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Demand forecasting for companies with many branches, low sales numbers per product, and non-recurring orderings

URN to cite this document: urn:nbn:de:bvb:703-opus-4255

Title data

Kurz, Sascha ; Rambau, Jörg:
Demand forecasting for companies with many branches, low sales numbers per product, and non-recurring orderings.
Bayreuth , 2006

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Abstract

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.

Abstract in another language

Die Nachfrageschätzung spielt für Unternehmen eine immer größer werdende Rolle. Wir führen einen neuen Index für die Schätzung der Nachfrage anhand historischer Verkaufsinformationen ein.

Further data

Item Type: Preprint, postprint
Additional notes (visible to public): msc: 90B05
Keywords: Operations Research; Revenue Management; Revenue Management; revenue management; demand forecasting
DDC Subjects: 500 Science > 510 Mathematics
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics in Economy
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics in Economy > Chair Mathematics in Economy - Univ.-Prof. Dr. Jörg Rambau
Faculties
Faculties > Faculty of Mathematics, Physics und Computer Science
Language: English
Originates at UBT: Yes
URN: urn:nbn:de:bvb:703-opus-4255
Date Deposited: 25 Apr 2014 12:34
Last Modified: 27 Mar 2019 13:00
URI: https://epub.uni-bayreuth.de/id/eprint/741

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