A Study on the Rapid Classification of Medical Consumable Supply Chains Based on K-means Clustering

The application of the K-means clustering algorithm in the refined management of medical consumables within a pharmaceutical supply chain's big data framework, and constructs a classification model. Methods: The K-means clustering algorithm was applied to examine its use in classifying hospital consumables. Results: Based on outbound volume and frequency, K-means clustering quickly classifies consumables. Combined with management requirements, different inventory strategies can be implemented. Conclusion: K-means clustering in the pharmaceutical supply chain's big data enhances hospital management efficiency and optimizes costs. Customized strategies can be developed based on the characteristics of each consumable category, thereby strengthening the refined management of medical consumables in the hospital’s central warehouse.