Skip to main content

Research Repository

Advanced Search

Intermittent demand, inventory obsolescence, and temporal aggregation forecasts

Sanguri, Kamal; Patra, Sabyasachi; Nikolopoulos, Konstantinos; Punia, Sushil

Intermittent demand, inventory obsolescence, and temporal aggregation forecasts Thumbnail


Authors

Kamal Sanguri

Sabyasachi Patra

Sushil Punia



Abstract

Forecasting for intermittent demand is considered a difficult task and becomes even more challenging in the presence of obsolescence. Traditionally the problem has been dealt with modifications in the conventional parametric methods such as Croston. However, these methods are generally applied at the observed frequency, ignoring any additional information, such as trend that becomes prominent at higher levels of aggregation. We evaluate established Temporal Aggregation (TA) methods: ADIDA, Forecast Combination, and Temporal Hierarchies in the said context. We further employ restricted least-squares estimation and propose two new combination approaches tailored to decreasing demand scenarios. Finally, we test our propositions on both simulated and real datasets. Our empirical findings support the use of variable forecast combination weights to improve TA’s performance in intermittent demand items with a risk of obsolescence.

Citation

Sanguri, K., Patra, S., Nikolopoulos, K., & Punia, S. (2024). Intermittent demand, inventory obsolescence, and temporal aggregation forecasts. International Journal of Production Research, 62(5), 1663-1685. https://doi.org/10.1080/00207543.2023.2199435

Journal Article Type Article
Acceptance Date Mar 27, 2023
Online Publication Date Apr 18, 2023
Publication Date 2024
Deposit Date Mar 30, 2023
Publicly Available Date May 16, 2023
Journal International Journal of Production Research
Print ISSN 0020-7543
Electronic ISSN 1366-588X
Publisher Taylor and Francis Group
Peer Reviewed Peer Reviewed
Volume 62
Issue 5
Pages 1663-1685
DOI https://doi.org/10.1080/00207543.2023.2199435
Public URL https://durham-repository.worktribe.com/output/1178135

Files

Published Journal Article (Advance Online Version) (4.7 Mb)
PDF

Publisher Licence URL
http://creativecommons.org/licenses/by-nc-nd/4.0/

Copyright Statement
© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.







You might also like



Downloadable Citations