NDLI: Demand Forecasting for Companies with Many Branches, Low Sales Numbers per Product, and Non-Recurring Orderings

Content Provider	IEEE Xplore Digital Library
Author	Kurz, S. Rambau, J.
Copyright Year	2007
Description	Author affiliation: Univ. of Bayreuth, Bayreuth (Kurz, S.; Rambau, J.)
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. 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.
Starting Page	196
Ending Page	204
File Size	473042
Page Count	9
File Format	PDF
ISBN	9780769529769
DOI	10.1109/ISDA.2007.83
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-20
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Supply and demand Demand forecasting Companies Inventory management Economic forecasting Marketing and sales Robustness Mathematics Clothing industry Intelligent systems
Content Type	Text
Resource Type	Article

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