Pharmacy Inventory Management with Unreliable Supply

Abstract

Oncology pharmacy inventory management faces significant challenges due to uncertain patient demand and unreliable supply. Chemotherapy regimens require timely drug availability, where disruptions can compromise treatment outcomes. This study develops a stochastic optimisation framework that integrates patient arrivals, modelled through a Poisson process, and treatment progression, represented by a discrete-time Markov chain. Monte Carlo simulations are employed to incorporate supply disruptions. The results indicate that deterministic models based on averages underestimate the volatility of daily demand, leading to risks of both shortages and wastage. Chemotherapy states generated highly variable demands, whereas hormonal therapy exhibited stable, long-term patterns suitable for lean inventory policies. The inclusion of discontinuation events highlighted critical implications for stock planning. By linking clinical pathways with operational models, the framework provides a practical tool for hospital pharmacies to design resilient inventory strategies that balance continuity of care with financial efficiency.