Using Designed Experiments for Cake Mix.docx

Using Designed Experiments (DOE) to Minimize Moisture Loss Marilyn Wheatley 20 February, 2017 11 11 Comments As a person who loves baking (and eating) cakes, I find it bothersome to go through all the effort of baking a cake when the end result is too dry for my taste. For that reason, I decided to use a designed experiment in Minitab to help me reduce the moisture loss in baked chocolate cakes, and find the optimal settings of my input factors to produce a moist baked chocolate cake. I’ll share the details of the design and the results in this post. Choosing Input Factors for the Designed Experiment Because I like to use premixed chocolate cake mixes, I decided to use two of my favorite cake mix brands for the experiment. For the purpose of this post, I’ll call the brands A and B. Thinking about what could impact the loss of moisture, it is likely that the baking time and the oven temperature will affect the results. Therefore, the factors or inputs that I decided to use for the experiment are: 1. Cake mix brand: A or B (categorical data) 2. Oven temperature: 350 or 380 degrees Fahrenheit (continuous data) 3. Baking time: 38 or 46 minutes (continuous data) Measuring the Response Next, I needed a way to measure the moisture loss. For this experiment, I used an electronic food scale to weigh each cake (in the same baking pan) before and after baking, and then used those weights in conjunction with the formula below to calculate the percent of moisture lost for each cake: % Moisture Loss = 100 x initial weight – final weight initial weight

Designing the Experiment For this experiment, I decided to construct a 2 3 full factorial design with center points to detect any possible curvature in the response surface. Since the cake mix brand is categorical and therefore has no center point between brand A and brand B, the number of center points will be doubled for that factor. Because of this, I’d have to bake 10 cakes which, even for me, is too many in a single day. Therefore, I decided to run the experiment over two days. Because differences between the days on which the data was collected could potentially introduce additional variation, I decided to add a block to the design to account for any potential variation due to the day. To create my design in Minitab, I use Stat > DOE > Factorial > Create Factorial Design : Minitab makes it easy to enter the details of the design. First, I selected 3 as the number of factors:

Next, I clicked on the Designs button above. In the Designs window, I can tell Minitab what type of design I’d like to use with my 3 factors: In the window above, I’ve selected a full 2 3 design, and also added 2 blocks (to account for variation between days), and 1 center point per block. After making the selections and clicking OK in the above window, I clicked on the Factors button in the main window to enter the details about each of my factors: Because center points are doubled for categorical factors, and because this design has two blocks, the final design will have a total of 4 center points. After clicking OK in the window above, I ended up with the design shown below with 12 runs:

Performing the Experiment and Analyzing the Data After spending an entire weekend baking cakes and calculating the moisture loss for each one, I entered the data into Minitab for the analysis. I also brought in a lot of cake to share with my colleagues at Minitab! With the moisture loss for each of my 12 cakes recorded in column C8 in the experiment worksheet, I’m ready to analyze the results.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help