In industry, designed experiments can be used to systematically investigate the process or product variables that affect product quality. After you identify the process conditions and product components that affect product quality, you can direct improvement efforts to enhance a product's manufacturability, reliability, quality, and field performance.
To add output from a DOE, go to Add and complete a form.
For example, a group of engineers plans an experiment to investigate the effects of three factors on the warping that occurs in a copper plate. They create a 2-level factorial design by specifying the design information, including blocks and center points. To see an example, go to Minitab Help: Example of Create 2-Level Factorial Design.
Use this form to record the data analysis from your experiment. Use the DOE Planning form to help you design the experiment.
For more information on available designs, go to Minitab Help: Factorial and fractional factorial designs.
For example, a marketing manager wants to study the influence that three categorical factors have on the ability of test subjects to recall an online advertisement. Because the experiment includes factors that have 3 levels, the manager uses a general full factorial design. To see an example, go to Minitab Help: Example of Create General Full Factorial Design.
Use this form to record the data analysis from your experiment. Use the DOE Planning form to help you design the experiment.
For more information on available designs, go to Minitab Help: Choose a factorial design.
General full factorial (GFF) designs are not recommended for use in screening, or reducing, the number of potentially important inputs. The size of the experiment can be large, and therefore, expensive. Also, for screening purposes, GFF designs provide much more information than you need. You should screen out all possible inputs using two levels, then add inputs needing more than two levels to the screened design.
Designs for these experiments are useful because many product design and development activities in industrial situations involve formulations or mixtures. In these situations, the response is a function of the proportions of the different ingredients in the mixture. For example, you might be developing a pancake mixture that is made of flour, baking powder, milk, eggs, and oil. Or, you might be developing an insecticide that blends four chemical ingredients. To see an example, go to Minitab Help: Example of Create Mixture Design (Simplex Centroid).
For more information on available designs, go to Minitab Help: Choose a mixture design.
An optimal design uses the "best" group of design points, selected from reducing or augmenting the number of experimental runs in the original design. Optimal design capabilities can be used with general full factorial designs, response surface designs, and mixture designs. To see an example, go to Minitab Help: Example of selecting a D-optimal response surface design.
The candidate points must be a general full factorial, response surface, or mixture design. The sample size and power should be desirable for a practically important effect size. Usually, you use optimal designs to decrease the number of experimental runs, but smaller sample sizes may not provide a design that can detect small effects with sufficient power. For details, go to Minitab Help: Data considerations for Select Optimal Design.
Response surface design methodology is often used to refine models after you have determined important factors using screening designs or factorial designs; especially if you suspect curvature in the response surface.
For example, an engineer wants to analyze the injection-molding process for a plastic part. First, the engineer performs a fractional factorial design, identifies the important factors (temperature, pressure, cooling rate), and determines that curvature is present in the data. Then, the engineer creates a central composite design to analyze the curvature and find the best factor settings. To see an example, go to Minitab Help: Example of Create Response Surface Design (Central Composite).
For more information on available designs, go to Minitab Help: What are response surface designs, central composite designs, and Box-Behnken designs?.