Assessing the Performance of Hierarchical Forecasting Methods on the Retail Sector

• Type: Article in International Scientific Journal
• Date: 2019

Authors

José Manuel Oliveira (Author) FEP; Patrícia Ramos (Author) Other

Journal

Title: Entropy

Vol. 21

Final page: 436

ISSN: 1099-4300

Publisher: MDPI

Indexing

ISI Web of Knowledge - 20 Citations

Scopus - 21 Citations

Other information

Authenticus ID: P-00Q-G8F

DOI: 10.3390/e21040436

Abstract (EN): Retailers need demand forecasts at different levels of aggregation in order to support a variety of decisions along the supply chain. To ensure aligned decision-making across the hierarchy, it is essential that forecasts at the most disaggregated level add up to forecasts at the aggregate levels above. It is not clear if these aggregate forecasts should be generated independently or by using an hierarchical forecasting method that ensures coherent decision-making at the different levels but does not guarantee, at least, the same accuracy. To give guidelines on this issue, our empirical study investigates the relative performance of independent and reconciled forecasting approaches, using real data from a Portuguese retailer. We consider two alternative forecasting model families for generating the base forecasts; namely, state space models and ARIMA. Appropriate models from both families are chosen for each time-series by minimising the bias-corrected Akaike information criteria. The results show significant improvements in forecast accuracy, providing valuable information to support management decisions. It is clear that reconciled forecasts using the Minimum Trace Shrinkage estimator (MinT-Shrink) generally improve on the accuracy of the ARIMA base forecasts for all levels and for the complete hierarchy, across all forecast horizons. The accuracy gains generally increase with the horizon, varying between 1.7% and 3.7% for the complete hierarchy. It is also evident that the gains in forecast accuracy are more substantial at the higher levels of aggregation, which means that the information about the individual dynamics of the series, which was lost due to aggregation, is brought back again from the lower levels of aggregation to the higher levels by the reconciliation process, substantially improving the forecast accuracy over the base forecasts.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 22