Let us now discuss the meaning and the potential validity of the hypotheses
of the preceeding section. The first point to be mentioned concerns
what is modeled. Using a probability function like 
can model our lack of actual knowledge concerning the future demand.
We may for example think that a better knowledge (at ordering time)
can be reached but that it's cost would stay beyond the additional
benefits resulting from this additional knowledge (a discussion of
such cost balancing is undertaken in [2]). Another
point of view is that markets are intrinsically wild so that the probability
function rather models the very nature of market.
The second point concerns what experimental procedure can be used
to determine . A ``gedachte Experiment''
is as follows: starting with a great number of exact copies of the
actual world, put different order quantities in these worlds, inducing
them to evolve (independently) in different manners and observe what
happens at selling time. One cannot escape this point of view by considering
approximations obtained from times series, since only ergodicity can
justify such approximations (without mentioning the fact that actual
times series are quite ever too short to conclude, even assuming ergodicity).
A third point is that the actual demand cannot be measured (even afterwards)
when the demand overflows the inventory. In such a case, the only
actual knowledge is 
. Therefore, a slight shift towards
oversizing the inventory could be a good policy since it results into
a better knowledge (for a slight cost).