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Nendi Sunendar
Universitas Nusa Mandiri
Computer Science & IT Electrical & Electronics Engineering

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AI-based benchmarking framework for heterogeneous hospital pharmaceutical demand forecasting Nendi Sunendar; Kursehi Falgenti; Farizal Gunawan
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2847-2857

Abstract

Accurate pharmaceutical demand forecasting is challenging because hospital medicine consumption exhibits heterogeneous patterns, including seasonal, volatile, and intermittent demand. This study proposes the regime-aware forecasting framework (RAFF), a reproducible benchmarking approach that combines structural demand diagnostics, leakage-controlled walk-forward validation, statistical comparison, and regime-stratified evaluation. Ten years of monthly demand records covering 300 pharmaceutical items were evaluated using Seasonal Naive, seasonal autoregressive integrated moving average with exogenous factors (SARIMAX), and long short-term memory (LSTM) models. Relative to Seasonal Naive, SARIMAX reduced mean absolute error by 18.20%, and root mean squared error by 20.58%, while LSTM reduced them by 16.48% and 21.75%, respectively. SARIMAX achieved the lowest aggregate mean absolute error, whereas LSTM achieved the lowest root mean squared error and greater stability as volatility increased. The results show that forecasting performance depends on demand structure, supporting regime-aware model selection for hospital inventory and procurement planning.
Co-Authors Farizal Gunawan Kursehi Falgenti