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Assessing the Performance of Hierarchical Forecasting Methods on the Retail Sector.

José Manuel Oliveira1,2, Patrícia Ramos1,3

  • 1INESC Technology and Science, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary

Reconciled demand forecasting methods, like Minimum Trace Shrinkage (MinT-Shrink), significantly improve accuracy over independent forecasts for retailers. These hierarchical approaches enhance supply chain decision-making by preserving granular data insights at aggregate levels.

Keywords:
ARIMAentropyhierarchical forecastinginformation criteriamodel selectionretailstate space models

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Area of Science:

  • Operations Research
  • Business Analytics
  • Time Series Forecasting

Background:

  • Retailers require demand forecasts across various aggregation levels for supply chain decisions.
  • Ensuring forecast consistency across hierarchical levels is crucial for aligned decision-making.
  • The choice between independent and hierarchical forecasting methods impacts accuracy and coherence.

Purpose of the Study:

  • To empirically compare the performance of independent versus reconciled forecasting approaches.
  • To provide guidelines for retailers on selecting appropriate forecasting methods.
  • To investigate forecast accuracy improvements using real retail data.

Main Methods:

  • Utilized state space models and ARIMA for base forecast generation.
  • Selected optimal models using the bias-corrected Akaike information criterion.
  • Applied the Minimum Trace Shrinkage (MinT-Shrink) estimator for forecast reconciliation.

Main Results:

  • Reconciled forecasts using MinT-Shrink consistently outperformed ARIMA base forecasts in accuracy.
  • Accuracy gains were observed across all hierarchy levels and forecast horizons.
  • Forecast accuracy improvements were more pronounced at higher aggregation levels.

Conclusions:

  • Hierarchical forecasting with reconciliation, specifically MinT-Shrink, enhances forecast accuracy for retailers.
  • Reconciliation effectively reintegrates lost information from lower aggregation levels into higher ones.
  • These findings support improved management decisions through more accurate, coherent demand forecasts.