  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.
