Demand forecasting in supply chains: a review of aggregation and hierarchical approaches

M. Zied Babai, John E. Boylan and Bahman Rostami-Tabar

International Journal of Production Research, 2022, vol. 60, issue 1, 324-348

Abstract: Demand forecasts are the basis of most decisions in supply chain management. The granularity of these decisions lead to different forecast requirements. For example, inventory replenishment decisions require forecasts at the individual SKU level over lead time, whereas forecasts at higher levels, over longer horizons, are required for supply chain strategic decisions. The most accurate forecasts are not always obtained from data at the 'natural' level of aggregation. In some cases, forecast accuracy may be improved by aggregating data or forecasts at lower levels, or disaggregating data or forecasts at higher levels, or by combining forecasts at multiple levels of aggregation. Temporal and cross-sectional aggregation approaches are well established in the literature. More recently, it has been argued that these two approaches do not make the fullest use of data available at the different hierarchical levels of the supply chain. Therefore, consideration of forecasting hierarchies (over time and other dimensions), and combinations of forecasts across hierarchical levels, have been recommended. This paper provides a comprehensive review of research dealing with aggregation and hierarchical forecasting in supply chains, based on a systematic search. The review enables the identification of major research gaps and the presentation of an agenda for further research.

Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2021.2005268 (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:taf:tprsxx:v:60:y:2022:i:1:p:324-348

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2021.2005268

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().