A Data-Driven Framework for Vaccine Demand Forecasting and Inventory Simulation in a Hospital Travel Clinic
6
Issued Date
2026-01-01
Resource Type
eISSN
21693536
Scopus ID
2-s2.0-105036518131
Journal Title
IEEE Access
Rights Holder(s)
SCOPUS
Bibliographic Citation
IEEE Access (2026)
Suggested Citation
Petchrompo S., Wangprasertkul B., Sungsee A., Piyaphanee W., Asawapaithulsert P., Padungwech W., Wangwittaya C., Jarumaneeroj P. A Data-Driven Framework for Vaccine Demand Forecasting and Inventory Simulation in a Hospital Travel Clinic. IEEE Access (2026). doi:10.1109/ACCESS.2026.3685781 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/116460
Title
A Data-Driven Framework for Vaccine Demand Forecasting and Inventory Simulation in a Hospital Travel Clinic
Corresponding Author(s)
Other Contributor(s)
Abstract
Forecasting vaccine demand and determining inventory policies are critical challenges in healthcare supply chains, where uncertainty poses significant operational risks. This study proposes a two-step data-driven framework to support vaccine planning under uncertainty. The first step leverages machine learning models—XGBoost and LightGBM—for daily demand forecasting using a recursive multi-day strategy, with model deviations generated via bootstrapping to characterize uncertainty. Forecast accuracy is evaluated using a sliding-window Mean Cumulative Absolute Percentage Error to capture cumulative deviations relevant to operational planning. The second step employs a stochastic Monte Carlo simulation and a custom performance-based heuristic to determine proper policy parameters. A key feature is the implementation of dynamic reorder points and order quantities that adapt to forecasted demand and volatility to ensure responsiveness. By incorporating data-driven forecast distributions, the simulation evaluates tradeoffs between stock-out risk and inventory efficiency using Value-at-Risk metrics. A case study examining vaccines in a hospital travel clinic confirms the framework’s real-world applicability and the effectiveness of this hybrid approach. Results reveal that XGBoost performs better for seasonal or volatile demand, while LightGBM excels with smoother profiles. Notably, both algorithms outperform benchmark algorithms including CatBoost, LSTM, and Prophet. Furthermore, the proposed heuristic identifies effective policy parameters for each vaccine within a computationally efficient timeframe. Inventory results show that the proposed method maintains inventory days within hospital targets to maintain vaccine potency while simultaneously minimizing the risk of stockout. This is particularly advantageous for travel clinics that manage diverse vaccine portfolios with unpredictable demand and strict shelf-life constraints.