Statistical methods such as Six Sigma are most effective when there is an appropriate blending of engineering theory, process and product knowledge, and statistical thinking methods. The design and development of a brake subsystem for a new product ..

The Thought Map

The thought map is invaluable in any focused work effort for capturing the multitude of questions that arise, the many possible paths for seeking solutions, the work performed, and the solutions obtained. A potential weakness in statistical techniques ..

A Graphical Tool for Detection of Outliers in Completely Randomized, Unreplicated 2 k and 2 k-P Factorials

ABSTRACT With the increased awareness of statistical methods in industry today, many non-statisticians are implementing statistical studies and conducting statistically designed experiments (DOEs). With this increased use of DOEs by non-statisticians in applied settings, there is a need for more graphical methodologies to support both analysis and interpretations of DOE results. In particular, there is a critical need for user-friendly means to investigate outlier effects of noise and active background variables in unreplicated DOEs. This article presents a profoundly simple, yet effective, methodology to identify outliers in unreplicated 2 k and 2 k-p factorial designs that integrates well-established, confirmatory statistical techniques with a simple graphical, exploratory tool.