Optimising centralisation in distribution networks for perishable products through mathematical modelling, parametric analysis, and machine learning

Alessandra Cantini, Leonardo Leoni, Saverio Ferraro and Filippo De Carlo

International Journal of Production Research, 2025, vol. 63, issue 17, 6291-6318

Abstract: The success of distribution companies for perishable products is enabled by optimally configuring distribution networks, which allows for reducing total logistic costs while ensuring reduced product spoilage and high service levels. Since customer demand for perishable products varies over time, the network configuration should not be optimised once, but periodically reviewed. Among the decisions to be reviewed, determining whether to centralise or decentralise inventory (i.e. stock allocation in distribution centres) is crucial. However, the literature overlooks stock allocation decisions, and existing methodologies to compare the economic performance of centralised, decentralised, and hybrid policies neglect important cost items, also requiring advanced computational technologies and skills to be applied. This paper addresses these gaps by providing two contributions. First, a novel mathematical model is offered to compare five stock allocation policies (ranging from centralisation to decentralisation, crossing through three hybrid policies), identifying the most cost-effective one under a comprehensive economic analysis. Next, a parametric analysis is accomplished and a machine learning algorithm is trained to obtain a quick and easy-to-use Decision Support System (DSS). The DSS results in a decision tree, which was tested in a case study and provided managerial insights on how to review the stock allocation of perishable products.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2025.2470983 (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:63:y:2025:i:17:p:6291-6318

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

DOI: 10.1080/00207543.2025.2470983

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 ().