Supply Chain Analytics in Healthcare

Hospitals and the healthcare system play an indispensable role in society by providing critical care to patients, often in unpredictable and challenging conditions. Despite their vital function, many hospitals still rely on traditional, non-data-oriented practices in their supply chain management. This thesis delves into the potential of implementing data-driven Operations Management and Operations Research (OM/OR) techniques to enhance hospital supply chains. This thesis comprises three articles, each addressing real-life use cases that highlight specific challenges presented by the hospital context and propose OM/OR approaches to tackle them.

The first article focuses on optimising bed flow management within a hospital, addressing the complexities of matching an unpredictable patient demand with limited bed resources, primarily reliant on human decision-making. We introduce a model that incorporates the impact of demand variability on the workforce, emphasising the efficiency gained by considering this human factor. This approach helps mitigate the risk of bed shortages and enhances the overall supply chain.

The second article centres on the sterilisation process of surgical tools, presenting a comprehensive model of the entire cycle, including a dedicated model of the surgical demand. We demonstrate the necessity of such a holistic approach in accurately identifying weaknesses within the cycle and devising effective solutions.

The third paper explores the allocation of departments within a hospital under construction. It illustrates how proactive planning for resource flexibility can harness benefits in terms of resource utilisation while addressing the challenges posed by increased transportation flows.

This thesis demonstrates how data-driven OM/OR methods can significantly improve hospital supply chains. However, it also highlights various challenges associated with the hospital context, including issues related to data quality, data availability, or the complexities of managing highly variable demand.