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

Autor: Blaž Kažič, Dunja Mladenic, Maja Škrjanc, Jože M. Rožanec, Blaž Fortuna
Rok vydání: 2021
Předmět:
Demand management
Technology
0209 industrial biotechnology
QH301-705.5
Computer science
QC1-999
Supply chain
Pooling
Automotive industry
02 engineering and technology
supply chain agility
020901 industrial engineering & automation
digital twin
0202 electrical engineering
electronic engineering
information engineering
General Materials Science
Use case
smart manufacturing
Biology (General)
Baseline (configuration management)
QD1-999
Instrumentation
Fluid Flow and Transfer Processes
business.industry
Physics
Process Chemistry and Technology
General Engineering
Demand forecasting
artificial intelligence
Engineering (General). Civil engineering (General)
Original equipment manufacturer
Computer Science Applications
demand forecasting
Chemistry
020201 artificial intelligence & image processing
TA1-2040
business
Algorithm
Zdroj: Applied Sciences, Vol 11, Iss 6787, p 6787 (2021)
Applied Sciences
Volume 11
Issue 15
ISSN: 2076-3417
DOI: 10.3390/app11156787
Popis: 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.
Databáze: OpenAIRE
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