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

Sascha Kurz (), Jörg Rambau (), Jörg Schlüchtermann () and Rainer Wolf ()

Annals of Operations Research, 2015, vol. 229, issue 1, 563 pages

Abstract: 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. Our approach individually measures 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. Copyright Springer Science+Business Media New York 2015

Keywords: Revenue management; Size optimization; Demand forecasting; Top-Dog-Index; Field study; Parallel blind testing; Primary: 90B05; Secondary: 90B90 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1007/s10479-014-1746-8 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:229:y:2015:i:1:p:541-563:10.1007/s10479-014-1746-8

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-014-1746-8

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().