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  • Sagaert_TemporalBigDataForecasting

    Rights statement: INFORMS © 2018

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Temporal big data for tire industry tactical sales forecasting

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Temporal big data for tire industry tactical sales forecasting. / Sagaert, Yves R.; Aghezzaf, El-Houssaine; Kourentzes, Nikolaos; Desmet, Bram.

In: Interfaces, Vol. 48, No. 2, 2018, p. 121-129.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Sagaert, YR, Aghezzaf, E-H, Kourentzes, N & Desmet, B 2018, 'Temporal big data for tire industry tactical sales forecasting', Interfaces, vol. 48, no. 2, pp. 121-129. https://doi.org/10.1287/inte.2017.0901

APA

Sagaert, Y. R., Aghezzaf, E-H., Kourentzes, N., & Desmet, B. (2018). Temporal big data for tire industry tactical sales forecasting. Interfaces, 48(2), 121-129. https://doi.org/10.1287/inte.2017.0901

Vancouver

Author

Sagaert, Yves R. ; Aghezzaf, El-Houssaine ; Kourentzes, Nikolaos ; Desmet, Bram. / Temporal big data for tire industry tactical sales forecasting. In: Interfaces. 2018 ; Vol. 48, No. 2. pp. 121-129.

Bibtex

@article{7b0e54ccb26b4585bbda5648451a6d63,
title = "Temporal big data for tire industry tactical sales forecasting",
abstract = "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.",
keywords = "Forecasting, Time Series, Regression, Temporal Big Data, Supply Chain Planning",
author = "Sagaert, {Yves R.} and El-Houssaine Aghezzaf and Nikolaos Kourentzes and Bram Desmet",
note = "INFORMS {\textcopyright} 2018",
year = "2018",
doi = "10.1287/inte.2017.0901",
language = "English",
volume = "48",
pages = "121--129",
journal = "Interfaces",
issn = "0092-2102",
publisher = "INFORMS Inst.for Operations Res.and the Management Sciences",
number = "2",

}

RIS

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 -