Non-stationary demand forecasting by cross-sectional aggregation

Rostami-Tabar, Bahman ORCID: https://orcid.org/0000-0002-3730-0045, Babai, Mohamed Zied, Ducq, Yves and Syntetos, Argyrios ORCID: https://orcid.org/0000-0003-4639-0756 2015. Non-stationary demand forecasting by cross-sectional aggregation. International Journal of Production Economics 170 (Part A) , pp. 297-309. 10.1016/j.ijpe.2015.10.001

Preview

PDF - Accepted Post-Print Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview

Abstract

In this paper the relative effectiveness of top-down (TD) versus bottom-up (BU) approaches is compared for cross-sectionally forecasting aggregate and sub-aggregate demand. We assume that the sub-aggregate demand follows a non-stationary Integrated Moving Average (IMA) process of order one and a Single Exponential Smoothing (SES) procedure is used to extrapolate future requirements. Such demand processes are often encountered in practice and SES is one of the standard estimators used in industry (in addition to being the optimal estimator for an IMA process). Theoretical variances of forecast error are derived for the BU and TD approach in order to contrast the relevant forecasting performances. The theoretical analysis is supported by an extensive numerical investigation at both the aggregate and sub-aggregate level, in addition to empirically validating our findings on a real dataset from a European superstore. The results demonstrate the increased benefit resulting from cross-sectional forecasting in a non-stationary environment than in a stationary one. Valuable insights are offered to demand planners and the paper closes with an agenda for further research in this area.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Subjects:	H Social Sciences > HD Industries. Land use. Labor
Uncontrolled Keywords:	Demand Forecasting; Cross-sectional aggregation; Non-Stationary Processes; Single Exponential Smoothing
Publisher:	Elsevier
ISSN:	0925-5273
Date of First Compliant Deposit:	30 March 2016
Date of Acceptance:	2 October 2015
Last Modified:	19 Nov 2024 01:15
URI:	https://orca.cardiff.ac.uk/id/eprint/79860

Citation Data

Cited 10 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item