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 Naïve, Holt, and Holt-Winters methods perform poorly.
| Original language | English |
|---|---|
| Pages (from-to) | 251-265 |
| Number of pages | 15 |
| Journal | International Journal of Forecasting |
| Volume | 35 |
| Issue number | 1 |
| Early online date | 27 Mar 2018 |
| DOIs | |
| Publication status | Published - 1 Jan 2019 |
Keywords
- Bullwhip effect
- Evaluation
- Forecasting
- Inventory
- Utility metrics
ASJC Scopus subject areas
- Business and International Management
Cite this
The inventory performance of forecasting methods: evidence from the M3-competition data. / Petropoulos, Fotios; Wang, Xun; Disney, Stephen M.
In: International Journal of Forecasting, Vol. 35, No. 1, 01.01.2019, p. 251-265.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - The inventory performance of forecasting methods:
T2 - evidence from the M3-competition data
AU - Petropoulos, Fotios
AU - Wang, Xun
AU - Disney, Stephen M.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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 Naïve, Holt, and Holt-Winters methods perform poorly.
AB - 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 Naïve, Holt, and Holt-Winters methods perform poorly.
KW - Bullwhip effect
KW - Evaluation
KW - Forecasting
KW - Inventory
KW - Utility metrics
UR - http://www.scopus.com/inward/record.url?scp=85044382408&partnerID=8YFLogxK
U2 - 10.1016/j.ijforecast.2018.01.004
DO - 10.1016/j.ijforecast.2018.01.004
M3 - Article
VL - 35
SP - 251
EP - 265
JO - International Journal of Forecasting
JF - International Journal of Forecasting
SN - 0169-2070
IS - 1
ER -