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Article

Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies

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Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia
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Qlector d.o.o., Rovšnikova 7, 1000 Ljubljana, Slovenia
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Jožef Stefan International Postgraduate School, Jamova 39, 1000 Ljubljana, Slovenia
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Author to whom correspondence should be addressed.
Academic Editor: Grzegorz Dudek
Appl. Sci. 2021, 11(15), 6787; https://doi.org/10.3390/app11156787
Received: 30 June 2021 / Revised: 19 July 2021 / Accepted: 21 July 2021 / Published: 23 July 2021
(This article belongs to the Special Issue Applied Machine Learning Ⅱ)
Demand forecasting is a crucial component of demand management, directly impacting manufacturing companies’ planning, revenues, and actors through the supply chain. We evaluate 21 baseline, statistical, and machine learning algorithms to forecast smooth and erratic demand on a real-world use case scenario. The products’ data were obtained from a European original equipment manufacturer targeting the global automotive industry market. Our research shows that global machine learning models achieve superior performance than local models. We show that forecast errors from global models can be constrained by pooling product data based on the past demand magnitude. We also propose a set of metrics and criteria for a comprehensive understanding of demand forecasting models’ performance. View Full-Text
Keywords: demand forecasting; smart manufacturing; artificial intelligence; supply chain agility; digital twin demand forecasting; smart manufacturing; artificial intelligence; supply chain agility; digital twin
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MDPI and ACS Style

Rožanec, J.M.; Kažič, B.; Škrjanc, M.; Fortuna, B.; Mladenić, D. Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies. Appl. Sci. 2021, 11, 6787. https://doi.org/10.3390/app11156787

AMA Style

Rožanec JM, Kažič B, Škrjanc M, Fortuna B, Mladenić D. Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies. Applied Sciences. 2021; 11(15):6787. https://doi.org/10.3390/app11156787

Chicago/Turabian Style

Rožanec, Jože M., Blaž Kažič, Maja Škrjanc, Blaž Fortuna, and Dunja Mladenić. 2021. "Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies" Applied Sciences 11, no. 15: 6787. https://doi.org/10.3390/app11156787

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