Now I want to know if there is any way the data I obtained can still be valid even without the two negative axial runs. I think that in theory it should still be acceptable since the objective of my study is to maximize the response value, which is achieved by (generally) maximizing the levels of the factors. Therefore the loss of the two negative runs should not have much of an effect. Or, if there is any way to use the data that I already have to fit a model without having to conduct further experiments, because I really have no more time to do so. At this point I can only make do with what I have.

I really hope you will be able to help me on this matter, I am quite desperate. Thank you very much.

]]>I have a set of data of orders delivered to customers. For every order we predict a commit date on which the order will be delivered. We are measuring the the performance by comparing te committed date and the actual date.

To know if my predictions are correct can I consider the commit date I predict as a forecast and measure Forecast accuracy (MAD, MAPE, SSE) & Bias(Mean Bias & confidence intervals).

I have this doubt because the above measures are usually used to check the accuracy of a forecasted demand & in this case I’m forecasting/predicting the date by when a certain order will be delivered.

Please let me know your views on this.

Appreicate your help.

Thanks & Best Regards

N. Chandramouli