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

Applied Sciences

Dunja Mladenić¹  Blaž Kažič¹  Blaž Fortuna¹  Jože M. Rožanec¹  Maja Škrjanc¹ 

[1] Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia;

关键词: demand forecasting;    smart manufacturing;    artificial intelligence;    supply chain agility;    digital twin;   

DOI  :  10.3390/app11156787

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown