Improving hospital pharmacy inventory management using data segmentation

Abstract

Abstract Purpose This case study describes the development and empirical validation of an easy-to-implement practical framework for improving hospital pharmacy inventory management. Summary Research suggests various inventory optimization models, which can lead to cost reductions while maintaining adequate service levels; however, they are facing limited adoption in healthcare settings. The main barriers appear to be the high effort and complexity of implementation, the dependence on data that are not available or might not be in the right form, and the one-size-fits-all approach often followed without addressing healthcare sector–specific particularities. A research framework was developed by adapting relevant inventory models to the healthcare context using the concept of data segmentation on the basis of a three-dimensional classification of hospital pharmacy inventory items based on their relative importance, clinical criticality, and consumption pattern. Suitable replenishment policies were assigned to high-impact classes, and an integrated performance-measurement component assesses the framework’s effectiveness. The suggested approach was implemented and empirically tested at the pharmacy of a large public hospital using longitudinal data. The results demonstrate substantial improvements with respect to all of the selected key performance indicators and translate into inventory cost savings due to reduced stockholding costs and better synchronization of inventories to demand. Conclusion Use of standard software functionalities combined with targeted data segmentation efforts significantly improves hospital pharmacy inventory cost performance.