Based on your reply, I suggest you read some books on DOE, or perhaps look through some of the on-line reference material on DOE provided by JMP, for example:
https://www.jmp.com/en_us/events/getting-started-with-jmp/doe-intro-kit.html
1. Pardon my over simplification, but DOE is not a test. In experimentation you are looking for clues as to what is the causal structure related to each response variable. A test is done to pick a winner. This is why running an experiment, you would likely start with multiple factors set at 2 bold levels to assess the linear effect, screen out the less interesting factors and then continue to iterate (move the space and augment ) with the remaining factors. If experiment on multiple factors at 2 levels you can get estimates of linear main effects and interaction effects, depending on the resolution of the design. If you add levels, you can get estimates of non-linear effects (3 levels - quadratic, 4 levels - cubic, etc.). Typically, you build your models in a Taylor series approach where you start with linear Maine effects, then linear interaction effects, then quadratic, etc. (building the model from first - 2nd to 3rd...order). But, the intent is not to create a complex model, the intent is to keep the model as simple as possible while still having a model that works for prediction.
2. Covariates are a way of handling a factor that you cannot specifically control/manage, but can be measured. We incorporate this random variable into the model of fixed effects (from the DOE). Accounting for the covariate increases the precision of the experiment without compromising the inference space. Of course, you are now analyzing a mixed model.
3. I'm not familiar with the quantitative descriptive analysis method, but it sounds interesting. I am always concerned with human sensory perception as a response variable. According to my cursory look, the measurement is interval. How to get consistency and mitigate bias is challenging. I don't see how this method handles either of these issues. There are many research papers on the number of units in a scale that can be consistently used for human perception.
"All models are wrong, some are useful" G.E.P. Box