  We propose the new Top-Dog-Index to quantify the historic deviation of the
supply data of many small branches for a commodity group from sales data. On
the one hand, the common parametric assumptions on the customer demand
distribution in the literature could not at all be supported in our real-world
data set. On the other hand, a reasonably-looking non-parametric approach to
estimate the demand distribution for the different branches directly from the
sales distribution could only provide us with statistically weak and unreliable
estimates for the future demand. Based on real-world sales data from our
industry partner we provide evidence that our Top-Dog-Index is statistically
robust. Using the Top-Dog-Index, we propose a heuristics to improve the
branch-dependent proportion between supply and demand. Our approach cannot
estimate the branch-dependent demand directly. It can, however, classify the
branches into a given number of clusters according to an historic oversupply or
undersupply. This classification of branches can iteratively be used to adapt
the branch distribution of supply and demand in the future.
