Rights statement: INFORMS © 2018
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Temporal big data for tire industry tactical sales forecasting
AU - Sagaert, Yves R.
AU - Aghezzaf, El-Houssaine
AU - Kourentzes, Nikolaos
AU - Desmet, Bram
N1 - INFORMS © 2018
PY - 2018
Y1 - 2018
N2 - We propose a forecasting method to improve accuracy for tactical sales predictions at a major supplier to the tire industry. This level of forecasting serves as direct input for the demand planning, steering the global supply chain and is typically up to a year ahead. The case company has a product portfolio that is strongly sensitive to external events. Univariate statistical methods, which are common in practice, are unable to anticipate and forecast changes in the market, while human expert forecasts are known to be biased and inconsistent. The proposed method is able to automatically identify key leading indicators that drive sales from a massive set of macro-economic indicators, across different regions and markets and produce accurate forecasts. Our method is able to handle the additional complexity of the short and long term dynamics from the product sales and the external indicators. We find that accuracy is improved by 16.1% over current practice with proportional benefits for the supply chain. Furthermore, our method provides transparency to the market dynamics, allowing managers to better understand the events and economic variables that affect the sales of their products.
AB - We propose a forecasting method to improve accuracy for tactical sales predictions at a major supplier to the tire industry. This level of forecasting serves as direct input for the demand planning, steering the global supply chain and is typically up to a year ahead. The case company has a product portfolio that is strongly sensitive to external events. Univariate statistical methods, which are common in practice, are unable to anticipate and forecast changes in the market, while human expert forecasts are known to be biased and inconsistent. The proposed method is able to automatically identify key leading indicators that drive sales from a massive set of macro-economic indicators, across different regions and markets and produce accurate forecasts. Our method is able to handle the additional complexity of the short and long term dynamics from the product sales and the external indicators. We find that accuracy is improved by 16.1% over current practice with proportional benefits for the supply chain. Furthermore, our method provides transparency to the market dynamics, allowing managers to better understand the events and economic variables that affect the sales of their products.
KW - Forecasting
KW - Time Series
KW - Regression
KW - Temporal Big Data
KW - Supply Chain Planning
U2 - 10.1287/inte.2017.0901
DO - 10.1287/inte.2017.0901
M3 - Journal article
VL - 48
SP - 121
EP - 129
JO - Interfaces
JF - Interfaces
SN - 0092-2102
IS - 2
ER -