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DoES 3.0 Windows Software
Design of Experiments Simplified
Tools for Variability Reduction

The Qualitron DoES™ 3.0 for Windows is an easy to use, Design of Experiments (DOE) software application for Microsoft Windows 95/98 and NT. This statistically powerful software was specifically developed to provide the engineer and scientist with the tools needed to systematically analyze and correct process variables and their interactions, dramatically reducing variation and improving quality.
Product Improvement Roadmap

DoES Product Improvement Roadmap

DoES is the only software on the market that captures the intuitive RED X Methodology for quality improvement, as described by Keki Bhote in his book World Class Quality. Based on the latest variation research and designed experiments techniques, DoES simplifies and automates the intensive math and interpretation process associated with DOE.

DoES mathematically reduces the data using statistically accepted formulae, helps interpret the data, provides valuable visual displays in the form of charts and graphs, maintains experimental data files, and prints both high resolution graphics and text. It is a powerful tool to guide the user through the step by step methodology of designed experiments.

Combined with data obtained from SPC, DoES provides the necessary methodology to analyze and eliminate out of control processes. What SPC was to the 80's, DOE is to the 90's and beyond. DoES will be there to help. When it comes to reducing variability, improving product designs and refining manufacturing processes, DoES will be at the center of your quality revolution.

DoES Tools for Variability Reduction:

Isoplot Analysis

  • Determines the repeatability and reproducibility of measurements or attribute rankings.
  • Checks for repeatability by time, operator, instrument or any other user definable variables.

Multi-Vari Analysis

  • Determines the family of variables that cause the greatest variation in the product or process and the variables that are significant. The purpose of the Multi-Vari Analysis is to reduce a larger number of possible causes of variation, in a product or process, to a smaller number which contains the Red X.
  • This tool uses a plotting technique to determine whether the major variation pattern is positional, cyclical or temporal.

Paired Comparisons

  • Reduces a large number of possible causes of variation down to the Primary family of the Red X.
  • This tool selects and pairs "good" and "bad" units in a systematic way to identify patterns of differences.

Paired Comparisons Analysis Variables Search Analysis
Paired Comparisons Analysis Variables Search Analysis

Components Search
  • Uses a procedure of interchanging components in search of the Red X, a primary cause of variation.
  • This tool is used primarily in assembly operations.

Variables Search

  • A very useful tool for efficiently reducing variation, separating the important variables, Red X, from the unimportant ones,Pink X.
  • The technique employs a honing-in binary technique that uses "high" and "low" levels of each variable and combines each with all the other variables at the opposite level.

Full Factorials

  • The 2K full factorial of this experiment is the real workhorse of the DOE used to determine the most important variable(s), Red X and Pink X, and to assign numerical values to determine individual and interactive effects on the quality of a product or process.
  • This experiment is used for prototype testing, pilot runs, and production problem solving where a limited number of variables are causing the variation.

½ Fractional Factorials

  • The fractional 2K designs are particularly useful for screening experiments to reduce the number of significant factors to 2 or 3.
  • The main advantage of this design is to reduce the number of lots that are required in an experiment.
  • This design is constructed using the Effect/Treatment Combinations chart.


  • This test is a tool that is used in the same manner as a B vs. C, except that the data must be measurable and two samples of data are compared.
  • It is used to validate the results of previous experiments by confirming that there is a statistically significant difference, comparing sample means from two normal populations or distributors and comparing a sample mean with a known population mean.

Full Factorial Analysis Scatter Plots Analysis
Full Factorial Analysis Scatter Plots Analysis

Scatter Plots

  • The purpose of this tool is to optimize the levels of important variables, the Red X, and establish realistic specification tolerances.
  • Scatter plots are a graphical technique in which at least thirty readings of a range of values of an independent variable are plotted, in a random time sequence, against the corresponding range of values of a dependent variable.
  • Using the scatter plot to identify target values (nominals) and tolerances for the Primary Red X cause of variation will help ensure zero defects.

B vs.C

  • Determines which of two alternatives result in a better product process or method.
  • This test is a nonparametric comparison that is qualitative rather than quantitative.
  • This tool is very simple to use and no assumption of normality is necessary for either B (Better experimental sample) or C (Control distribution or process) distributors.
  • This experiment is applicable in prototype and pilot runs, production capping runs and in non-technical fields of work or study such as sales, advertising, etc.

DoES Features:

  • 32-Bit Windows Software
  • Easy-to-use Graphical User Interface (GUI), prompting the user through the correct routines.
  • Excel-type spreadsheet for data entry. Tabs are located at the bottom of the spreadsheet for each Tool.
  • Context sensitive help with hypertext
  • Improved graphics capability
  • Data import / export capability
  • Improved log sheets
  • High resolution printouts of charts and data
  • Network compatibility with any Windows (95/98, NT), Novell, and other popular network file servers

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