I'm hoping for some help identifying the experiment design in this question:
A large bakery would like to run an experiment that consists of evaluating the volume of their cupcakes after baking. Four recipes among their many recipes are selected at random. The employees and the owner of this bakery agreed on using three cooking temperatures. It is desired to have six replications from each combination of the four recipes and the three cooking temperatures. However, only twenty-four cupcakes can be baked per day, so the experiment must have to be spread over three consecutive days in order to achieve the six replications. Six similar ovens, randomly chosen from the pool of all ovens, are available in the course of the entire experiment every day and each can bake four cupcakes at a time.
The related question is
Discuss in detail how you will implement such an experiment in light of the constraint(s) imposed. Identify the design that you would use.
I want to suggest a split-plot design with recipe as 24 the main treatment/whole plots, each divided into three temperatures (subplots/split-plots). I believe this achieves significant experimental efficiency, rather than 72 total 'runs' to achieve the 6 replicates. However, I'm not sure how to account for the 'similar' ovens in this design.
Should I consider a nested design? Is blocking for the effect of oven necessary (I think 'no' because they are chosen at random)? Or can I stick with the split-plot and effectively ignore the various ovens?