The Open UniversitySkip to content

Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments

Priore, Paolo; Ponte, Borja; Rosillo, Rafael and de la Fuente, David (2019). Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments. International Journal of Production Research, 57(11) pp. 3663–3677.

Full text available as:
Full text not publicly available (Accepted Manuscript)
Due to publisher licensing restrictions, this file is not available for public download until 5 December 2019
Click here to request a copy from the OU Author.
DOI (Digital Object Identifier) Link:
Google Scholar: Look up in Google Scholar


Firms currently operate in highly competitive scenarios, where the environmental conditions evolve over time. Many factors intervene simultaneously and their hard-to-interpret interactions throughout the supply chain greatly complicate decision-making. The complexity clearly manifests itself in the field of inventory management, in which determining the optimal replenishment rule often becomes an intractable problem. This paper applies machine learning to help managers understand these complex scenarios and better manage the inventory flow. Building on a dynamic framework, we employ an inductive learning algorithm for setting the most appropriate replenishment policy over time by reacting to the environmental changes. This approach proves to be effective in a three-echelon supply chain where the scenario is defined by seven variables (cost structure, demand variability, three lead times, and two partners’ inventory policy). Considering four alternatives, the algorithm determines the best replenishment rule around 88% of the time. This leads to a noticeable reduction of operating costs against static alternatives. Interestingly, we observe that the nodes are much more sensitive to inventory decisions in the lower echelons than in the upper echelons of the supply chain.

Item Type: Journal Item
Copyright Holders: 2018 Informa UK Limited, trading as Taylor & Francis Group
ISSN: 0020-7543
Keywords: Bullwhip Effect; inductive learning; inventory management; machine learning; replenishment policy; supply chain management
Academic Unit/School: Faculty of Business and Law (FBL) > Business > Department for People and Organisations
Faculty of Business and Law (FBL) > Business
Faculty of Business and Law (FBL)
Item ID: 58257
Depositing User: Borja Ponte Blanco
Date Deposited: 10 Dec 2018 09:05
Last Modified: 15 Jul 2019 09:21
Share this page:


Altmetrics from Altmetric

Citations from Dimensions

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU