Doug Sanders

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..

The Process Map

This article describes a modification of process flowcharts and cause-and-effect diagrams that are used in conjunction with other tools and techniques to facilitate and document process investigation and improvement...

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 ANALYTIC EXAMINATION OF TIME-DEPENDENT VARIANCE COMPONENTS

Dr. Deming states that the application of statistical theory to enumerative problems is generally correct, but the application of traditional techniques to analytic problems may be misleading. An example is the use of traditional nested designs to stud..

Recognition and Importance of Restrictions on Randomization in Industrial Experimentation

Statisticians typically recommend completely randomized experimental designs. The reasoning behind this advice is theoretically sound. Unfortunately, engineers who typically run industrial experiments frequently fail to recognize restrictions on randomization, i.e., split-plot experiments, and are often unaware of the risks associated with analyzing split-plot experiments as if they were randomized. Similarly, issues concerning the inference space of the experiment frequently are not given adequate consideration. Conversely, statisticians frequently are unaware that a restriction on randomization does not necessarily translate into less information than a completely randomized design. In this paper, we discuss a proactive methodology for identifying and incorporating information concerning restrictions on randomization and inference space in industrial experiments. We also present the factor relationship diagram (FRD), a tool that assists engineers in the recognition of restrictions of randomization and guides the development of questions that encourage the experimenter to understand those sources of variation that may contribute to a lack of precision in a split-plot experiment or lack of repeatability in inference space different from that studied in the experiment. Examples that illustrate the use of the methodology and the FRD are included.

Randomized Complete Block Designs in Industrial Studies

The use of randomized complete block designs is considered as a proactive method for collecting data to develop further understanding of the sources of variation affecting process outputs. The purpose of the randomized block design has traditionall..

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.

CONSIDERATIONS ASSOCIATED WITH RESTRICTIONS ON RANDOMIZATION IN INDUSTRIAL EXPERIMENTATION

A philosophical motivation and methodology for proactive use of factor relationship diagrams is examined. The method is useful for developing questions about the sources of variation contributing to a lack of precision. Responses to these questions, w..