In today’s competitive landscape, engineers and design teams are constantly searching for ways to save time, money and valuable resources. One of the current best methods to do so is the utilization of design of experiments (DOE) tools. Specifically design of experiment software.

Design of experiments was first developed in the 1920s by a British statistician named Ronald Fisher. Some 70 years later, Fisher's method has become a powerful software tool for engineers and researchers.

In essence design of experiments is a mathematical methodology that defines an optimal set of experiments in the design space, in order to obtain the most relevant information possible with the highest accuracy at the lowest cost. Scientific exploration of the design space replaces a tedious, manual, trial-and-error process, and is the fastest way to acquire the most relevant information with minimum computational effort.

DOE tools and DOE software allows the user to determine the objectives of a virtual experiment and then select the factors for the study. The best design of experiment software set up and analyze statistically solid design of experiments with ease. They do this by fitting data into mathematical equations that predict outcomes for any combination of values. They can then lay out a detailed experimental plan that defines the experiments to be run.

Well chosen experimental designs maximize the amount of valuable information that can be obtained for a given amount of experimental effort. By using DOE software, engineers, scientists and researchers in virtually any industry can optimize responses and discover winning combinations. This could mean cutting six to 12 months off production schedules and an annual savings of six figures or more.

When selecting DOE software, it's important to not only look for a statistical engine that's fast and accurate but also the following factors:

• A simple and intuitive user interface.
• A well-written manual with easy to understand tutorials.
• A wide selection of designs for screening and optimizing processes or product formulations.
• A flexible data entry spreadsheet flexible that can easily deal with missing data and changed factor levels.
Software that randomizes the order of experimental runs. (Randomization is important because it ensures that interference factors will spread randomly across all control factors.)
• Design evaluation tools that will reveal aliases and other potential pitfalls.

And of course, most important is picking a design of experiment software package that works best for you and your team. Once this is done you are on your way to saving time, money and energy by using DOE tools.

Author's Bio: 

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