I will add my thoughts to the excellent suggestions already noted:
Adding additional replicates of the same treatment combinations "later" adds additional noise to the study. You should be careful about how you analyze the additional runs. Since it appears that you (and/or your colleagues) are "selectively" choosing which treatments to replicate, be aware of this bias. (see papers on Randomized Complete (and incomplete, BIB) Block Designs), for example:
Sanders, D., Leitnaker M., and McLean R. (2002) “Randomized Complete Block Designs in Industrial Studies” Quality Engineering, Vol. 14, Issue 1
It can be hugely advantageous to increase the inference space by running replicates over changing noise. If this is done systematically/purposefully, you might get significantly more insight to the mechanisms at work (which factors have an effect) and if those factor effects are robust to changing conditions (noise). If this is done randomly, you increase the inference space, but negatively effect the precision of the design.
"All models are wrong, some are useful" G.E.P. Box