Data driven supply allocation to individual customers considering forecast bias

Alexander Seitz, Martin Grunow and Renzo Akkerman

International Journal of Production Economics, 2020, vol. 227, issue C

Abstract: We propose a data-driven allocation planning approach, which is designed for use in advanced planning systems as they are widely used in industrial environments. The approach exploits increasingly available data on individual customers and products by allocating supply on a highly granular level at high planning frequencies. It counteracts rationing gaming by customers, which we assume to be the reason for demand forecast biases.

Keywords: Allocation planning; Order promising; Demand fulfilment; Demand forecast bias; Big data; Supply chain planning (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0925527320300761
Full text for ScienceDirect subscribers only

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:eee:proeco:v:227:y:2020:i:c:s0925527320300761

DOI: 10.1016/j.ijpe.2020.107683

Access Statistics for this article

International Journal of Production Economics is currently edited by Stefan Minner

More articles in International Journal of Production Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().