Intermittent demand forecasting for spare parts with little historical information

Marco Caserta and Luca D’Angelo

Journal of the Operational Research Society, 2025, vol. 76, issue 2, 294-309

Abstract: We propose a framework to address the demand forecast problem for spare parts with little or no historical demand information. This problem is relevant, e.g., in the context of forecasting the demand of parts in the initial procurement phase. We depart from the traditional forecasting approach by focusing on the distance between two demand profiles, rather than directly predicting the demand. That is, we build a model on the space induced by some distance metrics applied on attribute-wise comparisons of the spare parts, rather than directly working on the space induced by the original attributes. The three steps of the framework are: (i) data preparation, comparing parts along a set of attributes; (ii) model training, linking the attribute-wise distances and the distance between the demand profiles; (iii) neighborhood construction and forecasting, reconstructing the profile of the demand of the part with no historical demand. We tested the algorithm on a real-world instance with over 28 thousands spare parts from 131 Amazon sites, with the goal of reconstructing the demand of parts with no historical demand. Empirical evidence showcases the significant improvement brought about by the proposed approach, compared with a pool of alternative models.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2024.2349734 (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:tjorxx:v:76:y:2025:i:2:p:294-309

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2024.2349734

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

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