Platelet Demand Forecasting Based on the SARIMA Model: Optimizing Blood Bank Resource Allocation and Clinical Supply.

Abstract

Background: With advances in medical technology and an aging population, the demand for single-donor platelet transfusions is increasing because of their significant therapeutic effects. However, the short shelf-life of platelets and the lack of large-scale reserves make accurate demand forecasting crucial for blood bank inventory management, resource allocation and clinical supply.

Objective: This study aims to forecast platelet demand trends via time series analysis, specifically the SARIMA model, to provide scientific evidence for blood banks, optimize resource allocation and improve clinical supply efficiency.

Methods: Monthly aggregate data from type A BPC units supplied by Huzhou Central Blood Station from January 2015 to December 2023 were collected. By analyzing these data, a SARIMA model was constructed to predict platelet demand in the first half of 2024.

Results: The SARIMA(0,1,1)(0,1,1)₁₂ model performed best in terms of goodness of fit and Bayesian information criterion (BIC) tests and accurately predicted platelet demand. The predicted results revealed that the actual monthly supply in the first half of 2024 was within the 95% confidence interval of the forecast, with a mean relative error of 3.61%.

Conclusion: The SARIMA model effectively predicts platelet demand, providing a practical tool for blood banks to optimize inventory management and clinical supply. Future research should explore further optimizations and improvements to better serve clinical needs and resource management.