Hierarchical Forecasting of Spare Parts Components on a Supply Chain Network

• Ravi B. D. Figueiredo Faculdade FPFtech
• Italo P. Caliari Faculdade FPFtech
• Wington L. Vital Faculdade FPFtech
• Natasha A. Caxias Faculdade FPFtech
• Fabrício S. Batalha Faculdade FPFtech

DOI: https://doi.org/10.5753/eniac.2026.27156

Resumo

This study evaluates hierarchical demand forecasting for approximately 900 electronic interchangeable component groups within a Brazilian spare parts supply chain. To overcome limited cloud computational resources, we implemented reconciliation methods—Bottom-Up, Top-Down, Min-Trace, and ERM—using PySpark for large-scale distributed processing. Results show that the Min-Trace (MinT) model, including proposed variations to handle negative values, outperformed other methods in MAE and RMSE. The findings confirm that hierarchical reconciliation improves forecasting accuracy and distribution efficiency across planning and distribution levels.

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