Prescriptive analytics for hospital drug inventory: A context-aware multi-channel neural network for integrated forecasting and optimization

TY - JOUR

T1 - Prescriptive analytics for hospital drug inventory

T2 - A context-aware multi-channel neural network for integrated forecasting and optimization

AU - Chen, Jie

AU - Xu, Zeshui

AU - Zhu, Bin

N1 - Publisher Copyright: © 2026 Elsevier Ltd

PY - 2026/9

Y1 - 2026/9

N2 - Inventory management in hospital pharmacy settings focuses on two interdependent tasks: forecasting drug demand and determining ordering quantities under uncertainty. However, effectively integrating these tasks remains challenging due to the underutilization of contextual data and the disconnect between prediction accuracy and decision quality. This study proposes a prescriptive analytics framework for hospital drug inventory that integrates forecasting and decision-making within a context-aware multi-channel neural network. Specifically, for demand forecasting, we develop a multi-channel neural network that captures heterogeneous temporal patterns from historical data including ordering records, epidemiological indicators, behavioral signals, environmental variables, and demographic factors. For inventory decisions, we train the model to directly generate ordering quantities by minimizing a decision-oriented loss function that accounts for the asymmetric costs of overstocking and understocking. We prove that incorporating multi-source contextual data and integrating prediction with decision-making leads to lower expected costs compared to conventional two-stage methods. Empirical results based on real data from hospitals in Beijing support this finding, showing consistent improvements in cost performance and inventory stability.

AB - Inventory management in hospital pharmacy settings focuses on two interdependent tasks: forecasting drug demand and determining ordering quantities under uncertainty. However, effectively integrating these tasks remains challenging due to the underutilization of contextual data and the disconnect between prediction accuracy and decision quality. This study proposes a prescriptive analytics framework for hospital drug inventory that integrates forecasting and decision-making within a context-aware multi-channel neural network. Specifically, for demand forecasting, we develop a multi-channel neural network that captures heterogeneous temporal patterns from historical data including ordering records, epidemiological indicators, behavioral signals, environmental variables, and demographic factors. For inventory decisions, we train the model to directly generate ordering quantities by minimizing a decision-oriented loss function that accounts for the asymmetric costs of overstocking and understocking. We prove that incorporating multi-source contextual data and integrating prediction with decision-making leads to lower expected costs compared to conventional two-stage methods. Empirical results based on real data from hospitals in Beijing support this finding, showing consistent improvements in cost performance and inventory stability.

KW - Demand forecasting

KW - Inventory decision-making

KW - Multi-channel neural network

KW - Multi-source data

KW - Prescriptive analytics

UR - https://www.scopus.com/pages/publications/105033405185

U2 - 10.1016/j.omega.2026.103557

DO - 10.1016/j.omega.2026.103557

M3 - Article

AN - SCOPUS:105033405185

SN - 0305-0483

VL - 143

JO - Omega (United Kingdom)

JF - Omega (United Kingdom)

M1 - 103557

ER -