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We propose the new Top-Dog-Index, a measure for the branch-dependent historicdeviation of the supply data of apparel sizes from the sales data of a fashiondiscounter. A common approach is to estimate demand for sizes directly from thesales data. This approach may yield information for the demand for sizes ifaggregated over all branches and products. However, as we will show in areal-world business case, this direct approach is in general not capable toprovide information about each branch's individual demand for sizes: the supplyper branch is so small that either the number of sales is statistically toosmall for a good estimate (early measurement) or there will be too muchunsatisfied demand neglected in the sales data (late measurement). Moreover, inour real-world data we could not verify any of the demand distributionassumptions suggested in the literature. Our approach cannot estimate thedemand for sizes directly. It can, however, individually measure for eachbranch the scarcest and the amplest sizes, aggregated over all products. Thismeasurement can iteratively be used to adapt the size distributions in thepre-pack orders for the future. A real-world blind study shows the potential ofthis distribution free heuristic optimization approach: The gross yieldmeasured in percent of gross value was almost one percentage point higher inthe test-group branches than in the control-group branches.

annals of operation research/annals of operations research

The Top-Dog Index: A New Measurement for the Demand Consistency of the  Size Distribution in Pre-Pack Orders for a Fashion Discounter with Many Small  Branches