DOE (design of experiments) helps you investigate the effects of input variables (factors) on an output variable (response) at the same time. These experiments consist of a series of runs, or tests, in which purposeful changes are made to the input variables. Data are collected at each run. You use DOE to identify the process conditions and product components that affect quality, and then determine the factor settings that optimize results.
Minitab offers four types of designs: factorial designs, response surface designs, mixture designs, and Taguchi designs (also called Taguchi robust designs). The steps you follow in Minitab to create, analyze, and visualize a designed experiment are similar for all types. After you perform the experiment and enter the results, Minitab provides several analytical tools and graph tools to help you understand the results. This chapter demonstrates the typical steps to create and analyze a factorial design. You can apply these steps to any design that you create in Minitab.
In this chapter, you investigate two factors that might decrease the time that is needed to prepare an order for shipment: the order-processing system and the packing procedure.
The Western center has a new order-processing system. You want to determine whether the new system decreases the time that is needed to prepare an order. The center also has two different packing procedures. You want to determine which procedure is more efficient. You decide to perform a factorial experiment to test which combination of factors enables the shortest time that is needed to prepare an order for shipment.
Before you can enter or analyze DOE data in Minitab, you must first create a designed experiment in the worksheet. Minitab offers a variety of designs.
You choose the appropriate design based on the requirements of your experiment. Choose the design from themenu. You can also open the appropriate toolbar by choosing . After you choose the design and its features, Minitab creates the design and stores it in the worksheet.
You want to create a factorial design to examine the relationship between two factors, order-processing system and packing procedure, and the time that is needed to prepare an order for shipping.
Minitab uses the factor names as the labels for the factors on the analysis output and graphs. If you do not enter factor levels, Minitab sets the low level at −1 and the high level at 1.
By default, Minitab randomizes the run order of all design types, except Taguchi designs. Randomization helps ensure that the model meets certain statistical assumptions. Randomization can also help reduce the effects of factors that are not included in the study.
Setting the base for the random data generator ensures that you obtain the same run order each time you create the design.
Each time you create a design, Minitab stores design information and factors in worksheet columns.
The RunOrder column (C2) indicates the order to collect data. If you do not randomize the design, the StdOrder and RunOrder columns are the same.
In this example, because you did not add center points or put runs into blocks, Minitab sets all the values in C3 and C4 to 1. The factors that you entered are stored in columns C5 (OrderSystem) and C6 (Pack).
You can useto switch between a random display and a standard-order display, and between a coded display and an uncoded display. To change the factor settings or names, use . If you need to change only the factor names, you can enter them directly in the worksheet.
After you perform the experiment and collect the data, you can enter the data into the worksheet.
The characteristic that you measure is called a response. In this example, the response that you measure is the number of hours that are needed to prepare an order for shipment. You obtain the following data from the experiment:
14.72 9.62 13.81 7.97 12.52 13.78 14.64 9.41 13.89 13.89 12.57 14.06
To print a data collection form, click in the worksheet and choose Print Grid Lines is selected. Use the form to record measurements during the experiment.. Verify that
After you create a design and enter the response data, you can fit a model to the data and generate graphs to assess the effects. Use the results from the fitted model and graphs to determine which factors are important to reduce the number of hours that are needed to prepare an order for shipment.
Because the worksheet contains a factorial design, Minitab enables the Analyze Factorial Design and Factorial Plots. In this example, you fit the model first.menu commands,
You use the Session window output and the two effects plots to determine which effects are important to your process. First, look at the Session window output.
You can also evaluate the normal probability plot and the Pareto chart of the standardized effects to see which effects influence the response, Hours.
You can use the stored model to perform additional analyses to better understand your results. Next, you create factorial plots to identify the best factor settings, and you use Minitab's Predict analysis to predict the number of hours for those settings.
You use the stored model to create a main effects plot and an interaction plot to visualize the effects.
The factorial plots include the main effects plot and the interaction plot. A main effect is the difference in the mean response between two levels of a factor. The main effects plot shows the means for Hours using both order-processing systems and the means for Hours using both packing procedures. The interaction plot shows the impact of both factors, order-processing system and packing procedure, on the response. Because an interaction means that the effect of one factor depends on the level of the other factor, assessing interactions is important.
Each point represents the mean processing time for one level of a factor. The horizontal center line shows the mean processing time for all runs. The left panel of the plot indicates that orders that were processed using the new order-processing system took less time than orders that were processed using the current order-processing system. The right panel of the plot indicates that orders that were processed using packing procedure B took less time than orders that were processed using packing procedure A.
If there were no significant interactions between the factors, a main effects plot would adequately describe the relationship between each factor and the response. However, because the interaction is significant, you should also examine the interaction plot. A significant interaction between two factors can affect the interpretation of the main effects.
Each point in the interaction plot shows the mean processing time at different combinations of factor levels. If the lines are not parallel, the plot indicates that there is an interaction between the two factors. The interaction plot indicates that book orders that were processed using the new order-processing system and packing procedure B took the fewest hours to prepare (9 hours). Orders that were processed using the current order-processing system and packing procedure A took the most hours to prepare (approximately 14.5 hours). Because the slope of the line for packing procedure B is steeper, you conclude that the new order-processing system has a greater effect when packing procedure B is used instead of packing procedure A.
Based on the results of the experiment, you recommend that the Western shipping center use the new order-processing system and packing procedure B to decrease the time to deliver orders.
You determined the best settings, which are stored in the DOE model in the worksheet. You can use the stored model to predict the processing time for these settings.
The Session window output displays the model equation and the variable settings. The fitted value (also called predicted value) for these settings is 9 hours. However, all estimates contain uncertainty because they use sample data. The 95% confidence interval is the range of likely values for the mean preparation time. If you use the new order-processing system and packing procedure B, you can be 95% confident that the mean preparation time for all orders will be between 8.11 and 9.89 hours.
The factorial experiment indicates that you can decrease the time that is needed to prepare orders at the Western shipping center by using the new order-processing system and packing procedure B. In the next chapter, you learn how to use command language and create and run exec files to quickly re-run an analysis when new data are collected.
Go to Using Session Commands.