The inventory performance of forecasting methods: Evidence from the M3 competition data
By Fotios Petropoulos, Xun Wang, Stephen M. Disney. 2019
58 citations recorded by the source corpus.
Abstract Forecasting competitions have been a major driver not only of improvements in forecasting methods’ performances, but also of the development of new forecasting approaches. However, despite the tremendous value and impact of these competitions, they do suffer from the limitation that performances are measured only in terms of the forecast accuracy and bias, ignoring utility metrics. Using the monthly industry series of the M3 competition, we empirically explore the inventory performances of various widely used forecasting techniques, including exponential smoothing, ARIMA models, the Theta method, and approaches based on multiple temporal aggregation. We employ a rolling simulation approach and analyse the results for the order-up-to policy under various lead times. We find that the methods that are based on combinations result in superior inventory performances, while the Naive, Holt, and Holt-Winters methods perform poorly.
Subfields: Economics and Econometrics, Management Science and Operations Research
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In Economics and Econometrics
In Management Science and Operations Research
- Ivan Svetunkov
- Fotios Petropoulos
- Vassilios Assimakopoulos
- Evangelos Spiliotis
- Konstantinos Nikolopoulos
- Rob J Hyndman
- Spyros Makridakis
Citations in the corpus
- A simple combination of univariate models (2020, background): “We observe that the good performance of SCUM involves only moderate increase in computational time compared to approaches that are widely considered as benchmarks in forecasting research (Petropoulos et al., 2018).”