Abstract
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.
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This work was sponsored by GlaxoSmithKline Biologicals SA.
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Angam, B., Beretta, A., De Poorter, E., Duvinage, M., Peralta, D. (2025). Forecast Reconciliation for Vaccine Supply Chain Optimization. In: Oliehoek, F.A., Kok, M., Verwer, S. (eds) Artificial Intelligence and Machine Learning. BNAIC/Benelearn 2023. Communications in Computer and Information Science, vol 2187. Springer, Cham. https://doi.org/10.1007/978-3-031-74650-5_6
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