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September 10, 2025

Performance Assessment of Predictive Forecasting Techniques for Enhancing Hospital Supply Chain Efficiency in Healthcare Logistics

Key Points

The hybrid LSTM-GRU model achieved 95.8% accuracy, outperforming other forecasting methods in supply chain management.
Metrics like precision (95.6%), recall (95.1%), and F1-score (95.8%) highlight the effectiveness of the predictive analytics approach.
Traditional forecasting methods struggle with the complex, non-linear nature of hospital inventory requirements and demand.
Incorporating deep learning into healthcare logistics can enhance inventory decision-making and improve patient care outcomes.

Abstract

The health care system is becoming too concerned about the acquisition of medications and medical equipment, collaboration with the wholesalers, the increased costs of their activity, and the management of the waste products. The nature (complex and non-linear) of the medical inventory requirements that are to be taken into account cannot be covered by the traditional rule-based or linear forecasting methodologies. The present study aims at optimizing hospital supply chain efficiency by evaluating the performance of forecasting methods that can be used to predict advanced forecasting. As part of preprocessing, the robustness of data was achieved by formatting the data type dates as datetimes, using one-hot encoding, and Min-Max normalization to gain quality data inputs using a real-world hospital supply chain dataset supplied by Kaggle. Hybrid-style deep learning (DL) of LSTM and GRU was implemented as a model to learn complex conditions within the supply-demand data series. Comparisons were carried out between this model and Gradient Boosting (GB), DBSCAN, K- Nearest Neighbors (KNN), and ARIMA to give a balanced analysis between supervised and unsupervised learning and time-series forecasting. The hybrid model, LSTM-GRU, performed the best having recorded an accuracy rate of 95.8% much higher than GB (94.30%), DBSCAN (92.7%), KNN (86%), and ARIMA (85%). Precision (95.6%), recall (95.1%), F1-score (95.8%) evaluated metrics and even the ROC (96%) further proved the efficacy of the model when processing supply-demand variability. This multi-model evaluation demonstrates the benefits of incorporating deep learning into healthcare logistics to provide data-based knowledge that may facilitate prompt inventory decision-making and contribute to better patient care outcomes. What this work emphasizes is the importance of predictive analytics in the creation of a more efficient, less costly, more patient-centric health infrastructure.

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