ABSTRACT
The outbreak of the COVID pandemic revealed that supply chains are not resilient to such a type of turmoil, and the food industry appeared to be particularly vulnerable. Meanwhile, customers expect uninterrupted deliveries and the products' selection responding to their preferences. In this article, we discuss several topics related to supply management that allow preparing a delivery plan for a distributed network of vending machines, considering each location individually. The developed solution takes advantage of the state-of-the-art machine learning methods. However, it is human-centric and aligned with the concept of Industry 5.0. We present the conceptual and technological side of the solution with a particular emphasis on the developed feature extraction framework, which uses selected indicators from the survival analysis. We present an analysis of the real data confirming that the proposed approach copes well with high uncertainty in data, addressing the cold-start problem. © 2005-2012 IEEE.