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

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 branchsWe 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 branchs 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.show moreshow less
Filialabhängige Bedarfsprognosen anhand von historischen Verkaufsinformationen sind sehr wichtig aber auch sehr schwierig zu treffen. Wir führen einen neuen Index, den Top-Dog-Index, ein, um zu messen, wie stark einzelne Filialen über- bzw. unterbeliefert sind. Mit diesem Ansatz kann man zwar den quantitativen Bedarf nicht direkt schätzen, aber zumindest die Belieferung iterativ an den Bedarf anpassen. Wir werten eine in der Praxis durchgeführte Blindstudie aus und belegen das Potential dieser MFilialabhängige Bedarfsprognosen anhand von historischen Verkaufsinformationen sind sehr wichtig aber auch sehr schwierig zu treffen. Wir führen einen neuen Index, den Top-Dog-Index, ein, um zu messen, wie stark einzelne Filialen über- bzw. unterbeliefert sind. Mit diesem Ansatz kann man zwar den quantitativen Bedarf nicht direkt schätzen, aber zumindest die Belieferung iterativ an den Bedarf anpassen. Wir werten eine in der Praxis durchgeführte Blindstudie aus und belegen das Potential dieser Methode anhand von signifikanten Steigerungen im Rohertrag.show moreshow less

Download full text files

Export metadata

  • Export Bibtex
  • Export RIS
  • frontdoor_exportcitavi

Additional Services

    Share in Twitter Search Google Scholar
Metadaten
Institutes:Mathematik
Wirtschaftswissenschaften
Author: Sascha Kurz, Jörg Rambau, Jörg Schlüchtermann, Rainer Wolf
Year of Completion:2008
SWD-Keyword:Blindversuch; Diskrete Optimierung; Feldforschung; Operations Research; Revenue Management
Tag:Doppel-Blind-Studie; Feldstudie; Grössenoptimierung; Revenue Management
Top-Dog-Index; demand forecasting; field study; parallel blind testing; revenue management; size optimization
Dewey Decimal Classification:330 Wirtschaft
MSC-Classification:90B05 Inventory, storage, reservoirs
URN:urn:nbn:de:bvb:703-opus-4219
Document Type:Preprint
Language:English
Date of Publication (online):15.04.2008