Vaccine supply chain optimization can benefit from hierarchical time series forecasting, when grouping the vaccines by type or location. However, forecasts of different hierarchy levels become incoherent when higher levels do not match the sum of the lower levels forecasts, which can be addressed by reconciliation methods. In this paper, we tackle the vaccine sale forecasting problem by modeling sales data from GSK between 2010 and 2021 as a hierarchical time series. After forecasting future values with several ARIMA models, we systematically compare the performance of various reconciliation methods, using statistical tests. We also compare the performance of the forecast before and after COVID. The results highlight Minimum Trace and Weighted Least Squares with Structural scaling as the best performing methods, which provided a coherent forecast while reducing the forecast error of the baseline ARIMA.

翻译：疫苗供应链优化可从分层时间序列预测中受益，尤其是按疫苗类型或地理位置分组时。然而，当高层级预测与低层级预测之和存在偏差时，不同层级间的预测会出现不一致问题，这可通过协调方法加以解决。本文以GSK公司2010年至2021年的销售数据为研究对象，将其建模为分层时间序列，通过多个ARIMA模型预测未来值后，采用统计检验系统比较了多种协调方法的性能，并分析了疫情前后预测效果的变化。结果表明，最小迹法与结构缩放的加权最小二乘法表现最优，能在保持预测一致性的同时，有效降低基准ARIMA模型的预测误差。