Inventory planning for self-serve pharmacy kiosks: A fill rate maximization approach

Abstract

In recent years, self-serve kiosks have become increasingly popular due to their 24/7 availability and lower setup and operational costs compared to traditional brick-and-mortar stores. However, the inventory planning for pharmacy kiosks is a challenge due to its limited capacity to store thousands of drugs ordered in various quantities, each with low and sporadic drug demand. In this work, we model the pharmacy kiosk inventory planning problem as a capacitated multiproduct newsvendor problem under fill rate maximization objective. We present a data-driven robust optimization framework where product demand lies within an uncertainty set generated from product clustering. A column-and-constraint generation based solution approach is proposed to solve industry scale instances. The proposed robust framework is tested on actual pharmacy sales data and randomly generated instances with 2000 products. The robust solutions outperform scenario-based stochastic solutions with an increase in out-of-sample fill rate of 5.8%, on average, and of up to 17%. Comparative analysis with profit objective reveals that the fill rate objective results in 17% higher out-of-sample fill rate by compromising 20% in profits, on average.