Clustering and Forecasting Implementation for Medical Consumables Stock Reccomendation
DOI:
https://doi.org/10.30871/jaic.v9i4.9717Keywords:
Agglomerative Hierarchical Clustering, Medical Consumables, Recommendations, Single Exponential SmoothingAbstract
Managing medical consumables (BMHP) in hospitals can be tricky because the demand often changes unpredictably. This study aims to help hospitals manage their BMHP stocks better by using two techniques: forecasting with Single Exponential Smoothing (SES) and grouping items using Agglomerative Hierarchical Clustering (AHC). SES is used to predict future needs based on previous usage, while AHC groups similar items based on how they're used, which helps make the predictions more accurate. Before applying clustering, the prediction error was quite high, with a MAPE of 61.77% and an MAE of 18,769.80. After clustering, these numbers dropped to 10.06% and 3,987.45, showing a significant improvement. The clustering itself was strong, with a Silhouette Coefficient of 0.727, meaning the item groups made sense. Each group of items got different stock suggestions. Items with high and unstable demand were advised to keep extra safety stock. Items with uncertain patterns needed a more flexible buffer stock. For items with stable use, average trends over the last few months were enough to guide stock planning. This approach helps hospitals avoid both overstock and stockouts by giving more accurate and tailored recommendations. Although this study only used data from one hospital, the results show that combining SES and AHC can make stock management smarter and more efficient.
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