  We propose the new Top-Dog-Index, a measure for the branch-dependent historic
deviation of the supply data of apparel sizes from the sales data of a fashion
discounter. A common approach is to estimate demand for sizes directly from the
sales data. This approach may yield information for the demand for sizes if
aggregated over all branches and products. However, as we will show in a
real-world business case, this direct approach is in general not capable to
provide information about each branch's individual demand for sizes: the supply
per branch is so small that either the number of sales is statistically too
small for a good estimate (early measurement) or there will be too much
unsatisfied demand neglected in the sales data (late measurement). Moreover, in
our real-world data we could not verify any of the demand distribution
assumptions suggested in the literature. Our approach cannot estimate the
demand for sizes directly. It can, however, individually measure for each
branch the scarcest and the amplest sizes, aggregated over all products. This
measurement can iteratively be used to adapt the size distributions in the
pre-pack orders for the future. A real-world blind study shows the potential of
this distribution free heuristic optimization approach: The gross yield
measured in percent of gross value was almost one percentage point higher in
the test-group branches than in the control-group branches.
