A resilient inventory management of pharmaceutical supply chains under demand disruption

Inspired by the global supply chain disruptions caused by the COVID-19 pandemic, we study optimal procurement and inventory decisions for a pharmaceutical supply chain over a finite planning horizon. To model disruption, we assume that the demand for medical drugs is uncertain and shows spatiotemporal variability. To address demand uncertainty, we propose a two-stage optimization framework, where in the first stage, the total cost of pre-positioning drugs at distribution centers and its associated risk is minimized, while the second stage minimizes the cost of recourse decisions (e.g., reallocation, inventory management). To allow for different risk preferences, we propose to capture the risk of demand uncertainty through the expectation and worst-case measures, leading to two different models, namely (risk-neutral) stochastic programming and (risk-averse) robust optimization. We consider a finite number of scenarios to represent the demand uncertainty, and to solve the resulting models efficiently, we propose L-shaped decomposition-based algorithms. Through extensive numerical experiments, we illustrate the impact of various parameters, such as travel time, product's shelf life, and waste due to transportation and storage, on the supply chain resiliency and cost, under optimal risk-neutral and risk-averse policies. These insights can assist decision makers in making informed choices.

Automated summary

Motivated by the disruptions in the global supply chain due to COVID-19, this study investigates the optimal procurement and inventory decisions in a pharmaceutical supply chain with uncertain and spatiotemporal demand. A two-stage optimization framework is proposed to address demand uncertainty, where the first stage minimizes the cost and risk associated with pre-positioning drugs, and the second stage minimizes the cost of recourse decisions. The study considers different risk preferences and proposes two models, stochastic programming and robust optimization, to capture the risk of demand uncertainty. Efficient algorithms are also proposed for solving these models.