Application of Medical Material Inventory Model Under Deep Learning in Supply Planning of Public Emergency

Abstract

The purposes are to solve inventory management problems in emergencies, optimize inventory management structure, and improve management efficiency. The medical material inventory management is taken as the research object. According to previous research, the existing material management model has problems such as management confusion, resource waste, and insufficient data analysis. Hence, the strengths of deep learning algorithms are utilized to address the above problems. A deep learning-based medical material inventory management model is constructed through the reasonable classification of material management methods. This model effectively utilizes the data by analyzing disaster data in different regions and establishes a corresponding inventory management model according to the classification standards. The numerical analysis of three examples further verifies the effectiveness of the proposed model. On this basis, the model is compared with the latest inventory management models to further determine its advantages. Results suggest that the medical supplies management model based on deep learning can interpret and analyze the data well and calculate the optimal inventory and management method of the model with limited funds based on the data. Compared with the latest inventory management models, the proposed model can provide a prediction accuracy as high as 92.45%. Under the same data, the analysis time of the medical material management method based on deep learning is only 35 minutes, which has undeniable advantages compared with other models. The proposed model can provide research ideas for material management in emergencies.