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Predictive analytics in health supply chains: Machine learning approaches for medicine demand prediction in Public Health facilities of Rwanda
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| dc.contributor.author | Mbonyinshuti, Francois | |
| dc.date.accessioned | 2025-10-23T10:30:49Z | |
| dc.date.available | 2025-10-23T10:30:49Z | |
| dc.date.issued | 2024-07 | |
| dc.identifier.uri | http://dr.ur.ac.rw/handle/123456789/2630 | |
| dc.description | Master's Dissertation | en_US |
| dc.description.abstract | Introduction: Recent innovations in healthcare, particularly the integration of artificial intelligence (AI) and predictive analytics, have catalyzed a data-centric revolution in health supply chains, paving the way for more accurate forecasting and enhanced operational efficiency. By leveraging predictive analytics and machine learning, health supply chains can transform inventory management and forecasting accuracy, thereby advancing operational efficiency and reliability in global healthcare delivery. | en_US |
| dc.language.iso | en | en_US |
| dc.subject | Health Supply Chain, Machine Learning, Linear Regression, Artificial Neural Networks, Random Forest, Medicines, LSTM, ARIMA, Prediction, Models, Healthcare, Data, Digital Technology, Rwanda | en_US |
| dc.title | Predictive analytics in health supply chains: Machine learning approaches for medicine demand prediction in Public Health facilities of Rwanda | en_US |
| dc.type | Dissertation | en_US |
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